PVD

.gitignore

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+.idea
+data
+output
+__pycache__
+*.png
+*.net
+*.npy
+*.npz
+eval_model
+eval_data

.gitmodules

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+[submodule "metrics/ChamferDistancePytorch"]
+	path = metrics/ChamferDistancePytorch
+	url = https://github.com/ThibaultGROUEIX/ChamferDistancePytorch

README.md

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+# Shape Generation and Completion Through Point-Voxel Diffusion
+
+[Project]() | [Paper]() 
+
+Implementation of 
+
+## Pretrained Models
+
+Pretrained models can be accessed [here](https://www.dropbox.com/s/a3xydf594fzaokl/cifar10_pretrained.rar?dl=0).
+
+## Requirements:
+
+Make sure the following environments are installed.
+
+```
+python==3.6
+pytorch==1.4.0
+torchvision==0.5.0
+cudatoolkit==10.1
+matplotlib==2.2.5
+tqdm==4.32.1
+open3d==0.9.0
+```
+The code was tested on Unbuntu with Titan RTX. 
+
+
+## Training on CIFAR-10:
+
+```bash
+$ python train_cifar.py
+```
+
+Please refer to the python file for optimal training parameters.
+
+## Results
+
+Some generative results are as follows.
+<p float="left">
+  <img src="example/cifar_gen.png" width="300"/>
+  <img src="example/lsun_gen.png" width="300"/>
+</p>
+
+
+
+## Reference
+
+```
+@inproceedings{han2020joint,
+  title={Joint Training of Variational Auto-Encoder and Latent Energy-Based Model},
+  author={Han, Tian and Nijkamp, Erik and Zhou, Linqi and Pang, Bo and Zhu, Song-Chun and Wu, Ying Nian},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={7978--7987},
+  year={2020}
+}
+```
+
+## Acknowledgement
+
+For any questions related to codes and experiment setting, please contact Linqi (Alex) Zhou (alexzhou907@gmail.com). For questions related to model and algorithm in the paper, please contact Tian Han (hantian@ucla.edu). Thanks to [@Tian Han ](https://github.com/hthth0801?tab=repositories) and [@Erik Njikamp](https://github.com/enijkamp) for their colloboration and guidance.

datasets/partnet.py

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+from torch.utils.data import Dataset, DataLoader
+import torch
+import numpy as np
+import os
+import json
+import random
+import trimesh
+import csv
+from plyfile import PlyData, PlyElement
+from glob import glob
+
+def project_pc_to_image(points, resolution=64):
+    """project point clouds into 2D image
+    :param points: (n, 3) range(-1, 1)
+    :return: binary image
+    """
+    img = []
+    for i in range(3):
+        canvas = np.zeros((resolution, resolution))
+        axis = [0, 1, 2]
+        axis.remove(i)
+        proj_points = (points[:, axis] + 1) / 2 * resolution
+        proj_points = proj_points.astype(np.int)
+        canvas[proj_points[:, 0], proj_points[:, 1]] = 1
+        img.append(canvas)
+    img = np.concatenate(img, axis=1)
+    return img
+
+
+def write_ply(points, filename, text=False):
+    """ input: Nx3, write points to filename as PLY format. """
+    points = [(points[i,0], points[i,1], points[i,2]) for i in range(points.shape[0])]
+    vertex = np.array(points, dtype=[('x', 'f4'), ('y', 'f4'),('z', 'f4')])
+    el = PlyElement.describe(vertex, 'vertex', comments=['vertices'])
+    with open(filename, mode='wb') as f:
+        PlyData([el], text=text).write(f)
+
+
+def rotate_point_cloud(points, transformation_mat):
+
+    new_points = np.dot(transformation_mat, points.T).T
+
+    return new_points
+
+
+def rotate_point_cloud_by_axis_angle(points, axis, angle_deg):
+    """ align 3depn shapes to shapenet coordinates"""
+    # angle = math.radians(angle_deg)
+    # rot_m = pymesh.Quaternion.fromAxisAngle(axis, angle)
+    # rot_m = rot_m.to_matrix()
+    rot_m = np.array([[ 2.22044605e-16,  0.00000000e+00,  1.00000000e+00],
+                      [ 0.00000000e+00,  1.00000000e+00,  0.00000000e+00],
+                      [-1.00000000e+00,  0.00000000e+00,  2.22044605e-16]])
+
+    new_points = rotate_point_cloud(points, rot_m)
+
+    return new_points
+
+
+def downsample_point_cloud(points, n_pts):
+    """downsample points by random choice
+    :param points: (n, 3)
+    :param n_pts: int
+    :return:
+    """
+    p_idx = random.choices(list(range(points.shape[0])), k=n_pts)
+    return points[p_idx]
+
+
+def upsample_point_cloud(points, n_pts):
+    """upsample points by random choice
+    :param points: (n, 3)
+    :param n_pts: int, > n
+    :return:
+    """
+    p_idx = random.choices(list(range(points.shape[0])), k=n_pts - points.shape[0])
+    dup_points = points[p_idx]
+    points = np.concatenate([points, dup_points], axis=0)
+    return points
+
+
+def sample_point_cloud_by_n(points, n_pts):
+    """resample point cloud to given number of points"""
+    if n_pts > points.shape[0]:
+        return upsample_point_cloud(points, n_pts)
+    elif n_pts < points.shape[0]:
+        return downsample_point_cloud(points, n_pts)
+    else:
+        return points
+
+
+
+def collect_data_id(split_dir, classname, phase):
+    filename = os.path.join(split_dir, "{}.{}.json".format(classname, phase))
+    if not os.path.exists(filename):
+        raise ValueError("Invalid filepath: {}".format(filename))
+
+    all_ids = []
+    with open(filename, 'r') as fp:
+        info = json.load(fp)
+    for item in info:
+        all_ids.append(item["anno_id"])
+
+    return all_ids
+
+
+
+class GANdatasetPartNet(Dataset):
+    def __init__(self, phase, data_root, category, n_pts):
+        super(GANdatasetPartNet, self).__init__()
+        if phase == "validation":
+            phase = "val"
+
+        self.phase = phase
+        self.aug = phase == "train"
+
+        self.data_root = data_root
+
+        shape_names = collect_data_id(os.path.join(self.data_root, 'partnet_labels/partnet_train_val_test_split'), category, phase)
+        self.shape_names = []
+        for name in shape_names:
+            path = os.path.join(self.data_root, 'partnet_labels/partnet_pc_label', name)
+            if os.path.exists(path):
+                self.shape_names.append(name)
+
+        self.n_pts = n_pts
+        self.raw_n_pts = self.n_pts // 2
+
+        self.rng = random.Random(1234)
+
+    @staticmethod
+    def load_point_cloud(path):
+        pc = trimesh.load(path)
+        pc = pc.vertices / 2.0 # scale to unit sphere
+        return pc
+
+    @staticmethod
+    def read_point_cloud_part_label(path):
+        with open(path, 'r') as fp:
+            labels = fp.readlines()
+        labels = np.array([int(x) for x in labels])
+        return labels
+
+    def random_rm_parts(self, raw_pc, part_labels):
+        part_ids = sorted(np.unique(part_labels).tolist())
+        if self.phase == "train":
+            random.shuffle(part_ids)
+            n_part_keep = random.randint(1, max(1, len(part_ids) - 1))
+        else:
+            self.rng.shuffle(part_ids)
+            n_part_keep = self.rng.randint(1, max(1, len(part_ids) - 1))
+        part_ids_keep = part_ids[:n_part_keep]
+        point_idx = []
+        for i in part_ids_keep:
+            point_idx.extend(np.where(part_labels == i)[0].tolist())
+        raw_pc = raw_pc[point_idx]
+        return raw_pc, n_part_keep
+
+    def __getitem__(self, index):
+        raw_shape_name = self.shape_names[index]
+        raw_ply_path = os.path.join(self.data_root, 'partnet_data', raw_shape_name, 'point_sample/ply-10000.ply')
+        raw_pc = self.load_point_cloud(raw_ply_path)
+
+        raw_label_path = os.path.join(self.data_root, 'partnet_labels/partnet_pc_label', raw_shape_name, 'label-merge-level1-10000.txt')
+        part_labels = self.read_point_cloud_part_label(raw_label_path)
+        raw_pc, n_part_keep = self.random_rm_parts(raw_pc, part_labels)
+        raw_pc = sample_point_cloud_by_n(raw_pc, self.raw_n_pts)
+        raw_pc = torch.tensor(raw_pc, dtype=torch.float32).transpose(1, 0)
+
+        real_shape_name = self.shape_names[index]
+        real_ply_path = os.path.join(self.data_root, 'partnet_data', real_shape_name, 'point_sample/ply-10000.ply')
+        real_pc = self.load_point_cloud(real_ply_path)
+        real_pc = sample_point_cloud_by_n(real_pc, self.n_pts)
+        real_pc = torch.tensor(real_pc, dtype=torch.float32).transpose(1, 0)
+
+        return {"raw": raw_pc, "real": real_pc, "raw_id": raw_shape_name, "real_id": real_shape_name,
+                'n_part_keep': n_part_keep, 'idx': index}
+
+    def __len__(self):
+        return len(self.shape_names)
+
+
+
+
+if __name__ == '__main__':
+    data_root = '/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetPointCloud'
+    data_raw_root = '/viscam/u/alexzhou907/01DATA/shapenet/shapenet_dim32_sdf_pc'
+    pc_dataroot = '/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k'
+
+    sn_root = '/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2'
+    classes = 'car'
+    npoints = 2048
+    # from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+    # pc_ds = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+    #                                categories=[classes], split='train',
+    #                                tr_sample_size=npoints,
+    #                                te_sample_size=npoints,
+    #                                scale=1.,
+    #                                normalize_per_shape=False,
+    #                                normalize_std_per_axis=False,
+    #                                random_subsample=True)
+
+    train_ds = GANdatasetPartNet('test', pc_dataroot, data_raw_root, classes, npoints, np.array([0,0,0]),
+                               np.array([1, 1, 1]))
+
+    d1 = train_ds[0]
+    real = d1['real']
+    raw = d1['raw']
+    m, s = d1['m'], d1['s']
+    x = (torch.cat([raw, real], dim=-1) * s + m).transpose(0,1)
+
+    write_ply(x.numpy(), 'x.ply')
+    pass

datasets/shapenet_data_pc.py

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+import os
+import torch
+import numpy as np
+from torch.utils.data import Dataset
+from torch.utils import data
+import random
+import open3d as o3d
+import numpy as np
+import torch.nn.functional as F
+
+# taken from https://github.com/optas/latent_3d_points/blob/8e8f29f8124ed5fc59439e8551ba7ef7567c9a37/src/in_out.py
+synsetid_to_cate = {
+    '02691156': 'airplane', '02773838': 'bag', '02801938': 'basket',
+    '02808440': 'bathtub', '02818832': 'bed', '02828884': 'bench',
+    '02876657': 'bottle', '02880940': 'bowl', '02924116': 'bus',
+    '02933112': 'cabinet', '02747177': 'can', '02942699': 'camera',
+    '02954340': 'cap', '02958343': 'car', '03001627': 'chair',
+    '03046257': 'clock', '03207941': 'dishwasher', '03211117': 'monitor',
+    '04379243': 'table', '04401088': 'telephone', '02946921': 'tin_can',
+    '04460130': 'tower', '04468005': 'train', '03085013': 'keyboard',
+    '03261776': 'earphone', '03325088': 'faucet', '03337140': 'file',
+    '03467517': 'guitar', '03513137': 'helmet', '03593526': 'jar',
+    '03624134': 'knife', '03636649': 'lamp', '03642806': 'laptop',
+    '03691459': 'speaker', '03710193': 'mailbox', '03759954': 'microphone',
+    '03761084': 'microwave', '03790512': 'motorcycle', '03797390': 'mug',
+    '03928116': 'piano', '03938244': 'pillow', '03948459': 'pistol',
+    '03991062': 'pot', '04004475': 'printer', '04074963': 'remote_control',
+    '04090263': 'rifle', '04099429': 'rocket', '04225987': 'skateboard',
+    '04256520': 'sofa', '04330267': 'stove', '04530566': 'vessel',
+    '04554684': 'washer', '02992529': 'cellphone',
+    '02843684': 'birdhouse', '02871439': 'bookshelf',
+    # '02858304': 'boat', no boat in our dataset, merged into vessels
+    # '02834778': 'bicycle', not in our taxonomy
+}
+cate_to_synsetid = {v: k for k, v in synsetid_to_cate.items()}
+
+
+class Uniform15KPC(Dataset):
+    def __init__(self, root_dir, subdirs, tr_sample_size=10000,
+                 te_sample_size=10000, split='train', scale=1.,
+                 normalize_per_shape=False, box_per_shape=False,
+                 random_subsample=False,
+                 normalize_std_per_axis=False,
+                 all_points_mean=None, all_points_std=None,
+                 input_dim=3, use_mask=False):
+        self.root_dir = root_dir
+        self.split = split
+        self.in_tr_sample_size = tr_sample_size
+        self.in_te_sample_size = te_sample_size
+        self.subdirs = subdirs
+        self.scale = scale
+        self.random_subsample = random_subsample
+        self.input_dim = input_dim
+        self.use_mask = use_mask
+        self.box_per_shape = box_per_shape
+        if use_mask:
+            self.mask_transform = PointCloudMasks(radius=5, elev=5, azim=90)
+
+        self.all_cate_mids = []
+        self.cate_idx_lst = []
+        self.all_points = []
+        for cate_idx, subd in enumerate(self.subdirs):
+            # NOTE: [subd] here is synset id
+            sub_path = os.path.join(root_dir, subd, self.split)
+            if not os.path.isdir(sub_path):
+                print("Directory missing : %s" % sub_path)
+                continue
+
+            all_mids = []
+            for x in os.listdir(sub_path):
+                if not x.endswith('.npy'):
+                    continue
+                all_mids.append(os.path.join(self.split, x[:-len('.npy')]))
+
+            # NOTE: [mid] contains the split: i.e. "train/<mid>" or "val/<mid>" or "test/<mid>"
+            for mid in all_mids:
+                # obj_fname = os.path.join(sub_path, x)
+                obj_fname = os.path.join(root_dir, subd, mid + ".npy")
+                try:
+                    point_cloud = np.load(obj_fname)  # (15k, 3)
+
+                except:
+                    continue
+
+                assert point_cloud.shape[0] == 15000
+                self.all_points.append(point_cloud[np.newaxis, ...])
+                self.cate_idx_lst.append(cate_idx)
+                self.all_cate_mids.append((subd, mid))
+
+        # Shuffle the index deterministically (based on the number of examples)
+        self.shuffle_idx = list(range(len(self.all_points)))
+        random.Random(38383).shuffle(self.shuffle_idx)
+        self.cate_idx_lst = [self.cate_idx_lst[i] for i in self.shuffle_idx]
+        self.all_points = [self.all_points[i] for i in self.shuffle_idx]
+        self.all_cate_mids = [self.all_cate_mids[i] for i in self.shuffle_idx]
+
+        # Normalization
+        self.all_points = np.concatenate(self.all_points)  # (N, 15000, 3)
+        self.normalize_per_shape = normalize_per_shape
+        self.normalize_std_per_axis = normalize_std_per_axis
+        if all_points_mean is not None and all_points_std is not None:  # using loaded dataset stats
+            self.all_points_mean = all_points_mean
+            self.all_points_std = all_points_std
+        elif self.normalize_per_shape:  # per shape normalization
+            B, N = self.all_points.shape[:2]
+            self.all_points_mean = self.all_points.mean(axis=1).reshape(B, 1, input_dim)
+            if normalize_std_per_axis:
+                self.all_points_std = self.all_points.reshape(B, N, -1).std(axis=1).reshape(B, 1, input_dim)
+            else:
+                self.all_points_std = self.all_points.reshape(B, -1).std(axis=1).reshape(B, 1, 1)
+        elif self.box_per_shape:
+            B, N = self.all_points.shape[:2]
+            self.all_points_mean = self.all_points.min(axis=1).reshape(B, 1, input_dim)
+
+            self.all_points_std = self.all_points.max(axis=1).reshape(B, 1, input_dim) - self.all_points.min(axis=1).reshape(B, 1, input_dim)
+
+        else:  # normalize across the dataset
+            self.all_points_mean = self.all_points.reshape(-1, input_dim).mean(axis=0).reshape(1, 1, input_dim)
+            if normalize_std_per_axis:
+                self.all_points_std = self.all_points.reshape(-1, input_dim).std(axis=0).reshape(1, 1, input_dim)
+            else:
+                self.all_points_std = self.all_points.reshape(-1).std(axis=0).reshape(1, 1, 1)
+
+        self.all_points = (self.all_points - self.all_points_mean) / self.all_points_std
+        if self.box_per_shape:
+            self.all_points = self.all_points - 0.5
+        self.train_points = self.all_points[:, :10000]
+        self.test_points = self.all_points[:, 10000:]
+
+        self.tr_sample_size = min(10000, tr_sample_size)
+        self.te_sample_size = min(5000, te_sample_size)
+        print("Total number of data:%d" % len(self.train_points))
+        print("Min number of points: (train)%d (test)%d"
+              % (self.tr_sample_size, self.te_sample_size))
+        assert self.scale == 1, "Scale (!= 1) is deprecated"
+
+    def get_pc_stats(self, idx):
+        if self.normalize_per_shape or self.box_per_shape:
+            m = self.all_points_mean[idx].reshape(1, self.input_dim)
+            s = self.all_points_std[idx].reshape(1, -1)
+            return m, s
+
+
+        return self.all_points_mean.reshape(1, -1), self.all_points_std.reshape(1, -1)
+
+    def renormalize(self, mean, std):
+        self.all_points = self.all_points * self.all_points_std + self.all_points_mean
+        self.all_points_mean = mean
+        self.all_points_std = std
+        self.all_points = (self.all_points - self.all_points_mean) / self.all_points_std
+        self.train_points = self.all_points[:, :10000]
+        self.test_points = self.all_points[:, 10000:]
+
+    def __len__(self):
+        return len(self.train_points)
+
+    def __getitem__(self, idx):
+        tr_out = self.train_points[idx]
+        if self.random_subsample:
+            tr_idxs = np.random.choice(tr_out.shape[0], self.tr_sample_size)
+        else:
+            tr_idxs = np.arange(self.tr_sample_size)
+        tr_out = torch.from_numpy(tr_out[tr_idxs, :]).float()
+
+        te_out = self.test_points[idx]
+        if self.random_subsample:
+            te_idxs = np.random.choice(te_out.shape[0], self.te_sample_size)
+        else:
+            te_idxs = np.arange(self.te_sample_size)
+        te_out = torch.from_numpy(te_out[te_idxs, :]).float()
+
+        m, s = self.get_pc_stats(idx)
+        cate_idx = self.cate_idx_lst[idx]
+        sid, mid = self.all_cate_mids[idx]
+
+        out = {
+            'idx': idx,
+            'train_points': tr_out,
+            'test_points': te_out,
+            'mean': m, 'std': s, 'cate_idx': cate_idx,
+            'sid': sid, 'mid': mid
+        }
+
+        if self.use_mask:
+            # masked = torch.from_numpy(self.mask_transform(self.all_points[idx]))
+            # ss = min(masked.shape[0], self.in_tr_sample_size//2)
+            # masked = masked[:ss]
+            #
+            # tr_mask = torch.ones_like(masked)
+            # masked = torch.cat([masked, torch.zeros(self.in_tr_sample_size - ss, 3)],dim=0)#F.pad(masked, (self.in_tr_sample_size-masked.shape[0], 0), "constant", 0)
+            #
+            # tr_mask =  torch.cat([tr_mask, torch.zeros(self.in_tr_sample_size- ss, 3)],dim=0)#F.pad(tr_mask, (self.in_tr_sample_size-tr_mask.shape[0], 0), "constant", 0)
+            # out['train_points_masked'] = masked
+            # out['train_masks'] = tr_mask
+            tr_mask = self.mask_transform(tr_out)
+            out['train_masks'] = tr_mask
+
+        return out
+
+
+class ShapeNet15kPointClouds(Uniform15KPC):
+    def __init__(self, root_dir="data/ShapeNetCore.v2.PC15k",
+                 categories=['airplane'], tr_sample_size=10000, te_sample_size=2048,
+                 split='train', scale=1., normalize_per_shape=False,
+                 normalize_std_per_axis=False, box_per_shape=False,
+                 random_subsample=False,
+                 all_points_mean=None, all_points_std=None,
+                 use_mask=False):
+        self.root_dir = root_dir
+        self.split = split
+        assert self.split in ['train', 'test', 'val']
+        self.tr_sample_size = tr_sample_size
+        self.te_sample_size = te_sample_size
+        self.cates = categories
+        if 'all' in categories:
+            self.synset_ids = list(cate_to_synsetid.values())
+        else:
+            self.synset_ids = [cate_to_synsetid[c] for c in self.cates]
+
+        # assert 'v2' in root_dir, "Only supporting v2 right now."
+        self.gravity_axis = 1
+        self.display_axis_order = [0, 2, 1]
+
+        super(ShapeNet15kPointClouds, self).__init__(
+            root_dir, self.synset_ids,
+            tr_sample_size=tr_sample_size,
+            te_sample_size=te_sample_size,
+            split=split, scale=scale,
+            normalize_per_shape=normalize_per_shape, box_per_shape=box_per_shape,
+            normalize_std_per_axis=normalize_std_per_axis,
+            random_subsample=random_subsample,
+            all_points_mean=all_points_mean, all_points_std=all_points_std,
+            input_dim=3, use_mask=use_mask)
+
+
+
+class PointCloudMasks(object):
+    '''
+    render a view then save mask
+    '''
+    def __init__(self, radius : float=10, elev: float =45, azim:float=315, ):
+
+        self.radius = radius
+        self.elev = elev
+        self.azim = azim
+
+
+    def __call__(self, points):
+
+        pcd = o3d.geometry.PointCloud()
+        pcd.points = o3d.utility.Vector3dVector(points)
+
+        camera = [self.radius * np.sin(90-self.elev) * np.cos(self.azim),
+                  self.radius * np.cos(90 - self.elev),
+                  self.radius * np.sin(90 - self.elev) * np.sin(self.azim),
+                  ]
+        # camera = [0,self.radius,0]
+        _, pt_map = pcd.hidden_point_removal(camera, self.radius)
+
+        mask = torch.zeros_like(points)
+        mask[pt_map] = 1
+
+        return mask #points[pt_map]
+
+
+####################################################################################
+
+

datasets/shapenet_data_sv.py

@@ -0,0 +1,257 @@
+import warnings
+from torch.utils.data import Dataset
+from tqdm import tqdm
+from pathlib import Path
+import open3d as o3d
+import os
+import numpy as np
+
+import hashlib
+import torch
+import matplotlib.pyplot as plt
+
+synset_to_label = {
+    '02691156': 'airplane', '02773838': 'bag', '02801938': 'basket',
+    '02808440': 'bathtub', '02818832': 'bed', '02828884': 'bench',
+    '02876657': 'bottle', '02880940': 'bowl', '02924116': 'bus',
+    '02933112': 'cabinet', '02747177': 'can', '02942699': 'camera',
+    '02954340': 'cap', '02958343': 'car', '03001627': 'chair',
+    '03046257': 'clock', '03207941': 'dishwasher', '03211117': 'monitor',
+    '04379243': 'table', '04401088': 'telephone', '02946921': 'tin_can',
+    '04460130': 'tower', '04468005': 'train', '03085013': 'keyboard',
+    '03261776': 'earphone', '03325088': 'faucet', '03337140': 'file',
+    '03467517': 'guitar', '03513137': 'helmet', '03593526': 'jar',
+    '03624134': 'knife', '03636649': 'lamp', '03642806': 'laptop',
+    '03691459': 'speaker', '03710193': 'mailbox', '03759954': 'microphone',
+    '03761084': 'microwave', '03790512': 'motorcycle', '03797390': 'mug',
+    '03928116': 'piano', '03938244': 'pillow', '03948459': 'pistol',
+    '03991062': 'pot', '04004475': 'printer', '04074963': 'remote_control',
+    '04090263': 'rifle', '04099429': 'rocket', '04225987': 'skateboard',
+    '04256520': 'sofa', '04330267': 'stove', '04530566': 'vessel',
+    '04554684': 'washer', '02992529': 'cellphone',
+    '02843684': 'birdhouse', '02871439': 'bookshelf',
+    # '02858304': 'boat', no boat in our dataset, merged into vessels
+    # '02834778': 'bicycle', not in our taxonomy
+}
+
+# Label to Synset mapping (for ShapeNet core classes)
+label_to_synset = {v: k for k, v in synset_to_label.items()}
+
+def _convert_categories(categories):
+    assert categories is not None, 'List of categories cannot be empty!'
+    if not (c in synset_to_label.keys() + label_to_synset.keys()
+            for c in categories):
+        warnings.warn('Some or all of the categories requested are not part of \
+            ShapeNetCore. Data loading may fail if these categories are not avaliable.')
+    synsets = [label_to_synset[c] if c in label_to_synset.keys()
+               else c for c in categories]
+    return synsets
+
+
+class ShapeNet_Multiview_Points(Dataset):
+    def __init__(self, root_pc:str, root_views: str, cache: str, categories: list = ['chair'], split: str= 'val',
+                 npoints=2048, sv_samples=800, all_points_mean=None, all_points_std=None, get_image=False):
+        self.root = Path(root_views)
+        self.split = split
+        self.get_image = get_image
+        params = {
+            'cat': categories,
+            'npoints': npoints,
+            'sv_samples': sv_samples,
+        }
+        params = tuple(sorted(pair for pair in params.items()))
+        self.cache_dir = Path(cache) / 'svpoints/{}/{}'.format('_'.join(categories), hashlib.md5(bytes(repr(params), 'utf-8')).hexdigest())
+
+        self.cache_dir.mkdir(parents=True, exist_ok=True)
+        self.paths = []
+        self.synset_idxs = []
+        self.synsets = _convert_categories(categories)
+        self.labels = [synset_to_label[s] for s in self.synsets]
+        self.npoints = npoints
+        self.sv_samples = sv_samples
+
+        self.all_points = []
+        self.all_points_sv = []
+
+        # loops through desired classes
+        for i in range(len(self.synsets)):
+
+            syn = self.synsets[i]
+            class_target = self.root / syn
+            if not class_target.exists():
+                raise ValueError('Class {0} ({1}) was not found at location {2}.'.format(
+                    syn, self.labels[i], str(class_target)))
+
+
+            sub_path_pc = os.path.join(root_pc, syn, split)
+            if not os.path.isdir(sub_path_pc):
+                print("Directory missing : %s" % sub_path_pc)
+                continue
+
+            self.all_mids = []
+            self.imgs = []
+            for x in os.listdir(sub_path_pc):
+                if not x.endswith('.npy'):
+                    continue
+                self.all_mids.append(os.path.join(split, x[:-len('.npy')]))
+
+            for mid in tqdm(self.all_mids):
+                # obj_fname = os.path.join(sub_path, x)
+                obj_fname = os.path.join(root_pc, syn, mid + ".npy")
+                cams_pths = list((self.root/ syn/ mid.split('/')[-1]).glob('*_cam_params.npz'))
+                if len(cams_pths) < 20:
+                    continue
+                point_cloud = np.load(obj_fname)
+                sv_points_group = []
+                img_path_group = []
+                (self.cache_dir / (mid.split('/')[-1])).mkdir(parents=True, exist_ok=True)
+                success = True
+                for i, cp in enumerate(cams_pths):
+                    cp = str(cp)
+                    vp = cp.split('cam_params')[0] + 'depth.png'
+                    depth_minmax_pth = cp.split('_cam_params')[0] + '.npy'
+                    cache_pth = str(self.cache_dir / mid.split('/')[-1] / os.path.basename(depth_minmax_pth) )
+
+                    cam_params = np.load(cp)
+                    extr = cam_params['extr']
+                    intr = cam_params['intr']
+
+                    self.transform = DepthToSingleViewPoints(cam_ext=extr, cam_int=intr)
+
+                    try:
+                        sv_point_cloud = self._render(cache_pth, vp, depth_minmax_pth)
+
+                        img_path_group.append(vp)
+
+                        sv_points_group.append(sv_point_cloud)
+                    except Exception as e:
+                        print(e)
+                        success=False
+                        break
+                if not success:
+                    continue
+                self.all_points_sv.append(np.stack(sv_points_group, axis=0))
+                self.all_points.append(point_cloud)
+                self.imgs.append(img_path_group)
+
+        self.all_points = np.stack(self.all_points, axis=0)
+
+        self.all_points_sv = np.stack(self.all_points_sv, axis=0)
+        if all_points_mean is not None and all_points_std is not None:  # using loaded dataset stats
+            self.all_points_mean = all_points_mean
+            self.all_points_std = all_points_std
+        else:  # normalize across the dataset
+            self.all_points_mean = self.all_points.reshape(-1, 3).mean(axis=0).reshape(1, 1, 3)
+            self.all_points_std = self.all_points.reshape(-1).std(axis=0).reshape(1, 1, 1)
+
+        self.all_points = (self.all_points - self.all_points_mean) / self.all_points_std
+        self.train_points = self.all_points[:,:10000]
+        self.test_points = self.all_points[:,10000:]
+        self.all_points_sv = (self.all_points_sv - self.all_points_mean) / self.all_points_std
+
+    def get_pc_stats(self, idx):
+
+        return self.all_points_mean.reshape(1,1, -1), self.all_points_std.reshape(1,1, -1)
+
+    def __len__(self):
+        """Returns the length of the dataset. """
+        return len(self.all_points)
+
+    def __getitem__(self, index):
+
+
+        tr_out = self.train_points[index]
+        tr_idxs = np.random.choice(tr_out.shape[0], self.npoints)
+        tr_out = tr_out[tr_idxs, :]
+
+        gt_points = self.test_points[index][:self.npoints]
+
+        m, s = self.get_pc_stats(index)
+
+        sv_points = self.all_points_sv[index]
+
+        idxs = np.arange(0, sv_points.shape[-2])[:self.sv_samples]#np.random.choice(sv_points.shape[0], 500, replace=False)
+
+        data = torch.cat([torch.from_numpy(sv_points[:,idxs]).float(),
+                          torch.zeros(sv_points.shape[0], self.npoints - idxs.shape[0], sv_points.shape[2])], dim=1)
+        masks = torch.zeros_like(data)
+        masks[:,:idxs.shape[0]] = 1
+
+        res = {'train_points': torch.from_numpy(tr_out).float(),
+                'test_points': torch.from_numpy(gt_points).float(),
+                'sv_points': data,
+                'masks': masks,
+                'std': s, 'mean': m,
+                'idx': index,
+               'name':self.all_mids[index]
+                }
+
+        if self.split != 'train' and self.get_image:
+
+            img_lst = []
+            for n in range(self.all_points_sv.shape[1]):
+
+                img = torch.from_numpy(plt.imread(self.imgs[index][n])).float().permute(2,0,1)[:3]
+
+                img_lst.append(img)
+
+            img = torch.stack(img_lst, dim=0)
+
+            res['image'] = img
+
+        return res
+
+
+
+    def _render(self, cache_path, depth_pth, depth_minmax_pth):
+        # if not os.path.exists(cache_path.split('.npy')[0] + '_color.png') and os.path.exists(cache_path):
+        #
+        #     os.remove(cache_path)
+
+        if os.path.exists(cache_path):
+            data = np.load(cache_path)
+        else:
+
+            data, depth = self.transform(depth_pth, depth_minmax_pth)
+            assert data.shape[0] > 600, 'Only {} points found'.format(data.shape[0])
+            data = data[np.random.choice(data.shape[0], 600, replace=False)]
+            np.save(cache_path, data)
+
+        return data
+
+
+
+
+class DepthToSingleViewPoints(object):
+    '''
+    render a view then save mask
+    '''
+    def __init__(self, cam_ext, cam_int):
+
+        self.cam_ext = cam_ext.reshape(4,4)
+        self.cam_int = cam_int.reshape(3,3)
+
+
+    def __call__(self, depth_pth, depth_minmax_pth):
+
+        depth_minmax = np.load(depth_minmax_pth)
+        depth_img = plt.imread(depth_pth)[...,0]
+        mask = np.where(depth_img == 0, -1.0, 1.0)
+        depth_img = 1 - depth_img
+        depth_img = (depth_img * (np.max(depth_minmax) - np.min(depth_minmax)) + np.min(depth_minmax)) * mask
+
+        intr = o3d.camera.PinholeCameraIntrinsic(depth_img.shape[0], depth_img.shape[1],
+                                                 self.cam_int[0, 0], self.cam_int[1, 1], self.cam_int[0,2],
+                                                 self.cam_int[1,2])
+
+        depth_im = o3d.geometry.Image(depth_img.astype(np.float32, copy=False))
+
+        # rgbd_im = o3d.geometry.RGBDImage.create_from_color_and_depth(color_im, depth_im)
+        pcd = o3d.geometry.PointCloud.create_from_depth_image(depth_im, intr, self.cam_ext, depth_scale=1.)
+        pc =  np.asarray(pcd.points)
+
+        return pc, depth_img
+
+    def __repr__(self):
+        return 'MeshToMaskedVoxel_'+str(self.radius)+str(self.resolution)+str(self.elev )+str(self.azim)+str(self.img_size )
+

metrics/.gitignore

@@ -0,0 +1 @@
+StructuralLosses

metrics/ChamferDistancePytorch/.gitignore

@@ -0,0 +1 @@
+*__pycache__*

metrics/ChamferDistancePytorch/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 ThibaultGROUEIX
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

metrics/ChamferDistancePytorch/README.md

@@ -0,0 +1,101 @@
+# Pytorch Chamfer Distance.
+
+Include a **CUDA** version, and a **PYTHON** version with pytorch standard operations.
+NB : In this depo, dist1 and dist2 are squared pointcloud euclidean distances, so you should adapt thresholds accordingly.
+
+- [x] F - Score  
+
+
+
+### CUDA VERSION
+
+- [x] JIT compilation
+- [x] Supports multi-gpu
+- [x] 2D  point clouds.
+- [x] 3D  point clouds.
+- [x] 5D  point clouds.
+- [x] Contiguous() safe.
+
+
+
+### Python Version
+
+- [x]  Supports any dimension
+
+
+
+### Usage
+
+```python
+import torch, chamfer3D.dist_chamfer_3D, fscore
+chamLoss = chamfer3D.dist_chamfer_3D.chamfer_3DDist()
+points1 = torch.rand(32, 1000, 3).cuda()
+points2 = torch.rand(32, 2000, 3, requires_grad=True).cuda()
+dist1, dist2, idx1, idx2 = chamLoss(points1, points2)
+f_score, precision, recall = fscore.fscore(dist1, dist2)
+```
+
+
+
+### Add it to your project as a submodule
+
+```shell
+git submodule add https://github.com/ThibaultGROUEIX/ChamferDistancePytorch
+```
+
+
+
+### Benchmark:  [forward + backward] pass
+- [x] CUDA 10.1, NVIDIA 435, Pytorch 1.4
+- [x] p1 : 32 x 2000 x dim
+- [x] p2 : 32 x 1000 x dim
+
+|  *Timing (sec * 1000)*  | 2D | 3D | 5D |
+| ---------- | -------- | ------- | ------- |
+| **Cuda Compiled**     | **1.2** | 1.4 |1.8 |
+| **Cuda JIT**     | 1.3 | **1.4** |**1.5** |
+| **Python**     | 37 | 37 | 37 |
+
+
+| *Memory (MB)* |  2D | 3D | 5D |
+| ---------- | -------- | ------- | ------- |
+| **Cuda Compiled**     | 529 | 529  | 549 |
+| **Cuda JIT**     | **520** | **529** |**549** |
+| **Python**     | 2495 | 2495 | 2495 |
+
+
+
+### What is the chamfer distance ? 
+
+[Stanford course](http://graphics.stanford.edu/courses/cs468-17-spring/LectureSlides/L14%20-%203d%20deep%20learning%20on%20point%20cloud%20representation%20(analysis).pdf) on 3D deep Learning
+
+
+
+### Aknowledgment 
+
+Original backbone from [Fei Xia](https://github.com/fxia22/pointGAN/blob/master/nndistance/src/nnd_cuda.cu).
+
+JIT cool trick from [Christian Diller](https://github.com/chrdiller)
+
+### Troubleshoot
+
+- `Undefined symbol: Zxxxxxxxxxxxxxxxxx `:
+
+--> Fix: Make sure to `import torch` before you `import chamfer`.
+--> Use pytorch.version >= 1.1.0
+
+-  [RuntimeError: Ninja is required to load C++ extension](https://github.com/zhanghang1989/PyTorch-Encoding/issues/167)
+
+```shell
+wget https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip
+sudo unzip ninja-linux.zip -d /usr/local/bin/
+sudo update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force 
+```
+
+
+
+
+
+#### TODO:
+
+* Discuss behaviour of torch.min() and tensor.min() which causes issues in some pytorch versions

metrics/ChamferDistancePytorch/chamfer2D/chamfer2D.cu

@@ -0,0 +1,182 @@
+
+#include <stdio.h>
+#include <ATen/ATen.h>
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+#include <vector>
+
+
+
+__global__ void NmDistanceKernel(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i){
+	const int batch=512;
+	__shared__ float buf[batch*2];
+	for (int i=blockIdx.x;i<b;i+=gridDim.x){
+		for (int k2=0;k2<m;k2+=batch){
+			int end_k=min(m,k2+batch)-k2;
+			for (int j=threadIdx.x;j<end_k*2;j+=blockDim.x){
+				buf[j]=xyz2[(i*m+k2)*2+j];
+			}
+			__syncthreads();
+			for (int j=threadIdx.x+blockIdx.y*blockDim.x;j<n;j+=blockDim.x*gridDim.y){
+				float x1=xyz[(i*n+j)*2+0];
+				float y1=xyz[(i*n+j)*2+1];
+				int best_i=0;
+				float best=0;
+				int end_ka=end_k-(end_k&2);
+				if (end_ka==batch){
+					for (int k=0;k<batch;k+=4){
+						{
+							float x2=buf[k*2+0]-x1;
+							float y2=buf[k*2+1]-y1;
+							float d=x2*x2+y2*y2;
+							if (k==0 || d<best){
+								best=d;
+								best_i=k+k2;
+							}
+						}
+						{
+							float x2=buf[k*2+2]-x1;
+							float y2=buf[k*2+3]-y1;
+							float d=x2*x2+y2*y2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+1;
+							}
+						}
+						{
+							float x2=buf[k*2+4]-x1;
+							float y2=buf[k*2+5]-y1;
+							float d=x2*x2+y2*y2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+2;
+							}
+						}
+						{
+							float x2=buf[k*2+6]-x1;
+							float y2=buf[k*2+7]-y1;
+							float d=x2*x2+y2*y2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+3;
+							}
+						}
+					}
+				}else{
+					for (int k=0;k<end_ka;k+=4){
+						{
+							float x2=buf[k*2+0]-x1;
+							float y2=buf[k*2+1]-y1;
+							float d=x2*x2+y2*y2;
+							if (k==0 || d<best){
+								best=d;
+								best_i=k+k2;
+							}
+						}
+						{
+							float x2=buf[k*2+2]-x1;
+							float y2=buf[k*2+3]-y1;
+							float d=x2*x2+y2*y2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+1;
+							}
+						}
+						{
+							float x2=buf[k*2+4]-x1;
+							float y2=buf[k*2+5]-y1;
+							float d=x2*x2+y2*y2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+2;
+							}
+						}
+						{
+							float x2=buf[k*2+6]-x1;
+							float y2=buf[k*2+7]-y1;
+							float d=x2*x2+y2*y2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+3;
+							}
+						}
+					}
+				}
+				for (int k=end_ka;k<end_k;k++){
+					float x2=buf[k*2+0]-x1;
+					float y2=buf[k*2+1]-y1;
+					float d=x2*x2+y2*y2;
+					if (k==0 || d<best){
+						best=d;
+						best_i=k+k2;
+					}
+				}
+				if (k2==0 || result[(i*n+j)]>best){
+					result[(i*n+j)]=best;
+					result_i[(i*n+j)]=best_i;
+				}
+			}
+			__syncthreads();
+		}
+	}
+}
+// int chamfer_cuda_forward(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i,float * result2,int * result2_i, cudaStream_t stream){
+int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2){
+
+	const auto batch_size = xyz1.size(0);
+	const auto n = xyz1.size(1); //num_points point cloud A
+	const auto m = xyz2.size(1); //num_points point cloud B
+
+	NmDistanceKernel<<<dim3(32,16,1),512>>>(batch_size, n, xyz1.data<float>(), m, xyz2.data<float>(), dist1.data<float>(), idx1.data<int>());
+	NmDistanceKernel<<<dim3(32,16,1),512>>>(batch_size, m, xyz2.data<float>(), n, xyz1.data<float>(), dist2.data<float>(), idx2.data<int>());
+
+	cudaError_t err = cudaGetLastError();
+	  if (err != cudaSuccess) {
+	    printf("error in nnd updateOutput: %s\n", cudaGetErrorString(err));
+	    //THError("aborting");
+	    return 0;
+	  }
+	  return 1;
+
+
+}
+__global__ void NmDistanceGradKernel(int b,int n,const float * xyz1,int m,const float * xyz2,const float * grad_dist1,const int * idx1,float * grad_xyz1,float * grad_xyz2){
+	for (int i=blockIdx.x;i<b;i+=gridDim.x){
+		for (int j=threadIdx.x+blockIdx.y*blockDim.x;j<n;j+=blockDim.x*gridDim.y){
+			float x1=xyz1[(i*n+j)*2+0];
+			float y1=xyz1[(i*n+j)*2+1];
+			int j2=idx1[i*n+j];
+			float x2=xyz2[(i*m+j2)*2+0];
+			float y2=xyz2[(i*m+j2)*2+1];
+			float g=grad_dist1[i*n+j]*2;
+			atomicAdd(&(grad_xyz1[(i*n+j)*2+0]),g*(x1-x2));
+			atomicAdd(&(grad_xyz1[(i*n+j)*2+1]),g*(y1-y2));
+			atomicAdd(&(grad_xyz2[(i*m+j2)*2+0]),-(g*(x1-x2)));
+			atomicAdd(&(grad_xyz2[(i*m+j2)*2+1]),-(g*(y1-y2)));
+		}
+	}
+}
+// int chamfer_cuda_backward(int b,int n,const float * xyz1,int m,const float * xyz2,const float * grad_dist1,const int * idx1,const float * grad_dist2,const int * idx2,float * grad_xyz1,float * grad_xyz2, cudaStream_t stream){
+int chamfer_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2){
+	// cudaMemset(grad_xyz1,0,b*n*3*4);
+	// cudaMemset(grad_xyz2,0,b*m*3*4);
+	
+	const auto batch_size = xyz1.size(0);
+	const auto n = xyz1.size(1); //num_points point cloud A
+	const auto m = xyz2.size(1); //num_points point cloud B
+
+	NmDistanceGradKernel<<<dim3(1,16,1),256>>>(batch_size,n,xyz1.data<float>(),m,xyz2.data<float>(),graddist1.data<float>(),idx1.data<int>(),gradxyz1.data<float>(),gradxyz2.data<float>());
+	NmDistanceGradKernel<<<dim3(1,16,1),256>>>(batch_size,m,xyz2.data<float>(),n,xyz1.data<float>(),graddist2.data<float>(),idx2.data<int>(),gradxyz2.data<float>(),gradxyz1.data<float>());
+	
+	cudaError_t err = cudaGetLastError();
+	  if (err != cudaSuccess) {
+	    printf("error in nnd get grad: %s\n", cudaGetErrorString(err));
+	    //THError("aborting");
+	    return 0;
+	  }
+	  return 1;
+	
+}
+

metrics/ChamferDistancePytorch/chamfer2D/chamfer_cuda.cpp

@@ -0,0 +1,33 @@
+#include <torch/torch.h>
+#include <vector>
+
+///TMP
+//#include "common.h"
+/// NOT TMP
+	
+
+int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2);
+
+
+int chamfer_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2);
+
+
+
+
+int chamfer_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2) {
+    return chamfer_cuda_forward(xyz1, xyz2, dist1, dist2, idx1, idx2);
+}
+
+
+int chamfer_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, 
+					  at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2) {
+
+    return chamfer_cuda_backward(xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2);
+}
+
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("forward", &chamfer_forward, "chamfer forward (CUDA)");
+  m.def("backward", &chamfer_backward, "chamfer backward (CUDA)");
+}

metrics/ChamferDistancePytorch/chamfer2D/dist_chamfer_2D.py

@@ -0,0 +1,73 @@
+from torch import nn
+from torch.autograd import Function
+import torch
+import importlib
+import os
+chamfer_found = importlib.find_loader("chamfer_2D") is not None
+if not chamfer_found:
+    ## Cool trick from https://github.com/chrdiller
+    print("Jitting Chamfer 2D")
+
+    from torch.utils.cpp_extension import load
+    chamfer_2D = load(name="chamfer_2D",
+                  sources=[
+                      "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer_cuda.cpp"]),
+                      "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer2D.cu"]),
+                  ])
+    print("Loaded JIT 2D CUDA chamfer distance")
+
+else:
+    import chamfer_2D
+    print("Loaded compiled 2D CUDA chamfer distance")
+
+# Chamfer's distance module @thibaultgroueix
+# GPU tensors only
+class chamfer_2DFunction(Function):
+    @staticmethod
+    def forward(ctx, xyz1, xyz2):
+        batchsize, n, _ = xyz1.size()
+        _, m, _ = xyz2.size()
+        device = xyz1.device
+
+        dist1 = torch.zeros(batchsize, n)
+        dist2 = torch.zeros(batchsize, m)
+
+        idx1 = torch.zeros(batchsize, n).type(torch.IntTensor)
+        idx2 = torch.zeros(batchsize, m).type(torch.IntTensor)
+
+        dist1 = dist1.to(device)
+        dist2 = dist2.to(device)
+        idx1 = idx1.to(device)
+        idx2 = idx2.to(device)
+        torch.cuda.set_device(device)
+
+        chamfer_2D.forward(xyz1, xyz2, dist1, dist2, idx1, idx2)
+        ctx.save_for_backward(xyz1, xyz2, idx1, idx2)
+        return dist1, dist2, idx1, idx2
+
+    @staticmethod
+    def backward(ctx, graddist1, graddist2, gradidx1, gradidx2):
+        xyz1, xyz2, idx1, idx2 = ctx.saved_tensors
+        graddist1 = graddist1.contiguous()
+        graddist2 = graddist2.contiguous()
+        device = graddist1.device
+
+        gradxyz1 = torch.zeros(xyz1.size())
+        gradxyz2 = torch.zeros(xyz2.size())
+
+        gradxyz1 = gradxyz1.to(device)
+        gradxyz2 = gradxyz2.to(device)
+        chamfer_2D.backward(
+            xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2
+        )
+        return gradxyz1, gradxyz2
+
+
+class chamfer_2DDist(nn.Module):
+    def __init__(self):
+        super(chamfer_2DDist, self).__init__()
+
+    def forward(self, input1, input2):
+        input1 = input1.contiguous()
+        input2 = input2.contiguous()
+        return chamfer_2DFunction.apply(input1, input2)

metrics/ChamferDistancePytorch/chamfer2D/setup.py

@@ -0,0 +1,16 @@
+from setuptools import setup
+from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+
+setup(
+    name='chamfer_2D',
+    ext_modules=[
+        CUDAExtension('chamfer_2D', [
+            "/".join(__file__.split('/')[:-1] + ['chamfer_cuda.cpp']),
+            "/".join(__file__.split('/')[:-1] + ['chamfer2D.cu']),
+        ]),
+    ],
+
+    extra_cuda_cflags=['--compiler-bindir=/usr/bin/gcc-8'],
+    cmdclass={
+        'build_ext': BuildExtension
+    })

metrics/ChamferDistancePytorch/chamfer3D/chamfer3D.cu

@@ -0,0 +1,196 @@
+
+#include <stdio.h>
+#include <ATen/ATen.h>
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+#include <vector>
+
+
+
+__global__ void NmDistanceKernel(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i){
+	const int batch=512;
+	__shared__ float buf[batch*3];
+	for (int i=blockIdx.x;i<b;i+=gridDim.x){
+		for (int k2=0;k2<m;k2+=batch){
+			int end_k=min(m,k2+batch)-k2;
+			for (int j=threadIdx.x;j<end_k*3;j+=blockDim.x){
+				buf[j]=xyz2[(i*m+k2)*3+j];
+			}
+			__syncthreads();
+			for (int j=threadIdx.x+blockIdx.y*blockDim.x;j<n;j+=blockDim.x*gridDim.y){
+				float x1=xyz[(i*n+j)*3+0];
+				float y1=xyz[(i*n+j)*3+1];
+				float z1=xyz[(i*n+j)*3+2];
+				int best_i=0;
+				float best=0;
+				int end_ka=end_k-(end_k&3);
+				if (end_ka==batch){
+					for (int k=0;k<batch;k+=4){
+						{
+							float x2=buf[k*3+0]-x1;
+							float y2=buf[k*3+1]-y1;
+							float z2=buf[k*3+2]-z1;
+							float d=x2*x2+y2*y2+z2*z2;
+							if (k==0 || d<best){
+								best=d;
+								best_i=k+k2;
+							}
+						}
+						{
+							float x2=buf[k*3+3]-x1;
+							float y2=buf[k*3+4]-y1;
+							float z2=buf[k*3+5]-z1;
+							float d=x2*x2+y2*y2+z2*z2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+1;
+							}
+						}
+						{
+							float x2=buf[k*3+6]-x1;
+							float y2=buf[k*3+7]-y1;
+							float z2=buf[k*3+8]-z1;
+							float d=x2*x2+y2*y2+z2*z2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+2;
+							}
+						}
+						{
+							float x2=buf[k*3+9]-x1;
+							float y2=buf[k*3+10]-y1;
+							float z2=buf[k*3+11]-z1;
+							float d=x2*x2+y2*y2+z2*z2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+3;
+							}
+						}
+					}
+				}else{
+					for (int k=0;k<end_ka;k+=4){
+						{
+							float x2=buf[k*3+0]-x1;
+							float y2=buf[k*3+1]-y1;
+							float z2=buf[k*3+2]-z1;
+							float d=x2*x2+y2*y2+z2*z2;
+							if (k==0 || d<best){
+								best=d;
+								best_i=k+k2;
+							}
+						}
+						{
+							float x2=buf[k*3+3]-x1;
+							float y2=buf[k*3+4]-y1;
+							float z2=buf[k*3+5]-z1;
+							float d=x2*x2+y2*y2+z2*z2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+1;
+							}
+						}
+						{
+							float x2=buf[k*3+6]-x1;
+							float y2=buf[k*3+7]-y1;
+							float z2=buf[k*3+8]-z1;
+							float d=x2*x2+y2*y2+z2*z2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+2;
+							}
+						}
+						{
+							float x2=buf[k*3+9]-x1;
+							float y2=buf[k*3+10]-y1;
+							float z2=buf[k*3+11]-z1;
+							float d=x2*x2+y2*y2+z2*z2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+3;
+							}
+						}
+					}
+				}
+				for (int k=end_ka;k<end_k;k++){
+					float x2=buf[k*3+0]-x1;
+					float y2=buf[k*3+1]-y1;
+					float z2=buf[k*3+2]-z1;
+					float d=x2*x2+y2*y2+z2*z2;
+					if (k==0 || d<best){
+						best=d;
+						best_i=k+k2;
+					}
+				}
+				if (k2==0 || result[(i*n+j)]>best){
+					result[(i*n+j)]=best;
+					result_i[(i*n+j)]=best_i;
+				}
+			}
+			__syncthreads();
+		}
+	}
+}
+// int chamfer_cuda_forward(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i,float * result2,int * result2_i, cudaStream_t stream){
+int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2){
+
+	const auto batch_size = xyz1.size(0);
+	const auto n = xyz1.size(1); //num_points point cloud A
+	const auto m = xyz2.size(1); //num_points point cloud B
+
+	NmDistanceKernel<<<dim3(32,16,1),512>>>(batch_size, n, xyz1.data<float>(), m, xyz2.data<float>(), dist1.data<float>(), idx1.data<int>());
+	NmDistanceKernel<<<dim3(32,16,1),512>>>(batch_size, m, xyz2.data<float>(), n, xyz1.data<float>(), dist2.data<float>(), idx2.data<int>());
+
+	cudaError_t err = cudaGetLastError();
+	  if (err != cudaSuccess) {
+	    printf("error in nnd updateOutput: %s\n", cudaGetErrorString(err));
+	    //THError("aborting");
+	    return 0;
+	  }
+	  return 1;
+
+
+}
+__global__ void NmDistanceGradKernel(int b,int n,const float * xyz1,int m,const float * xyz2,const float * grad_dist1,const int * idx1,float * grad_xyz1,float * grad_xyz2){
+	for (int i=blockIdx.x;i<b;i+=gridDim.x){
+		for (int j=threadIdx.x+blockIdx.y*blockDim.x;j<n;j+=blockDim.x*gridDim.y){
+			float x1=xyz1[(i*n+j)*3+0];
+			float y1=xyz1[(i*n+j)*3+1];
+			float z1=xyz1[(i*n+j)*3+2];
+			int j2=idx1[i*n+j];
+			float x2=xyz2[(i*m+j2)*3+0];
+			float y2=xyz2[(i*m+j2)*3+1];
+			float z2=xyz2[(i*m+j2)*3+2];
+			float g=grad_dist1[i*n+j]*2;
+			atomicAdd(&(grad_xyz1[(i*n+j)*3+0]),g*(x1-x2));
+			atomicAdd(&(grad_xyz1[(i*n+j)*3+1]),g*(y1-y2));
+			atomicAdd(&(grad_xyz1[(i*n+j)*3+2]),g*(z1-z2));
+			atomicAdd(&(grad_xyz2[(i*m+j2)*3+0]),-(g*(x1-x2)));
+			atomicAdd(&(grad_xyz2[(i*m+j2)*3+1]),-(g*(y1-y2)));
+			atomicAdd(&(grad_xyz2[(i*m+j2)*3+2]),-(g*(z1-z2)));
+		}
+	}
+}
+// int chamfer_cuda_backward(int b,int n,const float * xyz1,int m,const float * xyz2,const float * grad_dist1,const int * idx1,const float * grad_dist2,const int * idx2,float * grad_xyz1,float * grad_xyz2, cudaStream_t stream){
+int chamfer_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2){
+	// cudaMemset(grad_xyz1,0,b*n*3*4);
+	// cudaMemset(grad_xyz2,0,b*m*3*4);
+	
+	const auto batch_size = xyz1.size(0);
+	const auto n = xyz1.size(1); //num_points point cloud A
+	const auto m = xyz2.size(1); //num_points point cloud B
+
+	NmDistanceGradKernel<<<dim3(1,16,1),256>>>(batch_size,n,xyz1.data<float>(),m,xyz2.data<float>(),graddist1.data<float>(),idx1.data<int>(),gradxyz1.data<float>(),gradxyz2.data<float>());
+	NmDistanceGradKernel<<<dim3(1,16,1),256>>>(batch_size,m,xyz2.data<float>(),n,xyz1.data<float>(),graddist2.data<float>(),idx2.data<int>(),gradxyz2.data<float>(),gradxyz1.data<float>());
+	
+	cudaError_t err = cudaGetLastError();
+	  if (err != cudaSuccess) {
+	    printf("error in nnd get grad: %s\n", cudaGetErrorString(err));
+	    //THError("aborting");
+	    return 0;
+	  }
+	  return 1;
+	
+}
+

metrics/ChamferDistancePytorch/chamfer3D/chamfer_cuda.cpp

@@ -0,0 +1,33 @@
+#include <torch/torch.h>
+#include <vector>
+
+///TMP
+//#include "common.h"
+/// NOT TMP
+	
+
+int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2);
+
+
+int chamfer_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2);
+
+
+
+
+int chamfer_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2) {
+    return chamfer_cuda_forward(xyz1, xyz2, dist1, dist2, idx1, idx2);
+}
+
+
+int chamfer_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, 
+					  at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2) {
+
+    return chamfer_cuda_backward(xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2);
+}
+
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("forward", &chamfer_forward, "chamfer forward (CUDA)");
+  m.def("backward", &chamfer_backward, "chamfer backward (CUDA)");
+}

metrics/ChamferDistancePytorch/chamfer3D/dist_chamfer_3D.py

@@ -0,0 +1,77 @@
+from torch import nn
+from torch.autograd import Function
+import torch
+import importlib
+import os
+chamfer_found = importlib.find_loader("chamfer_3D") is not None
+if not chamfer_found:
+    ## Cool trick from https://github.com/chrdiller
+    print("Jitting Chamfer 3D")
+
+    from torch.utils.cpp_extension import load
+    chamfer_3D = load(name="chamfer_3D",
+          sources=[
+              "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer_cuda.cpp"]),
+              "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer3D.cu"]),
+              ],
+
+                      extra_cuda_cflags=['--compiler-bindir=/usr/bin/gcc-8'],)
+    print("Loaded JIT 3D CUDA chamfer distance")
+
+else:
+    import chamfer_3D
+    print("Loaded compiled 3D CUDA chamfer distance")
+
+
+# Chamfer's distance module @thibaultgroueix
+# GPU tensors only
+class chamfer_3DFunction(Function):
+    @staticmethod
+    def forward(ctx, xyz1, xyz2):
+        batchsize, n, _ = xyz1.size()
+        _, m, _ = xyz2.size()
+        device = xyz1.device
+
+        dist1 = torch.zeros(batchsize, n)
+        dist2 = torch.zeros(batchsize, m)
+
+        idx1 = torch.zeros(batchsize, n).type(torch.IntTensor)
+        idx2 = torch.zeros(batchsize, m).type(torch.IntTensor)
+
+        dist1 = dist1.to(device)
+        dist2 = dist2.to(device)
+        idx1 = idx1.to(device)
+        idx2 = idx2.to(device)
+        torch.cuda.set_device(device)
+
+        chamfer_3D.forward(xyz1, xyz2, dist1, dist2, idx1, idx2)
+        ctx.save_for_backward(xyz1, xyz2, idx1, idx2)
+        return dist1, dist2, idx1, idx2
+
+    @staticmethod
+    def backward(ctx, graddist1, graddist2, gradidx1, gradidx2):
+        xyz1, xyz2, idx1, idx2 = ctx.saved_tensors
+        graddist1 = graddist1.contiguous()
+        graddist2 = graddist2.contiguous()
+        device = graddist1.device
+
+        gradxyz1 = torch.zeros(xyz1.size())
+        gradxyz2 = torch.zeros(xyz2.size())
+
+        gradxyz1 = gradxyz1.to(device)
+        gradxyz2 = gradxyz2.to(device)
+        chamfer_3D.backward(
+            xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2
+        )
+        return gradxyz1, gradxyz2
+
+
+class chamfer_3DDist(nn.Module):
+    def __init__(self):
+        super(chamfer_3DDist, self).__init__()
+
+    def forward(self, input1, input2):
+        input1 = input1.contiguous()
+        input2 = input2.contiguous()
+        return chamfer_3DFunction.apply(input1, input2)
+

metrics/ChamferDistancePytorch/chamfer3D/setup.py

@@ -0,0 +1,16 @@
+from setuptools import setup
+from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+
+setup(
+    name='chamfer_3D',
+    ext_modules=[
+        CUDAExtension('chamfer_3D', [
+            "/".join(__file__.split('/')[:-1] + ['chamfer_cuda.cpp']),
+            "/".join(__file__.split('/')[:-1] + ['chamfer3D.cu']),
+        ]),
+    ],
+
+    extra_cuda_cflags=['--compiler-bindir=/usr/bin/gcc-8'],
+    cmdclass={
+        'build_ext': BuildExtension
+    })

metrics/ChamferDistancePytorch/chamfer5D/chamfer5D.cu

@@ -0,0 +1,223 @@
+
+#include <stdio.h>
+#include <ATen/ATen.h>
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+#include <vector>
+
+
+
+__global__ void NmDistanceKernel(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i){
+	const int batch=2048;
+	__shared__ float buf[batch*5];
+	for (int i=blockIdx.x;i<b;i+=gridDim.x){
+		for (int k2=0;k2<m;k2+=batch){
+			int end_k=min(m,k2+batch)-k2;
+			for (int j=threadIdx.x;j<end_k*5;j+=blockDim.x){
+				buf[j]=xyz2[(i*m+k2)*5+j];
+			}
+			__syncthreads();
+			for (int j=threadIdx.x+blockIdx.y*blockDim.x;j<n;j+=blockDim.x*gridDim.y){
+				float x1=xyz[(i*n+j)*5+0];
+				float y1=xyz[(i*n+j)*5+1];
+				float r1=xyz[(i*n+j)*5+2];
+				float g1=xyz[(i*n+j)*5+3];
+				float b1=xyz[(i*n+j)*5+4];
+				int best_i=0;
+				float best=0;
+				int end_ka=end_k-(end_k&5);
+				if (end_ka==batch){
+					for (int k=0;k<batch;k+=4){
+						{
+							float x2=buf[k*5+0]-x1;
+							float y2=buf[k*5+1]-y1;
+							float r2=buf[k*5+2]-r1;
+							float g2=buf[k*5+3]-g1;
+							float b2=buf[k*5+4]-b1;
+							float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
+							if (k==0 || d<best){
+								best=d;
+								best_i=k+k2;
+							}
+						}
+						{
+							float x2=buf[k*5+5]-x1;
+							float y2=buf[k*5+6]-y1;
+							float r2=buf[k*5+7]-r1;
+							float g2=buf[k*5+8]-g1;
+							float b2=buf[k*5+9]-b1;
+							float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+1;
+							}
+						}
+						{
+							float x2=buf[k*5+10]-x1;
+							float y2=buf[k*5+11]-y1;
+							float r2=buf[k*5+12]-r1;
+							float g2=buf[k*5+13]-g1;
+							float b2=buf[k*5+14]-b1;
+							float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+2;
+							}
+						}
+						{
+							float x2=buf[k*5+15]-x1;
+							float y2=buf[k*5+16]-y1;
+							float r2=buf[k*5+17]-r1;
+							float g2=buf[k*5+18]-g1;
+							float b2=buf[k*5+19]-b1;
+							float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+3;
+							}
+						}
+					}
+				}else{
+					for (int k=0;k<end_ka;k+=4){
+						{
+							float x2=buf[k*5+0]-x1;
+							float y2=buf[k*5+1]-y1;
+							float r2=buf[k*5+2]-r1;
+							float g2=buf[k*5+3]-g1;
+							float b2=buf[k*5+4]-b1;
+							float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
+							if (k==0 || d<best){
+								best=d;
+								best_i=k+k2;
+							}
+						}
+						{
+							float x2=buf[k*5+5]-x1;
+							float y2=buf[k*5+6]-y1;
+							float r2=buf[k*5+7]-r1;
+							float g2=buf[k*5+8]-g1;
+							float b2=buf[k*5+9]-b1;
+							float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+1;
+							}
+						}
+						{
+							float x2=buf[k*5+10]-x1;
+							float y2=buf[k*5+11]-y1;
+							float r2=buf[k*5+12]-r1;
+							float g2=buf[k*5+13]-g1;
+							float b2=buf[k*5+14]-b1;
+							float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+2;
+							}
+						}
+						{
+							float x2=buf[k*5+15]-x1;
+							float y2=buf[k*5+16]-y1;
+							float r2=buf[k*5+17]-r1;
+							float g2=buf[k*5+18]-g1;
+							float b2=buf[k*5+19]-b1;
+							float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
+							if (d<best){
+								best=d;
+								best_i=k+k2+3;
+							}
+						}
+					}
+				}
+				for (int k=end_ka;k<end_k;k++){
+					float x2=buf[k*5+0]-x1;
+					float y2=buf[k*5+1]-y1;
+					float r2=buf[k*5+2]-r1;
+					float g2=buf[k*5+3]-g1;
+					float b2=buf[k*5+4]-b1;
+					float d=x2*x2+y2*y2+r2*r2+g2*g2+b2*b2;
+					if (k==0 || d<best){
+						best=d;
+						best_i=k+k2;
+					}
+				}
+				if (k2==0 || result[(i*n+j)]>best){
+					result[(i*n+j)]=best;
+					result_i[(i*n+j)]=best_i;
+				}
+			}
+			__syncthreads();
+		}
+	}
+}
+// int chamfer_cuda_forward(int b,int n,const float * xyz,int m,const float * xyz2,float * result,int * result_i,float * result2,int * result2_i, cudaStream_t stream){
+int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2){
+
+	const auto batch_size = xyz1.size(0);
+	const auto n = xyz1.size(1); //num_points point cloud A
+	const auto m = xyz2.size(1); //num_points point cloud B
+
+	NmDistanceKernel<<<dim3(32,16,1),512>>>(batch_size, n, xyz1.data<float>(), m, xyz2.data<float>(), dist1.data<float>(), idx1.data<int>());
+	NmDistanceKernel<<<dim3(32,16,1),512>>>(batch_size, m, xyz2.data<float>(), n, xyz1.data<float>(), dist2.data<float>(), idx2.data<int>());
+
+	cudaError_t err = cudaGetLastError();
+	  if (err != cudaSuccess) {
+	    printf("error in nnd updateOutput: %s\n", cudaGetErrorString(err));
+	    //THError("aborting");
+	    return 0;
+	  }
+	  return 1;
+
+
+}
+__global__ void NmDistanceGradKernel(int b,int n,const float * xyz1,int m,const float * xyz2,const float * grad_dist1,const int * idx1,float * grad_xyz1,float * grad_xyz2){
+	for (int i=blockIdx.x;i<b;i+=gridDim.x){
+		for (int j=threadIdx.x+blockIdx.y*blockDim.x;j<n;j+=blockDim.x*gridDim.y){
+			float x1=xyz1[(i*n+j)*5+0];
+			float y1=xyz1[(i*n+j)*5+1];
+			float r1=xyz1[(i*n+j)*5+2];
+			float g1=xyz1[(i*n+j)*5+3];
+			float b1=xyz1[(i*n+j)*5+4];
+			int j2=idx1[i*n+j];
+			float x2=xyz2[(i*m+j2)*5+0];
+			float y2=xyz2[(i*m+j2)*5+1];
+			float r2=xyz2[(i*m+j2)*5+2];
+			float g2=xyz2[(i*m+j2)*5+3];
+			float b2=xyz2[(i*m+j2)*5+4];
+			float g=grad_dist1[i*n+j]*2;
+			atomicAdd(&(grad_xyz1[(i*n+j)*5+0]),g*(x1-x2));
+			atomicAdd(&(grad_xyz1[(i*n+j)*5+1]),g*(y1-y2));
+			atomicAdd(&(grad_xyz1[(i*n+j)*5+2]),g*(r1-r2));
+			atomicAdd(&(grad_xyz1[(i*n+j)*5+3]),g*(g1-g2));
+			atomicAdd(&(grad_xyz1[(i*n+j)*5+4]),g*(b1-b2));
+			atomicAdd(&(grad_xyz2[(i*m+j2)*5+0]),-(g*(x1-x2)));
+			atomicAdd(&(grad_xyz2[(i*m+j2)*5+1]),-(g*(y1-y2)));
+			atomicAdd(&(grad_xyz2[(i*m+j2)*5+2]),-(g*(r1-r2)));
+			atomicAdd(&(grad_xyz2[(i*m+j2)*5+3]),-(g*(g1-g2)));
+			atomicAdd(&(grad_xyz2[(i*m+j2)*5+4]),-(g*(b1-b2)));
+		}
+	}
+}
+// int chamfer_cuda_backward(int b,int n,const float * xyz1,int m,const float * xyz2,const float * grad_dist1,const int * idx1,const float * grad_dist2,const int * idx2,float * grad_xyz1,float * grad_xyz2, cudaStream_t stream){
+int chamfer_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2){
+	// cudaMemset(grad_xyz1,0,b*n*3*4);
+	// cudaMemset(grad_xyz2,0,b*m*3*4);
+
+	const auto batch_size = xyz1.size(0);
+	const auto n = xyz1.size(1); //num_points point cloud A
+	const auto m = xyz2.size(1); //num_points point cloud B
+
+	NmDistanceGradKernel<<<dim3(1,16,1),256>>>(batch_size,n,xyz1.data<float>(),m,xyz2.data<float>(),graddist1.data<float>(),idx1.data<int>(),gradxyz1.data<float>(),gradxyz2.data<float>());
+	NmDistanceGradKernel<<<dim3(1,16,1),256>>>(batch_size,m,xyz2.data<float>(),n,xyz1.data<float>(),graddist2.data<float>(),idx2.data<int>(),gradxyz2.data<float>(),gradxyz1.data<float>());
+
+	cudaError_t err = cudaGetLastError();
+	  if (err != cudaSuccess) {
+	    printf("error in nnd get grad: %s\n", cudaGetErrorString(err));
+	    //THError("aborting");
+	    return 0;
+	  }
+	  return 1;
+
+}

metrics/ChamferDistancePytorch/chamfer5D/chamfer_cuda.cpp

@@ -0,0 +1,33 @@
+#include <torch/torch.h>
+#include <vector>
+
+///TMP
+//#include "common.h"
+/// NOT TMP
+	
+
+int chamfer_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2);
+
+
+int chamfer_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2);
+
+
+
+
+int chamfer_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist1, at::Tensor dist2, at::Tensor idx1, at::Tensor idx2) {
+    return chamfer_cuda_forward(xyz1, xyz2, dist1, dist2, idx1, idx2);
+}
+
+
+int chamfer_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz1, at::Tensor gradxyz2, at::Tensor graddist1, 
+					  at::Tensor graddist2, at::Tensor idx1, at::Tensor idx2) {
+
+    return chamfer_cuda_backward(xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2);
+}
+
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("forward", &chamfer_forward, "chamfer forward (CUDA)");
+  m.def("backward", &chamfer_backward, "chamfer backward (CUDA)");
+}

metrics/ChamferDistancePytorch/chamfer5D/dist_chamfer_5D.py

@@ -0,0 +1,75 @@
+from torch import nn
+from torch.autograd import Function
+import torch
+import importlib
+import os
+
+chamfer_found = importlib.find_loader("chamfer_5D") is not None
+if not chamfer_found:
+    ## Cool trick from https://github.com/chrdiller
+    print("Jitting Chamfer 5D")
+
+    from torch.utils.cpp_extension import load
+    chamfer_5D = load(name="chamfer_5D",
+                      sources=[
+                          "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer_cuda.cpp"]),
+                          "/".join(os.path.abspath(__file__).split('/')[:-1] + ["chamfer5D.cu"]),
+                      ])
+    print("Loaded JIT 5D CUDA chamfer distance")
+
+else:
+    import chamfer_5D
+    print("Loaded compiled 5D CUDA chamfer distance")
+
+
+# Chamfer's distance module @thibaultgroueix
+# GPU tensors only
+class chamfer_5DFunction(Function):
+    @staticmethod
+    def forward(ctx, xyz1, xyz2):
+        batchsize, n, _ = xyz1.size()
+        _, m, _ = xyz2.size()
+        device = xyz1.device
+
+        dist1 = torch.zeros(batchsize, n)
+        dist2 = torch.zeros(batchsize, m)
+
+        idx1 = torch.zeros(batchsize, n).type(torch.IntTensor)
+        idx2 = torch.zeros(batchsize, m).type(torch.IntTensor)
+
+        dist1 = dist1.to(device)
+        dist2 = dist2.to(device)
+        idx1 = idx1.to(device)
+        idx2 = idx2.to(device)
+        torch.cuda.set_device(device)
+
+        chamfer_5D.forward(xyz1, xyz2, dist1, dist2, idx1, idx2)
+        ctx.save_for_backward(xyz1, xyz2, idx1, idx2)
+        return dist1, dist2, idx1, idx2
+
+    @staticmethod
+    def backward(ctx, graddist1, graddist2, gradidx1, gradidx2):
+        xyz1, xyz2, idx1, idx2 = ctx.saved_tensors
+        graddist1 = graddist1.contiguous()
+        graddist2 = graddist2.contiguous()
+        device = graddist1.device
+
+        gradxyz1 = torch.zeros(xyz1.size())
+        gradxyz2 = torch.zeros(xyz2.size())
+
+        gradxyz1 = gradxyz1.to(device)
+        gradxyz2 = gradxyz2.to(device)
+        chamfer_5D.backward(
+            xyz1, xyz2, gradxyz1, gradxyz2, graddist1, graddist2, idx1, idx2
+        )
+        return gradxyz1, gradxyz2
+
+
+class chamfer_5DDist(nn.Module):
+    def __init__(self):
+        super(chamfer_5DDist, self).__init__()
+
+    def forward(self, input1, input2):
+        input1 = input1.contiguous()
+        input2 = input2.contiguous()
+        return chamfer_5DFunction.apply(input1, input2)

metrics/ChamferDistancePytorch/chamfer5D/setup.py

@@ -0,0 +1,16 @@
+from setuptools import setup
+from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+
+setup(
+    name='chamfer_5D',
+    ext_modules=[
+        CUDAExtension('chamfer_5D', [
+            "/".join(__file__.split('/')[:-1] + ['chamfer_cuda.cpp']),
+            "/".join(__file__.split('/')[:-1] + ['chamfer5D.cu']),
+        ]),
+    ],
+
+    extra_cuda_cflags=['--compiler-bindir=/usr/bin/gcc-8'],
+    cmdclass={
+        'build_ext': BuildExtension
+    })

metrics/ChamferDistancePytorch/chamfer_python.py

@@ -0,0 +1,40 @@
+import torch
+
+
+def pairwise_dist(x, y):
+    xx, yy, zz = torch.mm(x, x.t()), torch.mm(y, y.t()), torch.mm(x, y.t())
+    rx = xx.diag().unsqueeze(0).expand_as(xx)
+    ry = yy.diag().unsqueeze(0).expand_as(yy)
+    P = rx.t() + ry - 2 * zz
+    return P
+
+
+def NN_loss(x, y, dim=0):
+    dist = pairwise_dist(x, y)
+    values, indices = dist.min(dim=dim)
+    return values.mean()
+
+
+def distChamfer(a, b):
+    """
+    :param a: Pointclouds Batch x nul_points x dim
+    :param b:  Pointclouds Batch x nul_points x dim
+    :return:
+    -closest point on b of points from a
+    -closest point on a of points from b
+    -idx of closest point on b of points from a
+    -idx of closest point on a of points from b
+    Works for pointcloud of any dimension
+    """
+    x, y = a.double(), b.double()
+    bs, num_points_x, points_dim = x.size()
+    bs, num_points_y, points_dim = y.size()
+
+    xx = torch.pow(x, 2).sum(2)
+    yy = torch.pow(y, 2).sum(2)
+    zz = torch.bmm(x, y.transpose(2, 1))
+    rx = xx.unsqueeze(1).expand(bs, num_points_y, num_points_x) # Diagonal elements xx
+    ry = yy.unsqueeze(1).expand(bs, num_points_x, num_points_y) # Diagonal elements yy
+    P = rx.transpose(2, 1) + ry - 2 * zz
+    return torch.min(P, 2)[0].float(), torch.min(P, 1)[0].float(), torch.min(P, 2)[1].int(), torch.min(P, 1)[1].int()
+

metrics/ChamferDistancePytorch/fscore.py

@@ -0,0 +1,17 @@
+import torch
+
+def fscore(dist1, dist2, threshold=0.001):
+    """
+    Calculates the F-score between two point clouds with the corresponding threshold value.
+    :param dist1: Batch, N-Points
+    :param dist2: Batch, N-Points
+    :param th: float
+    :return: fscore, precision, recall
+    """
+    # NB : In this depo, dist1 and dist2 are squared pointcloud euclidean distances, so you should adapt the threshold accordingly.
+    precision_1 = torch.mean((dist1 < threshold).float(), dim=1)
+    precision_2 = torch.mean((dist2 < threshold).float(), dim=1)
+    fscore = 2 * precision_1 * precision_2 / (precision_1 + precision_2)
+    fscore[torch.isnan(fscore)] = 0
+    return fscore, precision_1, precision_2
+

metrics/ChamferDistancePytorch/unit_test.py

@@ -0,0 +1,69 @@
+import torch, time
+import chamfer2D.dist_chamfer_2D
+import chamfer3D.dist_chamfer_3D
+import chamfer5D.dist_chamfer_5D
+import chamfer_python
+
+cham2D = chamfer2D.dist_chamfer_2D.chamfer_2DDist()
+cham3D = chamfer3D.dist_chamfer_3D.chamfer_3DDist()
+cham5D = chamfer5D.dist_chamfer_5D.chamfer_5DDist()
+
+from torch.autograd import Variable
+from fscore import fscore
+
+def test_chamfer(distChamfer, dim):
+    points1 = torch.rand(4, 100, dim).cuda()
+    points2 = torch.rand(4, 200, dim, requires_grad=True).cuda()
+    dist1, dist2, idx1, idx2= distChamfer(points1, points2)
+
+    loss = torch.sum(dist1)
+    loss.backward()
+
+    mydist1, mydist2, myidx1, myidx2 = chamfer_python.distChamfer(points1, points2)
+    d1 = (dist1 - mydist1) ** 2
+    d2 = (dist2 - mydist2) ** 2
+    assert (
+        torch.mean(d1) + torch.mean(d2) < 0.00000001
+    ), "chamfer cuda and chamfer normal are not giving the same results"
+
+    xd1 = idx1 - myidx1
+    xd2 = idx2 - myidx2
+    assert (
+            torch.norm(xd1.float()) + torch.norm(xd2.float()) == 0
+    ), "chamfer cuda and chamfer normal are not giving the same results"
+    print(f"fscore :", fscore(dist1, dist2))
+    print("Unit test passed")
+
+
+def timings(distChamfer, dim):
+    p1 = torch.rand(32, 2000, dim).cuda()
+    p2 = torch.rand(32, 1000, dim).cuda()
+    print("Timings : Start CUDA version")
+    start = time.time()
+    num_it = 100
+    for i in range(num_it):
+        points1 = Variable(p1, requires_grad=True)
+        points2 = Variable(p2)
+        mydist1, mydist2, idx1, idx2 = distChamfer(points1, points2)
+        loss = torch.sum(mydist1)
+        loss.backward()
+    print(f"Ellapsed time forward backward is {(time.time() - start)/num_it} seconds.")
+
+
+    print("Timings : Start Pythonic version")
+    start = time.time()
+    for i in range(num_it):
+        points1 = Variable(p1, requires_grad=True)
+        points2 = Variable(p2)
+        mydist1, mydist2, idx1, idx2 = chamfer_python.distChamfer(points1, points2)
+        loss = torch.sum(mydist1)
+        loss.backward()
+    print(f"Ellapsed time  forward backward  is {(time.time() - start)/num_it} seconds.")
+
+
+
+dims = [2,3,5]
+for i,cham in enumerate([cham2D, cham3D, cham5D]):
+    print(f"testing Chamfer {dims[i]}D")
+    test_chamfer(cham, dims[i])
+    timings(cham, dims[i])

metrics/PyTorchEMD/.gitignore

@@ -0,0 +1,5 @@
+__pycache__
+build
+dist
+emd_ext.egg-info
+*.so

metrics/PyTorchEMD/README.md

@@ -0,0 +1,31 @@
+# PyTorch Wrapper for Point-cloud Earth-Mover-Distance (EMD)
+
+## Dependency
+
+The code has been tested on Ubuntu 16.04, PyTorch 1.1.0, CUDA 9.0.
+
+## Usage
+
+First compile using
+        
+        python setup.py install
+
+Then, copy the lib file out to the main directory,
+
+        cp build/lib.linux-x86_64-3.6/emd_cuda.cpython-36m-x86_64-linux-gnu.so .
+
+Then, you can use it by simply
+
+        from emd import earth_mover_distance
+        d = earth_mover_distance(p1, p2, transpose=False)  # p1: B x N1 x 3, p2: B x N2 x 3
+
+Check `test_emd_loss.py` for example.
+
+## Author
+
+The cuda code is originally written by Haoqiang Fan. The PyTorch wrapper is written by Kaichun Mo. Also, Jiayuan Gu provided helps.
+
+## License
+
+MIT
+

metrics/PyTorchEMD/cuda/emd.cpp

@@ -0,0 +1,29 @@
+#ifndef _EMD
+#define _EMD
+
+#include <vector>
+#include <torch/extension.h>
+
+//CUDA declarations
+at::Tensor ApproxMatchForward(
+    const at::Tensor xyz1,
+    const at::Tensor xyz2);
+
+at::Tensor MatchCostForward(
+    const at::Tensor xyz1,
+    const at::Tensor xyz2,
+    const at::Tensor match);
+
+std::vector<at::Tensor> MatchCostBackward(
+    const at::Tensor grad_cost,
+    const at::Tensor xyz1,
+    const at::Tensor xyz2,
+    const at::Tensor match);
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("approxmatch_forward", &ApproxMatchForward,"ApproxMatch forward (CUDA)");
+  m.def("matchcost_forward", &MatchCostForward,"MatchCost forward (CUDA)");
+  m.def("matchcost_backward", &MatchCostBackward,"MatchCost backward (CUDA)");
+}
+
+#endif

metrics/PyTorchEMD/cuda/emd_kernel.cu

@@ -0,0 +1,400 @@
+/**********************************
+ * Original Author: Haoqiang Fan
+ * Modified by: Kaichun Mo
+ *********************************/
+
+#ifndef _EMD_KERNEL
+#define _EMD_KERNEL
+
+#include <cmath>
+#include <vector>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAApplyUtils.cuh>  // at::cuda::getApplyGrid
+#include <THC/THC.h>
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
+#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
+
+
+/********************************
+* Forward kernel for approxmatch
+*********************************/
+
+template<typename scalar_t>
+__global__ void approxmatch(int b,int n,int m,const scalar_t * __restrict__ xyz1,const scalar_t * __restrict__ xyz2,scalar_t * __restrict__ match,scalar_t * temp){
+	scalar_t * remainL=temp+blockIdx.x*(n+m)*2, * remainR=temp+blockIdx.x*(n+m)*2+n,*ratioL=temp+blockIdx.x*(n+m)*2+n+m,*ratioR=temp+blockIdx.x*(n+m)*2+n+m+n;
+	scalar_t multiL,multiR;
+	if (n>=m){
+		multiL=1;
+		multiR=n/m;
+	}else{
+		multiL=m/n;
+		multiR=1;
+	}
+	const int Block=1024;
+	__shared__ scalar_t buf[Block*4];
+	for (int i=blockIdx.x;i<b;i+=gridDim.x){
+		for (int j=threadIdx.x;j<n*m;j+=blockDim.x)
+			match[i*n*m+j]=0;
+		for (int j=threadIdx.x;j<n;j+=blockDim.x)
+			remainL[j]=multiL;
+		for (int j=threadIdx.x;j<m;j+=blockDim.x)
+			remainR[j]=multiR;
+		__syncthreads();
+		for (int j=7;j>=-2;j--){
+			scalar_t level=-powf(4.0f,j);
+			if (j==-2){
+				level=0;
+			}
+			for (int k0=0;k0<n;k0+=blockDim.x){
+				int k=k0+threadIdx.x;
+				scalar_t x1=0,y1=0,z1=0;
+				if (k<n){
+					x1=xyz1[i*n*3+k*3+0];
+					y1=xyz1[i*n*3+k*3+1];
+					z1=xyz1[i*n*3+k*3+2];
+				}
+				scalar_t suml=1e-9f;
+				for (int l0=0;l0<m;l0+=Block){
+					int lend=min(m,l0+Block)-l0;
+					for (int l=threadIdx.x;l<lend;l+=blockDim.x){
+						scalar_t x2=xyz2[i*m*3+l0*3+l*3+0];
+						scalar_t y2=xyz2[i*m*3+l0*3+l*3+1];
+						scalar_t z2=xyz2[i*m*3+l0*3+l*3+2];
+						buf[l*4+0]=x2;
+						buf[l*4+1]=y2;
+						buf[l*4+2]=z2;
+						buf[l*4+3]=remainR[l0+l];
+					}
+					__syncthreads();
+					for (int l=0;l<lend;l++){
+						scalar_t x2=buf[l*4+0];
+						scalar_t y2=buf[l*4+1];
+						scalar_t z2=buf[l*4+2];
+						scalar_t d=level*((x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1));
+						scalar_t w=__expf(d)*buf[l*4+3];
+						suml+=w;
+					}
+					__syncthreads();
+				}
+				if (k<n)
+					ratioL[k]=remainL[k]/suml;
+			}
+			__syncthreads();
+			for (int l0=0;l0<m;l0+=blockDim.x){
+				int l=l0+threadIdx.x;
+				scalar_t x2=0,y2=0,z2=0;
+				if (l<m){
+					x2=xyz2[i*m*3+l*3+0];
+					y2=xyz2[i*m*3+l*3+1];
+					z2=xyz2[i*m*3+l*3+2];
+				}
+				scalar_t sumr=0;
+				for (int k0=0;k0<n;k0+=Block){
+					int kend=min(n,k0+Block)-k0;
+					for (int k=threadIdx.x;k<kend;k+=blockDim.x){
+						buf[k*4+0]=xyz1[i*n*3+k0*3+k*3+0];
+						buf[k*4+1]=xyz1[i*n*3+k0*3+k*3+1];
+						buf[k*4+2]=xyz1[i*n*3+k0*3+k*3+2];
+						buf[k*4+3]=ratioL[k0+k];
+					}
+					__syncthreads();
+					for (int k=0;k<kend;k++){
+						scalar_t x1=buf[k*4+0];
+						scalar_t y1=buf[k*4+1];
+						scalar_t z1=buf[k*4+2];
+						scalar_t w=__expf(level*((x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1)))*buf[k*4+3];
+						sumr+=w;
+					}
+					__syncthreads();
+				}
+				if (l<m){
+					sumr*=remainR[l];
+					scalar_t consumption=fminf(remainR[l]/(sumr+1e-9f),1.0f);
+					ratioR[l]=consumption*remainR[l];
+					remainR[l]=fmaxf(0.0f,remainR[l]-sumr);
+				}
+			}
+			__syncthreads();
+			for (int k0=0;k0<n;k0+=blockDim.x){
+				int k=k0+threadIdx.x;
+				scalar_t x1=0,y1=0,z1=0;
+				if (k<n){
+					x1=xyz1[i*n*3+k*3+0];
+					y1=xyz1[i*n*3+k*3+1];
+					z1=xyz1[i*n*3+k*3+2];
+				}
+				scalar_t suml=0;
+				for (int l0=0;l0<m;l0+=Block){
+					int lend=min(m,l0+Block)-l0;
+					for (int l=threadIdx.x;l<lend;l+=blockDim.x){
+						buf[l*4+0]=xyz2[i*m*3+l0*3+l*3+0];
+						buf[l*4+1]=xyz2[i*m*3+l0*3+l*3+1];
+						buf[l*4+2]=xyz2[i*m*3+l0*3+l*3+2];
+						buf[l*4+3]=ratioR[l0+l];
+					}
+					__syncthreads();
+					scalar_t rl=ratioL[k];
+					if (k<n){
+						for (int l=0;l<lend;l++){
+							scalar_t x2=buf[l*4+0];
+							scalar_t y2=buf[l*4+1];
+							scalar_t z2=buf[l*4+2];
+							scalar_t w=__expf(level*((x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1)))*rl*buf[l*4+3];
+							match[i*n*m+(l0+l)*n+k]+=w;
+							suml+=w;
+						}
+					}
+					__syncthreads();
+				}
+				if (k<n)
+					remainL[k]=fmaxf(0.0f,remainL[k]-suml);
+			}
+			__syncthreads();
+		}
+	}
+}
+
+//void approxmatchLauncher(int b,int n,int m,const scalar_t * xyz1,const scalar_t * xyz2,scalar_t * match,scalar_t * temp){
+//	approxmatch<<<32,512>>>(b,n,m,xyz1,xyz2,match,temp);
+//}
+
+/* ApproxMatch forward interface
+Input:
+  xyz1: (B, N1, 3)  # dataset_points
+  xyz2: (B, N2, 3)  # query_points
+Output:
+  match: (B, N2, N1)
+*/
+at::Tensor ApproxMatchForward(
+    const at::Tensor xyz1,
+    const at::Tensor xyz2){
+  const auto b = xyz1.size(0);
+  const auto n = xyz1.size(1);
+  const auto m = xyz2.size(1);
+
+  CHECK_EQ(xyz2.size(0), b);
+  CHECK_EQ(xyz1.size(2), 3);
+  CHECK_EQ(xyz2.size(2), 3);
+  CHECK_INPUT(xyz1);
+  CHECK_INPUT(xyz2);
+
+  auto match = at::zeros({b, m, n}, xyz1.type());
+  auto temp = at::zeros({b, (n+m)*2}, xyz1.type());
+
+  AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "ApproxMatchForward", ([&] {
+        approxmatch<scalar_t><<<32,512>>>(b, n, m, xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), temp.data<scalar_t>());
+  }));
+  THCudaCheck(cudaGetLastError());
+
+  return match;
+}
+
+
+/********************************
+* Forward kernel for matchcost
+*********************************/
+
+template<typename scalar_t>
+__global__ void matchcost(int b,int n,int m,const scalar_t * __restrict__ xyz1,const scalar_t * __restrict__ xyz2,const scalar_t * __restrict__ match,scalar_t * __restrict__ out){
+	__shared__ scalar_t allsum[512];
+	const int Block=1024;
+	__shared__ scalar_t buf[Block*3];
+	for (int i=blockIdx.x;i<b;i+=gridDim.x){
+		scalar_t subsum=0;
+		for (int k0=0;k0<n;k0+=blockDim.x){
+			int k=k0+threadIdx.x;
+			scalar_t x1=0,y1=0,z1=0;
+			if (k<n){
+				x1=xyz1[i*n*3+k*3+0];
+				y1=xyz1[i*n*3+k*3+1];
+				z1=xyz1[i*n*3+k*3+2];
+			}
+			for (int l0=0;l0<m;l0+=Block){
+				int lend=min(m,l0+Block)-l0;
+				for (int l=threadIdx.x;l<lend*3;l+=blockDim.x)
+					buf[l]=xyz2[i*m*3+l0*3+l];
+				__syncthreads();
+				if (k<n){
+					for (int l=0;l<lend;l++){
+						scalar_t x2=buf[l*3+0];
+						scalar_t y2=buf[l*3+1];
+						scalar_t z2=buf[l*3+2];
+						scalar_t d=(x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1);
+						subsum+=d*match[i*n*m+(l0+l)*n+k];
+					}
+				}
+				__syncthreads();
+			}
+		}
+		allsum[threadIdx.x]=subsum;
+		for (int j=1;j<blockDim.x;j<<=1){
+			__syncthreads();
+			if ((threadIdx.x&j)==0 && threadIdx.x+j<blockDim.x){
+				allsum[threadIdx.x]+=allsum[threadIdx.x+j];
+			}
+		}
+		if (threadIdx.x==0)
+			out[i]=allsum[0];
+		__syncthreads();
+	}
+}
+
+//void matchcostLauncher(int b,int n,int m,const scalar_t * xyz1,const scalar_t * xyz2,const scalar_t * match,scalar_t * out){
+//	matchcost<<<32,512>>>(b,n,m,xyz1,xyz2,match,out);
+//}
+
+/* MatchCost forward interface
+Input:
+  xyz1: (B, N1, 3)  # dataset_points
+  xyz2: (B, N2, 3)  # query_points
+  match: (B, N2, N1)
+Output:
+  cost: (B)
+*/
+at::Tensor MatchCostForward(
+    const at::Tensor xyz1,
+    const at::Tensor xyz2,
+    const at::Tensor match){
+  const auto b = xyz1.size(0);
+  const auto n = xyz1.size(1);
+  const auto m = xyz2.size(1);
+
+  CHECK_EQ(xyz2.size(0), b);
+  CHECK_EQ(xyz1.size(2), 3);
+  CHECK_EQ(xyz2.size(2), 3);
+  CHECK_INPUT(xyz1);
+  CHECK_INPUT(xyz2);
+
+  auto cost = at::zeros({b}, xyz1.type());
+
+  AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostForward", ([&] {
+        matchcost<scalar_t><<<32,512>>>(b, n, m, xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), cost.data<scalar_t>());
+  }));
+  THCudaCheck(cudaGetLastError());
+
+  return cost;
+}
+
+
+/********************************
+* matchcostgrad2 kernel
+*********************************/
+
+template<typename scalar_t>
+__global__ void matchcostgrad2(int b,int n,int m,const scalar_t * __restrict__ grad_cost,const scalar_t * __restrict__ xyz1,const scalar_t * __restrict__ xyz2,const scalar_t * __restrict__ match,scalar_t * __restrict__ grad2){
+	__shared__ scalar_t sum_grad[256*3];
+	for (int i=blockIdx.x;i<b;i+=gridDim.x){
+		int kbeg=m*blockIdx.y/gridDim.y;
+		int kend=m*(blockIdx.y+1)/gridDim.y;
+		for (int k=kbeg;k<kend;k++){
+			scalar_t x2=xyz2[(i*m+k)*3+0];
+			scalar_t y2=xyz2[(i*m+k)*3+1];
+			scalar_t z2=xyz2[(i*m+k)*3+2];
+			scalar_t subsumx=0,subsumy=0,subsumz=0;
+			for (int j=threadIdx.x;j<n;j+=blockDim.x){
+				scalar_t x1=x2-xyz1[(i*n+j)*3+0];
+				scalar_t y1=y2-xyz1[(i*n+j)*3+1];
+				scalar_t z1=z2-xyz1[(i*n+j)*3+2];
+				scalar_t d=match[i*n*m+k*n+j]*2;
+				subsumx+=x1*d;
+				subsumy+=y1*d;
+				subsumz+=z1*d;
+			}
+			sum_grad[threadIdx.x*3+0]=subsumx;
+			sum_grad[threadIdx.x*3+1]=subsumy;
+			sum_grad[threadIdx.x*3+2]=subsumz;
+			for (int j=1;j<blockDim.x;j<<=1){
+				__syncthreads();
+				int j1=threadIdx.x;
+				int j2=threadIdx.x+j;
+				if ((j1&j)==0 && j2<blockDim.x){
+					sum_grad[j1*3+0]+=sum_grad[j2*3+0];
+					sum_grad[j1*3+1]+=sum_grad[j2*3+1];
+					sum_grad[j1*3+2]+=sum_grad[j2*3+2];
+				}
+			}
+			if (threadIdx.x==0){
+				grad2[(i*m+k)*3+0]=sum_grad[0]*grad_cost[i];
+				grad2[(i*m+k)*3+1]=sum_grad[1]*grad_cost[i];
+				grad2[(i*m+k)*3+2]=sum_grad[2]*grad_cost[i];
+			}
+			__syncthreads();
+		}
+	}
+}
+
+/********************************
+* matchcostgrad1 kernel
+*********************************/
+
+template<typename scalar_t>
+__global__ void matchcostgrad1(int b,int n,int m,const scalar_t * __restrict__ grad_cost,const scalar_t * __restrict__ xyz1,const scalar_t * __restrict__ xyz2,const scalar_t * __restrict__ match,scalar_t * __restrict__ grad1){
+	for (int i=blockIdx.x;i<b;i+=gridDim.x){
+		for (int l=threadIdx.x;l<n;l+=blockDim.x){
+			scalar_t x1=xyz1[i*n*3+l*3+0];
+			scalar_t y1=xyz1[i*n*3+l*3+1];
+			scalar_t z1=xyz1[i*n*3+l*3+2];
+			scalar_t dx=0,dy=0,dz=0;
+			for (int k=0;k<m;k++){
+				scalar_t x2=xyz2[i*m*3+k*3+0];
+				scalar_t y2=xyz2[i*m*3+k*3+1];
+				scalar_t z2=xyz2[i*m*3+k*3+2];
+				scalar_t d=match[i*n*m+k*n+l]*2;
+				dx+=(x1-x2)*d;
+				dy+=(y1-y2)*d;
+				dz+=(z1-z2)*d;
+			}
+			grad1[i*n*3+l*3+0]=dx*grad_cost[i];
+			grad1[i*n*3+l*3+1]=dy*grad_cost[i];
+			grad1[i*n*3+l*3+2]=dz*grad_cost[i];
+		}
+	}
+}
+
+//void matchcostgradLauncher(int b,int n,int m,const scalar_t * xyz1,const scalar_t * xyz2,const scalar_t * match,scalar_t * grad1,scalar_t * grad2){
+//	matchcostgrad1<<<32,512>>>(b,n,m,xyz1,xyz2,match,grad1);
+//	matchcostgrad2<<<dim3(32,32),256>>>(b,n,m,xyz1,xyz2,match,grad2);
+//}
+
+
+/* MatchCost backward interface
+Input:
+  grad_cost: (B)    # gradients on cost
+  xyz1: (B, N1, 3)  # dataset_points
+  xyz2: (B, N2, 3)  # query_points
+  match: (B, N2, N1)
+Output:
+  grad1: (B, N1, 3)
+  grad2: (B, N2, 3)
+*/
+std::vector<at::Tensor> MatchCostBackward(
+    const at::Tensor grad_cost,
+    const at::Tensor xyz1,
+    const at::Tensor xyz2,
+    const at::Tensor match){
+  const auto b = xyz1.size(0);
+  const auto n = xyz1.size(1);
+  const auto m = xyz2.size(1);
+
+  CHECK_EQ(xyz2.size(0), b);
+  CHECK_EQ(xyz1.size(2), 3);
+  CHECK_EQ(xyz2.size(2), 3);
+  CHECK_INPUT(xyz1);
+  CHECK_INPUT(xyz2);
+
+  auto grad1 = at::zeros({b, n, 3}, xyz1.type());
+  auto grad2 = at::zeros({b, m, 3}, xyz1.type());
+
+  AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostBackward", ([&] {
+        matchcostgrad1<scalar_t><<<32,512>>>(b, n, m, grad_cost.data<scalar_t>(), xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), grad1.data<scalar_t>());
+        matchcostgrad2<scalar_t><<<dim3(32,32),256>>>(b, n, m, grad_cost.data<scalar_t>(), xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), grad2.data<scalar_t>());
+  }));
+  THCudaCheck(cudaGetLastError());
+
+  return std::vector<at::Tensor>({grad1, grad2});
+}
+
+#endif

metrics/PyTorchEMD/emd.py

@@ -0,0 +1,47 @@
+import torch
+import emd_cuda
+
+
+class EarthMoverDistanceFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, xyz1, xyz2):
+        xyz1 = xyz1.contiguous()
+        xyz2 = xyz2.contiguous()
+        assert xyz1.is_cuda and xyz2.is_cuda, "Only support cuda currently."
+        match = emd_cuda.approxmatch_forward(xyz1, xyz2)
+        cost = emd_cuda.matchcost_forward(xyz1, xyz2, match)
+        ctx.save_for_backward(xyz1, xyz2, match)
+        return cost
+
+    @staticmethod
+    def backward(ctx, grad_cost):
+        xyz1, xyz2, match = ctx.saved_tensors
+        grad_cost = grad_cost.contiguous()
+        grad_xyz1, grad_xyz2 = emd_cuda.matchcost_backward(grad_cost, xyz1, xyz2, match)
+        return grad_xyz1, grad_xyz2
+
+
+def earth_mover_distance(xyz1, xyz2, transpose=True):
+    """Earth Mover Distance (Approx)
+
+    Args:
+        xyz1 (torch.Tensor): (b, 3, n1)
+        xyz2 (torch.Tensor): (b, 3, n1)
+        transpose (bool): whether to transpose inputs as it might be BCN format.
+            Extensions only support BNC format.
+
+    Returns:
+        cost (torch.Tensor): (b)
+
+    """
+    if xyz1.dim() == 2:
+        xyz1 = xyz1.unsqueeze(0)
+    if xyz2.dim() == 2:
+        xyz2 = xyz2.unsqueeze(0)
+    if transpose:
+        xyz1 = xyz1.transpose(1, 2)
+        xyz2 = xyz2.transpose(1, 2)
+    cost = EarthMoverDistanceFunction.apply(xyz1, xyz2)
+    cost = cost / xyz1.shape[1]
+    return cost
+

metrics/PyTorchEMD/setup.py

@@ -0,0 +1,27 @@
+"""Setup extension
+
+Notes:
+    If extra_compile_args is provided, you need to provide different instances for different extensions.
+    Refer to https://github.com/pytorch/pytorch/issues/20169
+
+"""
+
+from setuptools import setup
+from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+
+
+setup(
+    name='emd_ext',
+    ext_modules=[
+        CUDAExtension(
+            name='emd_cuda',
+            sources=[
+                'cuda/emd.cpp',
+                'cuda/emd_kernel.cu',
+            ],
+            extra_compile_args={'cxx': ['-g'], 'nvcc': ['-O2']}
+        ),
+    ],
+    cmdclass={
+        'build_ext': BuildExtension
+    })

metrics/PyTorchEMD/test_emd_loss.py

@@ -0,0 +1,44 @@
+import torch
+import numpy as np
+import time
+from emd import earth_mover_distance
+
+# gt
+p1 = torch.from_numpy(np.array([[[1.7, -0.1, 0.1], [0.1, 1.2, 0.3]]], dtype=np.float32)).cuda()
+p1 = p1.repeat(3, 1, 1)
+p2 = torch.from_numpy(np.array([[[0.3, 1.8, 0.2], [1.2, -0.2, 0.3]]], dtype=np.float32)).cuda()
+p2 = p2.repeat(3, 1, 1)
+print(p1)
+print(p2)
+p1.requires_grad = True
+p2.requires_grad = True
+
+gt_dist = (((p1[0, 0] - p2[0, 1])**2).sum() + ((p1[0, 1] - p2[0, 0])**2).sum()) / 2 +  \
+         (((p1[1, 0] - p2[1, 1])**2).sum() + ((p1[1, 1] - p2[1, 0])**2).sum()) * 2 + \
+         (((p1[2, 0] - p2[2, 1])**2).sum() + ((p1[2, 1] - p2[2, 0])**2).sum()) / 3
+print('gt_dist: ', gt_dist)
+
+gt_dist.backward()
+print(p1.grad)
+print(p2.grad)
+
+# emd
+p1 = torch.from_numpy(np.array([[[1.7, -0.1, 0.1], [0.1, 1.2, 0.3]]], dtype=np.float32)).cuda()
+p1 = p1.repeat(3, 1, 1)
+p2 = torch.from_numpy(np.array([[[0.3, 1.8, 0.2], [1.2, -0.2, 0.3]]], dtype=np.float32)).cuda()
+p2 = p2.repeat(3, 1, 1)
+print(p1)
+print(p2)
+p1.requires_grad = True
+p2.requires_grad = True
+
+d = earth_mover_distance(p1, p2, transpose=False)
+print(d)
+
+loss = d[0] / 2 + d[1] * 2 + d[2] / 3
+print(loss)
+
+loss.backward()
+print(p1.grad)
+print(p2.grad)
+

metrics/evaluation_metrics.py

@@ -0,0 +1,322 @@
+import torch
+import numpy as np
+import warnings
+from scipy.stats import entropy
+from sklearn.neighbors import NearestNeighbors
+from numpy.linalg import norm
+
+from metrics.PyTorchEMD.emd import earth_mover_distance as EMD
+from metrics.ChamferDistancePytorch.chamfer3D.dist_chamfer_3D import chamfer_3DDist
+from metrics.ChamferDistancePytorch.fscore import fscore
+from tqdm import tqdm
+
+cham3D = chamfer_3DDist()
+
+# Borrow from https://github.com/ThibaultGROUEIX/AtlasNet
+def distChamfer(a, b):
+    x, y = a, b
+    bs, num_points, points_dim = x.size()
+    xx = torch.bmm(x, x.transpose(2, 1))
+    yy = torch.bmm(y, y.transpose(2, 1))
+    zz = torch.bmm(x, y.transpose(2, 1))
+    diag_ind = torch.arange(0, num_points).to(a).long()
+    rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx)
+    ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy)
+    P = (rx.transpose(2, 1) + ry - 2 * zz)
+    return P.min(1)[0], P.min(2)[0]
+
+
+def EMD_CD(sample_pcs, ref_pcs, batch_size,  reduced=True):
+    N_sample = sample_pcs.shape[0]
+    N_ref = ref_pcs.shape[0]
+    assert N_sample == N_ref, "REF:%d SMP:%d" % (N_ref, N_sample)
+
+    cd_lst = []
+    emd_lst = []
+    fs_lst = []
+    iterator = range(0, N_sample, batch_size)
+
+    for b_start in iterator:
+        b_end = min(N_sample, b_start + batch_size)
+        sample_batch = sample_pcs[b_start:b_end]
+        ref_batch = ref_pcs[b_start:b_end]
+
+        dl, dr, _, _ = cham3D(sample_batch.cuda(), ref_batch.cuda())
+        fs = fscore(dl, dr)[0].cpu()
+        fs_lst.append(fs)
+        cd_lst.append(dl.mean(dim=1) + dr.mean(dim=1))
+
+        emd_batch = EMD(sample_batch.cuda(), ref_batch.cuda(), transpose=False)
+        emd_lst.append(emd_batch)
+
+    if reduced:
+        cd = torch.cat(cd_lst).mean()
+        emd = torch.cat(emd_lst).mean()
+    else:
+        cd = torch.cat(cd_lst)
+        emd = torch.cat(emd_lst)
+    fs_lst = torch.cat(fs_lst).mean()
+    results = {
+        'MMD-CD': cd,
+        'MMD-EMD': emd,
+        'fscore': fs_lst
+    }
+    return results
+
+def _pairwise_EMD_CD_(sample_pcs, ref_pcs, batch_size, accelerated_cd=True):
+    N_sample = sample_pcs.shape[0]
+    N_ref = ref_pcs.shape[0]
+    all_cd = []
+    all_emd = []
+    iterator = range(N_sample)
+    for sample_b_start in tqdm(iterator):
+        sample_batch = sample_pcs[sample_b_start]
+
+        cd_lst = []
+        emd_lst = []
+        for ref_b_start in range(0, N_ref, batch_size):
+            ref_b_end = min(N_ref, ref_b_start + batch_size)
+            ref_batch = ref_pcs[ref_b_start:ref_b_end]
+
+            batch_size_ref = ref_batch.size(0)
+            sample_batch_exp = sample_batch.view(1, -1, 3).expand(batch_size_ref, -1, -1)
+            sample_batch_exp = sample_batch_exp.contiguous()
+
+            dl, dr, _, _ = cham3D(sample_batch_exp.cuda(), ref_batch.cuda())
+            cd_lst.append((dl.mean(dim=1) + dr.mean(dim=1)).view(1, -1).detach().cpu())
+
+            emd_batch = EMD(sample_batch_exp.cuda(), ref_batch.cuda(), transpose=False)
+            emd_lst.append(emd_batch.view(1, -1).detach().cpu())
+
+        cd_lst = torch.cat(cd_lst, dim=1)
+        emd_lst = torch.cat(emd_lst, dim=1)
+        all_cd.append(cd_lst)
+        all_emd.append(emd_lst)
+
+    all_cd = torch.cat(all_cd, dim=0)  # N_sample, N_ref
+    all_emd = torch.cat(all_emd, dim=0)  # N_sample, N_ref
+
+    return all_cd, all_emd
+
+
+# Adapted from https://github.com/xuqiantong/GAN-Metrics/blob/master/framework/metric.py
+def knn(Mxx, Mxy, Myy, k, sqrt=False):
+    n0 = Mxx.size(0)
+    n1 = Myy.size(0)
+    label = torch.cat((torch.ones(n0), torch.zeros(n1))).to(Mxx)
+    M = torch.cat((torch.cat((Mxx, Mxy), 1), torch.cat((Mxy.transpose(0, 1), Myy), 1)), 0)
+    if sqrt:
+        M = M.abs().sqrt()
+    INFINITY = float('inf')
+    val, idx = (M + torch.diag(INFINITY * torch.ones(n0 + n1).to(Mxx))).topk(k, 0, False)
+
+    count = torch.zeros(n0 + n1).to(Mxx)
+    for i in range(0, k):
+        count = count + label.index_select(0, idx[i])
+    pred = torch.ge(count, (float(k) / 2) * torch.ones(n0 + n1).to(Mxx)).float()
+
+    s = {
+        'tp': (pred * label).sum(),
+        'fp': (pred * (1 - label)).sum(),
+        'fn': ((1 - pred) * label).sum(),
+        'tn': ((1 - pred) * (1 - label)).sum(),
+    }
+
+    s.update({
+        'precision': s['tp'] / (s['tp'] + s['fp'] + 1e-10),
+        'recall': s['tp'] / (s['tp'] + s['fn'] + 1e-10),
+        'acc_t': s['tp'] / (s['tp'] + s['fn'] + 1e-10),
+        'acc_f': s['tn'] / (s['tn'] + s['fp'] + 1e-10),
+        'acc': torch.eq(label, pred).float().mean(),
+    })
+    return s
+
+
+def lgan_mmd_cov(all_dist):
+    N_sample, N_ref = all_dist.size(0), all_dist.size(1)
+    min_val_fromsmp, min_idx = torch.min(all_dist, dim=1)
+    min_val, _ = torch.min(all_dist, dim=0)
+    mmd = min_val.mean()
+    mmd_smp = min_val_fromsmp.mean()
+    cov = float(min_idx.unique().view(-1).size(0)) / float(N_ref)
+    cov = torch.tensor(cov).to(all_dist)
+    return {
+        'lgan_mmd': mmd,
+        'lgan_cov': cov,
+        'lgan_mmd_smp': mmd_smp,
+    }
+
+
+def compute_all_metrics(sample_pcs, ref_pcs, batch_size):
+    results = {}
+
+    M_rs_cd, M_rs_emd = _pairwise_EMD_CD_(ref_pcs, sample_pcs, batch_size)
+
+    res_cd = lgan_mmd_cov(M_rs_cd.t())
+    results.update({
+        "%s-CD" % k: v for k, v in res_cd.items()
+    })
+
+    res_emd = lgan_mmd_cov(M_rs_emd.t())
+    results.update({
+        "%s-EMD" % k: v for k, v in res_emd.items()
+    })
+
+    M_rr_cd, M_rr_emd = _pairwise_EMD_CD_(ref_pcs, ref_pcs, batch_size)
+    M_ss_cd, M_ss_emd = _pairwise_EMD_CD_(sample_pcs, sample_pcs, batch_size)
+
+    # 1-NN results
+    one_nn_cd_res = knn(M_rr_cd, M_rs_cd, M_ss_cd, 1, sqrt=False)
+    results.update({
+        "1-NN-CD-%s" % k: v for k, v in one_nn_cd_res.items() if 'acc' in k
+    })
+    one_nn_emd_res = knn(M_rr_emd, M_rs_emd, M_ss_emd, 1, sqrt=False)
+    results.update({
+        "1-NN-EMD-%s" % k: v for k, v in one_nn_emd_res.items() if 'acc' in k
+    })
+
+    return results
+
+
+#######################################################
+# JSD : from https://github.com/optas/latent_3d_points
+#######################################################
+def unit_cube_grid_point_cloud(resolution, clip_sphere=False):
+    """Returns the center coordinates of each cell of a 3D grid with resolution^3 cells,
+    that is placed in the unit-cube.
+    If clip_sphere it True it drops the "corner" cells that lie outside the unit-sphere.
+    """
+    grid = np.ndarray((resolution, resolution, resolution, 3), np.float32)
+    spacing = 1.0 / float(resolution - 1)
+    for i in range(resolution):
+        for j in range(resolution):
+            for k in range(resolution):
+                grid[i, j, k, 0] = i * spacing - 0.5
+                grid[i, j, k, 1] = j * spacing - 0.5
+                grid[i, j, k, 2] = k * spacing - 0.5
+
+    if clip_sphere:
+        grid = grid.reshape(-1, 3)
+        grid = grid[norm(grid, axis=1) <= 0.5]
+
+    return grid, spacing
+
+
+def jsd_between_point_cloud_sets(sample_pcs, ref_pcs, resolution=28):
+    """Computes the JSD between two sets of point-clouds, as introduced in the paper
+    ```Learning Representations And Generative Models For 3D Point Clouds```.
+    Args:
+        sample_pcs: (np.ndarray S1xR2x3) S1 point-clouds, each of R1 points.
+        ref_pcs: (np.ndarray S2xR2x3) S2 point-clouds, each of R2 points.
+        resolution: (int) grid-resolution. Affects granularity of measurements.
+    """
+    in_unit_sphere = True
+    sample_grid_var = entropy_of_occupancy_grid(sample_pcs, resolution, in_unit_sphere)[1]
+    ref_grid_var = entropy_of_occupancy_grid(ref_pcs, resolution, in_unit_sphere)[1]
+    return jensen_shannon_divergence(sample_grid_var, ref_grid_var)
+
+
+def entropy_of_occupancy_grid(pclouds, grid_resolution, in_sphere=False, verbose=False):
+    """Given a collection of point-clouds, estimate the entropy of the random variables
+    corresponding to occupancy-grid activation patterns.
+    Inputs:
+        pclouds: (numpy array) #point-clouds x points per point-cloud x 3
+        grid_resolution (int) size of occupancy grid that will be used.
+    """
+    epsilon = 10e-4
+    bound = 0.5 + epsilon
+    if abs(np.max(pclouds)) > bound or abs(np.min(pclouds)) > bound:
+        if verbose:
+            warnings.warn('Point-clouds are not in unit cube.')
+
+    if in_sphere and np.max(np.sqrt(np.sum(pclouds ** 2, axis=2))) > bound:
+        if verbose:
+            warnings.warn('Point-clouds are not in unit sphere.')
+
+    grid_coordinates, _ = unit_cube_grid_point_cloud(grid_resolution, in_sphere)
+    grid_coordinates = grid_coordinates.reshape(-1, 3)
+    grid_counters = np.zeros(len(grid_coordinates))
+    grid_bernoulli_rvars = np.zeros(len(grid_coordinates))
+    nn = NearestNeighbors(n_neighbors=1).fit(grid_coordinates)
+
+    for pc in pclouds:
+        _, indices = nn.kneighbors(pc)
+        indices = np.squeeze(indices)
+        for i in indices:
+            grid_counters[i] += 1
+        indices = np.unique(indices)
+        for i in indices:
+            grid_bernoulli_rvars[i] += 1
+
+    acc_entropy = 0.0
+    n = float(len(pclouds))
+    for g in grid_bernoulli_rvars:
+        if g > 0:
+            p = float(g) / n
+            acc_entropy += entropy([p, 1.0 - p])
+
+    return acc_entropy / len(grid_counters), grid_counters
+
+
+def jensen_shannon_divergence(P, Q):
+    if np.any(P < 0) or np.any(Q < 0):
+        raise ValueError('Negative values.')
+    if len(P) != len(Q):
+        raise ValueError('Non equal size.')
+
+    P_ = P / np.sum(P)  # Ensure probabilities.
+    Q_ = Q / np.sum(Q)
+
+    e1 = entropy(P_, base=2)
+    e2 = entropy(Q_, base=2)
+    e_sum = entropy((P_ + Q_) / 2.0, base=2)
+    res = e_sum - ((e1 + e2) / 2.0)
+
+    res2 = _jsdiv(P_, Q_)
+
+    if not np.allclose(res, res2, atol=10e-5, rtol=0):
+        warnings.warn('Numerical values of two JSD methods don\'t agree.')
+
+    return res
+
+
+def _jsdiv(P, Q):
+    """another way of computing JSD"""
+
+    def _kldiv(A, B):
+        a = A.copy()
+        b = B.copy()
+        idx = np.logical_and(a > 0, b > 0)
+        a = a[idx]
+        b = b[idx]
+        return np.sum([v for v in a * np.log2(a / b)])
+
+    P_ = P / np.sum(P)
+    Q_ = Q / np.sum(Q)
+
+    M = 0.5 * (P_ + Q_)
+
+    return 0.5 * (_kldiv(P_, M) + _kldiv(Q_, M))
+
+
+if __name__ == "__main__":
+    B, N = 2, 10
+    x = torch.rand(B, N, 3)
+    y = torch.rand(B, N, 3)
+
+    min_l, min_r = distChamfer(x.cuda(), y.cuda())
+    print(min_l.shape)
+    print(min_r.shape)
+
+    l_dist = min_l.mean().cpu().detach().item()
+    r_dist = min_r.mean().cpu().detach().item()
+    print(l_dist, r_dist)
+
+
+    emd_batch = EMD(x.cuda(), y.cuda(), False)
+    print(emd_batch.shape)
+    print(emd_batch.mean().detach().item())
+
+    jsd = jsd_between_point_cloud_sets(x.numpy(), y.numpy())
+    print(jsd)
+

model/pvcnn_completion.py

@@ -0,0 +1,253 @@
+import functools
+
+import torch.nn as nn
+import torch
+import numpy as np
+from modules import SharedMLP, PVConv, PointNetSAModule, PointNetAModule, PointNetFPModule, Attention, Swish
+
+
+def _linear_gn_relu(in_channels, out_channels):
+    return nn.Sequential(nn.Linear(in_channels, out_channels), nn.GroupNorm(8,out_channels), Swish())
+
+
+def create_mlp_components(in_channels, out_channels, classifier=False, dim=2, width_multiplier=1):
+    r = width_multiplier
+
+    if dim == 1:
+        block = _linear_gn_relu
+    else:
+        block = SharedMLP
+    if not isinstance(out_channels, (list, tuple)):
+        out_channels = [out_channels]
+    if len(out_channels) == 0 or (len(out_channels) == 1 and out_channels[0] is None):
+        return nn.Sequential(), in_channels, in_channels
+
+    layers = []
+    for oc in out_channels[:-1]:
+        if oc < 1:
+            layers.append(nn.Dropout(oc))
+        else:
+            oc = int(r * oc)
+            layers.append(block(in_channels, oc))
+            in_channels = oc
+    if dim == 1:
+        if classifier:
+            layers.append(nn.Linear(in_channels, out_channels[-1]))
+        else:
+            layers.append(_linear_gn_relu(in_channels, int(r * out_channels[-1])))
+    else:
+        if classifier:
+            layers.append(nn.Conv1d(in_channels, out_channels[-1], 1))
+        else:
+            layers.append(SharedMLP(in_channels, int(r * out_channels[-1])))
+    return layers, out_channels[-1] if classifier else int(r * out_channels[-1])
+
+
+def create_pointnet_components(blocks, in_channels, embed_dim, with_se=False, normalize=True, eps=0,
+                               width_multiplier=1, voxel_resolution_multiplier=1):
+    r, vr = width_multiplier, voxel_resolution_multiplier
+
+    layers, concat_channels = [], 0
+    c = 0
+    for k, (out_channels, num_blocks, voxel_resolution) in enumerate(blocks):
+        out_channels = int(r * out_channels)
+        for p in range(num_blocks):
+            attention = k % 2 == 0 and k > 0 and p == 0
+            if voxel_resolution is None:
+                block = SharedMLP
+            else:
+                block = functools.partial(PVConv, kernel_size=3, resolution=int(vr * voxel_resolution), attention=attention,
+                                          with_se=with_se, normalize=normalize, eps=eps)
+
+            if c == 0:
+                layers.append(block(in_channels, out_channels))
+            else:
+                layers.append(block(in_channels+embed_dim, out_channels))
+            in_channels = out_channels
+            concat_channels += out_channels
+            c += 1
+    return layers, in_channels, concat_channels
+
+
+def create_pointnet2_sa_components(sa_blocks, extra_feature_channels, embed_dim=64, use_att=False,
+                                   dropout=0.1, with_se=False, normalize=True, eps=0,
+                                   width_multiplier=1, voxel_resolution_multiplier=1):
+    r, vr = width_multiplier, voxel_resolution_multiplier
+    in_channels = extra_feature_channels + 3
+
+    sa_layers, sa_in_channels = [], []
+    c = 0
+    for conv_configs, sa_configs in sa_blocks:
+        k = 0
+        sa_in_channels.append(in_channels)
+        sa_blocks = []
+
+        if conv_configs is not None:
+            out_channels, num_blocks, voxel_resolution = conv_configs
+            out_channels = int(r * out_channels)
+            for p in range(num_blocks):
+                attention = (c+1) % 2 == 0 and c > 0 and use_att and p == 0
+                if voxel_resolution is None:
+                    block = SharedMLP
+                else:
+                    block = functools.partial(PVConv, kernel_size=3, resolution=int(vr * voxel_resolution), attention=attention,
+                                              dropout=dropout,
+                                              with_se=with_se and not attention, with_se_relu=True,
+                                              normalize=normalize, eps=eps)
+
+                if c == 0:
+                    sa_blocks.append(block(in_channels, out_channels))
+                elif k ==0:
+                    sa_blocks.append(block(in_channels+embed_dim, out_channels))
+                in_channels = out_channels
+                k += 1
+            extra_feature_channels = in_channels
+        num_centers, radius, num_neighbors, out_channels = sa_configs
+        _out_channels = []
+        for oc in out_channels:
+            if isinstance(oc, (list, tuple)):
+                _out_channels.append([int(r * _oc) for _oc in oc])
+            else:
+                _out_channels.append(int(r * oc))
+        out_channels = _out_channels
+        if num_centers is None:
+            block = PointNetAModule
+        else:
+            block = functools.partial(PointNetSAModule, num_centers=num_centers, radius=radius,
+                                      num_neighbors=num_neighbors)
+        sa_blocks.append(block(in_channels=extra_feature_channels+(embed_dim if k==0 else 0 ), out_channels=out_channels,
+                               include_coordinates=True))
+        c += 1
+        in_channels = extra_feature_channels = sa_blocks[-1].out_channels
+        if len(sa_blocks) == 1:
+            sa_layers.append(sa_blocks[0])
+        else:
+            sa_layers.append(nn.Sequential(*sa_blocks))
+
+    return sa_layers, sa_in_channels, in_channels, 1 if num_centers is None else num_centers
+
+
+def create_pointnet2_fp_modules(fp_blocks, in_channels, sa_in_channels, sv_points, embed_dim=64, use_att=False,
+                                dropout=0.1,
+                                with_se=False, normalize=True, eps=0,
+                                width_multiplier=1, voxel_resolution_multiplier=1):
+    r, vr = width_multiplier, voxel_resolution_multiplier
+
+    fp_layers = []
+    c = 0
+    for fp_idx, (fp_configs, conv_configs) in enumerate(fp_blocks):
+        fp_blocks = []
+        out_channels = tuple(int(r * oc) for oc in fp_configs)
+        fp_blocks.append(
+            PointNetFPModule(in_channels=in_channels + sa_in_channels[-1 - fp_idx] + embed_dim, out_channels=out_channels)
+        )
+        in_channels = out_channels[-1]
+
+        if conv_configs is not None:
+            out_channels, num_blocks, voxel_resolution = conv_configs
+            out_channels = int(r * out_channels)
+            for p in range(num_blocks):
+                attention = c % 2 == 0 and c < len(fp_blocks) - 1 and use_att and p == 0
+                if voxel_resolution is None:
+                    block = SharedMLP
+                else:
+                    block = functools.partial(PVConv, kernel_size=3, resolution=int(vr * voxel_resolution), attention=attention,
+                                              dropout=dropout,
+                                              with_se=with_se and not attention,with_se_relu=True, normalize=normalize, eps=eps)
+
+                fp_blocks.append(block(in_channels, out_channels))
+                in_channels = out_channels
+        if len(fp_blocks) == 1:
+            fp_layers.append(fp_blocks[0])
+        else:
+            fp_layers.append(nn.Sequential(*fp_blocks))
+
+        c += 1
+
+    return fp_layers, in_channels
+
+
+class PVCNN2Base(nn.Module):
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att, dropout=0.1,
+                 extra_feature_channels=3, width_multiplier=1, voxel_resolution_multiplier=1):
+        super().__init__()
+        assert extra_feature_channels >= 0
+        self.embed_dim = embed_dim
+        self.sv_points = sv_points
+        self.in_channels = extra_feature_channels + 3
+
+        sa_layers, sa_in_channels, channels_sa_features, _ = create_pointnet2_sa_components(
+            sa_blocks=self.sa_blocks, extra_feature_channels=extra_feature_channels, with_se=True, embed_dim=embed_dim,
+            use_att=use_att, dropout=dropout,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+        self.sa_layers = nn.ModuleList(sa_layers)
+
+        self.global_att = None if not use_att else Attention(channels_sa_features, 8, D=1)
+
+        # only use extra features in the last fp module
+        sa_in_channels[0] = extra_feature_channels
+        fp_layers, channels_fp_features = create_pointnet2_fp_modules(
+            fp_blocks=self.fp_blocks, in_channels=channels_sa_features, sa_in_channels=sa_in_channels,sv_points=sv_points,
+            with_se=True, embed_dim=embed_dim,
+            use_att=use_att, dropout=dropout,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+        self.fp_layers = nn.ModuleList(fp_layers)
+
+
+        layers, _ = create_mlp_components(in_channels=channels_fp_features, out_channels=[128, 0.5, num_classes],
+                                          classifier=True, dim=2, width_multiplier=width_multiplier)
+        self.classifier = nn.Sequential(*layers)
+
+        self.embedf = nn.Sequential(
+            nn.Linear(embed_dim, embed_dim),
+            nn.LeakyReLU(0.1, inplace=True),
+            nn.Linear(embed_dim, embed_dim),
+        )
+
+    def get_timestep_embedding(self, timesteps, device):
+        assert len(timesteps.shape) == 1  # and timesteps.dtype == tf.int32
+
+        half_dim = self.embed_dim // 2
+        emb = np.log(10000) / (half_dim - 1)
+        emb = torch.from_numpy(np.exp(np.arange(0, half_dim) * -emb)).float().to(device)
+        # emb = tf.range(num_embeddings, dtype=DEFAULT_DTYPE)[:, None] * emb[None, :]
+        emb = timesteps[:, None] * emb[None, :]
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+        if self.embed_dim % 2 == 1:  # zero pad
+            # emb = tf.concat([emb, tf.zeros([num_embeddings, 1])], axis=1)
+            emb = nn.functional.pad(emb, (0, 1), "constant", 0)
+        assert emb.shape == torch.Size([timesteps.shape[0], self.embed_dim])
+        return emb
+
+    def forward(self, inputs, t):
+
+        temb =  self.embedf(self.get_timestep_embedding(t, inputs.device))[:,:,None].expand(-1,-1,inputs.shape[-1])
+
+        # inputs : [B, in_channels + S, N]
+        coords, features = inputs[:, :3, :].contiguous(), inputs
+        coords_list, in_features_list = [], []
+        for i, sa_blocks  in enumerate(self.sa_layers):
+            in_features_list.append(features)
+            coords_list.append(coords)
+            if i == 0:
+                features, coords, temb = sa_blocks ((features, coords, temb))
+            else:
+                features, coords, temb = sa_blocks ((torch.cat([features,temb],dim=1), coords, temb))
+        in_features_list[0] = inputs[:, 3:, :].contiguous()
+        if self.global_att is not None:
+            features = self.global_att(features)
+        for fp_idx, fp_blocks  in enumerate(self.fp_layers):
+            jump_coords = coords_list[-1 - fp_idx]
+            fump_feats = in_features_list[-1-fp_idx]
+            # if fp_idx == len(self.fp_layers) - 1:
+            #     jump_coords = jump_coords[:,:,self.sv_points:]
+            #     fump_feats = fump_feats[:,:,self.sv_points:]
+
+            features, coords, temb = fp_blocks((jump_coords, coords, torch.cat([features,temb],dim=1), fump_feats, temb))
+
+        return self.classifier(features)
+
+

model/pvcnn_generation.py

@@ -0,0 +1,247 @@
+import functools
+
+import torch.nn as nn
+import torch
+import numpy as np
+from modules import SharedMLP, PVConv, PointNetSAModule, PointNetAModule, PointNetFPModule, Attention, Swish
+
+
+def _linear_gn_relu(in_channels, out_channels):
+    return nn.Sequential(nn.Linear(in_channels, out_channels), nn.GroupNorm(8,out_channels), Swish())
+
+
+def create_mlp_components(in_channels, out_channels, classifier=False, dim=2, width_multiplier=1):
+    r = width_multiplier
+
+    if dim == 1:
+        block = _linear_gn_relu
+    else:
+        block = SharedMLP
+    if not isinstance(out_channels, (list, tuple)):
+        out_channels = [out_channels]
+    if len(out_channels) == 0 or (len(out_channels) == 1 and out_channels[0] is None):
+        return nn.Sequential(), in_channels, in_channels
+
+    layers = []
+    for oc in out_channels[:-1]:
+        if oc < 1:
+            layers.append(nn.Dropout(oc))
+        else:
+            oc = int(r * oc)
+            layers.append(block(in_channels, oc))
+            in_channels = oc
+    if dim == 1:
+        if classifier:
+            layers.append(nn.Linear(in_channels, out_channels[-1]))
+        else:
+            layers.append(_linear_gn_relu(in_channels, int(r * out_channels[-1])))
+    else:
+        if classifier:
+            layers.append(nn.Conv1d(in_channels, out_channels[-1], 1))
+        else:
+            layers.append(SharedMLP(in_channels, int(r * out_channels[-1])))
+    return layers, out_channels[-1] if classifier else int(r * out_channels[-1])
+
+
+def create_pointnet_components(blocks, in_channels, embed_dim, with_se=False, normalize=True, eps=0,
+                               width_multiplier=1, voxel_resolution_multiplier=1):
+    r, vr = width_multiplier, voxel_resolution_multiplier
+
+    layers, concat_channels = [], 0
+    c = 0
+    for k, (out_channels, num_blocks, voxel_resolution) in enumerate(blocks):
+        out_channels = int(r * out_channels)
+        for p in range(num_blocks):
+            attention = k % 2 == 0 and k > 0 and p == 0
+            if voxel_resolution is None:
+                block = SharedMLP
+            else:
+                block = functools.partial(PVConv, kernel_size=3, resolution=int(vr * voxel_resolution), attention=attention,
+                                          with_se=with_se, normalize=normalize, eps=eps)
+
+            if c == 0:
+                layers.append(block(in_channels, out_channels))
+            else:
+                layers.append(block(in_channels+embed_dim, out_channels))
+            in_channels = out_channels
+            concat_channels += out_channels
+            c += 1
+    return layers, in_channels, concat_channels
+
+
+def create_pointnet2_sa_components(sa_blocks, extra_feature_channels, embed_dim=64, use_att=False,
+                                   dropout=0.1, with_se=False, normalize=True, eps=0,
+                                   width_multiplier=1, voxel_resolution_multiplier=1):
+    r, vr = width_multiplier, voxel_resolution_multiplier
+    in_channels = extra_feature_channels + 3
+
+    sa_layers, sa_in_channels = [], []
+    c = 0
+    for conv_configs, sa_configs in sa_blocks:
+        k = 0
+        sa_in_channels.append(in_channels)
+        sa_blocks = []
+
+        if conv_configs is not None:
+            out_channels, num_blocks, voxel_resolution = conv_configs
+            out_channels = int(r * out_channels)
+            for p in range(num_blocks):
+                attention = (c+1) % 2 == 0 and use_att and p == 0
+                if voxel_resolution is None:
+                    block = SharedMLP
+                else:
+                    block = functools.partial(PVConv, kernel_size=3, resolution=int(vr * voxel_resolution), attention=attention,
+                                              dropout=dropout,
+                                              with_se=with_se, with_se_relu=True,
+                                              normalize=normalize, eps=eps)
+
+                if c == 0:
+                    sa_blocks.append(block(in_channels, out_channels))
+                elif k ==0:
+                    sa_blocks.append(block(in_channels+embed_dim, out_channels))
+                in_channels = out_channels
+                k += 1
+            extra_feature_channels = in_channels
+        num_centers, radius, num_neighbors, out_channels = sa_configs
+        _out_channels = []
+        for oc in out_channels:
+            if isinstance(oc, (list, tuple)):
+                _out_channels.append([int(r * _oc) for _oc in oc])
+            else:
+                _out_channels.append(int(r * oc))
+        out_channels = _out_channels
+        if num_centers is None:
+            block = PointNetAModule
+        else:
+            block = functools.partial(PointNetSAModule, num_centers=num_centers, radius=radius,
+                                      num_neighbors=num_neighbors)
+        sa_blocks.append(block(in_channels=extra_feature_channels+(embed_dim if k==0 else 0 ), out_channels=out_channels,
+                               include_coordinates=True))
+        c += 1
+        in_channels = extra_feature_channels = sa_blocks[-1].out_channels
+        if len(sa_blocks) == 1:
+            sa_layers.append(sa_blocks[0])
+        else:
+            sa_layers.append(nn.Sequential(*sa_blocks))
+
+    return sa_layers, sa_in_channels, in_channels, 1 if num_centers is None else num_centers
+
+
+def create_pointnet2_fp_modules(fp_blocks, in_channels, sa_in_channels, embed_dim=64, use_att=False,
+                                dropout=0.1,
+                                with_se=False, normalize=True, eps=0,
+                                width_multiplier=1, voxel_resolution_multiplier=1):
+    r, vr = width_multiplier, voxel_resolution_multiplier
+
+    fp_layers = []
+    c = 0
+    for fp_idx, (fp_configs, conv_configs) in enumerate(fp_blocks):
+        fp_blocks = []
+        out_channels = tuple(int(r * oc) for oc in fp_configs)
+        fp_blocks.append(
+            PointNetFPModule(in_channels=in_channels + sa_in_channels[-1 - fp_idx] + embed_dim, out_channels=out_channels)
+        )
+        in_channels = out_channels[-1]
+
+        if conv_configs is not None:
+            out_channels, num_blocks, voxel_resolution = conv_configs
+            out_channels = int(r * out_channels)
+            for p in range(num_blocks):
+                attention = (c+1) % 2 == 0 and c < len(fp_blocks) - 1 and use_att and p == 0
+                if voxel_resolution is None:
+                    block = SharedMLP
+                else:
+                    block = functools.partial(PVConv, kernel_size=3, resolution=int(vr * voxel_resolution), attention=attention,
+                                              dropout=dropout,
+                                              with_se=with_se, with_se_relu=True,
+                                              normalize=normalize, eps=eps)
+
+                fp_blocks.append(block(in_channels, out_channels))
+                in_channels = out_channels
+        if len(fp_blocks) == 1:
+            fp_layers.append(fp_blocks[0])
+        else:
+            fp_layers.append(nn.Sequential(*fp_blocks))
+
+        c += 1
+
+    return fp_layers, in_channels
+
+
+
+class PVCNN2Base(nn.Module):
+
+    def __init__(self, num_classes, embed_dim, use_att, dropout=0.1,
+                 extra_feature_channels=3, width_multiplier=1, voxel_resolution_multiplier=1):
+        super().__init__()
+        assert extra_feature_channels >= 0
+        self.embed_dim = embed_dim
+        self.in_channels = extra_feature_channels + 3
+
+        sa_layers, sa_in_channels, channels_sa_features, _ = create_pointnet2_sa_components(
+            sa_blocks=self.sa_blocks, extra_feature_channels=extra_feature_channels, with_se=True, embed_dim=embed_dim,
+            use_att=use_att, dropout=dropout,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+        self.sa_layers = nn.ModuleList(sa_layers)
+
+        self.global_att = None if not use_att else Attention(channels_sa_features, 8, D=1)
+
+        # only use extra features in the last fp module
+        sa_in_channels[0] = extra_feature_channels
+        fp_layers, channels_fp_features = create_pointnet2_fp_modules(
+            fp_blocks=self.fp_blocks, in_channels=channels_sa_features, sa_in_channels=sa_in_channels, with_se=True, embed_dim=embed_dim,
+            use_att=use_att, dropout=dropout,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+        self.fp_layers = nn.ModuleList(fp_layers)
+
+
+        layers, _ = create_mlp_components(in_channels=channels_fp_features, out_channels=[128, dropout, num_classes], # was 0.5
+                                          classifier=True, dim=2, width_multiplier=width_multiplier)
+        self.classifier = nn.Sequential(*layers)
+
+        self.embedf = nn.Sequential(
+            nn.Linear(embed_dim, embed_dim),
+            nn.LeakyReLU(0.1, inplace=True),
+            nn.Linear(embed_dim, embed_dim),
+        )
+
+    def get_timestep_embedding(self, timesteps, device):
+        assert len(timesteps.shape) == 1  # and timesteps.dtype == tf.int32
+
+        half_dim = self.embed_dim // 2
+        emb = np.log(10000) / (half_dim - 1)
+        emb = torch.from_numpy(np.exp(np.arange(0, half_dim) * -emb)).float().to(device)
+        # emb = tf.range(num_embeddings, dtype=DEFAULT_DTYPE)[:, None] * emb[None, :]
+        emb = timesteps[:, None] * emb[None, :]
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+        if self.embed_dim % 2 == 1:  # zero pad
+            # emb = tf.concat([emb, tf.zeros([num_embeddings, 1])], axis=1)
+            emb = nn.functional.pad(emb, (0, 1), "constant", 0)
+        assert emb.shape == torch.Size([timesteps.shape[0], self.embed_dim])
+        return emb
+
+    def forward(self, inputs, t):
+
+        temb =  self.embedf(self.get_timestep_embedding(t, inputs.device))[:,:,None].expand(-1,-1,inputs.shape[-1])
+
+        # inputs : [B, in_channels + S, N]
+        coords, features = inputs[:, :3, :].contiguous(), inputs
+        coords_list, in_features_list = [], []
+        for i, sa_blocks  in enumerate(self.sa_layers):
+            in_features_list.append(features)
+            coords_list.append(coords)
+            if i == 0:
+                features, coords, temb = sa_blocks ((features, coords, temb))
+            else:
+                features, coords, temb = sa_blocks ((torch.cat([features,temb],dim=1), coords, temb))
+        in_features_list[0] = inputs[:, 3:, :].contiguous()
+        if self.global_att is not None:
+            features = self.global_att(features)
+        for fp_idx, fp_blocks  in enumerate(self.fp_layers):
+            features, coords, temb = fp_blocks((coords_list[-1-fp_idx], coords, torch.cat([features,temb],dim=1), in_features_list[-1-fp_idx], temb))
+
+        return self.classifier(features)
+
+

modules/__init__.py

@@ -0,0 +1,8 @@
+from modules.ball_query import BallQuery
+from modules.frustum import FrustumPointNetLoss
+from modules.loss import KLLoss
+from modules.pointnet import PointNetAModule, PointNetSAModule, PointNetFPModule
+from modules.pvconv import PVConv, Attention, Swish, PVConvReLU
+from modules.se import SE3d
+from modules.shared_mlp import SharedMLP
+from modules.voxelization import Voxelization

modules/ball_query.py

@@ -0,0 +1,34 @@
+import torch
+import torch.nn as nn
+
+import modules.functional as F
+
+__all__ = ['BallQuery']
+
+
+class BallQuery(nn.Module):
+    def __init__(self, radius, num_neighbors, include_coordinates=True):
+        super().__init__()
+        self.radius = radius
+        self.num_neighbors = num_neighbors
+        self.include_coordinates = include_coordinates
+
+    def forward(self, points_coords, centers_coords, temb, points_features=None):
+        points_coords = points_coords.contiguous()
+        centers_coords = centers_coords.contiguous()
+        neighbor_indices = F.ball_query(centers_coords, points_coords, self.radius, self.num_neighbors)
+        neighbor_coordinates = F.grouping(points_coords, neighbor_indices)
+        neighbor_coordinates = neighbor_coordinates - centers_coords.unsqueeze(-1)
+
+        if points_features is None:
+            assert self.include_coordinates, 'No Features For Grouping'
+            neighbor_features = neighbor_coordinates
+        else:
+            neighbor_features = F.grouping(points_features, neighbor_indices)
+            if self.include_coordinates:
+                neighbor_features = torch.cat([neighbor_coordinates, neighbor_features], dim=1)
+        return neighbor_features, F.grouping(temb, neighbor_indices)
+
+    def extra_repr(self):
+        return 'radius={}, num_neighbors={}{}'.format(
+            self.radius, self.num_neighbors, ', include coordinates' if self.include_coordinates else '')

modules/frustum.py

@@ -0,0 +1,138 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import modules.functional as PF
+
+__all__ = ['FrustumPointNetLoss', 'get_box_corners_3d']
+
+
+class FrustumPointNetLoss(nn.Module):
+    def __init__(self, num_heading_angle_bins, num_size_templates, size_templates, box_loss_weight=1.0,
+                 corners_loss_weight=10.0, heading_residual_loss_weight=20.0, size_residual_loss_weight=20.0):
+        super().__init__()
+        self.box_loss_weight = box_loss_weight
+        self.corners_loss_weight = corners_loss_weight
+        self.heading_residual_loss_weight = heading_residual_loss_weight
+        self.size_residual_loss_weight = size_residual_loss_weight
+
+        self.num_heading_angle_bins = num_heading_angle_bins
+        self.num_size_templates = num_size_templates
+        self.register_buffer('size_templates', size_templates.view(self.num_size_templates, 3))
+        self.register_buffer(
+            'heading_angle_bin_centers', torch.arange(0, 2 * np.pi, 2 * np.pi / self.num_heading_angle_bins)
+        )
+
+    def forward(self, inputs, targets):
+        mask_logits = inputs['mask_logits']  # (B, 2, N)
+        center_reg = inputs['center_reg']  # (B, 3)
+        center = inputs['center']  # (B, 3)
+        heading_scores = inputs['heading_scores']  # (B, NH)
+        heading_residuals_normalized = inputs['heading_residuals_normalized']  # (B, NH)
+        heading_residuals = inputs['heading_residuals']  # (B, NH)
+        size_scores = inputs['size_scores']  # (B, NS)
+        size_residuals_normalized = inputs['size_residuals_normalized']  # (B, NS, 3)
+        size_residuals = inputs['size_residuals']  # (B, NS, 3)
+
+        mask_logits_target = targets['mask_logits']  # (B, N)
+        center_target = targets['center']  # (B, 3)
+        heading_bin_id_target = targets['heading_bin_id']  # (B, )
+        heading_residual_target = targets['heading_residual']  # (B, )
+        size_template_id_target = targets['size_template_id']  # (B, )
+        size_residual_target = targets['size_residual']  # (B, 3)
+
+        batch_size = center.size(0)
+        batch_id = torch.arange(batch_size, device=center.device)
+
+        # Basic Classification and Regression losses
+        mask_loss = F.cross_entropy(mask_logits, mask_logits_target)
+        heading_loss = F.cross_entropy(heading_scores, heading_bin_id_target)
+        size_loss = F.cross_entropy(size_scores, size_template_id_target)
+        center_loss = PF.huber_loss(torch.norm(center_target - center, dim=-1), delta=2.0)
+        center_reg_loss = PF.huber_loss(torch.norm(center_target - center_reg, dim=-1), delta=1.0)
+
+        # Refinement losses for size/heading
+        heading_residuals_normalized = heading_residuals_normalized[batch_id, heading_bin_id_target]  # (B, )
+        heading_residual_normalized_target = heading_residual_target / (np.pi / self.num_heading_angle_bins)
+        heading_residual_normalized_loss = PF.huber_loss(
+            heading_residuals_normalized - heading_residual_normalized_target, delta=1.0
+        )
+        size_residuals_normalized = size_residuals_normalized[batch_id, size_template_id_target]  # (B, 3)
+        size_residual_normalized_target = size_residual_target / self.size_templates[size_template_id_target]
+        size_residual_normalized_loss = PF.huber_loss(
+            torch.norm(size_residual_normalized_target - size_residuals_normalized, dim=-1), delta=1.0
+        )
+
+        # Bounding box losses
+        heading = (heading_residuals[batch_id, heading_bin_id_target]
+                   + self.heading_angle_bin_centers[heading_bin_id_target])  # (B, )
+        # Warning: in origin code, size_residuals are added twice (issue #43 and #49 in charlesq34/frustum-pointnets)
+        size = (size_residuals[batch_id, size_template_id_target]
+                + self.size_templates[size_template_id_target])  # (B, 3)
+        corners = get_box_corners_3d(centers=center, headings=heading, sizes=size, with_flip=False)  # (B, 3, 8)
+        heading_target = self.heading_angle_bin_centers[heading_bin_id_target] + heading_residual_target  # (B, )
+        size_target = self.size_templates[size_template_id_target] + size_residual_target  # (B, 3)
+        corners_target, corners_target_flip = get_box_corners_3d(centers=center_target, headings=heading_target,
+                                                                 sizes=size_target, with_flip=True)  # (B, 3, 8)
+        corners_loss = PF.huber_loss(torch.min(
+            torch.norm(corners - corners_target, dim=1), torch.norm(corners - corners_target_flip, dim=1)
+        ), delta=1.0)
+        # Summing up
+        loss = mask_loss + self.box_loss_weight * (
+                center_loss + center_reg_loss + heading_loss + size_loss
+                + self.heading_residual_loss_weight * heading_residual_normalized_loss
+                + self.size_residual_loss_weight * size_residual_normalized_loss
+                + self.corners_loss_weight * corners_loss
+        )
+
+        return loss
+
+
+def get_box_corners_3d(centers, headings, sizes, with_flip=False):
+    """
+    :param centers: coords of box centers, FloatTensor[N, 3]
+    :param headings: heading angles, FloatTensor[N, ]
+    :param sizes: box sizes, FloatTensor[N, 3]
+    :param with_flip: bool, whether to return flipped box (headings + np.pi)
+    :return:
+        coords of box corners, FloatTensor[N, 3, 8]
+        NOTE: corner points are in counter clockwise order, e.g.,
+          2--1
+        3--0 5
+        7--4
+    """
+    l = sizes[:, 0]  # (N,)
+    w = sizes[:, 1]  # (N,)
+    h = sizes[:, 2]  # (N,)
+    x_corners = torch.stack([l/2, l/2, -l/2, -l/2, l/2, l/2, -l/2, -l/2], dim=1)  # (N, 8)
+    y_corners = torch.stack([h/2, h/2, h/2, h/2, -h/2, -h/2, -h/2, -h/2], dim=1)  # (N, 8)
+    z_corners = torch.stack([w/2, -w/2, -w/2, w/2, w/2, -w/2, -w/2, w/2], dim=1)  # (N, 8)
+
+    c = torch.cos(headings)  # (N,)
+    s = torch.sin(headings)  # (N,)
+    o = torch.ones_like(headings)  # (N,)
+    z = torch.zeros_like(headings)  # (N,)
+
+    centers = centers.unsqueeze(-1)  # (B, 3, 1)
+    corners = torch.stack([x_corners, y_corners, z_corners], dim=1)  # (N, 3, 8)
+    R = torch.stack([c, z, s, z, o, z, -s, z, c], dim=1).view(-1, 3, 3)  # roty matrix: (N, 3, 3)
+    if with_flip:
+        R_flip = torch.stack([-c, z, -s, z, o, z, s, z, -c], dim=1).view(-1, 3, 3)
+        return torch.matmul(R, corners) + centers, torch.matmul(R_flip, corners) + centers
+    else:
+        return torch.matmul(R, corners) + centers
+
+    # centers = centers.unsqueeze(1)  # (B, 1, 3)
+    # corners = torch.stack([x_corners, y_corners, z_corners], dim=-1)  # (N, 8, 3)
+    # RT = torch.stack([c, z, -s, z, o, z, s, z, c], dim=1).view(-1, 3, 3)  # (N, 3, 3)
+    # if with_flip:
+    #     RT_flip = torch.stack([-c, z, s, z, o, z, -s, z, -c], dim=1).view(-1, 3, 3)  # (N, 3, 3)
+    #     return torch.matmul(corners, RT) + centers, torch.matmul(corners, RT_flip) + centers  # (N, 8, 3)
+    # else:
+    #     return torch.matmul(corners, RT) + centers  # (N, 8, 3)
+
+    # corners = torch.stack([x_corners, y_corners, z_corners], dim=1)  # (N, 3, 8)
+    # R = torch.stack([c, z, s, z, o, z, -s, z, c], dim=1).view(-1, 3, 3)  # (N, 3, 3)
+    # corners = torch.matmul(R, corners) + centers.unsqueeze(2)  # (N, 3, 8)
+    # corners = corners.transpose(1, 2)  # (N, 8, 3)

modules/functional/__init__.py

@@ -0,0 +1,7 @@
+from modules.functional.ball_query import ball_query
+from modules.functional.devoxelization import trilinear_devoxelize
+from modules.functional.grouping import grouping
+from modules.functional.interpolatation import nearest_neighbor_interpolate
+from modules.functional.loss import kl_loss, huber_loss
+from modules.functional.sampling import gather, furthest_point_sample, logits_mask
+from modules.functional.voxelization import avg_voxelize

modules/functional/backend.py

@@ -0,0 +1,26 @@
+import os
+
+from torch.utils.cpp_extension import load
+
+_src_path = os.path.dirname(os.path.abspath(__file__))
+_backend = load(name='_pvcnn_backend',
+                extra_cflags=['-O3', '-std=c++17'],
+                extra_cuda_cflags=['--compiler-bindir=/usr/bin/gcc-8'],
+                sources=[os.path.join(_src_path,'src', f) for f in [
+                    'ball_query/ball_query.cpp',
+                    'ball_query/ball_query.cu',
+                    'grouping/grouping.cpp',
+                    'grouping/grouping.cu',
+                    'interpolate/neighbor_interpolate.cpp',
+                    'interpolate/neighbor_interpolate.cu',
+                    'interpolate/trilinear_devox.cpp',
+                    'interpolate/trilinear_devox.cu',
+                    'sampling/sampling.cpp',
+                    'sampling/sampling.cu',
+                    'voxelization/vox.cpp',
+                    'voxelization/vox.cu',
+                    'bindings.cpp',
+                ]]
+                )
+
+__all__ = ['_backend']

modules/functional/ball_query.py

@@ -0,0 +1,19 @@
+from torch.autograd import Function
+
+from modules.functional.backend import _backend
+
+__all__ = ['ball_query']
+
+
+def ball_query(centers_coords, points_coords, radius, num_neighbors):
+        """
+        :param centers_coords: coordinates of centers, FloatTensor[B, 3, M]
+        :param points_coords: coordinates of points, FloatTensor[B, 3, N]
+        :param radius: float, radius of ball query
+        :param num_neighbors: int, maximum number of neighbors
+        :return:
+            neighbor_indices: indices of neighbors, IntTensor[B, M, U]
+        """
+        centers_coords = centers_coords.contiguous()
+        points_coords = points_coords.contiguous()
+        return _backend.ball_query(centers_coords, points_coords, radius, num_neighbors)

modules/functional/devoxelization.py

@@ -0,0 +1,42 @@
+from torch.autograd import Function
+
+from modules.functional.backend import _backend
+
+__all__ = ['trilinear_devoxelize']
+
+
+class TrilinearDevoxelization(Function):
+    @staticmethod
+    def forward(ctx, features, coords, resolution, is_training=True):
+        """
+        :param ctx:
+        :param coords: the coordinates of points, FloatTensor[B, 3, N]
+        :param features: FloatTensor[B, C, R, R, R]
+        :param resolution: int, the voxel resolution
+        :param is_training: bool, training mode
+        :return:
+            FloatTensor[B, C, N]
+        """
+        B, C = features.shape[:2]
+        features = features.contiguous().view(B, C, -1)
+        coords = coords.contiguous()
+        outs, inds, wgts = _backend.trilinear_devoxelize_forward(resolution, is_training, coords, features)
+        if is_training:
+            ctx.save_for_backward(inds, wgts)
+            ctx.r = resolution
+        return outs
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        """
+        :param ctx: 
+        :param grad_output: gradient of outputs, FloatTensor[B, C, N]
+        :return:
+            gradient of inputs, FloatTensor[B, C, R, R, R]
+        """
+        inds, wgts = ctx.saved_tensors
+        grad_inputs = _backend.trilinear_devoxelize_backward(grad_output.contiguous(), inds, wgts, ctx.r)
+        return grad_inputs.view(grad_output.size(0), grad_output.size(1), ctx.r, ctx.r, ctx.r), None, None, None
+
+
+trilinear_devoxelize = TrilinearDevoxelization.apply

modules/functional/grouping.py

@@ -0,0 +1,31 @@
+from torch.autograd import Function
+
+from modules.functional.backend import _backend
+
+__all__ = ['grouping']
+
+
+class Grouping(Function):
+    @staticmethod
+    def forward(ctx, features, indices):
+        """
+        :param ctx:
+        :param features: features of points, FloatTensor[B, C, N]
+        :param indices: neighbor indices of centers, IntTensor[B, M, U], M is #centers, U is #neighbors
+        :return:
+            grouped_features: grouped features, FloatTensor[B, C, M, U]
+        """
+        features = features.contiguous()
+        indices = indices.contiguous()
+        ctx.save_for_backward(indices)
+        ctx.num_points = features.size(-1)
+        return _backend.grouping_forward(features, indices)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        indices, = ctx.saved_tensors
+        grad_features = _backend.grouping_backward(grad_output.contiguous(), indices, ctx.num_points)
+        return grad_features, None
+
+
+grouping = Grouping.apply

modules/functional/interpolatation.py

@@ -0,0 +1,38 @@
+from torch.autograd import Function
+
+from modules.functional.backend import _backend
+
+__all__ = ['nearest_neighbor_interpolate']
+
+
+class NeighborInterpolation(Function):
+    @staticmethod
+    def forward(ctx, points_coords, centers_coords, centers_features):
+        """
+        :param ctx:
+        :param points_coords: coordinates of points, FloatTensor[B, 3, N]
+        :param centers_coords: coordinates of centers, FloatTensor[B, 3, M]
+        :param centers_features: features of centers, FloatTensor[B, C, M]
+        :return:
+            points_features: features of points, FloatTensor[B, C, N]
+        """
+        centers_coords = centers_coords.contiguous()
+        points_coords = points_coords.contiguous()
+        centers_features = centers_features.contiguous()
+        points_features, indices, weights = _backend.three_nearest_neighbors_interpolate_forward(
+            points_coords, centers_coords, centers_features
+        )
+        ctx.save_for_backward(indices, weights)
+        ctx.num_centers = centers_coords.size(-1)
+        return points_features
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        indices, weights = ctx.saved_tensors
+        grad_centers_features = _backend.three_nearest_neighbors_interpolate_backward(
+            grad_output.contiguous(), indices, weights, ctx.num_centers
+        )
+        return None, None, grad_centers_features
+
+
+nearest_neighbor_interpolate = NeighborInterpolation.apply

modules/functional/loss.py

@@ -0,0 +1,17 @@
+import torch
+import torch.nn.functional as F
+
+__all__ = ['kl_loss', 'huber_loss']
+
+
+def kl_loss(x, y):
+    x = F.softmax(x.detach(), dim=1)
+    y = F.log_softmax(y, dim=1)
+    return torch.mean(torch.sum(x * (torch.log(x) - y), dim=1))
+
+
+def huber_loss(error, delta):
+    abs_error = torch.abs(error)
+    quadratic = torch.min(abs_error, torch.full_like(abs_error, fill_value=delta))
+    losses = 0.5 * (quadratic ** 2) + delta * (abs_error - quadratic)
+    return torch.mean(losses)

modules/functional/sampling.py

@@ -0,0 +1,84 @@
+import numpy as np
+import torch
+from torch.autograd import Function
+
+from modules.functional.backend import _backend
+
+__all__ = ['gather', 'furthest_point_sample', 'logits_mask']
+
+
+class Gather(Function):
+    @staticmethod
+    def forward(ctx, features, indices):
+        """
+        Gather
+        :param ctx:
+        :param features: features of points, FloatTensor[B, C, N]
+        :param indices: centers' indices in points, IntTensor[b, m]
+        :return:
+            centers_coords: coordinates of sampled centers, FloatTensor[B, C, M]
+        """
+        features = features.contiguous()
+        indices = indices.int().contiguous()
+        ctx.save_for_backward(indices)
+        ctx.num_points = features.size(-1)
+        return _backend.gather_features_forward(features, indices)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        indices, = ctx.saved_tensors
+        grad_features = _backend.gather_features_backward(grad_output.contiguous(), indices, ctx.num_points)
+        return grad_features, None
+
+
+gather = Gather.apply
+
+
+def furthest_point_sample(coords, num_samples):
+    """
+    Uses iterative furthest point sampling to select a set of npoint features that have the largest
+    minimum distance to the sampled point set
+    :param coords: coordinates of points, FloatTensor[B, 3, N]
+    :param num_samples: int, M
+    :return:
+       centers_coords: coordinates of sampled centers, FloatTensor[B, 3, M]
+    """
+    coords = coords.contiguous()
+    indices = _backend.furthest_point_sampling(coords, num_samples)
+    return gather(coords, indices)
+
+
+def logits_mask(coords, logits, num_points_per_object):
+    """
+    Use logits to sample points
+    :param coords: coords of points, FloatTensor[B, 3, N]
+    :param logits: binary classification logits, FloatTensor[B, 2, N]
+    :param num_points_per_object: M, #points per object after masking, int
+    :return:
+        selected_coords: FloatTensor[B, 3, M]
+        masked_coords_mean: mean coords of selected points, FloatTensor[B, 3]
+        mask: mask to select points, BoolTensor[B, N]
+    """
+    batch_size, _, num_points = coords.shape
+    mask = torch.lt(logits[:, 0, :], logits[:, 1, :])   # [B, N]
+    num_candidates = torch.sum(mask, dim=-1, keepdim=True)  # [B, 1]
+    masked_coords = coords * mask.view(batch_size, 1, num_points)  # [B, C, N]
+    masked_coords_mean = torch.sum(masked_coords, dim=-1) / torch.max(num_candidates,
+                                                                      torch.ones_like(num_candidates)).float()  # [B, C]
+    selected_indices = torch.zeros((batch_size, num_points_per_object), device=coords.device, dtype=torch.int32)
+    for i in range(batch_size):
+        current_mask = mask[i]  # [N]
+        current_candidates = current_mask.nonzero().view(-1)
+        current_num_candidates = current_candidates.numel()
+        if current_num_candidates >= num_points_per_object:
+            choices = np.random.choice(current_num_candidates, num_points_per_object, replace=False)
+            selected_indices[i] = current_candidates[choices]
+        elif current_num_candidates > 0:
+            choices = np.concatenate([
+                np.arange(current_num_candidates).repeat(num_points_per_object // current_num_candidates),
+                np.random.choice(current_num_candidates, num_points_per_object % current_num_candidates, replace=False)
+            ])
+            np.random.shuffle(choices)
+            selected_indices[i] = current_candidates[choices]
+    selected_coords = gather(masked_coords - masked_coords_mean.view(batch_size, -1, 1), selected_indices)
+    return selected_coords, masked_coords_mean, mask

modules/functional/src/ball_query/ball_query.cpp

@@ -0,0 +1,30 @@
+#include "ball_query.hpp"
+#include "ball_query.cuh"
+
+#include "../utils.hpp"
+
+at::Tensor ball_query_forward(at::Tensor centers_coords,
+                              at::Tensor points_coords, const float radius,
+                              const int num_neighbors) {
+  CHECK_CUDA(centers_coords);
+  CHECK_CUDA(points_coords);
+  CHECK_CONTIGUOUS(centers_coords);
+  CHECK_CONTIGUOUS(points_coords);
+  CHECK_IS_FLOAT(centers_coords);
+  CHECK_IS_FLOAT(points_coords);
+
+  int b = centers_coords.size(0);
+  int m = centers_coords.size(2);
+  int n = points_coords.size(2);
+
+  at::Tensor neighbors_indices = torch::zeros(
+      {b, m, num_neighbors},
+      at::device(centers_coords.device()).dtype(at::ScalarType::Int));
+
+  ball_query(b, n, m, radius * radius, num_neighbors,
+             centers_coords.data_ptr<float>(),
+             points_coords.data_ptr<float>(),
+             neighbors_indices.data_ptr<int>());
+
+  return neighbors_indices;
+}

modules/functional/src/ball_query/ball_query.cu

@@ -0,0 +1,59 @@
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "../cuda_utils.cuh"
+
+/*
+  Function: ball query
+  Args:
+    b   : batch size
+    n   : number of points in point clouds
+    m   : number of query centers
+    r2  : ball query radius ** 2
+    u   : maximum number of neighbors
+    centers_coords: coordinates of centers, FloatTensor[b, 3, m]
+    points_coords : coordinates of points, FloatTensor[b, 3, n]
+    neighbors_indices : neighbor indices in points, IntTensor[b, m, u]
+*/
+__global__ void ball_query_kernel(int b, int n, int m, float r2, int u,
+                                  const float *__restrict__ centers_coords,
+                                  const float *__restrict__ points_coords,
+                                  int *__restrict__ neighbors_indices) {
+  int batch_index = blockIdx.x;
+  int index = threadIdx.x;
+  int stride = blockDim.x;
+  points_coords += batch_index * n * 3;
+  centers_coords += batch_index * m * 3;
+  neighbors_indices += batch_index * m * u;
+
+  for (int j = index; j < m; j += stride) {
+    float center_x = centers_coords[j];
+    float center_y = centers_coords[j + m];
+    float center_z = centers_coords[j + m + m];
+    for (int k = 0, cnt = 0; k < n && cnt < u; ++k) {
+      float dx = center_x - points_coords[k];
+      float dy = center_y - points_coords[k + n];
+      float dz = center_z - points_coords[k + n + n];
+      float d2 = dx * dx + dy * dy + dz * dz;
+      if (d2 < r2) {
+        if (cnt == 0) {
+          for (int v = 0; v < u; ++v) {
+            neighbors_indices[j * u + v] = k;
+          }
+        }
+        neighbors_indices[j * u + cnt] = k;
+        ++cnt;
+      }
+    }
+  }
+}
+
+void ball_query(int b, int n, int m, float r2, int u,
+                const float *centers_coords, const float *points_coords,
+                int *neighbors_indices) {
+  ball_query_kernel<<<b, optimal_num_threads(m), 0,
+                      at::cuda::getCurrentCUDAStream()>>>(
+      b, n, m, r2, u, centers_coords, points_coords, neighbors_indices);
+  CUDA_CHECK_ERRORS();
+}

modules/functional/src/ball_query/ball_query.cuh

@@ -0,0 +1,8 @@
+#ifndef _BALL_QUERY_CUH
+#define _BALL_QUERY_CUH
+
+void ball_query(int b, int n, int m, float r2, int u,
+                const float *centers_coords, const float *points_coords,
+                int *neighbors_indices);
+
+#endif

modules/functional/src/ball_query/ball_query.hpp

@@ -0,0 +1,10 @@
+#ifndef _BALL_QUERY_HPP
+#define _BALL_QUERY_HPP
+
+#include <torch/extension.h>
+
+at::Tensor ball_query_forward(at::Tensor centers_coords,
+                              at::Tensor points_coords, const float radius,
+                              const int num_neighbors);
+
+#endif

modules/functional/src/bindings.cpp

@@ -0,0 +1,37 @@
+#include <pybind11/pybind11.h>
+
+#include "ball_query/ball_query.hpp"
+#include "grouping/grouping.hpp"
+#include "interpolate/neighbor_interpolate.hpp"
+#include "interpolate/trilinear_devox.hpp"
+#include "sampling/sampling.hpp"
+#include "voxelization/vox.hpp"
+
+PYBIND11_MODULE(_pvcnn_backend, m) {
+  m.def("gather_features_forward", &gather_features_forward,
+        "Gather Centers' Features forward (CUDA)");
+  m.def("gather_features_backward", &gather_features_backward,
+        "Gather Centers' Features backward (CUDA)");
+  m.def("furthest_point_sampling", &furthest_point_sampling_forward,
+        "Furthest Point Sampling (CUDA)");
+  m.def("ball_query", &ball_query_forward, "Ball Query (CUDA)");
+  m.def("grouping_forward", &grouping_forward,
+        "Grouping Features forward (CUDA)");
+  m.def("grouping_backward", &grouping_backward,
+        "Grouping Features backward (CUDA)");
+  m.def("three_nearest_neighbors_interpolate_forward",
+        &three_nearest_neighbors_interpolate_forward,
+        "3 Nearest Neighbors Interpolate forward (CUDA)");
+  m.def("three_nearest_neighbors_interpolate_backward",
+        &three_nearest_neighbors_interpolate_backward,
+        "3 Nearest Neighbors Interpolate backward (CUDA)");
+
+  m.def("trilinear_devoxelize_forward", &trilinear_devoxelize_forward,
+        "Trilinear Devoxelization forward (CUDA)");
+  m.def("trilinear_devoxelize_backward", &trilinear_devoxelize_backward,
+        "Trilinear Devoxelization backward (CUDA)");
+  m.def("avg_voxelize_forward", &avg_voxelize_forward,
+        "Voxelization forward with average pooling (CUDA)");
+  m.def("avg_voxelize_backward", &avg_voxelize_backward,
+        "Voxelization backward (CUDA)");
+}

modules/functional/src/cuda_utils.cuh

@@ -0,0 +1,39 @@
+#ifndef _CUDA_UTILS_H
+#define _CUDA_UTILS_H
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <cmath>
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+#include <vector>
+
+#define MAXIMUM_THREADS 512
+
+inline int optimal_num_threads(int work_size) {
+  const int pow_2 = std::log2(static_cast<double>(work_size));
+  return max(min(1 << pow_2, MAXIMUM_THREADS), 1);
+}
+
+inline dim3 optimal_block_config(int x, int y) {
+  const int x_threads = optimal_num_threads(x);
+  const int y_threads =
+      max(min(optimal_num_threads(y), MAXIMUM_THREADS / x_threads), 1);
+  dim3 block_config(x_threads, y_threads, 1);
+  return block_config;
+}
+
+#define CUDA_CHECK_ERRORS()                                                    \
+  {                                                                            \
+    cudaError_t err = cudaGetLastError();                                      \
+    if (cudaSuccess != err) {                                                  \
+      fprintf(stderr, "CUDA kernel failed : %s\n%s at L:%d in %s\n",           \
+              cudaGetErrorString(err), __PRETTY_FUNCTION__, __LINE__,          \
+              __FILE__);                                                       \
+      exit(-1);                                                                \
+    }                                                                          \
+  }
+
+#endif

modules/functional/src/grouping/grouping.cpp

@@ -0,0 +1,44 @@
+#include "grouping.hpp"
+#include "grouping.cuh"
+
+#include "../utils.hpp"
+
+at::Tensor grouping_forward(at::Tensor features, at::Tensor indices) {
+  CHECK_CUDA(features);
+  CHECK_CUDA(indices);
+  CHECK_CONTIGUOUS(features);
+  CHECK_CONTIGUOUS(indices);
+  CHECK_IS_FLOAT(features);
+  CHECK_IS_INT(indices);
+
+  int b = features.size(0);
+  int c = features.size(1);
+  int n = features.size(2);
+  int m = indices.size(1);
+  int u = indices.size(2);
+  at::Tensor output = torch::zeros(
+      {b, c, m, u}, at::device(features.device()).dtype(at::ScalarType::Float));
+  grouping(b, c, n, m, u, features.data_ptr<float>(), indices.data_ptr<int>(),
+           output.data_ptr<float>());
+  return output;
+}
+
+at::Tensor grouping_backward(at::Tensor grad_y, at::Tensor indices,
+                             const int n) {
+  CHECK_CUDA(grad_y);
+  CHECK_CUDA(indices);
+  CHECK_CONTIGUOUS(grad_y);
+  CHECK_CONTIGUOUS(indices);
+  CHECK_IS_FLOAT(grad_y);
+  CHECK_IS_INT(indices);
+
+  int b = grad_y.size(0);
+  int c = grad_y.size(1);
+  int m = indices.size(1);
+  int u = indices.size(2);
+  at::Tensor grad_x = torch::zeros(
+      {b, c, n}, at::device(grad_y.device()).dtype(at::ScalarType::Float));
+  grouping_grad(b, c, n, m, u, grad_y.data_ptr<float>(),
+                indices.data_ptr<int>(), grad_x.data_ptr<float>());
+  return grad_x;
+}

modules/functional/src/grouping/grouping.cu

@@ -0,0 +1,85 @@
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "../cuda_utils.cuh"
+
+/*
+  Function: grouping features of neighbors (forward)
+  Args:
+    b   : batch size
+    c   : #channles of features
+    n   : number of points in point clouds
+    m   : number of query centers
+    u   : maximum number of neighbors
+    features: points' features, FloatTensor[b, c, n]
+    indices : neighbor indices in points, IntTensor[b, m, u]
+    out     : gathered features, FloatTensor[b, c, m, u]
+*/
+__global__ void grouping_kernel(int b, int c, int n, int m, int u,
+                                const float *__restrict__ features,
+                                const int *__restrict__ indices,
+                                float *__restrict__ out) {
+  int batch_index = blockIdx.x;
+  features += batch_index * n * c;
+  indices += batch_index * m * u;
+  out += batch_index * m * u * c;
+
+  const int index = threadIdx.y * blockDim.x + threadIdx.x;
+  const int stride = blockDim.y * blockDim.x;
+  for (int i = index; i < c * m; i += stride) {
+    const int l = i / m;
+    const int j = i % m;
+    for (int k = 0; k < u; ++k) {
+      out[(l * m + j) * u + k] = features[l * n + indices[j * u + k]];
+    }
+  }
+}
+
+void grouping(int b, int c, int n, int m, int u, const float *features,
+              const int *indices, float *out) {
+  grouping_kernel<<<b, optimal_block_config(m, c), 0,
+                    at::cuda::getCurrentCUDAStream()>>>(b, c, n, m, u, features,
+                                                        indices, out);
+  CUDA_CHECK_ERRORS();
+}
+
+/*
+  Function: grouping features of neighbors (backward)
+  Args:
+    b   : batch size
+    c   : #channles of features
+    n   : number of points in point clouds
+    m   : number of query centers
+    u   : maximum number of neighbors
+    grad_y : grad of gathered features, FloatTensor[b, c, m, u]
+    indices : neighbor indices in points, IntTensor[b, m, u]
+    grad_x: grad of points' features, FloatTensor[b, c, n]
+*/
+__global__ void grouping_grad_kernel(int b, int c, int n, int m, int u,
+                                     const float *__restrict__ grad_y,
+                                     const int *__restrict__ indices,
+                                     float *__restrict__ grad_x) {
+  int batch_index = blockIdx.x;
+  grad_y += batch_index * m * u * c;
+  indices += batch_index * m * u;
+  grad_x += batch_index * n * c;
+
+  const int index = threadIdx.y * blockDim.x + threadIdx.x;
+  const int stride = blockDim.y * blockDim.x;
+  for (int i = index; i < c * m; i += stride) {
+    const int l = i / m;
+    const int j = i % m;
+    for (int k = 0; k < u; ++k) {
+      atomicAdd(grad_x + l * n + indices[j * u + k],
+                grad_y[(l * m + j) * u + k]);
+    }
+  }
+}
+
+void grouping_grad(int b, int c, int n, int m, int u, const float *grad_y,
+                   const int *indices, float *grad_x) {
+  grouping_grad_kernel<<<b, optimal_block_config(m, c), 0,
+                         at::cuda::getCurrentCUDAStream()>>>(
+      b, c, n, m, u, grad_y, indices, grad_x);
+  CUDA_CHECK_ERRORS();
+}

modules/functional/src/grouping/grouping.cuh

@@ -0,0 +1,9 @@
+#ifndef _GROUPING_CUH
+#define _GROUPING_CUH
+
+void grouping(int b, int c, int n, int m, int u, const float *features,
+              const int *indices, float *out);
+void grouping_grad(int b, int c, int n, int m, int u, const float *grad_y,
+                   const int *indices, float *grad_x);
+
+#endif

modules/functional/src/grouping/grouping.hpp

@@ -0,0 +1,10 @@
+#ifndef _GROUPING_HPP
+#define _GROUPING_HPP
+
+#include <torch/extension.h>
+
+at::Tensor grouping_forward(at::Tensor features, at::Tensor indices);
+at::Tensor grouping_backward(at::Tensor grad_y, at::Tensor indices,
+                             const int n);
+
+#endif

modules/functional/src/interpolate/neighbor_interpolate.cpp

@@ -0,0 +1,65 @@
+#include "neighbor_interpolate.hpp"
+#include "neighbor_interpolate.cuh"
+
+#include "../utils.hpp"
+
+std::vector<at::Tensor>
+three_nearest_neighbors_interpolate_forward(at::Tensor points_coords,
+                                            at::Tensor centers_coords,
+                                            at::Tensor centers_features) {
+  CHECK_CUDA(points_coords);
+  CHECK_CUDA(centers_coords);
+  CHECK_CUDA(centers_features);
+  CHECK_CONTIGUOUS(points_coords);
+  CHECK_CONTIGUOUS(centers_coords);
+  CHECK_CONTIGUOUS(centers_features);
+  CHECK_IS_FLOAT(points_coords);
+  CHECK_IS_FLOAT(centers_coords);
+  CHECK_IS_FLOAT(centers_features);
+
+  int b = centers_features.size(0);
+  int c = centers_features.size(1);
+  int m = centers_features.size(2);
+  int n = points_coords.size(2);
+
+  at::Tensor indices = torch::zeros(
+      {b, 3, n}, at::device(points_coords.device()).dtype(at::ScalarType::Int));
+  at::Tensor weights = torch::zeros(
+      {b, 3, n},
+      at::device(points_coords.device()).dtype(at::ScalarType::Float));
+  at::Tensor output = torch::zeros(
+      {b, c, n},
+      at::device(centers_features.device()).dtype(at::ScalarType::Float));
+
+  three_nearest_neighbors_interpolate(
+      b, c, m, n, points_coords.data_ptr<float>(),
+      centers_coords.data_ptr<float>(), centers_features.data_ptr<float>(),
+      indices.data_ptr<int>(), weights.data_ptr<float>(),
+      output.data_ptr<float>());
+  return {output, indices, weights};
+}
+
+at::Tensor three_nearest_neighbors_interpolate_backward(at::Tensor grad_y,
+                                                        at::Tensor indices,
+                                                        at::Tensor weights,
+                                                        const int m) {
+  CHECK_CUDA(grad_y);
+  CHECK_CUDA(indices);
+  CHECK_CUDA(weights);
+  CHECK_CONTIGUOUS(grad_y);
+  CHECK_CONTIGUOUS(indices);
+  CHECK_CONTIGUOUS(weights);
+  CHECK_IS_FLOAT(grad_y);
+  CHECK_IS_INT(indices);
+  CHECK_IS_FLOAT(weights);
+
+  int b = grad_y.size(0);
+  int c = grad_y.size(1);
+  int n = grad_y.size(2);
+  at::Tensor grad_x = torch::zeros(
+      {b, c, m}, at::device(grad_y.device()).dtype(at::ScalarType::Float));
+  three_nearest_neighbors_interpolate_grad(
+      b, c, n, m, grad_y.data_ptr<float>(), indices.data_ptr<int>(),
+      weights.data_ptr<float>(), grad_x.data_ptr<float>());
+  return grad_x;
+}

modules/functional/src/interpolate/neighbor_interpolate.cu

@@ -0,0 +1,181 @@
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "../cuda_utils.cuh"
+
+/*
+  Function: three nearest neighbors
+  Args:
+    b   : batch size
+    n   : number of points in point clouds
+    m   : number of query centers
+    points_coords : coordinates of points, FloatTensor[b, 3, n]
+    centers_coords: coordinates of centers, FloatTensor[b, 3, m]
+    weights       : weights of nearest 3 centers to the point,
+                    FloatTensor[b, 3, n]
+    indices       : indices of nearest 3 centers to the point,
+                    IntTensor[b, 3, n]
+*/
+__global__ void three_nearest_neighbors_kernel(
+    int b, int n, int m, const float *__restrict__ points_coords,
+    const float *__restrict__ centers_coords, float *__restrict__ weights,
+    int *__restrict__ indices) {
+  int batch_index = blockIdx.x;
+  int index = threadIdx.x;
+  int stride = blockDim.x;
+  points_coords += batch_index * 3 * n;
+  weights += batch_index * 3 * n;
+  indices += batch_index * 3 * n;
+  centers_coords += batch_index * 3 * m;
+
+  for (int j = index; j < n; j += stride) {
+    float ux = points_coords[j];
+    float uy = points_coords[j + n];
+    float uz = points_coords[j + n + n];
+
+    double best0 = 1e40, best1 = 1e40, best2 = 1e40;
+    int besti0 = 0, besti1 = 0, besti2 = 0;
+    for (int k = 0; k < m; ++k) {
+      float x = centers_coords[k];
+      float y = centers_coords[k + m];
+      float z = centers_coords[k + m + m];
+      float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);
+      if (d < best2) {
+        best2 = d;
+        besti2 = k;
+        if (d < best1) {
+          best2 = best1;
+          besti2 = besti1;
+          best1 = d;
+          besti1 = k;
+          if (d < best0) {
+            best1 = best0;
+            besti1 = besti0;
+            best0 = d;
+            besti0 = k;
+          }
+        }
+      }
+    }
+    best0 = max(min(1e10f, best0), 1e-10f);
+    best1 = max(min(1e10f, best1), 1e-10f);
+    best2 = max(min(1e10f, best2), 1e-10f);
+    float d0d1 = best0 * best1;
+    float d0d2 = best0 * best2;
+    float d1d2 = best1 * best2;
+    float d0d1d2 = 1.0f / (d0d1 + d0d2 + d1d2);
+    weights[j] = d1d2 * d0d1d2;
+    indices[j] = besti0;
+    weights[j + n] = d0d2 * d0d1d2;
+    indices[j + n] = besti1;
+    weights[j + n + n] = d0d1 * d0d1d2;
+    indices[j + n + n] = besti2;
+  }
+}
+
+/*
+  Function: interpolate three nearest neighbors (forward)
+  Args:
+    b   : batch size
+    c   : #channels of features
+    m   : number of query centers
+    n   : number of points in point clouds
+    centers_features: features of centers, FloatTensor[b, c, m]
+    indices         : indices of nearest 3 centers to the point,
+                      IntTensor[b, 3, n]
+    weights         : weights for interpolation, FloatTensor[b, 3, n]
+    out             : features of points, FloatTensor[b, c, n]
+*/
+__global__ void three_nearest_neighbors_interpolate_kernel(
+    int b, int c, int m, int n, const float *__restrict__ centers_features,
+    const int *__restrict__ indices, const float *__restrict__ weights,
+    float *__restrict__ out) {
+  int batch_index = blockIdx.x;
+  centers_features += batch_index * m * c;
+  indices += batch_index * n * 3;
+  weights += batch_index * n * 3;
+  out += batch_index * n * c;
+
+  const int index = threadIdx.y * blockDim.x + threadIdx.x;
+  const int stride = blockDim.y * blockDim.x;
+  for (int i = index; i < c * n; i += stride) {
+    const int l = i / n;
+    const int j = i % n;
+    float w1 = weights[j];
+    float w2 = weights[j + n];
+    float w3 = weights[j + n + n];
+    int i1 = indices[j];
+    int i2 = indices[j + n];
+    int i3 = indices[j + n + n];
+
+    out[i] = centers_features[l * m + i1] * w1 +
+             centers_features[l * m + i2] * w2 +
+             centers_features[l * m + i3] * w3;
+  }
+}
+
+void three_nearest_neighbors_interpolate(int b, int c, int m, int n,
+                                         const float *points_coords,
+                                         const float *centers_coords,
+                                         const float *centers_features,
+                                         int *indices, float *weights,
+                                         float *out) {
+  three_nearest_neighbors_kernel<<<b, optimal_num_threads(n), 0,
+                                   at::cuda::getCurrentCUDAStream()>>>(
+      b, n, m, points_coords, centers_coords, weights, indices);
+  three_nearest_neighbors_interpolate_kernel<<<
+      b, optimal_block_config(n, c), 0, at::cuda::getCurrentCUDAStream()>>>(
+      b, c, m, n, centers_features, indices, weights, out);
+  CUDA_CHECK_ERRORS();
+}
+
+/*
+  Function: interpolate three nearest neighbors (backward)
+  Args:
+    b   : batch size
+    c   : #channels of features
+    m   : number of query centers
+    n   : number of points in point clouds
+    grad_y  : grad of features of points, FloatTensor[b, c, n]
+    indices : indices of nearest 3 centers to the point, IntTensor[b, 3, n]
+    weights : weights for interpolation, FloatTensor[b, 3, n]
+    grad_x  : grad of features of centers, FloatTensor[b, c, m]
+*/
+__global__ void three_nearest_neighbors_interpolate_grad_kernel(
+    int b, int c, int n, int m, const float *__restrict__ grad_y,
+    const int *__restrict__ indices, const float *__restrict__ weights,
+    float *__restrict__ grad_x) {
+  int batch_index = blockIdx.x;
+  grad_y += batch_index * n * c;
+  indices += batch_index * n * 3;
+  weights += batch_index * n * 3;
+  grad_x += batch_index * m * c;
+
+  const int index = threadIdx.y * blockDim.x + threadIdx.x;
+  const int stride = blockDim.y * blockDim.x;
+  for (int i = index; i < c * n; i += stride) {
+    const int l = i / n;
+    const int j = i % n;
+    float w1 = weights[j];
+    float w2 = weights[j + n];
+    float w3 = weights[j + n + n];
+    int i1 = indices[j];
+    int i2 = indices[j + n];
+    int i3 = indices[j + n + n];
+    atomicAdd(grad_x + l * m + i1, grad_y[i] * w1);
+    atomicAdd(grad_x + l * m + i2, grad_y[i] * w2);
+    atomicAdd(grad_x + l * m + i3, grad_y[i] * w3);
+  }
+}
+
+void three_nearest_neighbors_interpolate_grad(int b, int c, int n, int m,
+                                              const float *grad_y,
+                                              const int *indices,
+                                              const float *weights,
+                                              float *grad_x) {
+  three_nearest_neighbors_interpolate_grad_kernel<<<
+      b, optimal_block_config(n, c), 0, at::cuda::getCurrentCUDAStream()>>>(
+      b, c, n, m, grad_y, indices, weights, grad_x);
+  CUDA_CHECK_ERRORS();
+}

modules/functional/src/interpolate/neighbor_interpolate.cuh

@@ -0,0 +1,16 @@
+#ifndef _NEIGHBOR_INTERPOLATE_CUH
+#define _NEIGHBOR_INTERPOLATE_CUH
+
+void three_nearest_neighbors_interpolate(int b, int c, int m, int n,
+                                         const float *points_coords,
+                                         const float *centers_coords,
+                                         const float *centers_features,
+                                         int *indices, float *weights,
+                                         float *out);
+void three_nearest_neighbors_interpolate_grad(int b, int c, int n, int m,
+                                              const float *grad_y,
+                                              const int *indices,
+                                              const float *weights,
+                                              float *grad_x);
+
+#endif

modules/functional/src/interpolate/neighbor_interpolate.hpp

@@ -0,0 +1,16 @@
+#ifndef _NEIGHBOR_INTERPOLATE_HPP
+#define _NEIGHBOR_INTERPOLATE_HPP
+
+#include <torch/extension.h>
+#include <vector>
+
+std::vector<at::Tensor>
+three_nearest_neighbors_interpolate_forward(at::Tensor points_coords,
+                                            at::Tensor centers_coords,
+                                            at::Tensor centers_features);
+at::Tensor three_nearest_neighbors_interpolate_backward(at::Tensor grad_y,
+                                                        at::Tensor indices,
+                                                        at::Tensor weights,
+                                                        const int m);
+
+#endif

modules/functional/src/interpolate/trilinear_devox.cpp

@@ -0,0 +1,91 @@
+#include "trilinear_devox.hpp"
+#include "trilinear_devox.cuh"
+
+#include "../utils.hpp"
+
+/*
+  Function: trilinear devoxelization (forward)
+  Args:
+    r        : voxel resolution
+    trainig  : whether is training mode
+    coords   : the coordinates of points, FloatTensor[b, 3, n]
+    features : features, FloatTensor[b, c, s], s = r ** 3
+  Return:
+    outs : outputs, FloatTensor[b, c, n]
+    inds : the voxel coordinates of point cube, IntTensor[b, 8, n]
+    wgts : weight for trilinear interpolation, FloatTensor[b, 8, n]
+*/
+std::vector<at::Tensor>
+trilinear_devoxelize_forward(const int r, const bool is_training,
+                             const at::Tensor coords,
+                             const at::Tensor features) {
+  CHECK_CUDA(features);
+  CHECK_CUDA(coords);
+  CHECK_CONTIGUOUS(features);
+  CHECK_CONTIGUOUS(coords);
+  CHECK_IS_FLOAT(features);
+  CHECK_IS_FLOAT(coords);
+
+  int b = features.size(0);
+  int c = features.size(1);
+  int n = coords.size(2);
+  int r2 = r * r;
+  int r3 = r2 * r;
+  at::Tensor outs = torch::zeros(
+      {b, c, n}, at::device(features.device()).dtype(at::ScalarType::Float));
+  if (is_training) {
+    at::Tensor inds = torch::zeros(
+        {b, 8, n}, at::device(features.device()).dtype(at::ScalarType::Int));
+    at::Tensor wgts = torch::zeros(
+        {b, 8, n}, at::device(features.device()).dtype(at::ScalarType::Float));
+    trilinear_devoxelize(b, c, n, r, r2, r3, true, coords.data_ptr<float>(),
+                         features.data_ptr<float>(), inds.data_ptr<int>(),
+                         wgts.data_ptr<float>(), outs.data_ptr<float>());
+    return {outs, inds, wgts};
+  } else {
+    at::Tensor inds = torch::zeros(
+        {1}, at::device(features.device()).dtype(at::ScalarType::Int));
+    at::Tensor wgts = torch::zeros(
+        {1}, at::device(features.device()).dtype(at::ScalarType::Float));
+    trilinear_devoxelize(b, c, n, r, r2, r3, false, coords.data_ptr<float>(),
+                         features.data_ptr<float>(), inds.data_ptr<int>(),
+                         wgts.data_ptr<float>(), outs.data_ptr<float>());
+    return {outs, inds, wgts};
+  }
+}
+
+/*
+  Function: trilinear devoxelization (backward)
+  Args:
+    grad_y  : grad outputs, FloatTensor[b, c, n]
+    indices : the voxel coordinates of point cube, IntTensor[b, 8, n]
+    weights : weight for trilinear interpolation, FloatTensor[b, 8, n]
+    r       : voxel resolution
+  Return:
+    grad_x     : grad inputs, FloatTensor[b, c, s], s = r ** 3
+*/
+at::Tensor trilinear_devoxelize_backward(const at::Tensor grad_y,
+                                         const at::Tensor indices,
+                                         const at::Tensor weights,
+                                         const int r) {
+  CHECK_CUDA(grad_y);
+  CHECK_CUDA(weights);
+  CHECK_CUDA(indices);
+  CHECK_CONTIGUOUS(grad_y);
+  CHECK_CONTIGUOUS(weights);
+  CHECK_CONTIGUOUS(indices);
+  CHECK_IS_FLOAT(grad_y);
+  CHECK_IS_FLOAT(weights);
+  CHECK_IS_INT(indices);
+
+  int b = grad_y.size(0);
+  int c = grad_y.size(1);
+  int n = grad_y.size(2);
+  int r3 = r * r * r;
+  at::Tensor grad_x = torch::zeros(
+      {b, c, r3}, at::device(grad_y.device()).dtype(at::ScalarType::Float));
+  trilinear_devoxelize_grad(b, c, n, r3, indices.data_ptr<int>(),
+                            weights.data_ptr<float>(), grad_y.data_ptr<float>(),
+                            grad_x.data_ptr<float>());
+  return grad_x;
+}

modules/functional/src/interpolate/trilinear_devox.cu

@@ -0,0 +1,178 @@
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "../cuda_utils.cuh"
+
+/*
+  Function: trilinear devoxlization (forward)
+  Args:
+    b   : batch size
+    c   : #channels
+    n   : number of points
+    r   : voxel resolution
+    r2  : r ** 2
+    r3  : r ** 3
+    coords : the coordinates of points, FloatTensor[b, 3, n]
+    feat   : features, FloatTensor[b, c, r3]
+    inds   : the voxel indices of point cube, IntTensor[b, 8, n]
+    wgts   : weight for trilinear interpolation, FloatTensor[b, 8, n]
+    outs   : outputs, FloatTensor[b, c, n]
+*/
+__global__ void trilinear_devoxelize_kernel(int b, int c, int n, int r, int r2,
+                                            int r3, bool is_training,
+                                            const float *__restrict__ coords,
+                                            const float *__restrict__ feat,
+                                            int *__restrict__ inds,
+                                            float *__restrict__ wgts,
+                                            float *__restrict__ outs) {
+  int batch_index = blockIdx.x;
+  int stride = blockDim.x;
+  int index = threadIdx.x;
+  coords += batch_index * n * 3;
+  inds += batch_index * n * 8;
+  wgts += batch_index * n * 8;
+  feat += batch_index * c * r3;
+  outs += batch_index * c * n;
+
+  for (int i = index; i < n; i += stride) {
+    float x = coords[i];
+    float y = coords[i + n];
+    float z = coords[i + n + n];
+    float x_lo_f = floorf(x);
+    float y_lo_f = floorf(y);
+    float z_lo_f = floorf(z);
+
+    float x_d_1 = x - x_lo_f; // / (x_hi_f - x_lo_f + 1e-8f)
+    float y_d_1 = y - y_lo_f;
+    float z_d_1 = z - z_lo_f;
+    float x_d_0 = 1.0f - x_d_1;
+    float y_d_0 = 1.0f - y_d_1;
+    float z_d_0 = 1.0f - z_d_1;
+
+    float wgt000 = x_d_0 * y_d_0 * z_d_0;
+    float wgt001 = x_d_0 * y_d_0 * z_d_1;
+    float wgt010 = x_d_0 * y_d_1 * z_d_0;
+    float wgt011 = x_d_0 * y_d_1 * z_d_1;
+    float wgt100 = x_d_1 * y_d_0 * z_d_0;
+    float wgt101 = x_d_1 * y_d_0 * z_d_1;
+    float wgt110 = x_d_1 * y_d_1 * z_d_0;
+    float wgt111 = x_d_1 * y_d_1 * z_d_1;
+
+    int x_lo = static_cast<int>(x_lo_f);
+    int y_lo = static_cast<int>(y_lo_f);
+    int z_lo = static_cast<int>(z_lo_f);
+    int x_hi = (x_d_1 > 0) ? -1 : 0;
+    int y_hi = (y_d_1 > 0) ? -1 : 0;
+    int z_hi = (z_d_1 > 0) ? 1 : 0;
+
+    int idx000 = x_lo * r2 + y_lo * r + z_lo;
+    int idx001 = idx000 + z_hi;      // x_lo * r2 + y_lo * r + z_hi;
+    int idx010 = idx000 + (y_hi & r);  // x_lo * r2 + y_hi * r + z_lo;
+    int idx011 = idx010 + z_hi;      // x_lo * r2 + y_hi * r + z_hi;
+    int idx100 = idx000 + (x_hi & r2); // x_hi * r2 + y_lo * r + z_lo;
+    int idx101 = idx100 + z_hi;      // x_hi * r2 + y_lo * r + z_hi;
+    int idx110 = idx100 + (y_hi & r);  // x_hi * r2 + y_hi * r + z_lo;
+    int idx111 = idx110 + z_hi;      // x_hi * r2 + y_hi * r + z_hi;
+
+    if (is_training) {
+      wgts[i] = wgt000;
+      wgts[i + n] = wgt001;
+      wgts[i + n * 2] = wgt010;
+      wgts[i + n * 3] = wgt011;
+      wgts[i + n * 4] = wgt100;
+      wgts[i + n * 5] = wgt101;
+      wgts[i + n * 6] = wgt110;
+      wgts[i + n * 7] = wgt111;
+      inds[i] = idx000;
+      inds[i + n] = idx001;
+      inds[i + n * 2] = idx010;
+      inds[i + n * 3] = idx011;
+      inds[i + n * 4] = idx100;
+      inds[i + n * 5] = idx101;
+      inds[i + n * 6] = idx110;
+      inds[i + n * 7] = idx111;
+    }
+
+    for (int j = 0; j < c; j++) {
+      int jr3 = j * r3;
+      outs[j * n + i] =
+          wgt000 * feat[jr3 + idx000] + wgt001 * feat[jr3 + idx001] +
+          wgt010 * feat[jr3 + idx010] + wgt011 * feat[jr3 + idx011] +
+          wgt100 * feat[jr3 + idx100] + wgt101 * feat[jr3 + idx101] +
+          wgt110 * feat[jr3 + idx110] + wgt111 * feat[jr3 + idx111];
+    }
+  }
+}
+
+/*
+  Function: trilinear devoxlization (backward)
+  Args:
+    b   : batch size
+    c   : #channels
+    n   : number of points
+    r3  : voxel cube size = voxel resolution ** 3
+    inds   : the voxel indices of point cube, IntTensor[b, 8, n]
+    wgts   : weight for trilinear interpolation, FloatTensor[b, 8, n]
+    grad_y : grad outputs, FloatTensor[b, c, n]
+    grad_x : grad inputs, FloatTensor[b, c, r3]
+*/
+__global__ void trilinear_devoxelize_grad_kernel(
+    int b, int c, int n, int r3, const int *__restrict__ inds,
+    const float *__restrict__ wgts, const float *__restrict__ grad_y,
+    float *__restrict__ grad_x) {
+  int batch_index = blockIdx.x;
+  int stride = blockDim.x;
+  int index = threadIdx.x;
+  inds += batch_index * n * 8;
+  wgts += batch_index * n * 8;
+  grad_x += batch_index * c * r3;
+  grad_y += batch_index * c * n;
+
+  for (int i = index; i < n; i += stride) {
+    int idx000 = inds[i];
+    int idx001 = inds[i + n];
+    int idx010 = inds[i + n * 2];
+    int idx011 = inds[i + n * 3];
+    int idx100 = inds[i + n * 4];
+    int idx101 = inds[i + n * 5];
+    int idx110 = inds[i + n * 6];
+    int idx111 = inds[i + n * 7];
+    float wgt000 = wgts[i];
+    float wgt001 = wgts[i + n];
+    float wgt010 = wgts[i + n * 2];
+    float wgt011 = wgts[i + n * 3];
+    float wgt100 = wgts[i + n * 4];
+    float wgt101 = wgts[i + n * 5];
+    float wgt110 = wgts[i + n * 6];
+    float wgt111 = wgts[i + n * 7];
+
+    for (int j = 0; j < c; j++) {
+      int jr3 = j * r3;
+      float g = grad_y[j * n + i];
+      atomicAdd(grad_x + jr3 + idx000, wgt000 * g);
+      atomicAdd(grad_x + jr3 + idx001, wgt001 * g);
+      atomicAdd(grad_x + jr3 + idx010, wgt010 * g);
+      atomicAdd(grad_x + jr3 + idx011, wgt011 * g);
+      atomicAdd(grad_x + jr3 + idx100, wgt100 * g);
+      atomicAdd(grad_x + jr3 + idx101, wgt101 * g);
+      atomicAdd(grad_x + jr3 + idx110, wgt110 * g);
+      atomicAdd(grad_x + jr3 + idx111, wgt111 * g);
+    }
+  }
+}
+
+void trilinear_devoxelize(int b, int c, int n, int r, int r2, int r3,
+                          bool training, const float *coords, const float *feat,
+                          int *inds, float *wgts, float *outs) {
+  trilinear_devoxelize_kernel<<<b, optimal_num_threads(n)>>>(
+      b, c, n, r, r2, r3, training, coords, feat, inds, wgts, outs);
+  CUDA_CHECK_ERRORS();
+}
+
+void trilinear_devoxelize_grad(int b, int c, int n, int r3, const int *inds,
+                               const float *wgts, const float *grad_y,
+                               float *grad_x) {
+  trilinear_devoxelize_grad_kernel<<<b, optimal_num_threads(n)>>>(
+      b, c, n, r3, inds, wgts, grad_y, grad_x);
+  CUDA_CHECK_ERRORS();
+}

modules/functional/src/interpolate/trilinear_devox.cuh

@@ -0,0 +1,13 @@
+#ifndef _TRILINEAR_DEVOX_CUH
+#define _TRILINEAR_DEVOX_CUH
+
+// CUDA function declarations
+void trilinear_devoxelize(int b, int c, int n, int r, int r2, int r3,
+                          bool is_training, const float *coords,
+                          const float *feat, int *inds, float *wgts,
+                          float *outs);
+void trilinear_devoxelize_grad(int b, int c, int n, int r3, const int *inds,
+                               const float *wgts, const float *grad_y,
+                               float *grad_x);
+
+#endif

modules/functional/src/interpolate/trilinear_devox.hpp

@@ -0,0 +1,16 @@
+#ifndef _TRILINEAR_DEVOX_HPP
+#define _TRILINEAR_DEVOX_HPP
+
+#include <torch/torch.h>
+#include <vector>
+
+std::vector<at::Tensor> trilinear_devoxelize_forward(const int r,
+                                                     const bool is_training,
+                                                     const at::Tensor coords,
+                                                     const at::Tensor features);
+
+at::Tensor trilinear_devoxelize_backward(const at::Tensor grad_y,
+                                         const at::Tensor indices,
+                                         const at::Tensor weights, const int r);
+
+#endif

modules/functional/src/sampling/sampling.cpp

@@ -0,0 +1,58 @@
+#include "sampling.hpp"
+#include "sampling.cuh"
+
+#include "../utils.hpp"
+
+at::Tensor gather_features_forward(at::Tensor features, at::Tensor indices) {
+  CHECK_CUDA(features);
+  CHECK_CUDA(indices);
+  CHECK_CONTIGUOUS(features);
+  CHECK_CONTIGUOUS(indices);
+  CHECK_IS_FLOAT(features);
+  CHECK_IS_INT(indices);
+
+  int b = features.size(0);
+  int c = features.size(1);
+  int n = features.size(2);
+  int m = indices.size(1);
+  at::Tensor output = torch::zeros(
+      {b, c, m}, at::device(features.device()).dtype(at::ScalarType::Float));
+  gather_features(b, c, n, m, features.data_ptr<float>(),
+                  indices.data_ptr<int>(), output.data_ptr<float>());
+  return output;
+}
+
+at::Tensor gather_features_backward(at::Tensor grad_y, at::Tensor indices,
+                                    const int n) {
+  CHECK_CUDA(grad_y);
+  CHECK_CUDA(indices);
+  CHECK_CONTIGUOUS(grad_y);
+  CHECK_CONTIGUOUS(indices);
+  CHECK_IS_FLOAT(grad_y);
+  CHECK_IS_INT(indices);
+
+  int b = grad_y.size(0);
+  int c = grad_y.size(1);
+  at::Tensor grad_x = torch::zeros(
+      {b, c, n}, at::device(grad_y.device()).dtype(at::ScalarType::Float));
+  gather_features_grad(b, c, n, indices.size(1), grad_y.data_ptr<float>(),
+                       indices.data_ptr<int>(), grad_x.data_ptr<float>());
+  return grad_x;
+}
+
+at::Tensor furthest_point_sampling_forward(at::Tensor coords,
+                                           const int num_samples) {
+  CHECK_CUDA(coords);
+  CHECK_CONTIGUOUS(coords);
+  CHECK_IS_FLOAT(coords);
+
+  int b = coords.size(0);
+  int n = coords.size(2);
+  at::Tensor indices = torch::zeros(
+      {b, num_samples}, at::device(coords.device()).dtype(at::ScalarType::Int));
+  at::Tensor distances = torch::full(
+      {b, n}, 1e38f, at::device(coords.device()).dtype(at::ScalarType::Float));
+  furthest_point_sampling(b, n, num_samples, coords.data_ptr<float>(),
+                          distances.data_ptr<float>(), indices.data_ptr<int>());
+  return indices;
+}

modules/functional/src/sampling/sampling.cu

@@ -0,0 +1,174 @@
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "../cuda_utils.cuh"
+
+/*
+  Function: gather centers' features (forward)
+  Args:
+    b   : batch size
+    c   : #channles of features
+    n   : number of points in point clouds
+    m   : number of query/sampled centers
+    features: points' features, FloatTensor[b, c, n]
+    indices : centers' indices in points, IntTensor[b, m]
+    out     : gathered features, FloatTensor[b, c, m]
+*/
+__global__ void gather_features_kernel(int b, int c, int n, int m,
+                                       const float *__restrict__ features,
+                                       const int *__restrict__ indices,
+                                       float *__restrict__ out) {
+  int batch_index = blockIdx.x;
+  int channel_index = blockIdx.y;
+  int temp_index = batch_index * c + channel_index;
+  features += temp_index * n;
+  indices += batch_index * m;
+  out += temp_index * m;
+
+  for (int j = threadIdx.x; j < m; j += blockDim.x) {
+    out[j] = features[indices[j]];
+  }
+}
+
+void gather_features(int b, int c, int n, int m, const float *features,
+                     const int *indices, float *out) {
+  gather_features_kernel<<<dim3(b, c, 1), optimal_num_threads(m), 0,
+                           at::cuda::getCurrentCUDAStream()>>>(
+      b, c, n, m, features, indices, out);
+  CUDA_CHECK_ERRORS();
+}
+
+/*
+  Function: gather centers' features (backward)
+  Args:
+    b   : batch size
+    c   : #channles of features
+    n   : number of points in point clouds
+    m   : number of query/sampled centers
+    grad_y  : grad of gathered features, FloatTensor[b, c, m]
+    indices : centers' indices in points, IntTensor[b, m]
+    grad_x  : grad of points' features, FloatTensor[b, c, n]
+*/
+__global__ void gather_features_grad_kernel(int b, int c, int n, int m,
+                                            const float *__restrict__ grad_y,
+                                            const int *__restrict__ indices,
+                                            float *__restrict__ grad_x) {
+  int batch_index = blockIdx.x;
+  int channel_index = blockIdx.y;
+  int temp_index = batch_index * c + channel_index;
+  grad_y += temp_index * m;
+  indices += batch_index * m;
+  grad_x += temp_index * n;
+
+  for (int j = threadIdx.x; j < m; j += blockDim.x) {
+    atomicAdd(grad_x + indices[j], grad_y[j]);
+  }
+}
+
+void gather_features_grad(int b, int c, int n, int m, const float *grad_y,
+                          const int *indices, float *grad_x) {
+  gather_features_grad_kernel<<<dim3(b, c, 1), optimal_num_threads(m), 0,
+                                at::cuda::getCurrentCUDAStream()>>>(
+      b, c, n, m, grad_y, indices, grad_x);
+  CUDA_CHECK_ERRORS();
+}
+
+/*
+  Function: furthest point sampling
+  Args:
+    b   : batch size
+    n   : number of points in point clouds
+    m   : number of query/sampled centers
+    coords    : points' coords, FloatTensor[b, 3, n]
+    distances : minimum distance of a point to the set, IntTensor[b, n]
+    indices   : sampled centers' indices in points, IntTensor[b, m]
+*/
+__global__ void furthest_point_sampling_kernel(int b, int n, int m,
+                                               const float *__restrict__ coords,
+                                               float *__restrict__ distances,
+                                               int *__restrict__ indices) {
+  if (m <= 0)
+    return;
+  int batch_index = blockIdx.x;
+  coords += batch_index * n * 3;
+  distances += batch_index * n;
+  indices += batch_index * m;
+
+  const int BlockSize = 512;
+  __shared__ float dists[BlockSize];
+  __shared__ int dists_i[BlockSize];
+  const int BufferSize = 3072;
+  __shared__ float buf[BufferSize * 3];
+
+  int old = 0;
+  if (threadIdx.x == 0)
+    indices[0] = old;
+
+  for (int j = threadIdx.x; j < min(BufferSize, n); j += blockDim.x) {
+    buf[j] = coords[j];
+    buf[j + BufferSize] = coords[j + n];
+    buf[j + BufferSize + BufferSize] = coords[j + n + n];
+  }
+  __syncthreads();
+
+  for (int j = 1; j < m; j++) {
+    int besti = 0;   // best index
+    float best = -1; // farthest distance
+    // calculating the distance with the latest sampled point
+    float x1 = coords[old];
+    float y1 = coords[old + n];
+    float z1 = coords[old + n + n];
+    for (int k = threadIdx.x; k < n; k += blockDim.x) {
+      // fetch distance at block n, thread k
+      float td = distances[k];
+      float x2, y2, z2;
+      if (k < BufferSize) {
+        x2 = buf[k];
+        y2 = buf[k + BufferSize];
+        z2 = buf[k + BufferSize + BufferSize];
+      } else {
+        x2 = coords[k];
+        y2 = coords[k + n];
+        z2 = coords[k + n + n];
+      }
+      float d =
+          (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);
+      float d2 = min(d, td);
+      // update "point-to-set" distance
+      if (d2 != td)
+        distances[k] = d2;
+      // update the farthest distance at sample step j
+      if (d2 > best) {
+        best = d2;
+        besti = k;
+      }
+    }
+
+    dists[threadIdx.x] = best;
+    dists_i[threadIdx.x] = besti;
+    for (int u = 0; (1 << u) < blockDim.x; u++) {
+      __syncthreads();
+      if (threadIdx.x < (blockDim.x >> (u + 1))) {
+        int i1 = (threadIdx.x * 2) << u;
+        int i2 = (threadIdx.x * 2 + 1) << u;
+        if (dists[i1] < dists[i2]) {
+          dists[i1] = dists[i2];
+          dists_i[i1] = dists_i[i2];
+        }
+      }
+    }
+    __syncthreads();
+
+    // finish sample step j; old is the sampled index
+    old = dists_i[0];
+    if (threadIdx.x == 0)
+      indices[j] = old;
+  }
+}
+
+void furthest_point_sampling(int b, int n, int m, const float *coords,
+                             float *distances, int *indices) {
+  furthest_point_sampling_kernel<<<b, 512>>>(b, n, m, coords, distances,
+                                             indices);
+  CUDA_CHECK_ERRORS();
+}

modules/functional/src/sampling/sampling.cuh

@@ -0,0 +1,11 @@
+#ifndef _SAMPLING_CUH
+#define _SAMPLING_CUH
+
+void gather_features(int b, int c, int n, int m, const float *features,
+                     const int *indices, float *out);
+void gather_features_grad(int b, int c, int n, int m, const float *grad_y,
+                          const int *indices, float *grad_x);
+void furthest_point_sampling(int b, int n, int m, const float *coords,
+                             float *distances, int *indices);
+
+#endif

modules/functional/src/sampling/sampling.hpp

@@ -0,0 +1,12 @@
+#ifndef _SAMPLING_HPP
+#define _SAMPLING_HPP
+
+#include <torch/extension.h>
+
+at::Tensor gather_features_forward(at::Tensor features, at::Tensor indices);
+at::Tensor gather_features_backward(at::Tensor grad_y, at::Tensor indices,
+                                    const int n);
+at::Tensor furthest_point_sampling_forward(at::Tensor coords,
+                                           const int num_samples);
+
+#endif

modules/functional/src/utils.hpp

@@ -0,0 +1,20 @@
+#ifndef _UTILS_HPP
+#define _UTILS_HPP
+
+#include <ATen/cuda/CUDAContext.h>
+#include <torch/extension.h>
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor")
+
+#define CHECK_CONTIGUOUS(x)                                                    \
+  TORCH_CHECK(x.is_contiguous(), #x " must be a contiguous tensor")
+
+#define CHECK_IS_INT(x)                                                        \
+  TORCH_CHECK(x.scalar_type() == at::ScalarType::Int,                             \
+           #x " must be an int tensor")
+
+#define CHECK_IS_FLOAT(x)                                                      \
+  TORCH_CHECK(x.scalar_type() == at::ScalarType::Float,                           \
+           #x " must be a float tensor")
+
+#endif

modules/functional/src/voxelization/vox.cpp

@@ -0,0 +1,76 @@
+#include "vox.hpp"
+#include "vox.cuh"
+
+#include "../utils.hpp"
+
+/*
+  Function: average pool voxelization (forward)
+  Args:
+    features: features, FloatTensor[b, c, n]
+    coords  : coords of each point, IntTensor[b, 3, n]
+    resolution : voxel resolution
+  Return:
+    out : outputs, FloatTensor[b, c, s], s = r ** 3
+    ind : voxel index of each point, IntTensor[b, n]
+    cnt : #points in each voxel index, IntTensor[b, s]
+*/
+std::vector<at::Tensor> avg_voxelize_forward(const at::Tensor features,
+                                             const at::Tensor coords,
+                                             const int resolution) {
+  CHECK_CUDA(features);
+  CHECK_CUDA(coords);
+  CHECK_CONTIGUOUS(features);
+  CHECK_CONTIGUOUS(coords);
+  CHECK_IS_FLOAT(features);
+  CHECK_IS_INT(coords);
+
+  int b = features.size(0);
+  int c = features.size(1);
+  int n = features.size(2);
+  int r = resolution;
+  int r2 = r * r;
+  int r3 = r2 * r;
+  at::Tensor ind = torch::zeros(
+      {b, n}, at::device(features.device()).dtype(at::ScalarType::Int));
+  at::Tensor out = torch::zeros(
+      {b, c, r3}, at::device(features.device()).dtype(at::ScalarType::Float));
+  at::Tensor cnt = torch::zeros(
+      {b, r3}, at::device(features.device()).dtype(at::ScalarType::Int));
+  avg_voxelize(b, c, n, r, r2, r3, coords.data_ptr<int>(),
+               features.data_ptr<float>(), ind.data_ptr<int>(),
+               cnt.data_ptr<int>(), out.data_ptr<float>());
+  return {out, ind, cnt};
+}
+
+/*
+  Function: average pool voxelization (backward)
+  Args:
+    grad_y : grad outputs, FloatTensor[b, c, s]
+    indices: voxel index of each point, IntTensor[b, n]
+    cnt    : #points in each voxel index, IntTensor[b, s]
+  Return:
+    grad_x : grad inputs, FloatTensor[b, c, n]
+*/
+at::Tensor avg_voxelize_backward(const at::Tensor grad_y,
+                                 const at::Tensor indices,
+                                 const at::Tensor cnt) {
+  CHECK_CUDA(grad_y);
+  CHECK_CUDA(indices);
+  CHECK_CUDA(cnt);
+  CHECK_CONTIGUOUS(grad_y);
+  CHECK_CONTIGUOUS(indices);
+  CHECK_CONTIGUOUS(cnt);
+  CHECK_IS_FLOAT(grad_y);
+  CHECK_IS_INT(indices);
+  CHECK_IS_INT(cnt);
+
+  int b = grad_y.size(0);
+  int c = grad_y.size(1);
+  int s = grad_y.size(2);
+  int n = indices.size(1);
+  at::Tensor grad_x = torch::zeros(
+      {b, c, n}, at::device(grad_y.device()).dtype(at::ScalarType::Float));
+  avg_voxelize_grad(b, c, n, s, indices.data_ptr<int>(), cnt.data_ptr<int>(),
+                    grad_y.data_ptr<float>(), grad_x.data_ptr<float>());
+  return grad_x;
+}

modules/functional/src/voxelization/vox.cu

@@ -0,0 +1,126 @@
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "../cuda_utils.cuh"
+
+/*
+  Function: get how many points in each voxel grid
+  Args:
+    b      : batch size
+    n      : number of points
+    r      : voxel resolution
+    r2     : = r * r
+    r3     : s, voxel cube size = r ** 3
+    coords : coords of each point, IntTensor[b, 3, n]
+    ind    : voxel index of each point, IntTensor[b, n]
+    cnt    : #points in each voxel index, IntTensor[b, s]
+*/
+__global__ void grid_stats_kernel(int b, int n, int r, int r2, int r3,
+                                  const int *__restrict__ coords,
+                                  int *__restrict__ ind, int *cnt) {
+  int batch_index = blockIdx.x;
+  int stride = blockDim.x;
+  int index = threadIdx.x;
+  coords += batch_index * n * 3;
+  ind += batch_index * n;
+  cnt += batch_index * r3;
+
+  for (int i = index; i < n; i += stride) {
+    // if (ind[i] == -1)
+    //   continue;
+    ind[i] = coords[i] * r2 + coords[i + n] * r + coords[i + n + n];
+    atomicAdd(cnt + ind[i], 1);
+  }
+}
+
+/*
+  Function: average pool voxelization (forward)
+  Args:
+    b   : batch size
+    c   : #channels
+    n   : number of points
+    s   : voxel cube size = voxel resolution ** 3
+    ind : voxel index of each point, IntTensor[b, n]
+    cnt : #points in each voxel index, IntTensor[b, s]
+    feat: features, FloatTensor[b, c, n]
+    out : outputs, FloatTensor[b, c, s]
+*/
+__global__ void avg_voxelize_kernel(int b, int c, int n, int s,
+                                    const int *__restrict__ ind,
+                                    const int *__restrict__ cnt,
+                                    const float *__restrict__ feat,
+                                    float *__restrict__ out) {
+  int batch_index = blockIdx.x;
+  int stride = blockDim.x;
+  int index = threadIdx.x;
+  ind += batch_index * n;
+  feat += batch_index * c * n;
+  out += batch_index * c * s;
+  cnt += batch_index * s;
+  for (int i = index; i < n; i += stride) {
+    int pos = ind[i];
+    // if (pos == -1)
+    //   continue;
+    int cur_cnt = cnt[pos];
+    if (cur_cnt > 0) {
+      float div_cur_cnt = 1.0 / static_cast<float>(cur_cnt);
+      for (int j = 0; j < c; j++) {
+        atomicAdd(out + j * s + pos, feat[j * n + i] * div_cur_cnt);
+      }
+    }
+  }
+}
+
+/*
+  Function: average pool voxelization (backward)
+  Args:
+    b      : batch size
+    c      : #channels
+    n      : number of points
+    r3     : voxel cube size = voxel resolution ** 3
+    ind    : voxel index of each point, IntTensor[b, n]
+    cnt    : #points in each voxel index, IntTensor[b, s]
+    grad_y : grad outputs, FloatTensor[b, c, s]
+    grad_x : grad inputs, FloatTensor[b, c, n]
+*/
+__global__ void avg_voxelize_grad_kernel(int b, int c, int n, int r3,
+                                         const int *__restrict__ ind,
+                                         const int *__restrict__ cnt,
+                                         const float *__restrict__ grad_y,
+                                         float *__restrict__ grad_x) {
+  int batch_index = blockIdx.x;
+  int stride = blockDim.x;
+  int index = threadIdx.x;
+  ind += batch_index * n;
+  grad_x += batch_index * c * n;
+  grad_y += batch_index * c * r3;
+  cnt += batch_index * r3;
+  for (int i = index; i < n; i += stride) {
+    int pos = ind[i];
+    // if (pos == -1)
+    //   continue;
+    int cur_cnt = cnt[pos];
+    if (cur_cnt > 0) {
+      float div_cur_cnt = 1.0 / static_cast<float>(cur_cnt);
+      for (int j = 0; j < c; j++) {
+        atomicAdd(grad_x + j * n + i, grad_y[j * r3 + pos] * div_cur_cnt);
+      }
+    }
+  }
+}
+
+void avg_voxelize(int b, int c, int n, int r, int r2, int r3, const int *coords,
+                  const float *feat, int *ind, int *cnt, float *out) {
+  grid_stats_kernel<<<b, optimal_num_threads(n)>>>(b, n, r, r2, r3, coords, ind,
+                                                   cnt);
+  avg_voxelize_kernel<<<b, optimal_num_threads(n)>>>(b, c, n, r3, ind, cnt,
+                                                     feat, out);
+  CUDA_CHECK_ERRORS();
+}
+
+void avg_voxelize_grad(int b, int c, int n, int s, const int *ind,
+                       const int *cnt, const float *grad_y, float *grad_x) {
+  avg_voxelize_grad_kernel<<<b, optimal_num_threads(n)>>>(b, c, n, s, ind, cnt,
+                                                          grad_y, grad_x);
+  CUDA_CHECK_ERRORS();
+}

modules/functional/src/voxelization/vox.cuh

@@ -0,0 +1,10 @@
+#ifndef _VOX_CUH
+#define _VOX_CUH
+
+// CUDA function declarations
+void avg_voxelize(int b, int c, int n, int r, int r2, int r3, const int *coords,
+                  const float *feat, int *ind, int *cnt, float *out);
+void avg_voxelize_grad(int b, int c, int n, int s, const int *idx,
+                       const int *cnt, const float *grad_y, float *grad_x);
+
+#endif

modules/functional/src/voxelization/vox.hpp

@@ -0,0 +1,15 @@
+#ifndef _VOX_HPP
+#define _VOX_HPP
+
+#include <torch/torch.h>
+#include <vector>
+
+std::vector<at::Tensor> avg_voxelize_forward(const at::Tensor features,
+                                             const at::Tensor coords,
+                                             const int resolution);
+
+at::Tensor avg_voxelize_backward(const at::Tensor grad_y,
+                                 const at::Tensor indices,
+                                 const at::Tensor cnt);
+
+#endif

modules/functional/voxelization.py

@@ -0,0 +1,40 @@
+from torch.autograd import Function
+
+from modules.functional.backend import _backend
+
+__all__ = ['avg_voxelize']
+
+
+class AvgVoxelization(Function):
+    @staticmethod
+    def forward(ctx, features, coords, resolution):
+        """
+        :param ctx:
+        :param features: Features of the point cloud, FloatTensor[B, C, N]
+        :param coords: Voxelized Coordinates of each point, IntTensor[B, 3, N]
+        :param resolution: Voxel resolution
+        :return:
+            Voxelized Features, FloatTensor[B, C, R, R, R]
+        """
+        features = features.contiguous()
+        coords = coords.int().contiguous()
+        b, c, _ = features.shape
+        out, indices, counts = _backend.avg_voxelize_forward(features, coords, resolution)
+        ctx.save_for_backward(indices, counts)
+        return out.view(b, c, resolution, resolution, resolution)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        """
+        :param ctx:
+        :param grad_output: gradient of output, FloatTensor[B, C, R, R, R]
+        :return:
+            gradient of inputs, FloatTensor[B, C, N]
+        """
+        b, c = grad_output.shape[:2]
+        indices, counts = ctx.saved_tensors
+        grad_features = _backend.avg_voxelize_backward(grad_output.contiguous().view(b, c, -1), indices, counts)
+        return grad_features, None, None
+
+
+avg_voxelize = AvgVoxelization.apply

modules/loss.py

@@ -0,0 +1,10 @@
+import torch.nn as nn
+
+import modules.functional as F
+
+__all__ = ['KLLoss']
+
+
+class KLLoss(nn.Module):
+    def forward(self, x, y):
+        return F.kl_loss(x, y)

modules/pointnet.py

@@ -0,0 +1,113 @@
+import torch
+import torch.nn as nn
+
+import modules.functional as F
+from modules.ball_query import BallQuery
+from modules.shared_mlp import SharedMLP
+
+__all__ = ['PointNetAModule', 'PointNetSAModule', 'PointNetFPModule']
+
+
+class PointNetAModule(nn.Module):
+    def __init__(self, in_channels, out_channels, include_coordinates=True):
+        super().__init__()
+        if not isinstance(out_channels, (list, tuple)):
+            out_channels = [[out_channels]]
+        elif not isinstance(out_channels[0], (list, tuple)):
+            out_channels = [out_channels]
+
+        mlps = []
+        total_out_channels = 0
+        for _out_channels in out_channels:
+            mlps.append(
+                SharedMLP(in_channels=in_channels + (3 if include_coordinates else 0),
+                          out_channels=_out_channels, dim=1)
+            )
+            total_out_channels += _out_channels[-1]
+
+        self.include_coordinates = include_coordinates
+        self.out_channels = total_out_channels
+        self.mlps = nn.ModuleList(mlps)
+
+    def forward(self, inputs):
+        features, coords = inputs
+        if self.include_coordinates:
+            features = torch.cat([features, coords], dim=1)
+        coords = torch.zeros((coords.size(0), 3, 1), device=coords.device)
+        if len(self.mlps) > 1:
+            features_list = []
+            for mlp in self.mlps:
+                features_list.append(mlp(features).max(dim=-1, keepdim=True).values)
+            return torch.cat(features_list, dim=1), coords
+        else:
+            return self.mlps[0](features).max(dim=-1, keepdim=True).values, coords
+
+    def extra_repr(self):
+        return f'out_channels={self.out_channels}, include_coordinates={self.include_coordinates}'
+
+
+class PointNetSAModule(nn.Module):
+    def __init__(self, num_centers, radius, num_neighbors, in_channels, out_channels, include_coordinates=True):
+        super().__init__()
+        if not isinstance(radius, (list, tuple)):
+            radius = [radius]
+        if not isinstance(num_neighbors, (list, tuple)):
+            num_neighbors = [num_neighbors] * len(radius)
+        assert len(radius) == len(num_neighbors)
+        if not isinstance(out_channels, (list, tuple)):
+            out_channels = [[out_channels]] * len(radius)
+        elif not isinstance(out_channels[0], (list, tuple)):
+            out_channels = [out_channels] * len(radius)
+        assert len(radius) == len(out_channels)
+
+        groupers, mlps = [], []
+        total_out_channels = 0
+        for _radius, _out_channels, _num_neighbors in zip(radius, out_channels, num_neighbors):
+            groupers.append(
+                BallQuery(radius=_radius, num_neighbors=_num_neighbors, include_coordinates=include_coordinates)
+            )
+            mlps.append(
+                SharedMLP(in_channels=in_channels + (3 if include_coordinates else 0),
+                          out_channels=_out_channels, dim=2)
+            )
+            total_out_channels += _out_channels[-1]
+
+        self.num_centers = num_centers
+        self.out_channels = total_out_channels
+        self.groupers = nn.ModuleList(groupers)
+        self.mlps = nn.ModuleList(mlps)
+
+    def forward(self, inputs):
+        features, coords, temb = inputs
+        centers_coords = F.furthest_point_sample(coords, self.num_centers)
+        features_list = []
+        for grouper, mlp in zip(self.groupers, self.mlps):
+            features, temb = mlp(grouper(coords, centers_coords, temb, features))
+            features_list.append(features.max(dim=-1).values)
+        if len(features_list) > 1:
+            return features_list[0], centers_coords, temb.max(dim=-1).values if temb.shape[1] > 0 else temb
+        else:
+            return features_list[0], centers_coords, temb.max(dim=-1).values if temb.shape[1] > 0 else temb
+
+    def extra_repr(self):
+        return f'num_centers={self.num_centers}, out_channels={self.out_channels}'
+
+
+class PointNetFPModule(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+        self.mlp = SharedMLP(in_channels=in_channels, out_channels=out_channels, dim=1)
+
+    def forward(self, inputs):
+        if len(inputs) == 3:
+            points_coords, centers_coords, centers_features, temb = inputs
+            points_features = None
+        else:
+            points_coords, centers_coords, centers_features, points_features, temb = inputs
+        interpolated_features = F.nearest_neighbor_interpolate(points_coords, centers_coords, centers_features)
+        interpolated_temb = F.nearest_neighbor_interpolate(points_coords, centers_coords, temb)
+        if points_features is not None:
+            interpolated_features = torch.cat(
+                [interpolated_features, points_features], dim=1
+            )
+        return self.mlp(interpolated_features), points_coords, interpolated_temb

modules/pvconv.py

@@ -0,0 +1,132 @@
+import torch.nn as nn
+import torch
+import modules.functional as F
+from modules.voxelization import Voxelization
+from modules.shared_mlp import SharedMLP
+from modules.se import SE3d
+
+__all__ = ['PVConv', 'Attention', 'Swish', 'PVConvReLU']
+
+
+class Swish(nn.Module):
+    def forward(self,x):
+        return  x * torch.sigmoid(x)
+
+
+class Attention(nn.Module):
+    def __init__(self, in_ch, num_groups, D=3):
+        super(Attention, self).__init__()
+        assert in_ch % num_groups == 0
+        if D == 3:
+            self.q = nn.Conv3d(in_ch, in_ch, 1)
+            self.k = nn.Conv3d(in_ch, in_ch, 1)
+            self.v = nn.Conv3d(in_ch, in_ch, 1)
+
+            self.out = nn.Conv3d(in_ch, in_ch, 1)
+        elif D == 1:
+            self.q = nn.Conv1d(in_ch, in_ch, 1)
+            self.k = nn.Conv1d(in_ch, in_ch, 1)
+            self.v = nn.Conv1d(in_ch, in_ch, 1)
+
+            self.out = nn.Conv1d(in_ch, in_ch, 1)
+
+        self.norm = nn.GroupNorm(num_groups, in_ch)
+        self.nonlin = Swish()
+
+        self.sm = nn.Softmax(-1)
+
+
+    def forward(self, x):
+        B, C = x.shape[:2]
+        h = x
+
+
+
+
+        q = self.q(h).reshape(B,C,-1)
+        k = self.k(h).reshape(B,C,-1)
+        v = self.v(h).reshape(B,C,-1)
+
+        qk = torch.matmul(q.permute(0, 2, 1), k) #* (int(C) ** (-0.5))
+
+        w = self.sm(qk)
+
+        h = torch.matmul(v, w.permute(0, 2, 1)).reshape(B,C,*x.shape[2:])
+
+        h = self.out(h)
+
+        x = h + x
+
+        x = self.nonlin(self.norm(x))
+
+        return x
+
+class PVConv(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, resolution, attention=False,
+                 dropout=0.1, with_se=False, with_se_relu=False, normalize=True, eps=0):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.resolution = resolution
+
+        self.voxelization = Voxelization(resolution, normalize=normalize, eps=eps)
+        voxel_layers = [
+            nn.Conv3d(in_channels, out_channels, kernel_size, stride=1, padding=kernel_size // 2),
+            nn.GroupNorm(num_groups=8, num_channels=out_channels),
+            Swish()
+        ]
+        voxel_layers += [nn.Dropout(dropout)] if dropout is not None else []
+        voxel_layers += [
+            nn.Conv3d(out_channels, out_channels, kernel_size, stride=1, padding=kernel_size // 2),
+            nn.GroupNorm(num_groups=8, num_channels=out_channels),
+            Attention(out_channels, 8) if attention else Swish()
+        ]
+        if with_se:
+            voxel_layers.append(SE3d(out_channels, use_relu=with_se_relu))
+        self.voxel_layers = nn.Sequential(*voxel_layers)
+        self.point_features = SharedMLP(in_channels, out_channels)
+
+    def forward(self, inputs):
+        features, coords, temb = inputs
+        voxel_features, voxel_coords = self.voxelization(features, coords)
+        voxel_features = self.voxel_layers(voxel_features)
+        voxel_features = F.trilinear_devoxelize(voxel_features, voxel_coords, self.resolution, self.training)
+        fused_features = voxel_features + self.point_features(features)
+        return fused_features, coords, temb
+
+
+
+class PVConvReLU(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, resolution, attention=False, leak=0.2,
+                 dropout=0.1, with_se=False, with_se_relu=False, normalize=True, eps=0):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.resolution = resolution
+
+        self.voxelization = Voxelization(resolution, normalize=normalize, eps=eps)
+        voxel_layers = [
+            nn.Conv3d(in_channels, out_channels, kernel_size, stride=1, padding=kernel_size // 2),
+            nn.BatchNorm3d(out_channels),
+            nn.LeakyReLU(leak, True)
+        ]
+        voxel_layers += [nn.Dropout(dropout)] if dropout is not None else []
+        voxel_layers += [
+            nn.Conv3d(out_channels, out_channels, kernel_size, stride=1, padding=kernel_size // 2),
+            nn.BatchNorm3d(out_channels),
+            Attention(out_channels, 8) if attention else nn.LeakyReLU(leak, True)
+        ]
+        if with_se:
+            voxel_layers.append(SE3d(out_channels, use_relu=with_se_relu))
+        self.voxel_layers = nn.Sequential(*voxel_layers)
+        self.point_features = SharedMLP(in_channels, out_channels)
+
+    def forward(self, inputs):
+        features, coords, temb = inputs
+        voxel_features, voxel_coords = self.voxelization(features, coords)
+        voxel_features = self.voxel_layers(voxel_features)
+        voxel_features = F.trilinear_devoxelize(voxel_features, voxel_coords, self.resolution, self.training)
+        fused_features = voxel_features + self.point_features(features)
+        return fused_features, coords, temb

modules/se.py

@@ -0,0 +1,19 @@
+import torch.nn as nn
+import torch
+__all__ = ['SE3d']
+
+class Swish(nn.Module):
+    def forward(self,x):
+        return  x * torch.sigmoid(x)
+class SE3d(nn.Module):
+    def __init__(self, channel, reduction=8, use_relu=False):
+        super().__init__()
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction, bias=False),
+            nn.ReLU(True) if use_relu else Swish() ,
+            nn.Linear(channel // reduction, channel, bias=False),
+            nn.Sigmoid()
+        )
+
+    def forward(self, inputs):
+        return inputs * self.fc(inputs.mean(-1).mean(-1).mean(-1)).view(inputs.shape[0], inputs.shape[1], 1, 1, 1)

modules/shared_mlp.py

@@ -0,0 +1,38 @@
+import torch.nn as nn
+import torch
+
+__all__ = ['SharedMLP']
+
+
+class Swish(nn.Module):
+    def forward(self,x):
+        return  x * torch.sigmoid(x)
+
+class SharedMLP(nn.Module):
+    def __init__(self, in_channels, out_channels, dim=1):
+        super().__init__()
+        if dim == 1:
+            conv = nn.Conv1d
+            bn = nn.GroupNorm
+        elif dim == 2:
+            conv = nn.Conv2d
+            bn = nn.GroupNorm
+        else:
+            raise ValueError
+        if not isinstance(out_channels, (list, tuple)):
+            out_channels = [out_channels]
+        layers = []
+        for oc in out_channels:
+            layers.extend([
+                conv(in_channels, oc, 1),
+                bn(8, oc),
+                Swish(),
+            ])
+            in_channels = oc
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, inputs):
+        if isinstance(inputs, (list, tuple)):
+            return (self.layers(inputs[0]), *inputs[1:])
+        else:
+            return self.layers(inputs)

modules/voxelization.py

@@ -0,0 +1,28 @@
+import torch
+import torch.nn as nn
+
+import modules.functional as F
+
+__all__ = ['Voxelization']
+
+
+class Voxelization(nn.Module):
+    def __init__(self, resolution, normalize=True, eps=0):
+        super().__init__()
+        self.r = int(resolution)
+        self.normalize = normalize
+        self.eps = eps
+
+    def forward(self, features, coords):
+        coords = coords.detach()
+        norm_coords = coords - coords.mean(2, keepdim=True)
+        if self.normalize:
+            norm_coords = norm_coords / (norm_coords.norm(dim=1, keepdim=True).max(dim=2, keepdim=True).values * 2.0 + self.eps) + 0.5
+        else:
+            norm_coords = (norm_coords + 1) / 2.0
+        norm_coords = torch.clamp(norm_coords * self.r, 0, self.r - 1)
+        vox_coords = torch.round(norm_coords).to(torch.int32)
+        return F.avg_voxelize(features, vox_coords, self.r), norm_coords
+
+    def extra_repr(self):
+        return 'resolution={}{}'.format(self.r, ', normalized eps = {}'.format(self.eps) if self.normalize else '')

requirement_voxel.txt

@@ -0,0 +1,26 @@
+conda:
+python==3.6
+torch==1.4.0
+torchvision==0.5.0
+cudatoolkit==10.1
+kaolin==0.1.0
+pytorch3d==0.2.5
+lutorpy=1.3.7
+xmltodict=0.12.0
+numba=0.51.2
+pycuda=2019.1.2
+matplotlib
+
+pip:
+torch-scatter==2.0.4
+torch-sparse==0.6.1
+torch-cluster==1.5.4
+torch-spline-conv==1.2.0
+descartes==1.1.0
+fire==0.3.1
+jupyter==1.0.0
+opencv_python==4.3.0
+Shapely==1.7.0
+Pillow==6.2.1
+torch_geometric==1.6.0
+open3d

shape_completion/12_res32_partnet_chair_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do.py

@@ -0,0 +1,825 @@
+import torch.multiprocessing as mp
+import torch.nn as nn
+import torch.optim as optim
+import torch.utils.data
+
+import argparse
+from model.unet import get_model
+from torch.distributions import Normal
+
+from utils.file_utils import *
+from utils.visualize import *
+from model.pvcnn_completion import PVCNN2Base
+import torch.distributed as dist
+from datasets.partnet import GANdatasetPartNet
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+'''
+some utils
+'''
+def rotation_matrix(axis, theta):
+    """
+    Return the rotation matrix associated with counterclockwise rotation about
+    the given axis by theta radians.
+    """
+    axis = np.asarray(axis)
+    axis = axis / np.sqrt(np.dot(axis, axis))
+    a = np.cos(theta / 2.0)
+    b, c, d = -axis * np.sin(theta / 2.0)
+    aa, bb, cc, dd = a * a, b * b, c * c, d * d
+    bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d
+    return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)],
+                     [2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)],
+                     [2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]])
+
+def rotate(vertices, faces):
+    '''
+    vertices: [numpoints, 3]
+    '''
+    M = rotation_matrix([0, 1, 0], np.pi / 2).transpose()
+    N = rotation_matrix([1, 0, 0], -np.pi / 4).transpose()
+    K = rotation_matrix([0, 0, 1], np.pi).transpose()
+
+    v, f = vertices[:,[1,2,0]].dot(M).dot(N).dot(K), faces[:,[1,2,0]]
+    return v, f
+
+def norm(v, f):
+    v = (v - v.min())/(v.max() - v.min()) - 0.5
+
+    return v, f
+
+def getGradNorm(net):
+    pNorm = torch.sqrt(sum(torch.sum(p ** 2) for p in net.parameters()))
+    gradNorm = torch.sqrt(sum(torch.sum(p.grad ** 2) for p in net.parameters()))
+    return pNorm, gradNorm
+
+
+def weights_init(m):
+    """
+    xavier initialization
+    """
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1 and m.weight is not None:
+        torch.nn.init.xavier_normal_(m.weight)
+    elif classname.find('BatchNorm') != -1:
+        m.weight.data.normal_()
+        m.bias.data.fill_(0)
+
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+    def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
+        imgs = [img_t]
+        for t in reversed(range(0,total_steps)):
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised,
+                                  return_pred_xstart=False)
+            if t % freq == 0 or t == total_steps-1:
+                imgs.append(img_t)
+
+        assert imgs[-1].shape == shape
+        return imgs
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+def get_dataset(data_root, data_raw_root, pc_dataroot, npoints, category):
+
+    train_ds = GANdatasetPartNet('train', data_root, category, npoints)
+    return train_ds
+
+
+def get_dataloader(opt, train_dataset, test_dataset=None):
+
+    if opt.distribution_type == 'multi':
+        train_sampler = torch.utils.data.distributed.DistributedSampler(
+            train_dataset,
+            num_replicas=opt.world_size,
+            rank=opt.rank
+        )
+        if test_dataset is not None:
+            test_sampler = torch.utils.data.distributed.DistributedSampler(
+                test_dataset,
+                num_replicas=opt.world_size,
+                rank=opt.rank
+            )
+        else:
+            test_sampler = None
+    else:
+        train_sampler = None
+        test_sampler = None
+
+    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.bs,sampler=train_sampler,
+                                                   shuffle=train_sampler is None, num_workers=int(opt.workers), drop_last=True)
+
+    if test_dataset is not None:
+        test_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.bs,sampler=test_sampler,
+                                                   shuffle=False, num_workers=int(opt.workers), drop_last=False)
+    else:
+        test_dataloader = None
+
+    return train_dataloader, test_dataloader, train_sampler, test_sampler
+
+
+def train(gpu, opt, output_dir, noises_init):
+
+    set_seed(opt)
+    logger = setup_logging(output_dir)
+    if opt.distribution_type == 'multi':
+        should_diag = gpu==0
+    else:
+        should_diag = True
+    if should_diag:
+        outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    if opt.distribution_type == 'multi':
+        if opt.dist_url == "env://" and opt.rank == -1:
+            opt.rank = int(os.environ["RANK"])
+
+        base_rank =  opt.rank * opt.ngpus_per_node
+        opt.rank = base_rank + gpu
+        dist.init_process_group(backend=opt.dist_backend, init_method=opt.dist_url,
+                                world_size=opt.world_size, rank=opt.rank)
+
+        opt.bs = int(opt.bs / opt.ngpus_per_node)
+        opt.workers = 0
+
+        opt.saveIter =  int(opt.saveIter / opt.ngpus_per_node)
+        opt.diagIter = int(opt.diagIter / opt.ngpus_per_node)
+        opt.vizIter = int(opt.vizIter / opt.ngpus_per_node)
+
+
+    ''' data '''
+    train_dataset = get_dataset(opt.data_root, opt.data_raw_root,opt.pc_dataroot, opt.npoints,opt.classes)
+    dataloader, _, train_sampler, _ = get_dataloader(opt, train_dataset, None)
+
+
+    '''
+    create networks
+    '''
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    netE = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.distribution_type == 'multi':  # Multiple processes, single GPU per process
+        def _transform_(m):
+            return nn.parallel.DistributedDataParallel(
+                m, device_ids=[gpu], output_device=gpu)
+
+        torch.cuda.set_device(gpu)
+        netE.cuda(gpu)
+        netE.multi_gpu_wrapper(_transform_)
+
+
+    elif opt.distribution_type == 'single':
+        def _transform_(m):
+            return nn.parallel.DataParallel(m)
+        netE = netE.cuda()
+        netE.multi_gpu_wrapper(_transform_)
+
+    elif gpu is not None:
+        torch.cuda.set_device(gpu)
+        netE = netE.cuda(gpu)
+    else:
+        raise ValueError('distribution_type = multi | single | None')
+
+    if should_diag:
+        logger.info(opt)
+
+    optimizer= optim.Adam(netE.parameters(), lr=opt.lrE, weight_decay=opt.e_decay, betas=(opt.beta1, 0.999))
+
+    lr_scheduler = optim.lr_scheduler.ExponentialLR(optimizer, opt.e_gamma)
+
+    if opt.netE != '':
+        ckpt = torch.load(opt.netE)
+        netE.load_state_dict(ckpt['model_state'])
+        optimizer.load_state_dict(ckpt['optimizer_state'])
+
+    if opt.netE != '':
+        start_epoch = torch.load(opt.netE)['epoch'] + 1
+    else:
+        start_epoch = 0
+
+
+    for epoch in range(start_epoch, opt.niter):
+
+        if opt.distribution_type == 'multi':
+            train_sampler.set_epoch(epoch)
+
+        lr_scheduler.step(epoch)
+
+        for i, data in enumerate(dataloader):
+
+            x = data['real']
+            sv_x = data['raw']
+
+            sv_x = torch.cat([sv_x, x[:, :, opt.svpoints:]], dim=-1)
+            noises_batch = noises_init[data['idx']]
+
+            '''
+            train diffusion
+            '''
+
+            if opt.distribution_type == 'multi' or (opt.distribution_type is None and gpu is not None):
+                sv_x = sv_x.cuda(gpu)
+                noises_batch = noises_batch.cuda(gpu)
+            elif opt.distribution_type == 'single':
+                sv_x = sv_x.cuda()
+                noises_batch = noises_batch.cuda()
+
+            loss = netE.get_loss_iter(sv_x, noises_batch).mean()
+
+            optimizer.zero_grad()
+            loss.backward()
+            netpNorm, netgradNorm = getGradNorm(netE)
+            if opt.grad_clip is not None:
+                torch.nn.utils.clip_grad_norm_(netE.parameters(), opt.grad_clip)
+
+            optimizer.step()
+
+
+            if i % opt.print_freq == 0 and should_diag:
+
+                logger.info('[{:>3d}/{:>3d}][{:>3d}/{:>3d}]    loss: {:>10.4f},    '
+                             'netpNorm: {:>10.2f},   netgradNorm: {:>10.2f}     '
+                             .format(
+                        epoch, opt.niter, i, len(dataloader),loss.item(),
+                    netpNorm, netgradNorm,
+                        ))
+
+        if (epoch + 1) % opt.diagIter == 0 and should_diag:
+
+            logger.info('Diagnosis:')
+
+            x_range = [x.min().item(), x.max().item()]
+            kl_stats = netE.all_kl(sv_x)
+            logger.info('      [{:>3d}/{:>3d}]    '
+                         'x_range: [{:>10.4f}, {:>10.4f}],   '
+                         'total_bpd_b: {:>10.4f},    '
+                         'terms_bpd: {:>10.4f},  '
+                         'prior_bpd_b: {:>10.4f}    '
+                         'mse_bt: {:>10.4f}  '
+                .format(
+                epoch, opt.niter,
+                *x_range,
+                kl_stats['total_bpd_b'].item(),
+                kl_stats['terms_bpd'].item(), kl_stats['prior_bpd_b'].item(), kl_stats['mse_bt'].item()
+            ))
+
+
+
+        if (epoch + 1) % opt.vizIter == 0 and should_diag:
+            logger.info('Generation: eval')
+
+            netE.eval()
+            m, s = torch.tensor([[0,0,0]]).transpose(0,1)[None], torch.tensor([[1,1,1]]).transpose(0,1)[None]#train_dataset.get_pc_stats(0)
+
+            with torch.no_grad():
+
+                x_gen_eval = netE.gen_samples(sv_x[:,:,:opt.svpoints], sv_x[:,:,opt.svpoints:].shape, sv_x.device, clip_denoised=False).detach().cpu()
+
+
+                gen_stats = [x_gen_eval.mean(), x_gen_eval.std()]
+                gen_eval_range = [x_gen_eval.min().item(), x_gen_eval.max().item()]
+
+                logger.info('      [{:>3d}/{:>3d}]  '
+                             'eval_gen_range: [{:>10.4f}, {:>10.4f}]     '
+                             'eval_gen_stats: [mean={:>10.4f}, std={:>10.4f}]      '
+                    .format(
+                    epoch, opt.niter,
+                    *gen_eval_range, *gen_stats,
+                ))
+
+            export_to_pc_batch('%s/epoch_%03d_samples_eval' % (outf_syn, epoch),
+                                    (x_gen_eval*s+m).transpose(1, 2).numpy()*3)
+
+            export_to_pc_batch('%s/epoch_%03d_ground_truth' % (outf_syn, epoch),
+                               (sv_x.detach().cpu()*s+m).transpose(1, 2).numpy()*3)
+
+            export_to_pc_batch('%s/epoch_%03d_partial' % (outf_syn, epoch),
+                               (sv_x[:,:,:opt.svpoints].detach().cpu()*s+m).transpose(1, 2).numpy()*3)
+
+
+            netE.train()
+
+
+
+
+
+
+
+        if (epoch + 1) % opt.saveIter == 0:
+
+            if should_diag:
+
+
+                save_dict = {
+                    'epoch': epoch,
+                    'model_state': netE.state_dict(),
+                    'optimizer_state': optimizer.state_dict()
+                }
+
+                torch.save(save_dict, '%s/epoch_%d.pth' % (output_dir, epoch))
+
+
+            if opt.distribution_type == 'multi':
+                dist.barrier()
+                map_location = {'cuda:%d' % 0: 'cuda:%d' % gpu}
+                netE.load_state_dict(
+                    torch.load('%s/epoch_%d.pth' % (output_dir, epoch), map_location=map_location)['model_state'])
+
+    dist.destroy_process_group()
+
+def main():
+    opt = parse_args()
+
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+
+    ''' workaround '''
+
+    train_dataset = get_dataset(opt.data_root, opt.data_raw_root, opt.pc_dataroot, opt.npoints,opt.classes)
+    noises_init = torch.randn(len(train_dataset), opt.nc, opt.npoints-opt.svpoints)
+
+    if opt.dist_url == "env://" and opt.world_size == -1:
+        opt.world_size = int(os.environ["WORLD_SIZE"])
+
+    if opt.distribution_type == 'multi':
+        opt.ngpus_per_node = torch.cuda.device_count()
+        opt.world_size = opt.ngpus_per_node * opt.world_size
+        mp.spawn(train, nprocs=opt.ngpus_per_node, args=(opt, output_dir, noises_init))
+    else:
+        train(opt.gpu, opt, output_dir, noises_init)
+
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_root', default='/home/ubuntu/01DATA/partnet/data_v0', help='input batch size')
+    parser.add_argument('--data_raw_root', default='/viscam/u/alexzhou907/01DATA/shapenet/shapenet_dim32_sdf_pc',
+                        help='input batch size')
+    parser.add_argument('--pc_dataroot', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k',
+                        help='input batch size')
+    parser.add_argument('--classes', default='Chair')
+
+    parser.add_argument('--bs', type=int, default=64, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=1024)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+    parser.add_argument('--lrE', type=float, default=2e-4, help='learning rate for E, default=0.0002')
+    parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
+    parser.add_argument('--e_decay', type=float, default=0, help='weight decay for EBM')
+    parser.add_argument('--grad_clip', type=float, default=None, help='weight decay for EBM')
+    parser.add_argument('--e_gamma', type=float, default=0.998, help='lr decay for EBM')
+
+    parser.add_argument('--netE', default='', help="path to netE (to continue training)")
+
+
+    '''distributed'''
+    parser.add_argument('--world_size', default=1, type=int,
+                        help='Number of distributed nodes.')
+    parser.add_argument('--dist_url', default='tcp://127.0.0.1:9991', type=str,
+                        help='url used to set up distributed training')
+    parser.add_argument('--dist_backend', default='nccl', type=str,
+                        help='distributed backend')
+    parser.add_argument('--distribution_type', default='single', choices=['multi', 'single', None],
+                        help='Use multi-processing distributed training to launch '
+                             'N processes per node, which has N GPUs. This is the '
+                             'fastest way to use PyTorch for either single node or '
+                             'multi node data parallel training')
+    parser.add_argument('--rank', default=0, type=int,
+                        help='node rank for distributed training')
+    parser.add_argument('--gpu', default=None, type=int,
+                        help='GPU id to use. None means using all available GPUs.')
+
+    '''eval'''
+    parser.add_argument('--saveIter', default=100, type=int,help='unit: epoch')
+    parser.add_argument('--diagIter', default=50, type=int, help='unit: epoch')
+    parser.add_argument('--vizIter', default=50, type=int,help='unit: epoch')
+    parser.add_argument('--print_freq', default=50, type=int,help='unit: iter')
+
+    parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
+
+
+    opt = parser.parse_args()
+
+    return opt
+
+if __name__ == '__main__':
+    main()

shape_completion/13_res32_partnet_table_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do.py

@@ -0,0 +1,825 @@
+import torch.multiprocessing as mp
+import torch.nn as nn
+import torch.optim as optim
+import torch.utils.data
+
+import argparse
+from model.unet import get_model
+from torch.distributions import Normal
+
+from utils.file_utils import *
+from utils.visualize import *
+from model.pvcnn_completion import PVCNN2Base
+import torch.distributed as dist
+from datasets.partnet import GANdatasetPartNet
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+'''
+some utils
+'''
+def rotation_matrix(axis, theta):
+    """
+    Return the rotation matrix associated with counterclockwise rotation about
+    the given axis by theta radians.
+    """
+    axis = np.asarray(axis)
+    axis = axis / np.sqrt(np.dot(axis, axis))
+    a = np.cos(theta / 2.0)
+    b, c, d = -axis * np.sin(theta / 2.0)
+    aa, bb, cc, dd = a * a, b * b, c * c, d * d
+    bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d
+    return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)],
+                     [2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)],
+                     [2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]])
+
+def rotate(vertices, faces):
+    '''
+    vertices: [numpoints, 3]
+    '''
+    M = rotation_matrix([0, 1, 0], np.pi / 2).transpose()
+    N = rotation_matrix([1, 0, 0], -np.pi / 4).transpose()
+    K = rotation_matrix([0, 0, 1], np.pi).transpose()
+
+    v, f = vertices[:,[1,2,0]].dot(M).dot(N).dot(K), faces[:,[1,2,0]]
+    return v, f
+
+def norm(v, f):
+    v = (v - v.min())/(v.max() - v.min()) - 0.5
+
+    return v, f
+
+def getGradNorm(net):
+    pNorm = torch.sqrt(sum(torch.sum(p ** 2) for p in net.parameters()))
+    gradNorm = torch.sqrt(sum(torch.sum(p.grad ** 2) for p in net.parameters()))
+    return pNorm, gradNorm
+
+
+def weights_init(m):
+    """
+    xavier initialization
+    """
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1 and m.weight is not None:
+        torch.nn.init.xavier_normal_(m.weight)
+    elif classname.find('BatchNorm') != -1:
+        m.weight.data.normal_()
+        m.bias.data.fill_(0)
+
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+    def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
+        imgs = [img_t]
+        for t in reversed(range(0,total_steps)):
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised,
+                                  return_pred_xstart=False)
+            if t % freq == 0 or t == total_steps-1:
+                imgs.append(img_t)
+
+        assert imgs[-1].shape == shape
+        return imgs
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+def get_dataset(data_root, data_raw_root, pc_dataroot, npoints, category):
+
+    train_ds = GANdatasetPartNet('train', data_root, category, npoints)
+    return train_ds
+
+
+def get_dataloader(opt, train_dataset, test_dataset=None):
+
+    if opt.distribution_type == 'multi':
+        train_sampler = torch.utils.data.distributed.DistributedSampler(
+            train_dataset,
+            num_replicas=opt.world_size,
+            rank=opt.rank
+        )
+        if test_dataset is not None:
+            test_sampler = torch.utils.data.distributed.DistributedSampler(
+                test_dataset,
+                num_replicas=opt.world_size,
+                rank=opt.rank
+            )
+        else:
+            test_sampler = None
+    else:
+        train_sampler = None
+        test_sampler = None
+
+    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.bs,sampler=train_sampler,
+                                                   shuffle=train_sampler is None, num_workers=int(opt.workers), drop_last=True)
+
+    if test_dataset is not None:
+        test_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.bs,sampler=test_sampler,
+                                                   shuffle=False, num_workers=int(opt.workers), drop_last=False)
+    else:
+        test_dataloader = None
+
+    return train_dataloader, test_dataloader, train_sampler, test_sampler
+
+
+def train(gpu, opt, output_dir, noises_init):
+
+    set_seed(opt)
+    logger = setup_logging(output_dir)
+    if opt.distribution_type == 'multi':
+        should_diag = gpu==0
+    else:
+        should_diag = True
+    if should_diag:
+        outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    if opt.distribution_type == 'multi':
+        if opt.dist_url == "env://" and opt.rank == -1:
+            opt.rank = int(os.environ["RANK"])
+
+        base_rank =  opt.rank * opt.ngpus_per_node
+        opt.rank = base_rank + gpu
+        dist.init_process_group(backend=opt.dist_backend, init_method=opt.dist_url,
+                                world_size=opt.world_size, rank=opt.rank)
+
+        opt.bs = int(opt.bs / opt.ngpus_per_node)
+        opt.workers = 0
+
+        opt.saveIter =  int(opt.saveIter / opt.ngpus_per_node)
+        opt.diagIter = int(opt.diagIter / opt.ngpus_per_node)
+        opt.vizIter = int(opt.vizIter / opt.ngpus_per_node)
+
+
+    ''' data '''
+    train_dataset = get_dataset(opt.data_root, opt.data_raw_root,opt.pc_dataroot, opt.npoints,opt.classes)
+    dataloader, _, train_sampler, _ = get_dataloader(opt, train_dataset, None)
+
+
+    '''
+    create networks
+    '''
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    netE = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.distribution_type == 'multi':  # Multiple processes, single GPU per process
+        def _transform_(m):
+            return nn.parallel.DistributedDataParallel(
+                m, device_ids=[gpu], output_device=gpu)
+
+        torch.cuda.set_device(gpu)
+        netE.cuda(gpu)
+        netE.multi_gpu_wrapper(_transform_)
+
+
+    elif opt.distribution_type == 'single':
+        def _transform_(m):
+            return nn.parallel.DataParallel(m)
+        netE = netE.cuda()
+        netE.multi_gpu_wrapper(_transform_)
+
+    elif gpu is not None:
+        torch.cuda.set_device(gpu)
+        netE = netE.cuda(gpu)
+    else:
+        raise ValueError('distribution_type = multi | single | None')
+
+    if should_diag:
+        logger.info(opt)
+
+    optimizer= optim.Adam(netE.parameters(), lr=opt.lrE, weight_decay=opt.e_decay, betas=(opt.beta1, 0.999))
+
+    lr_scheduler = optim.lr_scheduler.ExponentialLR(optimizer, opt.e_gamma)
+
+    if opt.netE != '':
+        ckpt = torch.load(opt.netE)
+        netE.load_state_dict(ckpt['model_state'])
+        optimizer.load_state_dict(ckpt['optimizer_state'])
+
+    if opt.netE != '':
+        start_epoch = torch.load(opt.netE)['epoch'] + 1
+    else:
+        start_epoch = 0
+
+
+    for epoch in range(start_epoch, opt.niter):
+
+        if opt.distribution_type == 'multi':
+            train_sampler.set_epoch(epoch)
+
+        lr_scheduler.step(epoch)
+
+        for i, data in enumerate(dataloader):
+
+            x = data['real']
+            sv_x = data['raw']
+
+            sv_x = torch.cat([sv_x, x[:, :, opt.svpoints:]], dim=-1)
+            noises_batch = noises_init[data['idx']]
+
+            '''
+            train diffusion
+            '''
+
+            if opt.distribution_type == 'multi' or (opt.distribution_type is None and gpu is not None):
+                sv_x = sv_x.cuda(gpu)
+                noises_batch = noises_batch.cuda(gpu)
+            elif opt.distribution_type == 'single':
+                sv_x = sv_x.cuda()
+                noises_batch = noises_batch.cuda()
+
+            loss = netE.get_loss_iter(sv_x, noises_batch).mean()
+
+            optimizer.zero_grad()
+            loss.backward()
+            netpNorm, netgradNorm = getGradNorm(netE)
+            if opt.grad_clip is not None:
+                torch.nn.utils.clip_grad_norm_(netE.parameters(), opt.grad_clip)
+
+            optimizer.step()
+
+
+            if i % opt.print_freq == 0 and should_diag:
+
+                logger.info('[{:>3d}/{:>3d}][{:>3d}/{:>3d}]    loss: {:>10.4f},    '
+                             'netpNorm: {:>10.2f},   netgradNorm: {:>10.2f}     '
+                             .format(
+                        epoch, opt.niter, i, len(dataloader),loss.item(),
+                    netpNorm, netgradNorm,
+                        ))
+
+        if (epoch + 1) % opt.diagIter == 0 and should_diag:
+
+            logger.info('Diagnosis:')
+
+            x_range = [x.min().item(), x.max().item()]
+            kl_stats = netE.all_kl(sv_x)
+            logger.info('      [{:>3d}/{:>3d}]    '
+                         'x_range: [{:>10.4f}, {:>10.4f}],   '
+                         'total_bpd_b: {:>10.4f},    '
+                         'terms_bpd: {:>10.4f},  '
+                         'prior_bpd_b: {:>10.4f}    '
+                         'mse_bt: {:>10.4f}  '
+                .format(
+                epoch, opt.niter,
+                *x_range,
+                kl_stats['total_bpd_b'].item(),
+                kl_stats['terms_bpd'].item(), kl_stats['prior_bpd_b'].item(), kl_stats['mse_bt'].item()
+            ))
+
+
+
+        if (epoch + 1) % opt.vizIter == 0 and should_diag:
+            logger.info('Generation: eval')
+
+            netE.eval()
+            m, s = torch.tensor([[0,0,0]]).transpose(0,1)[None], torch.tensor([[1,1,1]]).transpose(0,1)[None]#train_dataset.get_pc_stats(0)
+
+            with torch.no_grad():
+
+                x_gen_eval = netE.gen_samples(sv_x[:,:,:opt.svpoints], sv_x[:,:,opt.svpoints:].shape, sv_x.device, clip_denoised=False).detach().cpu()
+
+
+                gen_stats = [x_gen_eval.mean(), x_gen_eval.std()]
+                gen_eval_range = [x_gen_eval.min().item(), x_gen_eval.max().item()]
+
+                logger.info('      [{:>3d}/{:>3d}]  '
+                             'eval_gen_range: [{:>10.4f}, {:>10.4f}]     '
+                             'eval_gen_stats: [mean={:>10.4f}, std={:>10.4f}]      '
+                    .format(
+                    epoch, opt.niter,
+                    *gen_eval_range, *gen_stats,
+                ))
+
+            export_to_pc_batch('%s/epoch_%03d_samples_eval' % (outf_syn, epoch),
+                                    (x_gen_eval*s+m).transpose(1, 2).numpy()*3)
+
+            export_to_pc_batch('%s/epoch_%03d_ground_truth' % (outf_syn, epoch),
+                               (sv_x.detach().cpu()*s+m).transpose(1, 2).numpy()*3)
+
+            export_to_pc_batch('%s/epoch_%03d_partial' % (outf_syn, epoch),
+                               (sv_x[:,:,:opt.svpoints].detach().cpu()*s+m).transpose(1, 2).numpy()*3)
+
+
+            netE.train()
+
+
+
+
+
+
+
+        if (epoch + 1) % opt.saveIter == 0:
+
+            if should_diag:
+
+
+                save_dict = {
+                    'epoch': epoch,
+                    'model_state': netE.state_dict(),
+                    'optimizer_state': optimizer.state_dict()
+                }
+
+                torch.save(save_dict, '%s/epoch_%d.pth' % (output_dir, epoch))
+
+
+            if opt.distribution_type == 'multi':
+                dist.barrier()
+                map_location = {'cuda:%d' % 0: 'cuda:%d' % gpu}
+                netE.load_state_dict(
+                    torch.load('%s/epoch_%d.pth' % (output_dir, epoch), map_location=map_location)['model_state'])
+
+    dist.destroy_process_group()
+
+def main():
+    opt = parse_args()
+
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+
+    ''' workaround '''
+
+    train_dataset = get_dataset(opt.data_root, opt.data_raw_root, opt.pc_dataroot, opt.npoints,opt.classes)
+    noises_init = torch.randn(len(train_dataset), opt.nc, opt.npoints-opt.svpoints)
+
+    if opt.dist_url == "env://" and opt.world_size == -1:
+        opt.world_size = int(os.environ["WORLD_SIZE"])
+
+    if opt.distribution_type == 'multi':
+        opt.ngpus_per_node = torch.cuda.device_count()
+        opt.world_size = opt.ngpus_per_node * opt.world_size
+        mp.spawn(train, nprocs=opt.ngpus_per_node, args=(opt, output_dir, noises_init))
+    else:
+        train(opt.gpu, opt, output_dir, noises_init)
+
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_root', default='/home/ubuntu/01DATA/partnet/', help='input batch size')
+    parser.add_argument('--data_raw_root', default='/viscam/u/alexzhou907/01DATA/shapenet/shapenet_dim32_sdf_pc',
+                        help='input batch size')
+    parser.add_argument('--pc_dataroot', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k',
+                        help='input batch size')
+    parser.add_argument('--classes', default='Table')
+
+    parser.add_argument('--bs', type=int, default=64, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=1024)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+    parser.add_argument('--lrE', type=float, default=2e-4, help='learning rate for E, default=0.0002')
+    parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
+    parser.add_argument('--e_decay', type=float, default=0, help='weight decay for EBM')
+    parser.add_argument('--grad_clip', type=float, default=None, help='weight decay for EBM')
+    parser.add_argument('--e_gamma', type=float, default=0.998, help='lr decay for EBM')
+
+    parser.add_argument('--netE', default='', help="path to netE (to continue training)")
+
+
+    '''distributed'''
+    parser.add_argument('--world_size', default=1, type=int,
+                        help='Number of distributed nodes.')
+    parser.add_argument('--dist_url', default='tcp://127.0.0.1:9991', type=str,
+                        help='url used to set up distributed training')
+    parser.add_argument('--dist_backend', default='nccl', type=str,
+                        help='distributed backend')
+    parser.add_argument('--distribution_type', default='single', choices=['multi', 'single', None],
+                        help='Use multi-processing distributed training to launch '
+                             'N processes per node, which has N GPUs. This is the '
+                             'fastest way to use PyTorch for either single node or '
+                             'multi node data parallel training')
+    parser.add_argument('--rank', default=0, type=int,
+                        help='node rank for distributed training')
+    parser.add_argument('--gpu', default=None, type=int,
+                        help='GPU id to use. None means using all available GPUs.')
+
+    '''eval'''
+    parser.add_argument('--saveIter', default=100, type=int,help='unit: epoch')
+    parser.add_argument('--diagIter', default=50, type=int, help='unit: epoch')
+    parser.add_argument('--vizIter', default=50, type=int,help='unit: epoch')
+    parser.add_argument('--print_freq', default=50, type=int,help='unit: iter')
+
+    parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
+
+
+    opt = parser.parse_args()
+
+    return opt
+
+if __name__ == '__main__':
+    main()

shape_completion/14_res32_partnet_lamp_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do.py

@@ -0,0 +1,822 @@
+import torch.multiprocessing as mp
+import torch.nn as nn
+import torch.optim as optim
+import torch.utils.data
+
+import argparse
+from model.unet import get_model
+from torch.distributions import Normal
+
+from utils.file_utils import *
+from utils.visualize import *
+from model.pvcnn_completion import PVCNN2Base
+import torch.distributed as dist
+from datasets.partnet import GANdatasetPartNet
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+'''
+some utils
+'''
+def rotation_matrix(axis, theta):
+    """
+    Return the rotation matrix associated with counterclockwise rotation about
+    the given axis by theta radians.
+    """
+    axis = np.asarray(axis)
+    axis = axis / np.sqrt(np.dot(axis, axis))
+    a = np.cos(theta / 2.0)
+    b, c, d = -axis * np.sin(theta / 2.0)
+    aa, bb, cc, dd = a * a, b * b, c * c, d * d
+    bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d
+    return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)],
+                     [2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)],
+                     [2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]])
+
+def rotate(vertices, faces):
+    '''
+    vertices: [numpoints, 3]
+    '''
+    M = rotation_matrix([0, 1, 0], np.pi / 2).transpose()
+    N = rotation_matrix([1, 0, 0], -np.pi / 4).transpose()
+    K = rotation_matrix([0, 0, 1], np.pi).transpose()
+
+    v, f = vertices[:,[1,2,0]].dot(M).dot(N).dot(K), faces[:,[1,2,0]]
+    return v, f
+
+def norm(v, f):
+    v = (v - v.min())/(v.max() - v.min()) - 0.5
+
+    return v, f
+
+def getGradNorm(net):
+    pNorm = torch.sqrt(sum(torch.sum(p ** 2) for p in net.parameters()))
+    gradNorm = torch.sqrt(sum(torch.sum(p.grad ** 2) for p in net.parameters()))
+    return pNorm, gradNorm
+
+
+def weights_init(m):
+    """
+    xavier initialization
+    """
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1 and m.weight is not None:
+        torch.nn.init.xavier_normal_(m.weight)
+    elif classname.find('BatchNorm') != -1:
+        m.weight.data.normal_()
+        m.bias.data.fill_(0)
+
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+    def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
+        imgs = [img_t]
+        for t in reversed(range(0,total_steps)):
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised,
+                                  return_pred_xstart=False)
+            if t % freq == 0 or t == total_steps-1:
+                imgs.append(img_t)
+
+        assert imgs[-1].shape == shape
+        return imgs
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+def get_dataset(data_root, npoints, category):
+
+    train_ds = GANdatasetPartNet('train', data_root, category, npoints)
+    return train_ds
+
+
+def get_dataloader(opt, train_dataset, test_dataset=None):
+
+    if opt.distribution_type == 'multi':
+        train_sampler = torch.utils.data.distributed.DistributedSampler(
+            train_dataset,
+            num_replicas=opt.world_size,
+            rank=opt.rank
+        )
+        if test_dataset is not None:
+            test_sampler = torch.utils.data.distributed.DistributedSampler(
+                test_dataset,
+                num_replicas=opt.world_size,
+                rank=opt.rank
+            )
+        else:
+            test_sampler = None
+    else:
+        train_sampler = None
+        test_sampler = None
+
+    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.bs,sampler=train_sampler,
+                                                   shuffle=train_sampler is None, num_workers=int(opt.workers), drop_last=True)
+
+    if test_dataset is not None:
+        test_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.bs,sampler=test_sampler,
+                                                   shuffle=False, num_workers=int(opt.workers), drop_last=False)
+    else:
+        test_dataloader = None
+
+    return train_dataloader, test_dataloader, train_sampler, test_sampler
+
+
+def train(gpu, opt, output_dir, noises_init):
+
+    set_seed(opt)
+    logger = setup_logging(output_dir)
+    if opt.distribution_type == 'multi':
+        should_diag = gpu==0
+    else:
+        should_diag = True
+    if should_diag:
+        outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    if opt.distribution_type == 'multi':
+        if opt.dist_url == "env://" and opt.rank == -1:
+            opt.rank = int(os.environ["RANK"])
+
+        base_rank =  opt.rank * opt.ngpus_per_node
+        opt.rank = base_rank + gpu
+        dist.init_process_group(backend=opt.dist_backend, init_method=opt.dist_url,
+                                world_size=opt.world_size, rank=opt.rank)
+
+        opt.bs = int(opt.bs / opt.ngpus_per_node)
+        opt.workers = 0
+
+        opt.saveIter =  int(opt.saveIter / opt.ngpus_per_node)
+        opt.diagIter = int(opt.diagIter / opt.ngpus_per_node)
+        opt.vizIter = int(opt.vizIter / opt.ngpus_per_node)
+
+
+    ''' data '''
+    train_dataset = get_dataset(opt.data_root, opt.npoints,opt.classes)
+    dataloader, _, train_sampler, _ = get_dataloader(opt, train_dataset, None)
+
+
+    '''
+    create networks
+    '''
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    netE = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.distribution_type == 'multi':  # Multiple processes, single GPU per process
+        def _transform_(m):
+            return nn.parallel.DistributedDataParallel(
+                m, device_ids=[gpu], output_device=gpu)
+
+        torch.cuda.set_device(gpu)
+        netE.cuda(gpu)
+        netE.multi_gpu_wrapper(_transform_)
+
+
+    elif opt.distribution_type == 'single':
+        def _transform_(m):
+            return nn.parallel.DataParallel(m)
+        netE = netE.cuda()
+        netE.multi_gpu_wrapper(_transform_)
+
+    elif gpu is not None:
+        torch.cuda.set_device(gpu)
+        netE = netE.cuda(gpu)
+    else:
+        raise ValueError('distribution_type = multi | single | None')
+
+    if should_diag:
+        logger.info(opt)
+
+    optimizer= optim.Adam(netE.parameters(), lr=opt.lrE, weight_decay=opt.e_decay, betas=(opt.beta1, 0.999))
+
+    lr_scheduler = optim.lr_scheduler.ExponentialLR(optimizer, opt.e_gamma)
+
+    if opt.netE != '':
+        ckpt = torch.load(opt.netE)
+        netE.load_state_dict(ckpt['model_state'])
+        optimizer.load_state_dict(ckpt['optimizer_state'])
+
+    if opt.netE != '':
+        start_epoch = torch.load(opt.netE)['epoch'] + 1
+    else:
+        start_epoch = 0
+
+
+    for epoch in range(start_epoch, opt.niter):
+
+        if opt.distribution_type == 'multi':
+            train_sampler.set_epoch(epoch)
+
+        lr_scheduler.step(epoch)
+
+        for i, data in enumerate(dataloader):
+
+            x = data['real']
+            sv_x = data['raw']
+
+            sv_x = torch.cat([sv_x, x[:, :, opt.svpoints:]], dim=-1)
+            noises_batch = noises_init[data['idx']]
+
+            '''
+            train diffusion
+            '''
+
+            if opt.distribution_type == 'multi' or (opt.distribution_type is None and gpu is not None):
+                sv_x = sv_x.cuda(gpu)
+                noises_batch = noises_batch.cuda(gpu)
+            elif opt.distribution_type == 'single':
+                sv_x = sv_x.cuda()
+                noises_batch = noises_batch.cuda()
+
+            loss = netE.get_loss_iter(sv_x, noises_batch).mean()
+
+            optimizer.zero_grad()
+            loss.backward()
+            netpNorm, netgradNorm = getGradNorm(netE)
+            if opt.grad_clip is not None:
+                torch.nn.utils.clip_grad_norm_(netE.parameters(), opt.grad_clip)
+
+            optimizer.step()
+
+
+            if i % opt.print_freq == 0 and should_diag:
+
+                logger.info('[{:>3d}/{:>3d}][{:>3d}/{:>3d}]    loss: {:>10.4f},    '
+                             'netpNorm: {:>10.2f},   netgradNorm: {:>10.2f}     '
+                             .format(
+                        epoch, opt.niter, i, len(dataloader),loss.item(),
+                    netpNorm, netgradNorm,
+                        ))
+
+        if (epoch + 1) % opt.diagIter == 0 and should_diag:
+
+            logger.info('Diagnosis:')
+
+            x_range = [x.min().item(), x.max().item()]
+            kl_stats = netE.all_kl(sv_x)
+            logger.info('      [{:>3d}/{:>3d}]    '
+                         'x_range: [{:>10.4f}, {:>10.4f}],   '
+                         'total_bpd_b: {:>10.4f},    '
+                         'terms_bpd: {:>10.4f},  '
+                         'prior_bpd_b: {:>10.4f}    '
+                         'mse_bt: {:>10.4f}  '
+                .format(
+                epoch, opt.niter,
+                *x_range,
+                kl_stats['total_bpd_b'].item(),
+                kl_stats['terms_bpd'].item(), kl_stats['prior_bpd_b'].item(), kl_stats['mse_bt'].item()
+            ))
+
+
+
+        if (epoch + 1) % opt.vizIter == 0 and should_diag:
+            logger.info('Generation: eval')
+
+            netE.eval()
+            m, s = torch.tensor([[0,0,0]]).transpose(0,1)[None], torch.tensor([[1,1,1]]).transpose(0,1)[None]#train_dataset.get_pc_stats(0)
+
+            with torch.no_grad():
+
+                x_gen_eval = netE.gen_samples(sv_x[:,:,:opt.svpoints], sv_x[:,:,opt.svpoints:].shape, sv_x.device, clip_denoised=False).detach().cpu()
+
+
+                gen_stats = [x_gen_eval.mean(), x_gen_eval.std()]
+                gen_eval_range = [x_gen_eval.min().item(), x_gen_eval.max().item()]
+
+                logger.info('      [{:>3d}/{:>3d}]  '
+                             'eval_gen_range: [{:>10.4f}, {:>10.4f}]     '
+                             'eval_gen_stats: [mean={:>10.4f}, std={:>10.4f}]      '
+                    .format(
+                    epoch, opt.niter,
+                    *gen_eval_range, *gen_stats,
+                ))
+
+            export_to_pc_batch('%s/epoch_%03d_samples_eval' % (outf_syn, epoch),
+                                    (x_gen_eval*s+m).transpose(1, 2).numpy()*3)
+
+            export_to_pc_batch('%s/epoch_%03d_ground_truth' % (outf_syn, epoch),
+                               (sv_x.detach().cpu()*s+m).transpose(1, 2).numpy()*3)
+
+            export_to_pc_batch('%s/epoch_%03d_partial' % (outf_syn, epoch),
+                               (sv_x[:,:,:opt.svpoints].detach().cpu()*s+m).transpose(1, 2).numpy()*3)
+
+
+            netE.train()
+
+
+
+
+
+
+
+        if (epoch + 1) % opt.saveIter == 0:
+
+            if should_diag:
+
+
+                save_dict = {
+                    'epoch': epoch,
+                    'model_state': netE.state_dict(),
+                    'optimizer_state': optimizer.state_dict()
+                }
+
+                torch.save(save_dict, '%s/epoch_%d.pth' % (output_dir, epoch))
+
+
+            if opt.distribution_type == 'multi':
+                dist.barrier()
+                map_location = {'cuda:%d' % 0: 'cuda:%d' % gpu}
+                netE.load_state_dict(
+                    torch.load('%s/epoch_%d.pth' % (output_dir, epoch), map_location=map_location)['model_state'])
+
+    dist.destroy_process_group()
+
+def main():
+    opt = parse_args()
+
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+
+    ''' workaround '''
+
+    train_dataset = get_dataset(opt.data_root, opt.npoints,opt.classes)
+    noises_init = torch.randn(len(train_dataset), opt.nc, opt.npoints-opt.svpoints)
+
+    if opt.dist_url == "env://" and opt.world_size == -1:
+        opt.world_size = int(os.environ["WORLD_SIZE"])
+
+    if opt.distribution_type == 'multi':
+        opt.ngpus_per_node = torch.cuda.device_count()
+        opt.world_size = opt.ngpus_per_node * opt.world_size
+        mp.spawn(train, nprocs=opt.ngpus_per_node, args=(opt, output_dir, noises_init))
+    else:
+        train(opt.gpu, opt, output_dir, noises_init)
+
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_root', default='/viscam/u/alexzhou907/01DATA/partnet/', help='input batch size')
+
+    parser.add_argument('--classes', default='Table')
+
+    parser.add_argument('--bs', type=int, default=64, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=1024)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+    parser.add_argument('--lrE', type=float, default=2e-4, help='learning rate for E, default=0.0002')
+    parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
+    parser.add_argument('--e_decay', type=float, default=0, help='weight decay for EBM')
+    parser.add_argument('--grad_clip', type=float, default=None, help='weight decay for EBM')
+    parser.add_argument('--e_gamma', type=float, default=0.998, help='lr decay for EBM')
+
+    parser.add_argument('--netE', default='', help="path to netE (to continue training)")
+
+
+    '''distributed'''
+    parser.add_argument('--world_size', default=1, type=int,
+                        help='Number of distributed nodes.')
+    parser.add_argument('--dist_url', default='tcp://127.0.0.1:9991', type=str,
+                        help='url used to set up distributed training')
+    parser.add_argument('--dist_backend', default='nccl', type=str,
+                        help='distributed backend')
+    parser.add_argument('--distribution_type', default='single', choices=['multi', 'single', None],
+                        help='Use multi-processing distributed training to launch '
+                             'N processes per node, which has N GPUs. This is the '
+                             'fastest way to use PyTorch for either single node or '
+                             'multi node data parallel training')
+    parser.add_argument('--rank', default=0, type=int,
+                        help='node rank for distributed training')
+    parser.add_argument('--gpu', default=None, type=int,
+                        help='GPU id to use. None means using all available GPUs.')
+
+    '''eval'''
+    parser.add_argument('--saveIter', default=100, type=int,help='unit: epoch')
+    parser.add_argument('--diagIter', default=50, type=int, help='unit: epoch')
+    parser.add_argument('--vizIter', default=50, type=int,help='unit: epoch')
+    parser.add_argument('--print_freq', default=50, type=int,help='unit: iter')
+
+    parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
+
+
+    opt = parser.parse_args()
+
+    return opt
+
+if __name__ == '__main__':
+    main()

shape_completion/control_gen_chair.py

@@ -0,0 +1,660 @@
+
+from pprint import pprint
+from metrics.evaluation_metrics import compute_all_metrics, EMD_CD
+
+import torch.nn as nn
+import torch.utils.data
+
+import argparse
+from torch.distributions import Normal
+from utils.visualize import *
+from utils.file_utils import *
+from utils.mitsuba_renderer import write_to_xml_batch
+from model.pvcnn_completion import PVCNN2Base
+
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+from datasets.shapenet_data_sv import *
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+    def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
+        imgs = [img_t]
+        for t in reversed(range(0,total_steps)):
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised,
+                                  return_pred_xstart=False)
+            if t % freq == 0 or t == total_steps-1:
+                imgs.append(img_t)
+
+        assert imgs[-1].shape == shape
+        return imgs
+
+
+
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+    def interpolate(self, x0_part, x1_part, x0_sv, x1_sv, t, lamb, denoise_fn, noise_fn=torch.randn):
+
+        assert t >= 1
+
+        t_vec = torch.empty(x0_part.shape[0], dtype=torch.int64, device=x0_part.device).fill_(t-1)
+        encoding0 = self.q_sample(x0_part, t_vec)
+        encoding1 = self.q_sample(x1_part, t_vec)
+
+        enc = encoding0 * (1-lamb) + (lamb) * encoding1
+
+        img_t = torch.cat([torch.cat([x0_sv[:,:,:int(self.sv_points*(1-lamb))], x1_sv[:,:,:(self.sv_points - int(self.sv_points*(1-lamb)))]], dim=-1), enc], dim=-1)
+
+        for k in reversed(range(0,t)):
+            t_ = torch.empty(img_t.shape[0], dtype=torch.int64, device=img_t.device).fill_(k)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=False, return_pred_xstart=False).detach()
+
+
+        return img_t
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+    def interpolate(self, x0_part, x1_part, x0_sv, x1_sv, t, lamb):
+
+        return self.diffusion.interpolate(x0_part, x1_part, x0_sv, x1_sv, t, lamb, self._denoise)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+
+#############################################################################
+def get_svr_dataset(pc_dataroot, mesh_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Singleview_Points(root_pc=pc_dataroot, root_mesh=mesh_root,
+                                            cache=os.path.join(mesh_root, '../cache'), split='val',
+        categories=category,
+        radius=3, elev=-89, azim=180, img_size=512, focal_length=1000,
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return te_dataset
+
+def get_mvr_dataset(pc_dataroot, views_root, npoints,category, get_image=True):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Multiview_Points(root_pc=pc_dataroot, root_views=views_root,
+                                            cache=os.path.join(pc_dataroot, '../cache'), split='val',
+        categories=category,
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std, get_image=get_image,
+    )
+    return te_dataset
+
+
+def generate_multimodal(opt, netE, save_dir, logger):
+    test_dataset = get_mvr_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.classes)
+    # _, test_dataset =  get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=True)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+        if i!=3:
+            continue
+        gt_all = data['test_points']
+        x_all = data['sv_points']
+        img_all = data['image']
+
+        for v in range(20):
+
+            recons = []
+            svs = []
+            for p in [0,1]:
+                x = x_all[:,p].transpose(1, 2).contiguous()
+                img = img_all[:,p]
+
+                m, s = data['mean'].float(), data['std'].float()
+
+                recon = netE.gen_samples(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda',
+                                         clip_denoised=False).detach().cpu()
+
+                recon = recon.transpose(1, 2).contiguous()
+                x = x.transpose(1, 2).contiguous()
+
+                recons.append(recon)
+                svs.append(x[:, :opt.svpoints,:])
+
+                for l, d in enumerate(zip(list(gt_all), list(recon), list(x), list(img))):
+
+                    # im = np.fliplr(np.flipud(d[-1]))
+                    write_to_xml_batch(os.path.join(save_dir, 'x_%03d_%03d'%(i,l), 'mode_%03d'%v, 'depth_%d'%p),
+                                       (torch.stack(d[:-1], dim=0)* s[0] + m[0]).numpy(), cat='chair')
+
+                    export_to_pc_batch(os.path.join(save_dir, 'x_ply_%03d_%03d'%(i,l), 'mode_%03d'%v, 'depth_%d'%p),
+                                       (torch.stack(d[:-1], dim=0)* s[0] + m[0]).numpy())
+                    plt.imsave(os.path.join(save_dir, 'x_ply_%03d_%03d' % (i, l), 'mode_%03d' % v, 'depth_%d.png' % p),
+                               d[-1].permute(1, 2, 0), cmap='gray')
+
+            x0_part = recons[0].transpose(1, 2).contiguous()[:,:,opt.svpoints:].cuda()
+            x1_part = recons[1].transpose(1, 2).contiguous()[:,:,opt.svpoints:].cuda()
+            x0_sv = svs[0].transpose(1,2).cuda()
+            x1_sv = svs[1].transpose(1,2).cuda()
+
+            interres = []
+            for lamb in np.linspace(0.1, 0.9, 5):
+                res = netE.interpolate(x0_part, x1_part, x0_sv, x1_sv, 1000, lamb)
+
+                res = torch.cat([x0_sv, x1_sv, res[:,:,opt.svpoints:]], dim=-1).detach().cpu().transpose(1,2).contiguous()
+                interres.append(res)
+            for l, d in enumerate(torch.stack(interres, dim=1)):
+                write_to_xml_batch(os.path.join(save_dir, 'x_%03d_%03d' % (i, l), 'mode_%03d' % v),
+                                   (d* s[0] + m[0]).numpy(), cat='chair')
+                export_to_pc_batch(os.path.join(save_dir, 'x_ply_%03d_%03d' % (i, l), 'mode_%03d' % v),
+                                   (d * s[0] + m[0]).numpy())
+
+def main(opt):
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+    logger = setup_logging(output_dir)
+
+    outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    netE = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.cuda:
+        netE.cuda()
+
+    def _transform_(m):
+        return nn.parallel.DataParallel(m)
+
+    netE = netE.cuda()
+    netE.multi_gpu_wrapper(_transform_)
+
+    netE.eval()
+
+    ckpts = [os.path.join(opt.ckpt_dir, f) for f in os.listdir(opt.ckpt_dir) if f.endswith('.pth')]
+
+    with torch.no_grad():
+        for ckpt in reversed(sorted(ckpts, key=lambda x: int(x.strip('.pth').split('_')[-1]) )):
+
+            opt.netE = ckpt
+            logger.info("Resume Path:%s" % opt.netE)
+
+            resumed_param = torch.load(opt.netE)
+            netE.load_state_dict(resumed_param['model_state'])
+
+
+            generate_multimodal(opt, netE, outf_syn, logger)
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataroot_pc', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
+    parser.add_argument('--dataroot_sv', default='/viscam/u/alexzhou907/01DATA/shapenet/shapenet_mit_preprocessed',
+                        help='input batch size')
+    parser.add_argument('--classes', default=['chair'])
+
+    parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--eval_recon_mvr', default=False)
+    parser.add_argument('--generate_multimodal', default=False)
+    parser.add_argument('--eval_saved', default=False)
+    parser.add_argument('--eval_redwood', default=True)
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=200)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+
+    # constrain function
+    parser.add_argument('--constrain_eps', default=0.2)
+    parser.add_argument('--constrain_steps', type=int, default=1)
+
+    parser.add_argument('--ckpt_dir', default='/viscam/u/alexzhou907/research/diffusion/shape_completion/output/6_res32_pc_chair_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do/2020-11-01-19-21-18', help="path to netE (to continue training)")
+
+    '''eval'''
+
+    parser.add_argument('--eval_path',
+                        default='/viscam/u/alexzhou907/research/diffusion/shapenet/output/test/2020-10-10-20-11-46/syn/epoch_2799_samples.pth')
+
+    parser.add_argument('--manualSeed', default=None, type=int, help='random seed')
+
+    parser.add_argument('--gpu', type=int, default=0, metavar='S', help='gpu id (default: 0)')
+
+    opt = parser.parse_args()
+
+    if torch.cuda.is_available():
+        opt.cuda = True
+    else:
+        opt.cuda = False
+
+    return opt
+if __name__ == '__main__':
+    opt = parse_args()
+    set_seed(opt)
+    main(opt)

shape_completion/teaser_chair.py

@@ -0,0 +1,706 @@
+
+
+import torch.nn as nn
+import torch.utils.data
+
+import argparse
+from torch.distributions import Normal
+from utils.visualize import *
+from utils.file_utils import *
+from utils.mitsuba_renderer2 import write_to_xml_batch, write_to_xml
+from model.pvcnn_completion import PVCNN2Base
+
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+from datasets.shapenet_data_sv import *
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+
+    def p_sample_loop_trajectory2(self, partial_x, denoise_fn, shape, device, num_save,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        scale = np.exp(np.log(1/total_steps)/num_save)
+        save_step = total_steps
+
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        imgs = [img_t.detach().cpu()]
+        for t in reversed(range(0,total_steps)):
+            if (t+1) == save_step and t > 0 and len(imgs)<num_save:
+                imgs.append(img_t.detach().cpu())
+                save_step = int(save_step * scale)
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        imgs.append(img_t.detach().cpu())
+        assert imgs[-1][:,:,self.sv_points:].shape == shape
+        return imgs
+
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+    def gen_samples_traj2(self, partial_x, shape, device, noise_fn=torch.randn, num_save=20,
+                    clip_denoised=False,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop_trajectory2(partial_x, self._denoise, shape=shape, device=device, num_save=num_save, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+
+#############################################################################
+def get_svr_dataset(pc_dataroot, mesh_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Singleview_Points(root_pc=pc_dataroot, root_mesh=mesh_root,
+                                            cache=os.path.join(mesh_root, '../cache'), split='val',
+        categories=category,
+        radius=3, elev=-89, azim=180, img_size=512, focal_length=1000,
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return te_dataset
+
+def get_mvr_dataset(pc_dataroot, views_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Multiview_Points(root_pc=pc_dataroot, root_views=views_root,
+                                            cache=os.path.join(pc_dataroot, '../cache'), split='val',
+        categories=category,
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return te_dataset
+
+
+
+def generate_video(netE, opt, save_dir):
+    test_dataset = get_svr_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.classes)
+    # _, test_dataset =  get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=True)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+
+        # gt_all = data['test_points']
+        x_all = data['sv_points']
+
+        img_all = data['image']
+
+        export_to_pc_batch(
+            os.path.join(save_dir, 'batch_%03d_ply' % i), x_all[:, :opt.svpoints, :].numpy())
+        write_to_xml_batch(os.path.join(save_dir, 'batch_%03d' % i),
+                           x_all[:, :opt.svpoints, :].numpy(), cat='chair')
+
+        for v in range(6):
+            x = x_all.transpose(1, 2).contiguous()
+            img = img_all
+            m, s = data['mean'].float(), data['std'].float()
+
+            gen_all = netE.gen_samples_traj2(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda', num_save=55,
+                                     clip_denoised=False)
+            gen_all = torch.stack(gen_all, dim=1).detach().cpu()
+            gen_all = gen_all.transpose(2, 3).contiguous()
+
+            gen_all = gen_all * s[:, None] + m[:, None]
+
+            x = x.transpose(1, 2).contiguous()
+
+
+            for p, d in enumerate(zip(list(gen_all), list(img))):
+
+                im = np.fliplr(np.flipud(d[-1]))
+                gen = d[0]
+                plt.imsave(os.path.join(save_dir, 'depth_%03d_%03d.png'%(i,p)), im, cmap='gray')
+                # gen
+                write_to_xml_batch(os.path.join(save_dir, 'batch_%03d'%i, 'sample_%03d/mode_%03d/xml/gen_process/' % (p,v)),
+                                   gen.numpy(), cat='chair')
+
+            for p, gen in enumerate(gen_all[:,-1]):
+                Path(os.path.join(save_dir, 'batch_%03d_ply' % i, 'sample_%03d' % p,
+                                  'mode_%03d' % v)).mkdir(parents=True, exist_ok=True)
+                pcwrite(
+                    os.path.join(save_dir, 'batch_%03d_ply' % i, 'sample_%03d/mode_%03d/partial.ply' % (p,v)), gen.numpy())
+
+            for k, pcl in enumerate(gen_all[:, -1].cpu().numpy()):
+                dir_ = os.path.join(save_dir, 'batch_%03d' % i, 'sample_%03d/mode_%03d/xml/rotate_final/' % (k, v))
+                Path(dir_).mkdir(parents=True, exist_ok=True)
+                for azim in np.linspace(45, 405 - (360 / 50), 50):
+                    write_to_xml(
+                        os.path.join(dir_, 'azim_%03d.xml' % azim),
+                        pcl, cat='chair', elev=19.471, azim=azim)
+
+
+
+def generate_video_redwood(netE, opt, save_dir):
+    import open3d as o3d
+    pth = "/viscam/u/alexzhou907/research/diffusion/redwood/09620_pc_partial.ply"
+    pth_gt = "/viscam/u/alexzhou907/research/diffusion/redwood/09620_pc.ply"
+
+    points = np.asarray(o3d.io.read_point_cloud(pth).points)
+
+    gt_points = np.asarray(o3d.io.read_point_cloud(pth_gt).points)
+
+    np.save('gt.npy', gt_points)
+
+    test_dataset = ShapeNet15kPointClouds(root_dir=opt.dataroot_pc,
+                           categories=opt.classes, split='train',
+                           tr_sample_size=opt.npoints,
+                           te_sample_size=opt.npoints,
+                           scale=1.,
+                           normalize_per_shape=False,
+                           normalize_std_per_axis=False,
+                           random_subsample=True)
+
+    m, s = torch.from_numpy(test_dataset[0]['mean']).float(), torch.from_numpy(test_dataset[0]['std']).float()
+
+    x = torch.from_numpy(points[np.random.choice(points.shape[0], size=opt.svpoints, replace=False)]).float()
+    x = (x - m) / s
+
+    x = x[None].transpose(1, 2).cuda()
+
+    shape = list(x.shape)
+    shape[-1] = opt.npoints - shape[-1]
+
+    res = []
+    for v in tqdm(range(20)):
+        gen_all = netE.gen_samples_traj2(x.cuda(), torch.Size(shape), 'cuda', num_save=55,
+                                 clip_denoised=False)
+        gen_all = torch.stack(gen_all, dim=1).detach().cpu()
+        gen_all = gen_all.transpose(2, 3).contiguous()
+
+        gen_all = gen_all * s[:, None] + m[:, None]
+
+        res.append(gen_all[:, -1].cpu())
+
+        for p, gen in enumerate(gen_all):
+            # gen
+            write_to_xml_batch(
+                os.path.join(save_dir, 'mode_%03d/xml/gen_process/' % ( v)),
+                gen.numpy(), cat='chair')
+
+
+        for k, pcl in enumerate(gen_all[:, -1].cpu().numpy()):
+            dir_ = os.path.join(save_dir, 'mode_%03d/xml/rotate_final/' % ( v))
+            Path(dir_).mkdir(parents=True, exist_ok=True)
+            for azim in np.linspace(45, 405 - (360 / 50), 50):
+                write_to_xml(
+                    os.path.join(dir_, 'azim_%03d.xml' % azim),
+                    pcl, cat='chair', elev=19.471, azim=azim)
+
+        pcwrite(os.path.join(save_dir, 'mode_%03d.ply'%v), gen_all[:, -1].cpu().numpy()[0])
+
+
+    pcwrite(os.path.join(save_dir, 'gt.ply'),
+            gt_points[np.random.choice(gt_points.shape[0], size=opt.npoints, replace=False)])
+def main(opt):
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+    logger = setup_logging(output_dir)
+
+    outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    netE = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.cuda:
+        netE.cuda()
+
+    def _transform_(m):
+        return nn.parallel.DataParallel(m)
+
+    netE = netE.cuda()
+    netE.multi_gpu_wrapper(_transform_)
+
+    netE.eval()
+
+    ckpts = [os.path.join(opt.ckpt_dir, f) for f in os.listdir(opt.ckpt_dir) if f.endswith('.pth')]
+
+    with torch.no_grad():
+        for ckpt in reversed(sorted(ckpts, key=lambda x: int(x.strip('.pth').split('_')[-1]) )):
+
+            opt.netE = ckpt
+            logger.info("Resume Path:%s" % opt.netE)
+
+            resumed_param = torch.load(opt.netE)
+            netE.load_state_dict(resumed_param['model_state'])
+
+
+            generate_video_redwood( netE,opt, outf_syn)
+
+            exit()
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataroot_pc', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
+    parser.add_argument('--dataroot_sv', default='/viscam/u/alexzhou907/01DATA/shapenet/shapenet_mit_preprocessed',
+                        help='input batch size')
+    parser.add_argument('--classes', default=['chair'])
+
+    parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--eval_recon_mvr', default=False)
+    parser.add_argument('--generate_multimodal', default=False)
+    parser.add_argument('--eval_saved', default=False)
+    parser.add_argument('--eval_redwood', default=True)
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=200)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+
+    # constrain function
+    parser.add_argument('--constrain_eps', default=0.2)
+    parser.add_argument('--constrain_steps', type=int, default=1)
+
+    parser.add_argument('--ckpt_dir', default='/viscam/u/alexzhou907/research/diffusion/shape_completion/output/6_res32_pc_chair_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do/2020-11-01-19-21-18', help="path to netE (to continue training)")
+
+    '''eval'''
+
+    parser.add_argument('--eval_path',
+                        default='/viscam/u/alexzhou907/research/diffusion/shapenet/output/test/2020-10-10-20-11-46/syn/epoch_2799_samples.pth')
+
+    parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
+
+    parser.add_argument('--gpu', type=int, default=0, metavar='S', help='gpu id (default: 0)')
+
+    opt = parser.parse_args()
+
+    if torch.cuda.is_available():
+        opt.cuda = True
+    else:
+        opt.cuda = False
+
+    return opt
+if __name__ == '__main__':
+    opt = parse_args()
+
+    main(opt)

shape_completion/test_car.py

@@ -0,0 +1,681 @@
+
+from pprint import pprint
+from metrics.evaluation_metrics import compute_all_metrics, EMD_CD
+
+import torch.nn as nn
+import torch.utils.data
+
+import argparse
+from torch.distributions import Normal
+
+from utils.file_utils import *
+from utils.mitsuba_renderer import write_to_xml_batch
+from model.pvcnn_completion import PVCNN2Base
+
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+from datasets.shapenet_data_sv import *
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+    def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
+        imgs = [img_t]
+        for t in reversed(range(0,total_steps)):
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised,
+                                  return_pred_xstart=False)
+            if t % freq == 0 or t == total_steps-1:
+                imgs.append(img_t)
+
+        assert imgs[-1].shape == shape
+        return imgs
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+
+#############################################################################
+def get_svr_dataset(pc_dataroot, mesh_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Singleview_Points(root_pc=pc_dataroot, root_mesh=mesh_root,
+                                            cache=os.path.join(mesh_root, '../cache'), split='val',
+        categories=category,
+        radius=3, elev=0, azim=0, img_size=512, focal_length=1000,
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return te_dataset
+
+def get_mvr_dataset(pc_dataroot, views_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Multiview_Points(root_pc=pc_dataroot, root_views=views_root,
+                                            cache=os.path.join(pc_dataroot, '../cache'), split='val',
+        categories=category,
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return te_dataset
+
+
+def evaluate_recon_mvr(opt, netE, save_dir, logger):
+    test_dataset = get_mvr_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.classes)
+    # _, test_dataset =  get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=True)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+    ref = []
+    samples = []
+    images = []
+    masked = []
+    k = 0
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+
+        gt_all = data['test_points']
+        x_all = data['sv_points']
+        img_all = data['image']
+
+
+        B,V,N,C = x_all.shape
+        gt_all = gt_all[:,None,:,:].expand(-1, V, -1,-1)
+
+        x = x_all.reshape(B * V, N, C).transpose(1, 2).contiguous()
+        img = img_all.reshape(B * V, *img_all.shape[2:])
+
+        m, s = data['mean'].float(), data['std'].float()
+
+        recon = netE.gen_samples(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda',
+                                      clip_denoised=False).detach().cpu()
+
+        recon = recon.transpose(1, 2).contiguous()
+        x = x.transpose(1, 2).contiguous()
+
+
+        x_adj = x.reshape(B,V,N,C)* s + m
+        recon_adj = recon.reshape(B,V,N,C)* s + m
+        img = img.reshape(B,V,*img.shape[1:])
+
+        ref.append( gt_all * s + m)
+        masked.append(x_adj[:,:,:test_dataloader.dataset.sv_samples,:])
+        samples.append(recon_adj)
+        images.append(img)
+
+    ref_pcs = torch.cat(ref, dim=0)
+    sample_pcs = torch.cat(samples, dim=0)
+    images = torch.cat(images, dim=0)
+    masked = torch.cat(masked, dim=0)
+
+    B, V, N, C = ref_pcs.shape
+
+
+    torch.save(ref_pcs.reshape(B,V, N, C), os.path.join(save_dir, 'recon_gt.pth'))
+    # torch.save(images.reshape(B,V, *images.shape[2:]), os.path.join(save_dir, 'recon_depth.pth'))
+    torch.save(masked.reshape(B,V, *masked.shape[2:]), os.path.join(save_dir, 'recon_masked.pth'))
+    # Compute metrics
+    results = EMD_CD(sample_pcs.reshape(B*V, N, C),
+                     ref_pcs.reshape(B*V, N, C), opt.batch_size, reduced=False)
+
+    results = {ky: val.reshape(B,V) if val.shape == torch.Size([B*V,]) else val for ky, val in results.items()}
+
+    pprint({key: val.mean().item() for key, val in results.items()})
+    logger.info({key: val.mean().item() for key, val in results.items()})
+
+    results['pc'] = sample_pcs
+    torch.save(results, os.path.join(save_dir, 'ours_results.pth'))
+
+
+
+def generate_multimodal(opt, netE, save_dir, logger):
+    test_dataset = get_svr_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.classes)
+    # _, test_dataset =  get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=True)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+
+        gt_all = data['test_points']
+        x_all = data['sv_points']
+        img_all = data['image']
+
+        Path(os.path.join(save_dir, 'x_%03d'%k)).mkdir(exist_ok=True, parents=True)
+        Path(os.path.join(save_dir, 'x_ply_%03d' % k)).mkdir(exist_ok=True, parents=True)
+
+        for v in range(5):
+            x = x_all.transpose(1, 2).contiguous()
+            img = img_all
+
+            m, s = data['mean'].float(), data['std'].float()
+
+            recon = netE.gen_samples(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda',
+                                     clip_denoised=False).detach().cpu()
+
+            recon = recon.transpose(1, 2).contiguous()
+            x = x.transpose(1, 2).contiguous()
+
+            for p, d in enumerate(zip(list(gt_all), list(recon), list(x), list(img))):
+
+                im = np.fliplr(np.flipud(d[-1]))
+                plt.imsave(os.path.join(save_dir, 'depth_%03d_%03d.png'%(i,p)), im, cmap='gray')
+                write_to_xml_batch(os.path.join(save_dir, 'x_%03d_%03d'%(i,p), 'mode_%03d'%v), (torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
+
+                export_to_pc_batch(os.path.join(save_dir, 'x_ply_%03d_%03d'%(i,p), 'mode_%03d'%v),(torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
+
+
+def main(opt):
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+    logger = setup_logging(output_dir)
+
+    outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    netE = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.cuda:
+        netE.cuda()
+
+    def _transform_(m):
+        return nn.parallel.DataParallel(m)
+
+    netE = netE.cuda()
+    netE.multi_gpu_wrapper(_transform_)
+
+    netE.eval()
+
+    ckpts = [os.path.join(opt.ckpt_dir, f) for f in os.listdir(opt.ckpt_dir) if f.endswith('.pth')]
+
+    with torch.no_grad():
+        for ckpt in reversed(sorted(ckpts, key=lambda x: int(x.strip('.pth').split('_')[-1]) )):
+
+            opt.netE = ckpt
+            logger.info("Resume Path:%s" % opt.netE)
+
+            resumed_param = torch.load(opt.netE)
+            netE.load_state_dict(resumed_param['model_state'])
+
+
+            if opt.eval_recon_mvr:
+                # Evaluate generation
+                evaluate_recon_mvr(opt, netE, outf_syn, logger)
+
+            if opt.generate_multimodal:
+
+                generate_multimodal(opt, netE, outf_syn, logger)
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataroot_pc', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
+    parser.add_argument('--dataroot_sv', default='/viscam/u/alexzhou907/01DATA/shapenet/shapenet_mit_preprocessed',
+                        help='input batch size')
+    parser.add_argument('--classes', default=['car'])
+
+    parser.add_argument('--batch_size', type=int, default=8, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--eval_recon_mvr', default=True)
+    parser.add_argument('--generate_multimodal', default=False)
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=200)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+
+    # constrain function
+    parser.add_argument('--constrain_eps', default=0.2)
+    parser.add_argument('--constrain_steps', type=int, default=1)
+
+    parser.add_argument('--ckpt_dir', default='/viscam/u/alexzhou907/research/diffusion/shape_completion/output/3_res32_pc_car_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do/2020-11-01-03-08-40', help="path to netE (to continue training)")
+
+    '''eval'''
+
+    parser.add_argument('--eval_path',
+                        default='/viscam/u/alexzhou907/research/diffusion/shapenet/output/test/2020-10-10-20-11-46/syn/epoch_2799_samples.pth')
+
+    parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
+
+    parser.add_argument('--gpu', type=int, default=0, metavar='S', help='gpu id (default: 0)')
+
+    opt = parser.parse_args()
+
+    if torch.cuda.is_available():
+        opt.cuda = True
+    else:
+        opt.cuda = False
+
+    return opt
+if __name__ == '__main__':
+    opt = parse_args()
+    set_seed(opt)
+
+    main(opt)

shape_completion/test_chair.py

@@ -0,0 +1,753 @@
+
+from pprint import pprint
+from metrics.evaluation_metrics import compute_all_metrics, EMD_CD
+
+import torch.nn as nn
+import torch.utils.data
+
+import argparse
+from torch.distributions import Normal
+from utils.visualize import *
+from utils.file_utils import *
+from utils.mitsuba_renderer import write_to_xml_batch
+from model.pvcnn_completion import PVCNN2Base
+
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+from datasets.shapenet_data_sv import *
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+    def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
+        imgs = [img_t]
+        for t in reversed(range(0,total_steps)):
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised,
+                                  return_pred_xstart=False)
+            if t % freq == 0 or t == total_steps-1:
+                imgs.append(img_t)
+
+        assert imgs[-1].shape == shape
+        return imgs
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+
+#############################################################################
+def get_svr_dataset(pc_dataroot, mesh_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Singleview_Points(root_pc=pc_dataroot, root_mesh=mesh_root,
+                                            cache=os.path.join(mesh_root, '../cache'), split='val',
+        categories=category,
+        radius=3, elev=-89, azim=180, img_size=512, focal_length=1000,
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return te_dataset
+
+def get_mvr_dataset(pc_dataroot, views_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Multiview_Points(root_pc=pc_dataroot, root_views=views_root,
+                                            cache=os.path.join(pc_dataroot, '../cache'), split='val',
+        categories=category,
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return te_dataset
+
+
+def evaluate_recon_mvr(opt, netE, save_dir, logger):
+    test_dataset = get_mvr_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.classes)
+    # _, test_dataset =  get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=True)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+    ref = []
+    samples = []
+    images = []
+    masked = []
+    k = 0
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+
+        gt_all = data['test_points']
+        x_all = data['sv_points']
+        # img_all = data['image']
+
+
+        B,V,N,C = x_all.shape
+        gt_all = gt_all[:,None,:,:].expand(-1, V, -1,-1)
+
+        x = x_all.reshape(B * V, N, C).transpose(1, 2).contiguous()
+        # img = img_all.reshape(B * V, *img_all.shape[2:])
+
+        m, s = data['mean'].float(), data['std'].float()
+
+        recon = netE.gen_samples(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda',
+                                      clip_denoised=False).detach().cpu()
+
+        recon = recon.transpose(1, 2).contiguous()
+        x = x.transpose(1, 2).contiguous()
+
+
+        x_adj = x.reshape(B,V,N,C)* s + m
+        recon_adj = recon.reshape(B,V,N,C)* s + m
+        # img = img.reshape(B,V,*img.shape[1:])
+
+        ref.append( gt_all * s + m)
+        masked.append(x_adj[:,:,:test_dataloader.dataset.sv_samples,:])
+        samples.append(recon_adj)
+        # images.append(img)
+
+    ref_pcs = torch.cat(ref, dim=0)
+    sample_pcs = torch.cat(samples, dim=0)
+    # images = torch.cat(images, dim=0)
+    masked = torch.cat(masked, dim=0)
+
+    B, V, N, C = ref_pcs.shape
+
+
+    torch.save(ref_pcs.reshape(B,V, N, C), os.path.join(save_dir, 'recon_gt.pth'))
+    # torch.save(images.reshape(B,V, *images.shape[2:]), os.path.join(save_dir, 'recon_depth.pth'))
+    torch.save(masked.reshape(B,V, *masked.shape[2:]), os.path.join(save_dir, 'recon_masked.pth'))
+    # Compute metrics
+    results = EMD_CD(sample_pcs.reshape(B*V, N, C),
+                     ref_pcs.reshape(B*V, N, C), opt.batch_size, reduced=False)
+
+    results = {ky: val.reshape(B,V) if val.shape == torch.Size([B*V,]) else val for ky, val in results.items()}
+
+    pprint({key: val.mean().item() for key, val in results.items()})
+    logger.info({key: val.mean().item() for key, val in results.items()})
+
+    results['pc'] = sample_pcs
+    torch.save(results, os.path.join(save_dir, 'ours_results.pth'))
+
+    del ref_pcs, masked, results
+
+def evaluate_saved(opt, netE, save_dir, logger):
+    ours_base = '/viscam/u/alexzhou907/research/diffusion/shape_completion/output/test_chair/2020-11-04-02-10-38/syn'
+
+    gt_pth = ours_base + '/recon_gt.pth'
+    ours_pth = ours_base + '/ours_results.pth'
+    gt = torch.load(gt_pth).permute(1,0,2,3)
+    ours = torch.load(ours_pth)['pc'].permute(1,0,2,3)
+
+    all_res = {}
+    for i, (gt_, ours_) in enumerate(zip(gt, ours)):
+        results = compute_all_metrics(gt_, ours_, opt.batch_size)
+
+        for key, val in results.items():
+            if i == 0:
+                all_res[key] = val
+            else:
+                all_res[key] += val
+        pprint(results)
+    for key, val in all_res.items():
+        all_res[key] = val / gt.shape[0]
+
+    pprint({key: val.mean().item() for key, val in all_res.items()})
+
+
+def generate_multimodal(opt, netE, save_dir, logger):
+    test_dataset = get_svr_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.classes)
+    # _, test_dataset =  get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=True)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+
+        gt_all = data['test_points']
+        x_all = data['sv_points']
+        img_all = data['image']
+
+        for v in range(6):
+            x = x_all.transpose(1, 2).contiguous()
+            img = img_all
+
+            m, s = data['mean'].float(), data['std'].float()
+
+            recon = netE.gen_samples(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda',
+                                     clip_denoised=False).detach().cpu()
+
+            recon = recon.transpose(1, 2).contiguous()
+            x = x.transpose(1, 2).contiguous()
+
+            for p, d in enumerate(zip(list(gt_all), list(recon), list(x), list(img))):
+
+                im = np.fliplr(np.flipud(d[-1]))
+                plt.imsave(os.path.join(save_dir, 'depth_%03d_%03d.png'%(i,p)), im, cmap='gray')
+                write_to_xml_batch(os.path.join(save_dir, 'x_%03d_%03d'%(i,p), 'mode_%03d'%v), (torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy(), cat='chair')
+
+                export_to_pc_batch(os.path.join(save_dir, 'x_ply_%03d_%03d'%(i,p), 'mode_%03d'%v),(torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
+
+
+def redwood_demo(opt, netE, save_dir, logger):
+    import open3d as o3d
+    pth = "/viscam/u/alexzhou907/01DATA/redwood/09515_pc_partial.ply"
+    pth_gt = "/viscam/u/alexzhou907/01DATA/redwood/09515_pc.ply"
+
+    points = np.asarray(o3d.io.read_point_cloud(pth).points)
+
+    gt_points = np.asarray(o3d.io.read_point_cloud(pth_gt).points)
+
+    np.save('gt.npy', gt_points)
+
+    test_dataset = get_mvr_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.classes)
+
+    m, s = torch.from_numpy(test_dataset[0]['mean']).float(), torch.from_numpy(test_dataset[0]['std']).float()
+
+    x = torch.from_numpy(points[np.random.choice(points.shape[0], size=opt.npoints, replace=False)]).float()
+    x = (x-m)/s
+
+    x = x.transpose(1,2).cuda()
+
+    res = []
+    for k in range(20):
+        recon = netE.gen_samples(x[:,:,:opt.svpoints], x[:,:,opt.svpoints:].shape, 'cuda',
+                                 clip_denoised=False).detach().cpu()
+        recon = recon.transpose(1, 2).contiguous()
+        recon = recon * s+ m
+        res.append(recon)
+    res = torch.cat(res, dim=0)
+
+    write_to_xml_batch(os.path.join(save_dir, 'xml'),
+                       (res).numpy(), cat='chair')
+
+    export_to_pc_batch(os.path.join(save_dir, 'ply'),
+                       (res).numpy())
+
+    torch.save(res, os.path.join(save_dir, 'redwood_demo.pth'))
+
+    pcwrite(os.path.join(save_dir, 'ply', 'gt.ply'),
+            gt_points[np.random.choice(gt_points.shape[0], size=opt.npoints, replace=False)])
+    write_to_xml_batch(os.path.join(save_dir, 'xml_gt'),
+                       gt_points[None], cat='chair')
+
+    exit()
+
+def main(opt):
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+    logger = setup_logging(output_dir)
+
+    outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    netE = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.cuda:
+        netE.cuda()
+
+    def _transform_(m):
+        return nn.parallel.DataParallel(m)
+
+    netE = netE.cuda()
+    netE.multi_gpu_wrapper(_transform_)
+
+    netE.eval()
+
+    ckpts = [os.path.join(opt.ckpt_dir, f) for f in os.listdir(opt.ckpt_dir) if f.endswith('.pth')]
+
+    with torch.no_grad():
+        for ckpt in reversed(sorted(ckpts, key=lambda x: int(x.strip('.pth').split('_')[-1]) )):
+
+            opt.netE = ckpt
+            logger.info("Resume Path:%s" % opt.netE)
+
+            resumed_param = torch.load(opt.netE)
+            netE.load_state_dict(resumed_param['model_state'])
+
+
+            if opt.eval_recon_mvr:
+                # Evaluate generation
+                evaluate_recon_mvr(opt, netE, outf_syn, logger)
+
+            if opt.generate_multimodal:
+
+                generate_multimodal(opt, netE, outf_syn, logger)
+
+            if opt.eval_saved:
+                evaluate_saved(opt, netE, outf_syn, logger)
+
+            if opt.eval_redwood:
+                redwood_demo(opt, netE, outf_syn, logger)
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataroot_pc', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
+    parser.add_argument('--dataroot_sv', default='/viscam/u/alexzhou907/01DATA/shapenet/shapenet_mit_preprocessed',
+                        help='input batch size')
+    parser.add_argument('--classes', default=['chair'])
+
+    parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--eval_recon_mvr', default=False)
+    parser.add_argument('--generate_multimodal', default=False)
+    parser.add_argument('--eval_saved', default=False)
+    parser.add_argument('--eval_redwood', default=True)
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=200)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+
+    # constrain function
+    parser.add_argument('--constrain_eps', default=0.2)
+    parser.add_argument('--constrain_steps', type=int, default=1)
+
+    parser.add_argument('--ckpt_dir', default='/viscam/u/alexzhou907/research/diffusion/shape_completion/output/6_res32_pc_chair_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do/2020-11-01-19-21-18', help="path to netE (to continue training)")
+
+    '''eval'''
+
+    parser.add_argument('--eval_path',
+                        default='/viscam/u/alexzhou907/research/diffusion/shapenet/output/test/2020-10-10-20-11-46/syn/epoch_2799_samples.pth')
+
+    parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
+
+    parser.add_argument('--gpu', type=int, default=0, metavar='S', help='gpu id (default: 0)')
+
+    opt = parser.parse_args()
+
+    if torch.cuda.is_available():
+        opt.cuda = True
+    else:
+        opt.cuda = False
+
+    return opt
+if __name__ == '__main__':
+    opt = parse_args()
+
+    main(opt)

shape_completion/test_completion.py

@@ -0,0 +1,634 @@
+
+from pprint import pprint
+from metrics.evaluation_metrics import compute_all_metrics, EMD_CD
+
+import torch.nn as nn
+import torch.utils.data
+
+import argparse
+from torch.distributions import Normal
+from utils.visualize import *
+from utils.file_utils import *
+from utils.mitsuba_renderer import write_to_xml_batch
+from model.pvcnn_completion import PVCNN2Base
+
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+from datasets.shapenet_data_sv import *
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+    def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
+        imgs = [img_t]
+        for t in reversed(range(0,total_steps)):
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised,
+                                  return_pred_xstart=False)
+            if t % freq == 0 or t == total_steps-1:
+                imgs.append(img_t)
+
+        assert imgs[-1].shape == shape
+        return imgs
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+
+#############################################################################
+
+def get_mvr_dataset(pc_dataroot, views_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=[category], split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Multiview_Points(root_pc=pc_dataroot, root_views=views_root,
+                                            cache=os.path.join(pc_dataroot, '../cache'), split='val',
+        categories=[category],
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return te_dataset
+
+
+def evaluate_recon_mvr(opt, model, save_dir, logger):
+    test_dataset = get_mvr_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.category)
+
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+    ref = []
+    samples = []
+    masked = []
+    k = 0
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+
+        gt_all = data['test_points']
+        x_all = data['sv_points']
+
+        B,V,N,C = x_all.shape
+        gt_all = gt_all[:,None,:,:].expand(-1, V, -1,-1)
+
+        x = x_all.reshape(B * V, N, C).transpose(1, 2).contiguous()
+
+        m, s = data['mean'].float(), data['std'].float()
+
+        recon = model.gen_samples(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda',
+                                      clip_denoised=False).detach().cpu()
+
+        recon = recon.transpose(1, 2).contiguous()
+        x = x.transpose(1, 2).contiguous()
+
+
+        x_adj = x.reshape(B,V,N,C)* s + m
+        recon_adj = recon.reshape(B,V,N,C)* s + m
+
+        ref.append( gt_all * s + m)
+        masked.append(x_adj[:,:,:test_dataloader.dataset.sv_samples,:])
+        samples.append(recon_adj)
+
+    ref_pcs = torch.cat(ref, dim=0)
+    sample_pcs = torch.cat(samples, dim=0)
+    masked = torch.cat(masked, dim=0)
+
+    B, V, N, C = ref_pcs.shape
+
+
+    torch.save(ref_pcs.reshape(B,V, N, C), os.path.join(save_dir, 'recon_gt.pth'))
+
+    torch.save(masked.reshape(B,V, *masked.shape[2:]), os.path.join(save_dir, 'recon_masked.pth'))
+    # Compute metrics
+    results = EMD_CD(sample_pcs.reshape(B*V, N, C),
+                     ref_pcs.reshape(B*V, N, C), opt.batch_size, reduced=False)
+
+    results = {ky: val.reshape(B,V) if val.shape == torch.Size([B*V,]) else val for ky, val in results.items()}
+
+    pprint({key: val.mean().item() for key, val in results.items()})
+    logger.info({key: val.mean().item() for key, val in results.items()})
+
+    results['pc'] = sample_pcs
+    torch.save(results, os.path.join(save_dir, 'ours_results.pth'))
+
+    del ref_pcs, masked, results
+
+def evaluate_saved(opt, saved_dir):
+    # ours_base = '/viscam/u/alexzhou907/research/diffusion/shape_completion/output/test_chair/2020-11-04-02-10-38/syn'
+
+    gt_pth = saved_dir + '/recon_gt.pth'
+    ours_pth = saved_dir + '/ours_results.pth'
+    gt = torch.load(gt_pth).permute(1,0,2,3)
+    ours = torch.load(ours_pth)['pc'].permute(1,0,2,3)
+
+    all_res = {}
+    for i, (gt_, ours_) in enumerate(zip(gt, ours)):
+        results = compute_all_metrics(gt_, ours_, opt.batch_size)
+
+        for key, val in results.items():
+            if i == 0:
+                all_res[key] = val
+            else:
+                all_res[key] += val
+        pprint(results)
+    for key, val in all_res.items():
+        all_res[key] = val / gt.shape[0]
+
+    pprint({key: val.mean().item() for key, val in all_res.items()})
+
+
+
+def main(opt):
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+    logger = setup_logging(output_dir)
+
+    outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    model = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.cuda:
+        model.cuda()
+
+    def _transform_(m):
+        return nn.parallel.DataParallel(m)
+
+    model = model.cuda()
+    model.multi_gpu_wrapper(_transform_)
+
+    model.eval()
+
+    with torch.no_grad():
+
+        logger.info("Resume Path:%s" % opt.model)
+
+        resumed_param = torch.load(opt.model)
+        model.load_state_dict(resumed_param['model_state'])
+
+
+        if opt.eval_recon_mvr:
+            # Evaluate generation
+            evaluate_recon_mvr(opt, model, outf_syn, logger)
+
+        if opt.eval_saved:
+            evaluate_saved(opt, outf_syn)
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataroot_pc', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
+    parser.add_argument('--dataroot_sv', default='/viscam/u/alexzhou907/01DATA/shapenet/shapenet_mit_preprocessed',
+                        help='input batch size')
+    parser.add_argument('--category', default='chair')
+
+    parser.add_argument('--batch_size', type=int, default=50, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--eval_recon_mvr', default=True)
+    parser.add_argument('--eval_saved', default=True)
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=200)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+
+    parser.add_argument('--model', default='', required=True, help="path to model (to continue training)")
+
+    '''eval'''
+
+    parser.add_argument('--eval_path',
+                        default='')
+
+    parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
+
+    parser.add_argument('--gpu', type=int, default=0, metavar='S', help='gpu id (default: 0)')
+
+    opt = parser.parse_args()
+
+    if torch.cuda.is_available():
+        opt.cuda = True
+    else:
+        opt.cuda = False
+
+    return opt
+if __name__ == '__main__':
+    opt = parse_args()
+
+    main(opt)

shape_completion/test_partnet_chair.py

@@ -0,0 +1,599 @@
+
+from pprint import pprint
+from tqdm import tqdm
+import torch.nn as nn
+import torch.utils.data
+
+import argparse
+from torch.distributions import Normal
+from utils.visualize import *
+from utils.file_utils import *
+from utils.mitsuba_renderer import write_to_xml_batch
+from model.pvcnn_completion import PVCNN2Base
+
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+from datasets.partnet import GANdatasetPartNet
+import trimesh
+import csv
+import numpy as np
+import random
+from plyfile import PlyData, PlyElement
+
+
+def write_ply(points, filename, text=False):
+    """ input: Nx3, write points to filename as PLY format. """
+    points = [(points[i,0], points[i,1], points[i,2]) for i in range(points.shape[0])]
+    vertex = np.array(points, dtype=[('x', 'f4'), ('y', 'f4'),('z', 'f4')])
+    el = PlyElement.describe(vertex, 'vertex', comments=['vertices'])
+    with open(filename, mode='wb') as f:
+        PlyData([el], text=text).write(f)
+
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+    def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
+        imgs = [img_t]
+        for t in reversed(range(0,total_steps)):
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised,
+                                  return_pred_xstart=False)
+            if t % freq == 0 or t == total_steps-1:
+                imgs.append(img_t)
+
+        assert imgs[-1].shape == shape
+        return imgs
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+
+#############################################################################
+def get_dataset(data_root, npoints, category):
+
+    train_ds = GANdatasetPartNet('test', data_root, category, npoints)
+    return train_ds
+
+
+def generate_multimodal(opt, netE, save_dir, logger):
+    test_dataset = get_dataset(opt.data_root, opt.npoints,opt.classes)
+    # _, test_dataset =  get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=True)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+
+        gt_all = data['real']
+        x_all = data['raw']
+
+        for j in range(5):
+            x = torch.cat([x_all, gt_all[:, :, opt.svpoints:]], dim=-1)
+
+            recon = netE.gen_samples(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda',
+                                     clip_denoised=False).detach().cpu()
+
+
+
+
+            for p, d in enumerate(zip(list(x_all), list(recon), list(data['raw_id']))):
+
+                partial = torch.cat([d[0], d[0][:, 0:1].expand(-1, opt.svpoints)], dim=-1)
+                rec = d[1]
+                rid = d[2]
+                write_to_xml_batch(os.path.join(save_dir, 'x_%03d_%03d' % (i, p), 'mode_%03d' % j),
+                                   (torch.stack([partial, rec], dim=0)).transpose(1, 2).numpy(), cat='chair')
+
+                export_to_pc_batch(os.path.join(save_dir, 'x_ply_%03d_%03d' % (i, p), 'mode_%03d' % j),
+                                   (torch.stack([partial, rec], dim=0)).transpose(1, 2).numpy())
+
+                raw_id = rid.split('.')[0]
+                save_sample_dir = os.path.join(save_dir, "{}".format(raw_id))
+                Path(save_sample_dir).mkdir(parents=True, exist_ok=True)
+                # save input partial shape
+                if j == 0:
+                    save_path = os.path.join(save_sample_dir, "raw.ply")
+                    write_ply((partial.detach().cpu()).transpose(0, 1).numpy(), save_path)
+                # save completed shape
+                save_path = os.path.join(save_sample_dir, "fake-z{}.ply".format(j))
+                write_ply((rec.detach().cpu()).transpose(0, 1).numpy(), save_path)
+
+
+def main(opt):
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+    logger = setup_logging(output_dir)
+
+    outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    netE = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.cuda:
+        netE.cuda()
+
+    def _transform_(m):
+        return nn.parallel.DataParallel(m)
+
+    netE = netE.cuda()
+    netE.multi_gpu_wrapper(_transform_)
+
+    netE.eval()
+
+    ckpts = [os.path.join(opt.ckpt_dir, f) for f in os.listdir(opt.ckpt_dir) if f.endswith('.pth')]
+
+    with torch.no_grad():
+        for ckpt in reversed(sorted(ckpts, key=lambda x: int(x.strip('.pth').split('_')[-1]) )):
+
+            opt.netE = ckpt
+            logger.info("Resume Path:%s" % opt.netE)
+
+            resumed_param = torch.load(opt.netE)
+            netE.load_state_dict(resumed_param['model_state'])
+
+
+
+            if opt.generate_multimodal:
+
+                generate_multimodal(opt, netE, outf_syn, logger)
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_root', default='/viscam/u/alexzhou907/01DATA/partnet',
+                        help='input batch size')
+
+    parser.add_argument('--classes', default='Chair')
+
+    parser.add_argument('--batch_size', type=int, default=64, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--eval_recon_mvr', default=False)
+    parser.add_argument('--generate_multimodal', default=True)
+    parser.add_argument('--eval_saved', default=False)
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=1024)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+
+    # constrain function
+    parser.add_argument('--constrain_eps', default=0.2)
+    parser.add_argument('--constrain_steps', type=int, default=1)
+
+    parser.add_argument('--ckpt_dir', default='/viscam/u/alexzhou907/research/diffusion/shape_completion/output/epn3d_chair', help="path to netE (to continue training)")
+
+    '''eval'''
+
+    parser.add_argument('--eval_path',
+                        default='/viscam/u/alexzhou907/research/diffusion/shapenet/output/test/2020-10-10-20-11-46/syn/epoch_2799_samples.pth')
+
+    parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
+
+    parser.add_argument('--gpu', type=int, default=0, metavar='S', help='gpu id (default: 0)')
+
+    opt = parser.parse_args()
+
+    if torch.cuda.is_available():
+        opt.cuda = True
+    else:
+        opt.cuda = False
+
+    return opt
+if __name__ == '__main__':
+    opt = parse_args()
+
+    main(opt)

shape_completion/test_partnet_table.py

@@ -0,0 +1,599 @@
+
+from pprint import pprint
+from tqdm import tqdm
+import torch.nn as nn
+import torch.utils.data
+
+import argparse
+from torch.distributions import Normal
+from utils.visualize import *
+from utils.file_utils import *
+from utils.mitsuba_renderer import write_to_xml_batch
+from model.pvcnn_completion import PVCNN2Base
+
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+from datasets.partnet import GANdatasetPartNet
+import trimesh
+import csv
+import numpy as np
+import random
+from plyfile import PlyData, PlyElement
+
+
+def write_ply(points, filename, text=False):
+    """ input: Nx3, write points to filename as PLY format. """
+    points = [(points[i,0], points[i,1], points[i,2]) for i in range(points.shape[0])]
+    vertex = np.array(points, dtype=[('x', 'f4'), ('y', 'f4'),('z', 'f4')])
+    el = PlyElement.describe(vertex, 'vertex', comments=['vertices'])
+    with open(filename, mode='wb') as f:
+        PlyData([el], text=text).write(f)
+
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+    def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
+        imgs = [img_t]
+        for t in reversed(range(0,total_steps)):
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised,
+                                  return_pred_xstart=False)
+            if t % freq == 0 or t == total_steps-1:
+                imgs.append(img_t)
+
+        assert imgs[-1].shape == shape
+        return imgs
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+
+#############################################################################
+def get_dataset(data_root, npoints, category):
+
+    train_ds = GANdatasetPartNet('test', data_root, category, npoints)
+    return train_ds
+
+
+def generate_multimodal(opt, netE, save_dir, logger):
+    test_dataset = get_dataset(opt.data_root, opt.npoints,opt.classes)
+    # _, test_dataset =  get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=True)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+
+        gt_all = data['real']
+        x_all = data['raw']
+
+        for j in range(5):
+            x = torch.cat([x_all, gt_all[:, :, opt.svpoints:]], dim=-1)
+
+            recon = netE.gen_samples(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda',
+                                     clip_denoised=False).detach().cpu()
+
+
+
+
+            for p, d in enumerate(zip(list(x_all), list(recon), list(data['raw_id']))):
+
+                partial = torch.cat([d[0], d[0][:, 0:1].expand(-1, opt.svpoints)], dim=-1)
+                rec = d[1]
+                rid = d[2]
+                write_to_xml_batch(os.path.join(save_dir, 'x_%03d_%03d' % (i, p), 'mode_%03d' % j),
+                                   (torch.stack([partial, rec], dim=0)).transpose(1, 2).numpy(), cat='chair')
+
+                export_to_pc_batch(os.path.join(save_dir, 'x_ply_%03d_%03d' % (i, p), 'mode_%03d' % j),
+                                   (torch.stack([partial, rec], dim=0)).transpose(1, 2).numpy())
+
+                raw_id = rid.split('.')[0]
+                save_sample_dir = os.path.join(save_dir, "{}".format(raw_id))
+                Path(save_sample_dir).mkdir(parents=True, exist_ok=True)
+                # save input partial shape
+                if j == 0:
+                    save_path = os.path.join(save_sample_dir, "raw.ply")
+                    write_ply((partial.detach().cpu()).transpose(0, 1).numpy(), save_path)
+                # save completed shape
+                save_path = os.path.join(save_sample_dir, "fake-z{}.ply".format(j))
+                write_ply((rec.detach().cpu()).transpose(0, 1).numpy(), save_path)
+
+
+def main(opt):
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+    logger = setup_logging(output_dir)
+
+    outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    netE = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.cuda:
+        netE.cuda()
+
+    def _transform_(m):
+        return nn.parallel.DataParallel(m)
+
+    netE = netE.cuda()
+    netE.multi_gpu_wrapper(_transform_)
+
+    netE.eval()
+
+    ckpts = [os.path.join(opt.ckpt_dir, f) for f in os.listdir(opt.ckpt_dir) if f.endswith('.pth')]
+
+    with torch.no_grad():
+        for ckpt in reversed(sorted(ckpts, key=lambda x: int(x.strip('.pth').split('_')[-1]) )):
+
+            opt.netE = ckpt
+            logger.info("Resume Path:%s" % opt.netE)
+
+            resumed_param = torch.load(opt.netE)
+            netE.load_state_dict(resumed_param['model_state'])
+
+
+
+            if opt.generate_multimodal:
+
+                generate_multimodal(opt, netE, outf_syn, logger)
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_root', default='/viscam/u/alexzhou907/01DATA/partnet',
+                        help='input batch size')
+
+    parser.add_argument('--classes', default='Table')
+
+    parser.add_argument('--batch_size', type=int, default=64, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--eval_recon_mvr', default=False)
+    parser.add_argument('--generate_multimodal', default=True)
+    parser.add_argument('--eval_saved', default=False)
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=1024)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+
+    # constrain function
+    parser.add_argument('--constrain_eps', default=0.2)
+    parser.add_argument('--constrain_steps', type=int, default=1)
+
+    parser.add_argument('--ckpt_dir', default='/viscam/u/alexzhou907/research/diffusion/shape_completion/output/epn3d_chair', help="path to netE (to continue training)")
+
+    '''eval'''
+
+    parser.add_argument('--eval_path',
+                        default='/viscam/u/alexzhou907/research/diffusion/shapenet/output/test/2020-10-10-20-11-46/syn/epoch_2799_samples.pth')
+
+    parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
+
+    parser.add_argument('--gpu', type=int, default=0, metavar='S', help='gpu id (default: 0)')
+
+    opt = parser.parse_args()
+
+    if torch.cuda.is_available():
+        opt.cuda = True
+    else:
+        opt.cuda = False
+
+    return opt
+if __name__ == '__main__':
+    opt = parse_args()
+
+    main(opt)

shape_completion/test_plane.py

@@ -0,0 +1,681 @@
+
+from pprint import pprint
+from metrics.evaluation_metrics import compute_all_metrics, EMD_CD
+
+import torch.nn as nn
+import torch.utils.data
+
+import argparse
+from torch.distributions import Normal
+
+from utils.file_utils import *
+from utils.mitsuba_renderer import write_to_xml_batch
+from model.pvcnn_completion import PVCNN2Base
+
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+from datasets.shapenet_data_sv import *
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+    def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
+        imgs = [img_t]
+        for t in reversed(range(0,total_steps)):
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised,
+                                  return_pred_xstart=False)
+            if t % freq == 0 or t == total_steps-1:
+                imgs.append(img_t)
+
+        assert imgs[-1].shape == shape
+        return imgs
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+
+#############################################################################
+def get_svr_dataset(pc_dataroot, mesh_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Singleview_Points(root_pc=pc_dataroot, root_mesh=mesh_root,
+                                            cache=os.path.join(mesh_root, '../cache'), split='val',
+        categories=category,
+        radius=3, elev=0, azim=0, img_size=512, focal_length=1000,
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return te_dataset
+
+def get_mvr_dataset(pc_dataroot, views_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Multiview_Points(root_pc=pc_dataroot, root_views=views_root,
+                                            cache=os.path.join(pc_dataroot, '../cache'), split='val',
+        categories=category,
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return te_dataset
+
+
+def evaluate_recon_mvr(opt, netE, save_dir, logger):
+    test_dataset = get_mvr_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.classes)
+    # _, test_dataset =  get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=True)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+    ref = []
+    samples = []
+    images = []
+    masked = []
+    k = 0
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+
+        gt_all = data['test_points']
+        x_all = data['sv_points']
+        img_all = data['image']
+
+
+        B,V,N,C = x_all.shape
+        gt_all = gt_all[:,None,:,:].expand(-1, V, -1,-1)
+
+        x = x_all.reshape(B * V, N, C).transpose(1, 2).contiguous()
+        img = img_all.reshape(B * V, *img_all.shape[2:])
+
+        m, s = data['mean'].float(), data['std'].float()
+
+        recon = netE.gen_samples(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda',
+                                      clip_denoised=False).detach().cpu()
+
+        recon = recon.transpose(1, 2).contiguous()
+        x = x.transpose(1, 2).contiguous()
+
+
+        x_adj = x.reshape(B,V,N,C)* s + m
+        recon_adj = recon.reshape(B,V,N,C)* s + m
+        img = img.reshape(B,V,*img.shape[1:])
+
+        ref.append( gt_all * s + m)
+        masked.append(x_adj[:,:,:test_dataloader.dataset.sv_samples,:])
+        samples.append(recon_adj)
+        images.append(img)
+
+    ref_pcs = torch.cat(ref, dim=0)
+    sample_pcs = torch.cat(samples, dim=0)
+    images = torch.cat(images, dim=0)
+    masked = torch.cat(masked, dim=0)
+
+    B, V, N, C = ref_pcs.shape
+
+
+    torch.save(ref_pcs.reshape(B,V, N, C), os.path.join(save_dir, 'recon_gt.pth'))
+    # torch.save(images.reshape(B,V, *images.shape[2:]), os.path.join(save_dir, 'recon_depth.pth'))
+    torch.save(masked.reshape(B,V, *masked.shape[2:]), os.path.join(save_dir, 'recon_masked.pth'))
+    # Compute metrics
+    results = EMD_CD(sample_pcs.reshape(B*V, N, C),
+                     ref_pcs.reshape(B*V, N, C), opt.batch_size, reduced=False)
+
+    results = {ky: val.reshape(B,V) if val.shape == torch.Size([B*V,]) else val for ky, val in results.items()}
+
+    pprint({key: val.mean().item() for key, val in results.items()})
+    logger.info({key: val.mean().item() for key, val in results.items()})
+
+    results['pc'] = sample_pcs
+    torch.save(results, os.path.join(save_dir, 'ours_results.pth'))
+
+
+
+def generate_multimodal(opt, netE, save_dir, logger):
+    test_dataset = get_svr_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.classes)
+    # _, test_dataset =  get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=True)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+
+        gt_all = data['test_points']
+        x_all = data['sv_points']
+        img_all = data['image']
+
+        Path(os.path.join(save_dir, 'x_%03d'%k)).mkdir(exist_ok=True, parents=True)
+        Path(os.path.join(save_dir, 'x_ply_%03d' % k)).mkdir(exist_ok=True, parents=True)
+
+        for v in range(5):
+            x = x_all.transpose(1, 2).contiguous()
+            img = img_all
+
+            m, s = data['mean'].float(), data['std'].float()
+
+            recon = netE.gen_samples(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda',
+                                     clip_denoised=False).detach().cpu()
+
+            recon = recon.transpose(1, 2).contiguous()
+            x = x.transpose(1, 2).contiguous()
+
+            for p, d in enumerate(zip(list(gt_all), list(recon), list(x), list(img))):
+
+                im = np.fliplr(np.flipud(d[-1]))
+                plt.imsave(os.path.join(save_dir, 'depth_%03d_%03d.png'%(i,p)), im, cmap='gray')
+                write_to_xml_batch(os.path.join(save_dir, 'x_%03d_%03d'%(i,p), 'mode_%03d'%v), (torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
+
+                export_to_pc_batch(os.path.join(save_dir, 'x_ply_%03d_%03d'%(i,p), 'mode_%03d'%v),(torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
+
+
+def main(opt):
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+    logger = setup_logging(output_dir)
+
+    outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    netE = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.cuda:
+        netE.cuda()
+
+    def _transform_(m):
+        return nn.parallel.DataParallel(m)
+
+    netE = netE.cuda()
+    netE.multi_gpu_wrapper(_transform_)
+
+    netE.eval()
+
+    ckpts = [os.path.join(opt.ckpt_dir, f) for f in os.listdir(opt.ckpt_dir) if f.endswith('.pth')]
+
+    with torch.no_grad():
+        for ckpt in reversed(sorted(ckpts, key=lambda x: int(x.strip('.pth').split('_')[-1]) )):
+
+            opt.netE = ckpt
+            logger.info("Resume Path:%s" % opt.netE)
+
+            resumed_param = torch.load(opt.netE)
+            netE.load_state_dict(resumed_param['model_state'])
+
+
+            if opt.eval_recon_mvr:
+                # Evaluate generation
+                evaluate_recon_mvr(opt, netE, outf_syn, logger)
+
+            if opt.generate_multimodal:
+
+                generate_multimodal(opt, netE, outf_syn, logger)
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataroot_pc', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
+    parser.add_argument('--dataroot_sv', default='/viscam/u/alexzhou907/01DATA/shapenet/shapenet_mit_preprocessed',
+                        help='input batch size')
+    parser.add_argument('--classes', default=['airplane'])
+
+    parser.add_argument('--batch_size', type=int, default=8, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--eval_recon_mvr', default=True)
+    parser.add_argument('--generate_multimodal', default=False)
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=200)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+
+    # constrain function
+    parser.add_argument('--constrain_eps', default=0.2)
+    parser.add_argument('--constrain_steps', type=int, default=1)
+
+    parser.add_argument('--ckpt_dir', default='/viscam/u/alexzhou907/research/diffusion/shape_completion/output/airplane_ckpt/', help="path to netE (to continue training)")
+
+    '''eval'''
+
+    parser.add_argument('--eval_path',
+                        default='/viscam/u/alexzhou907/research/diffusion/shapenet/output/test/2020-10-10-20-11-46/syn/epoch_2799_samples.pth')
+
+    parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
+
+    parser.add_argument('--gpu', type=int, default=0, metavar='S', help='gpu id (default: 0)')
+
+    opt = parser.parse_args()
+
+    if torch.cuda.is_available():
+        opt.cuda = True
+    else:
+        opt.cuda = False
+
+    return opt
+if __name__ == '__main__':
+    opt = parse_args()
+    set_seed(opt)
+
+    main(opt)

shape_completion/test_table.py

@@ -0,0 +1,764 @@
+
+from pprint import pprint
+from metrics.evaluation_metrics import compute_all_metrics, EMD_CD
+
+import torch.nn as nn
+import torch.utils.data
+
+import argparse
+from torch.distributions import Normal
+from utils.visualize import *
+from utils.file_utils import *
+from utils.mitsuba_renderer import write_to_xml_batch
+from model.pvcnn_completion import PVCNN2Base
+
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+from datasets.shapenet_data_sv import *
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+    def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
+        imgs = [img_t]
+        for t in reversed(range(0,total_steps)):
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised,
+                                  return_pred_xstart=False)
+            if t % freq == 0 or t == total_steps-1:
+                imgs.append(img_t)
+
+        assert imgs[-1].shape == shape
+        return imgs
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+
+#############################################################################
+def get_pc_dataset(pc_dataroot, mesh_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    return tr_dataset
+
+def get_svr_dataset(pc_dataroot, mesh_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Singleview_Points(root_pc=pc_dataroot, root_mesh=mesh_root,
+                                            cache=os.path.join(mesh_root, '../cache'), split='val',
+        categories=category,
+        radius=3, elev=-89, azim=180, img_size=512, focal_length=1000,
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return te_dataset
+
+def get_mvr_dataset(pc_dataroot, views_root, npoints,category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=pc_dataroot,
+        categories=category, split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    te_dataset = ShapeNet_Multiview_Points(root_pc=pc_dataroot, root_views=views_root,
+                                            cache=os.path.join(pc_dataroot, '../cache'), split='val',
+        categories=category,
+        npoints=npoints, sv_samples=200,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return te_dataset
+
+
+def evaluate_recon_mvr(opt, netE, save_dir, logger):
+    test_dataset = get_mvr_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.classes)
+    # _, test_dataset =  get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=True)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+    ref = []
+    samples = []
+    images = []
+    masked = []
+    k = 0
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+
+        gt_all = data['test_points']
+        x_all = data['sv_points']
+        # img_all = data['image']
+
+
+        B,V,N,C = x_all.shape
+        gt_all = gt_all[:,None,:,:].expand(-1, V, -1,-1)
+
+        x = x_all.reshape(B * V, N, C).transpose(1, 2).contiguous()
+        # img = img_all.reshape(B * V, *img_all.shape[2:])
+
+        m, s = data['mean'].float(), data['std'].float()
+
+        recon = netE.gen_samples(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda',
+                                      clip_denoised=False).detach().cpu()
+
+        recon = recon.transpose(1, 2).contiguous()
+        x = x.transpose(1, 2).contiguous()
+
+
+        x_adj = x.reshape(B,V,N,C)* s + m
+        recon_adj = recon.reshape(B,V,N,C)* s + m
+        # img = img.reshape(B,V,*img.shape[1:])
+
+        ref.append( gt_all * s + m)
+        masked.append(x_adj[:,:,:test_dataloader.dataset.sv_samples,:])
+        samples.append(recon_adj)
+        # images.append(img)
+
+    ref_pcs = torch.cat(ref, dim=0)
+    sample_pcs = torch.cat(samples, dim=0)
+    # images = torch.cat(images, dim=0)
+    masked = torch.cat(masked, dim=0)
+
+    B, V, N, C = ref_pcs.shape
+
+
+    torch.save(ref_pcs.reshape(B,V, N, C), os.path.join(save_dir, 'recon_gt.pth'))
+    # torch.save(images.reshape(B,V, *images.shape[2:]), os.path.join(save_dir, 'recon_depth.pth'))
+    torch.save(masked.reshape(B,V, *masked.shape[2:]), os.path.join(save_dir, 'recon_masked.pth'))
+    # Compute metrics
+    results = EMD_CD(sample_pcs.reshape(B*V, N, C),
+                     ref_pcs.reshape(B*V, N, C), opt.batch_size, reduced=False)
+
+    results = {ky: val.reshape(B,V) if val.shape == torch.Size([B*V,]) else val for ky, val in results.items()}
+
+    pprint({key: val.mean().item() for key, val in results.items()})
+    logger.info({key: val.mean().item() for key, val in results.items()})
+
+    results['pc'] = sample_pcs
+    torch.save(results, os.path.join(save_dir, 'ours_results.pth'))
+
+    del ref_pcs, masked, results
+
+def evaluate_saved(opt, netE, save_dir, logger):
+    ours_base = '/viscam/u/alexzhou907/research/diffusion/shape_completion/output/test_chair/2020-11-04-02-10-38/syn'
+
+    gt_pth = ours_base + '/recon_gt.pth'
+    ours_pth = ours_base + '/ours_results.pth'
+    gt = torch.load(gt_pth).permute(1,0,2,3)
+    ours = torch.load(ours_pth)['pc'].permute(1,0,2,3)
+
+    all_res = {}
+    for i, (gt_, ours_) in enumerate(zip(gt, ours)):
+        results = compute_all_metrics(gt_, ours_, opt.batch_size)
+
+        for key, val in results.items():
+            if i == 0:
+                all_res[key] = val
+            else:
+                all_res[key] += val
+        pprint(results)
+    for key, val in all_res.items():
+        all_res[key] = val / gt.shape[0]
+
+    pprint({key: val.mean().item() for key, val in all_res.items()})
+
+
+def generate_multimodal(opt, netE, save_dir, logger):
+    test_dataset = get_svr_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.classes)
+    # _, test_dataset =  get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=True)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
+                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
+
+    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Reconstructing Samples'):
+
+        gt_all = data['test_points']
+        x_all = data['sv_points']
+        img_all = data['image']
+
+        for v in range(6):
+            x = x_all.transpose(1, 2).contiguous()
+            img = img_all
+
+            m, s = data['mean'].float(), data['std'].float()
+
+            recon = netE.gen_samples(x[:, :, :opt.svpoints].cuda(), x[:, :, opt.svpoints:].shape, 'cuda',
+                                     clip_denoised=False).detach().cpu()
+
+            recon = recon.transpose(1, 2).contiguous()
+            x = x.transpose(1, 2).contiguous()
+
+            for p, d in enumerate(zip(list(gt_all), list(recon), list(x), list(img))):
+
+                im = np.fliplr(np.flipud(d[-1]))
+                plt.imsave(os.path.join(save_dir, 'depth_%03d_%03d.png'%(i,p)), im, cmap='gray')
+                write_to_xml_batch(os.path.join(save_dir, 'x_%03d_%03d'%(i,p), 'mode_%03d'%v), (torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy(), cat='chair')
+
+                export_to_pc_batch(os.path.join(save_dir, 'x_ply_%03d_%03d'%(i,p), 'mode_%03d'%v),(torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
+
+
+def redwood_demo(opt, netE, save_dir, logger):
+    import open3d as o3d
+    pth = "/viscam/u/alexzhou907/01DATA/redwood/01605_sample_1.ply"
+    pth_gt = "/viscam/u/alexzhou907/01DATA/redwood/01605_pc_gt.ply"
+
+    points = np.asarray(o3d.io.read_point_cloud(pth).points)
+
+    gt_points = np.asarray(o3d.io.read_point_cloud(pth_gt).points)
+
+    np.save('gt.npy', gt_points[np.random.choice(gt_points.shape[0], size=opt.npoints, replace=False)])
+
+    write_to_xml_batch(os.path.join(save_dir, 'xml_gt'),
+                       gt_points[np.random.choice(gt_points.shape[0], size=opt.npoints, replace=False)][None], cat='table')
+
+    test_dataset = get_pc_dataset(opt.dataroot_pc, opt.dataroot_sv,
+                                      opt.npoints, opt.classes)
+
+    m, s = torch.from_numpy(test_dataset[0]['mean']).float(), torch.from_numpy(test_dataset[0]['std']).float()
+
+    x = torch.from_numpy(points[np.random.choice(points.shape[0], size=opt.npoints, replace=False)]).float()
+
+    x = (x-m)/s
+
+
+    x = x[None].transpose(1,2).cuda()
+
+    res = []
+    for k in range(20):
+        recon = netE.gen_samples(x[:,:,:opt.svpoints], x[:,:,opt.svpoints:].shape, 'cuda',
+                                 clip_denoised=False).detach().cpu()
+        recon = recon.transpose(1, 2).contiguous()
+        recon = recon * s+ m
+        res.append(recon)
+    res = torch.cat(res, dim=0)
+
+    write_to_xml_batch(os.path.join(save_dir, 'xml'),
+                       (res).numpy(), cat='table')
+
+    export_to_pc_batch(os.path.join(save_dir, 'ply'),
+                       (res).numpy())
+
+    torch.save(res, os.path.join(save_dir, 'redwood_demo.pth'))
+
+    exit()
+
+def main(opt):
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+    logger = setup_logging(output_dir)
+
+    outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    netE = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.cuda:
+        netE.cuda()
+
+    def _transform_(m):
+        return nn.parallel.DataParallel(m)
+
+    netE = netE.cuda()
+    netE.multi_gpu_wrapper(_transform_)
+
+    netE.eval()
+
+    ckpts = [os.path.join(opt.ckpt_dir, f) for f in os.listdir(opt.ckpt_dir) if f.endswith('.pth')]
+
+    with torch.no_grad():
+        for ckpt in reversed(sorted(ckpts, key=lambda x: int(x.strip('.pth').split('_')[-1]) )):
+
+            opt.netE = ckpt
+            logger.info("Resume Path:%s" % opt.netE)
+
+            resumed_param = torch.load(opt.netE)
+            netE.load_state_dict(resumed_param['model_state'])
+
+
+            if opt.eval_recon_mvr:
+                # Evaluate generation
+                evaluate_recon_mvr(opt, netE, outf_syn, logger)
+
+            if opt.generate_multimodal:
+
+                generate_multimodal(opt, netE, outf_syn, logger)
+
+            if opt.eval_saved:
+                evaluate_saved(opt, netE, outf_syn, logger)
+
+            if opt.eval_redwood:
+                redwood_demo(opt, netE, outf_syn, logger)
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataroot_pc', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
+    parser.add_argument('--dataroot_sv', default='/viscam/u/alexzhou907/01DATA/shapenet/shapenet_mit_preprocessed',
+                        help='input batch size')
+    parser.add_argument('--classes', default=['table'])
+
+    parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--eval_recon_mvr', default=False)
+    parser.add_argument('--generate_multimodal', default=False)
+    parser.add_argument('--eval_saved', default=False)
+    parser.add_argument('--eval_redwood', default=True)
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=200)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+
+    # constrain function
+    parser.add_argument('--constrain_eps', default=0.2)
+    parser.add_argument('--constrain_steps', type=int, default=1)
+
+    parser.add_argument('--ckpt_dir', default='/viscam/u/alexzhou907/research/diffusion/shape_completion/output/9_res32_pc_table_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do/2020-12-16-14-09-50', help="path to netE (to continue training)")
+
+    '''eval'''
+
+    parser.add_argument('--eval_path',
+                        default='/viscam/u/alexzhou907/research/diffusion/shapenet/output/test/2020-10-10-20-11-46/syn/epoch_2799_samples.pth')
+
+    parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
+
+    parser.add_argument('--gpu', type=int, default=0, metavar='S', help='gpu id (default: 0)')
+
+    opt = parser.parse_args()
+
+    if torch.cuda.is_available():
+        opt.cuda = True
+    else:
+        opt.cuda = False
+
+    return opt
+if __name__ == '__main__':
+    opt = parse_args()
+
+    main(opt)

shape_completion/train_completion.py

@@ -0,0 +1,841 @@
+import torch.multiprocessing as mp
+import torch.nn as nn
+import torch.optim as optim
+import torch.utils.data
+
+import argparse
+from model.unet import get_model
+from torch.distributions import Normal
+
+from utils.file_utils import *
+from utils.visualize import *
+from model.pvcnn_completion import PVCNN2Base
+import torch.distributed as dist
+from datasets.shapenet_data_pc import ShapeNet15kPointClouds
+from datasets.shapenet_data_sv import ShapeNet_Multiview_Points
+'''
+some utils
+'''
+def rotation_matrix(axis, theta):
+    """
+    Return the rotation matrix associated with counterclockwise rotation about
+    the given axis by theta radians.
+    """
+    axis = np.asarray(axis)
+    axis = axis / np.sqrt(np.dot(axis, axis))
+    a = np.cos(theta / 2.0)
+    b, c, d = -axis * np.sin(theta / 2.0)
+    aa, bb, cc, dd = a * a, b * b, c * c, d * d
+    bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d
+    return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)],
+                     [2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)],
+                     [2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]])
+
+def rotate(vertices, faces):
+    '''
+    vertices: [numpoints, 3]
+    '''
+    M = rotation_matrix([0, 1, 0], np.pi / 2).transpose()
+    N = rotation_matrix([1, 0, 0], -np.pi / 4).transpose()
+    K = rotation_matrix([0, 0, 1], np.pi).transpose()
+
+    v, f = vertices[:,[1,2,0]].dot(M).dot(N).dot(K), faces[:,[1,2,0]]
+    return v, f
+
+def norm(v, f):
+    v = (v - v.min())/(v.max() - v.min()) - 0.5
+
+    return v, f
+
+def getGradNorm(net):
+    pNorm = torch.sqrt(sum(torch.sum(p ** 2) for p in net.parameters()))
+    gradNorm = torch.sqrt(sum(torch.sum(p.grad ** 2) for p in net.parameters()))
+    return pNorm, gradNorm
+
+
+def weights_init(m):
+    """
+    xavier initialization
+    """
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1 and m.weight is not None:
+        torch.nn.init.xavier_normal_(m.weight)
+    elif classname.find('BatchNorm') != -1:
+        m.weight.data.normal_()
+        m.bias.data.fill_(0)
+
+'''
+models
+'''
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    KL divergence between normal distributions parameterized by mean and log-variance.
+    """
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
+                + (mean1 - mean2)**2 * torch.exp(-logvar2))
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    # Assumes data is integers [0, 1]
+    assert x.shape == means.shape == log_scales.shape
+    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
+
+    centered_x = x - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 0.5)
+    cdf_plus = px0.cdf(plus_in)
+    min_in = inv_stdv * (centered_x - .5)
+    cdf_min = px0.cdf(min_in)
+    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
+    log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min,  torch.ones_like(cdf_min)*1e-12))
+    cdf_delta = cdf_plus - cdf_min
+
+    log_probs = torch.where(
+    x < 0.001, log_cdf_plus,
+    torch.where(x > 0.999, log_one_minus_cdf_min,
+             torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
+    assert log_probs.shape == x.shape
+    return log_probs
+
+class GaussianDiffusion:
+    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
+        self.loss_type = loss_type
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        assert isinstance(betas, np.ndarray)
+        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
+        assert (betas > 0).all() and (betas <= 1).all()
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.sv_points = sv_points
+        # initialize twice the actual length so we can keep running for eval
+        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
+        alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
+
+        self.betas = torch.from_numpy(betas).float()
+        self.alphas_cumprod = alphas_cumprod.float()
+        self.alphas_cumprod_prev = alphas_cumprod_prev.float()
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
+        self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
+        self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
+        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
+
+        betas = torch.from_numpy(betas).float()
+        alphas = torch.from_numpy(alphas).float()
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.posterior_variance = posterior_variance
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
+        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
+
+    @staticmethod
+    def _extract(a, t, x_shape):
+        """
+        Extract some coefficients at specified timesteps,
+        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
+        """
+        bs, = t.shape
+        assert x_shape[0] == bs
+        out = torch.gather(a, 0, t)
+        assert out.shape == torch.Size([bs])
+        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
+
+
+
+    def q_mean_variance(self, x_start, t):
+        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+        variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
+        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data (t == 0 means diffused for 1 step)
+        """
+        if noise is None:
+            noise = torch.randn(x_start.shape, device=x_start.device)
+        assert noise.shape == x_start.shape
+        return (
+                self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+                self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
+        )
+
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+                self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
+                self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
+        )
+        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
+        posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
+        assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
+                x_start.shape[0])
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+
+    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
+
+        model_output = denoise_fn(data, t)[:,:,self.sv_points:]
+
+
+        if self.model_var_type in ['fixedsmall', 'fixedlarge']:
+            # below: only log_variance is used in the KL computations
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
+                'fixedlarge': (self.betas.to(data.device),
+                               torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
+                'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
+            }[self.model_var_type]
+            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
+            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
+        else:
+            raise NotImplementedError(self.model_var_type)
+
+        if self.model_mean_type == 'eps':
+            x_recon = self._predict_xstart_from_eps(data[:,:,self.sv_points:], t=t, eps=model_output)
+
+
+            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:,:,self.sv_points:], t=t)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+
+        assert model_mean.shape == x_recon.shape
+        assert model_variance.shape == model_log_variance.shape
+        if return_pred_xstart:
+            return model_mean, model_variance, model_log_variance, x_recon
+        else:
+            return model_mean, model_variance, model_log_variance
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+                self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
+                self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
+        )
+
+    ''' samples '''
+
+    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
+        """
+        Sample from the model
+        """
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
+                                                                 return_pred_xstart=True)
+        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)
+
+        # no noise when t == 0
+        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))
+
+        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
+        sample = torch.cat([data[:, :, :self.sv_points], sample], dim=-1)
+        return (sample, pred_xstart) if return_pred_xstart else sample
+
+
+    def p_sample_loop(self, partial_x, denoise_fn, shape, device,
+                      noise_fn=torch.randn, clip_denoised=True, keep_running=False):
+        """
+        Generate samples
+        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
+
+        """
+
+        assert isinstance(shape, (tuple, list))
+        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
+        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised, return_pred_xstart=False)
+
+        assert img_t[:,:,self.sv_points:].shape == shape
+        return img_t
+
+    def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
+                                 noise_fn=torch.randn,clip_denoised=True, keep_running=False):
+        """
+        Generate samples, returning intermediate images
+        Useful for visualizing how denoised images evolve over time
+        Args:
+          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
+            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
+        """
+        assert isinstance(shape, (tuple, list))
+
+        total_steps =  self.num_timesteps if not keep_running else len(self.betas)
+
+        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
+        imgs = [img_t]
+        for t in reversed(range(0,total_steps)):
+
+            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
+            img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
+                                  clip_denoised=clip_denoised,
+                                  return_pred_xstart=False)
+            if t % freq == 0 or t == total_steps-1:
+                imgs.append(img_t)
+
+        assert imgs[-1].shape == shape
+        return imgs
+
+    '''losses'''
+
+    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start[:,:,self.sv_points:], x_t=data_t[:,:,self.sv_points:], t=t)
+        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
+            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
+
+        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
+        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.)
+
+        return (kl, pred_xstart) if return_pred_xstart else kl
+
+    def p_losses(self, denoise_fn, data_start, t, noise=None):
+        """
+        Training loss calculation
+        """
+        B, D, N = data_start.shape
+        assert t.shape == torch.Size([B])
+
+        if noise is None:
+            noise = torch.randn(data_start[:,:,self.sv_points:].shape, dtype=data_start.dtype, device=data_start.device)
+
+        data_t = self.q_sample(x_start=data_start[:,:,self.sv_points:], t=t, noise=noise)
+
+        if self.loss_type == 'mse':
+            # predict the noise instead of x_start. seems to be weighted naturally like SNR
+            eps_recon = denoise_fn(torch.cat([data_start[:,:,:self.sv_points], data_t], dim=-1), t)[:,:,self.sv_points:]
+
+            losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
+        elif self.loss_type == 'kl':
+            losses = self._vb_terms_bpd(
+                denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
+                return_pred_xstart=False)
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        assert losses.shape == torch.Size([B])
+        return losses
+
+    '''debug'''
+
+    def _prior_bpd(self, x_start):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
+            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
+            kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
+                                 mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
+            assert kl_prior.shape == x_start.shape
+            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
+
+    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
+
+        with torch.no_grad():
+            B, T = x_start.shape[0], self.num_timesteps
+
+            vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
+            for t in reversed(range(T)):
+
+                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
+                # Calculate VLB term at the current timestep
+                data_t = torch.cat([x_start[:, :, :self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points:], t=t_b)], dim=-1)
+                new_vals_b, pred_xstart = self._vb_terms_bpd(
+                    denoise_fn, data_start=x_start, data_t=data_t, t=t_b,
+                    clip_denoised=clip_denoised, return_pred_xstart=True)
+                # MSE for progressive prediction loss
+                assert pred_xstart.shape == x_start[:, :, self.sv_points:].shape
+                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points:]) ** 2).mean(dim=list(range(1, len(pred_xstart.shape))))
+                assert new_vals_b.shape == new_mse_b.shape ==  torch.Size([B])
+                # Insert the calculated term into the tensor of all terms
+                mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
+                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
+                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
+                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
+
+            prior_bpd_b = self._prior_bpd(x_start[:,:,self.sv_points:])
+            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
+            assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
+                   total_bpd_b.shape == prior_bpd_b.shape ==  torch.Size([B])
+            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
+
+
+class PVCNN2(PVCNN2Base):
+    sa_blocks = [
+        ((32, 2, 32), (1024, 0.1, 32, (32, 64))),
+        ((64, 3, 16), (256, 0.2, 32, (64, 128))),
+        ((128, 3, 8), (64, 0.4, 32, (128, 256))),
+        (None, (16, 0.8, 32, (256, 256, 512))),
+    ]
+    fp_blocks = [
+        ((256, 256), (256, 3, 8)),
+        ((256, 256), (256, 3, 8)),
+        ((256, 128), (128, 2, 16)),
+        ((128, 128, 64), (64, 2, 32)),
+    ]
+
+    def __init__(self, num_classes, sv_points, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
+                 voxel_resolution_multiplier=1):
+        super().__init__(
+            num_classes=num_classes, sv_points=sv_points, embed_dim=embed_dim, use_att=use_att,
+            dropout=dropout, extra_feature_channels=extra_feature_channels,
+            width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
+        super(Model, self).__init__()
+        self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type, args.svpoints)
+
+        self.model = PVCNN2(num_classes=args.nc, sv_points=args.svpoints, embed_dim=args.embed_dim, use_att=args.attention,
+                            dropout=args.dropout, extra_feature_channels=0)
+
+    def prior_kl(self, x0):
+        return self.diffusion._prior_bpd(x0)
+
+    def all_kl(self, x0, clip_denoised=True):
+        total_bpd_b, vals_bt, prior_bpd_b, mse_bt =  self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
+
+        return {
+            'total_bpd_b': total_bpd_b,
+            'terms_bpd': vals_bt,
+            'prior_bpd_b': prior_bpd_b,
+            'mse_bt':mse_bt
+        }
+
+
+    def _denoise(self, data, t):
+        B, D,N= data.shape
+        assert data.dtype == torch.float
+        assert t.shape == torch.Size([B]) and t.dtype == torch.int64
+
+        out = self.model(data, t)
+
+        return out
+
+    def get_loss_iter(self, data, noises=None):
+        B, D, N = data.shape
+        t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
+
+        if noises is not None:
+            noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
+
+        losses = self.diffusion.p_losses(
+            denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
+        assert losses.shape == t.shape == torch.Size([B])
+        return losses
+
+    def gen_samples(self, partial_x, shape, device, noise_fn=torch.randn,
+                    clip_denoised=True,
+                    keep_running=False):
+        return self.diffusion.p_sample_loop(partial_x, self._denoise, shape=shape, device=device, noise_fn=noise_fn,
+                                            clip_denoised=clip_denoised,
+                                            keep_running=keep_running)
+
+
+    def train(self):
+        self.model.train()
+
+    def eval(self):
+        self.model.eval()
+
+    def multi_gpu_wrapper(self, f):
+        self.model = f(self.model)
+
+
+def get_betas(schedule_type, b_start, b_end, time_num):
+    if schedule_type == 'linear':
+        betas = np.linspace(b_start, b_end, time_num)
+    elif schedule_type == 'warm0.1':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.1)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.2':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.2)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    elif schedule_type == 'warm0.5':
+
+        betas = b_end * np.ones(time_num, dtype=np.float64)
+        warmup_time = int(time_num * 0.5)
+        betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
+    else:
+        raise NotImplementedError(schedule_type)
+    return betas
+
+
+def get_dataset(dataroot_pc, dataroot_sv, npoints, svpoints, category):
+    tr_dataset = ShapeNet15kPointClouds(root_dir=dataroot_pc,
+        categories=[category], split='train',
+        tr_sample_size=npoints,
+        te_sample_size=npoints,
+        scale=1.,
+        normalize_per_shape=False,
+        normalize_std_per_axis=False,
+        random_subsample=True)
+    tr_dataset = ShapeNet_Multiview_Points(root_pc=dataroot_pc, root_views=dataroot_sv,
+                                            cache=os.path.join(dataroot_pc, '../cache'), split='train',
+        categories=[category],
+        npoints=npoints, sv_samples=svpoints,
+        all_points_mean=tr_dataset.all_points_mean,
+        all_points_std=tr_dataset.all_points_std,
+    )
+    return tr_dataset
+
+
+def get_dataloader(opt, train_dataset, test_dataset=None):
+
+    if opt.distribution_type == 'multi':
+        train_sampler = torch.utils.data.distributed.DistributedSampler(
+            train_dataset,
+            num_replicas=opt.world_size,
+            rank=opt.rank
+        )
+        if test_dataset is not None:
+            test_sampler = torch.utils.data.distributed.DistributedSampler(
+                test_dataset,
+                num_replicas=opt.world_size,
+                rank=opt.rank
+            )
+        else:
+            test_sampler = None
+    else:
+        train_sampler = None
+        test_sampler = None
+
+    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.bs,sampler=train_sampler,
+                                                   shuffle=train_sampler is None, num_workers=int(opt.workers), drop_last=True)
+
+    if test_dataset is not None:
+        test_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.bs,sampler=test_sampler,
+                                                   shuffle=False, num_workers=int(opt.workers), drop_last=False)
+    else:
+        test_dataloader = None
+
+    return train_dataloader, test_dataloader, train_sampler, test_sampler
+
+
+def train(gpu, opt, output_dir, noises_init):
+
+    set_seed(opt)
+    logger = setup_logging(output_dir)
+    if opt.distribution_type == 'multi':
+        should_diag = gpu==0
+    else:
+        should_diag = True
+    if should_diag:
+        outf_syn, = setup_output_subdirs(output_dir, 'syn')
+
+    if opt.distribution_type == 'multi':
+        if opt.dist_url == "env://" and opt.rank == -1:
+            opt.rank = int(os.environ["RANK"])
+
+        base_rank =  opt.rank * opt.ngpus_per_node
+        opt.rank = base_rank + gpu
+        dist.init_process_group(backend=opt.dist_backend, init_method=opt.dist_url,
+                                world_size=opt.world_size, rank=opt.rank)
+
+        opt.bs = int(opt.bs / opt.ngpus_per_node)
+        opt.workers = 0
+
+        opt.saveIter =  int(opt.saveIter / opt.ngpus_per_node)
+        opt.diagIter = int(opt.diagIter / opt.ngpus_per_node)
+        opt.vizIter = int(opt.vizIter / opt.ngpus_per_node)
+
+
+    ''' data '''
+    train_dataset = get_dataset(opt.dataroot_pc, opt.dataroot_sv, opt.npoints, opt.svpoints,opt.classes)
+    dataloader, _, train_sampler, _ = get_dataloader(opt, train_dataset, None)
+
+
+    '''
+    create networks
+    '''
+
+    betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
+    model = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
+
+    if opt.distribution_type == 'multi':  # Multiple processes, single GPU per process
+        def _transform_(m):
+            return nn.parallel.DistributedDataParallel(
+                m, device_ids=[gpu], output_device=gpu)
+
+        torch.cuda.set_device(gpu)
+        model.cuda(gpu)
+        model.multi_gpu_wrapper(_transform_)
+
+
+    elif opt.distribution_type == 'single':
+        def _transform_(m):
+            return nn.parallel.DataParallel(m)
+        model = model.cuda()
+        model.multi_gpu_wrapper(_transform_)
+
+    elif gpu is not None:
+        torch.cuda.set_device(gpu)
+        model = model.cuda(gpu)
+    else:
+        raise ValueError('distribution_type = multi | single | None')
+
+    if should_diag:
+        logger.info(opt)
+
+    optimizer= optim.Adam(model.parameters(), lr=opt.lr, weight_decay=opt.decay, betas=(opt.beta1, 0.999))
+
+    lr_scheduler = optim.lr_scheduler.ExponentialLR(optimizer, opt.lr_gamma)
+
+    if opt.model != '':
+        ckpt = torch.load(opt.model)
+        model.load_state_dict(ckpt['model_state'])
+        optimizer.load_state_dict(ckpt['optimizer_state'])
+
+    if opt.model != '':
+        start_epoch = torch.load(opt.model)['epoch'] + 1
+    else:
+        start_epoch = 0
+
+    def new_x_chain(x, num_chain):
+        return torch.randn(num_chain, *x.shape[1:], device=x.device)
+
+
+
+    for epoch in range(start_epoch, opt.niter):
+
+        if opt.distribution_type == 'multi':
+            train_sampler.set_epoch(epoch)
+
+        lr_scheduler.step(epoch)
+
+        for i, data in enumerate(dataloader):
+            randind = np.random.choice(20) #20 views
+            x = data['train_points'].transpose(1,2)
+            sv_x = data['sv_points'][:,randind].transpose(1,2)
+
+            sv_x[:,:,opt.svpoints:] = x[:,:,opt.svpoints:]
+            noises_batch = noises_init[data['idx']].transpose(1,2)
+
+            '''
+            train diffusion
+            '''
+
+            if opt.distribution_type == 'multi' or (opt.distribution_type is None and gpu is not None):
+                sv_x = sv_x.cuda(gpu)
+                noises_batch = noises_batch.cuda(gpu)
+            elif opt.distribution_type == 'single':
+                sv_x = sv_x.cuda()
+                noises_batch = noises_batch.cuda()
+
+            loss = model.get_loss_iter(sv_x, noises_batch).mean()
+
+            optimizer.zero_grad()
+            loss.backward()
+            netpNorm, netgradNorm = getGradNorm(model)
+            if opt.grad_clip is not None:
+                torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
+
+            optimizer.step()
+
+
+            if i % opt.print_freq == 0 and should_diag:
+
+                logger.info('[{:>3d}/{:>3d}][{:>3d}/{:>3d}]    loss: {:>10.4f},    '
+                             'netpNorm: {:>10.2f},   netgradNorm: {:>10.2f}     '
+                             .format(
+                        epoch, opt.niter, i, len(dataloader),loss.item(),
+                    netpNorm, netgradNorm,
+                        ))
+
+
+        if (epoch + 1) % opt.diagIter == 0 and should_diag:
+
+            logger.info('Diagnosis:')
+
+            x_range = [x.min().item(), x.max().item()]
+            kl_stats = model.all_kl(sv_x)
+            logger.info('      [{:>3d}/{:>3d}]    '
+                         'x_range: [{:>10.4f}, {:>10.4f}],   '
+                         'total_bpd_b: {:>10.4f},    '
+                         'terms_bpd: {:>10.4f},  '
+                         'prior_bpd_b: {:>10.4f}    '
+                         'mse_bt: {:>10.4f}  '
+                .format(
+                epoch, opt.niter,
+                *x_range,
+                kl_stats['total_bpd_b'].item(),
+                kl_stats['terms_bpd'].item(), kl_stats['prior_bpd_b'].item(), kl_stats['mse_bt'].item()
+            ))
+
+
+
+        if (epoch + 1) % opt.vizIter == 0 and should_diag:
+            logger.info('Generation: eval')
+
+            model.eval()
+            m, s = train_dataset.all_points_mean.reshape(1, -1), train_dataset.all_points_std.reshape(1, -1)
+
+            with torch.no_grad():
+
+                x_gen_eval = model.gen_samples(sv_x[:,:,:opt.svpoints], sv_x[:,:,opt.svpoints:].shape, sv_x.device, clip_denoised=False).detach().cpu()
+
+
+                gen_stats = [x_gen_eval.mean(), x_gen_eval.std()]
+                gen_eval_range = [x_gen_eval.min().item(), x_gen_eval.max().item()]
+
+                logger.info('      [{:>3d}/{:>3d}]  '
+                             'eval_gen_range: [{:>10.4f}, {:>10.4f}]     '
+                             'eval_gen_stats: [mean={:>10.4f}, std={:>10.4f}]      '
+                    .format(
+                    epoch, opt.niter,
+                    *gen_eval_range, *gen_stats,
+                ))
+
+            export_to_pc_batch('%s/epoch_%03d_samples_eval' % (outf_syn, epoch),
+                                    (x_gen_eval.transpose(1, 2)*s+m).numpy()*3)
+
+            export_to_pc_batch('%s/epoch_%03d_ground_truth' % (outf_syn, epoch),
+                               (sv_x.transpose(1, 2).detach().cpu()*s+m).numpy()*3)
+
+            export_to_pc_batch('%s/epoch_%03d_partial' % (outf_syn, epoch),
+                               (sv_x[:,:,:opt.svpoints].transpose(1, 2).detach().cpu()*s+m).numpy()*3)
+
+
+            model.train()
+
+
+
+
+
+
+
+        if (epoch + 1) % opt.saveIter == 0:
+
+            if should_diag:
+
+
+                save_dict = {
+                    'epoch': epoch,
+                    'model_state': model.state_dict(),
+                    'optimizer_state': optimizer.state_dict()
+                }
+
+                torch.save(save_dict, '%s/epoch_%d.pth' % (output_dir, epoch))
+
+
+            if opt.distribution_type == 'multi':
+                dist.barrier()
+                map_location = {'cuda:%d' % 0: 'cuda:%d' % gpu}
+                model.load_state_dict(
+                    torch.load('%s/epoch_%d.pth' % (output_dir, epoch), map_location=map_location)['model_state'])
+
+    dist.destroy_process_group()
+
+def main():
+    opt = parse_args()
+
+    exp_id = os.path.splitext(os.path.basename(__file__))[0]
+    dir_id = os.path.dirname(__file__)
+    output_dir = get_output_dir(dir_id, exp_id)
+    copy_source(__file__, output_dir)
+
+    ''' workaround '''
+
+    train_dataset = get_dataset(opt.dataroot_pc, opt.dataroot_sv, opt.npoints, opt.svpoints,opt.classes)
+    noises_init = torch.randn(len(train_dataset), opt.npoints-opt.svpoints, opt.nc)
+
+    if opt.dist_url == "env://" and opt.world_size == -1:
+        opt.world_size = int(os.environ["WORLD_SIZE"])
+
+    if opt.distribution_type == 'multi':
+        opt.ngpus_per_node = torch.cuda.device_count()
+        opt.world_size = opt.ngpus_per_node * opt.world_size
+        mp.spawn(train, nprocs=opt.ngpus_per_node, args=(opt, output_dir, noises_init))
+    else:
+        train(opt.gpu, opt, output_dir, noises_init)
+
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataroot_pc', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
+    parser.add_argument('--dataroot_sv', default='/viscam/u/alexzhou907/01DATA/shapenet/shapenet_mit_preprocessed',
+                        help='input batch size')
+    parser.add_argument('--category', default='chair')
+
+    parser.add_argument('--bs', type=int, default=48, help='input batch size')
+    parser.add_argument('--workers', type=int, default=16, help='workers')
+    parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
+
+    parser.add_argument('--nc', default=3)
+    parser.add_argument('--npoints', default=2048)
+    parser.add_argument('--svpoints', default=200)
+    '''model'''
+    parser.add_argument('--beta_start', default=0.0001)
+    parser.add_argument('--beta_end', default=0.02)
+    parser.add_argument('--schedule_type', default='linear')
+    parser.add_argument('--time_num', default=1000)
+
+    #params
+    parser.add_argument('--attention', default=True)
+    parser.add_argument('--dropout', default=0.1)
+    parser.add_argument('--embed_dim', type=int, default=64)
+    parser.add_argument('--loss_type', default='mse')
+    parser.add_argument('--model_mean_type', default='eps')
+    parser.add_argument('--model_var_type', default='fixedsmall')
+
+    parser.add_argument('--lr', type=float, default=2e-4, help='learning rate for E, default=0.0002')
+    parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
+    parser.add_argument('--decay', type=float, default=0, help='weight decay for EBM')
+    parser.add_argument('--grad_clip', type=float, default=None, help='weight decay for EBM')
+    parser.add_argument('--lr_gamma', type=float, default=0.998, help='lr decay for EBM')
+
+    parser.add_argument('--model', default='', help="path to model (to continue training)")
+
+
+    '''distributed'''
+    parser.add_argument('--world_size', default=1, type=int,
+                        help='Number of distributed nodes.')
+    parser.add_argument('--dist_url', default='tcp://127.0.0.1:9991', type=str,
+                        help='url used to set up distributed training')
+    parser.add_argument('--dist_backend', default='nccl', type=str,
+                        help='distributed backend')
+    parser.add_argument('--distribution_type', default='single', choices=['multi', 'single', None],
+                        help='Use multi-processing distributed training to launch '
+                             'N processes per node, which has N GPUs. This is the '
+                             'fastest way to use PyTorch for either single node or '
+                             'multi node data parallel training')
+    parser.add_argument('--rank', default=0, type=int,
+                        help='node rank for distributed training')
+    parser.add_argument('--gpu', default=None, type=int,
+                        help='GPU id to use. None means using all available GPUs.')
+
+    '''eval'''
+    parser.add_argument('--saveIter', default=100, help='unit: epoch')
+    parser.add_argument('--diagIter', default=50, help='unit: epoch')
+    parser.add_argument('--vizIter', default=50, help='unit: epoch')
+    parser.add_argument('--print_freq', default=50, help='unit: iter')
+
+    parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
+
+
+    opt = parser.parse_args()
+
+    return opt
+
+if __name__ == '__main__':
+    main()