...

README.md

@@ -1,12 +1,13 @@
 # Shape Generation and Completion Through Point-Voxel Diffusion
+<p float="left">
+  <img src="assets/pvd_teaser.gif" width="80%"/>
+</p>
 
-[Project]() | [Paper]() 
+[Project](https://alexzhou907.github.io/pvd) | [Paper](https://arxiv.org/abs/2104.03670) 
 
-Implementation of 
+Implementation of Shape Generation and Completion Through Point-Voxel Diffusion
 
-## Pretrained Models
-
-Pretrained models can be accessed [here](https://www.dropbox.com/s/a3xydf594fzaokl/cifar10_pretrained.rar?dl=0).
+[Linqi Zhou](https://alexzhou907.github.io), [Yilun Du](https://yilundu.github.io/), [Jiajun Wu](https://jiajunwu.com/)
 
 ## Requirements:
 
@@ -20,24 +21,56 @@ cudatoolkit==10.1
 matplotlib==2.2.5
 tqdm==4.32.1
 open3d==0.9.0
+trimesh=3.7.12
+scipy==1.5.1
 ```
+
+Install PyTorchEMD by
+```
+cd metrics/PyTorchEMD
+python setup.py install
+cp build/**/emd_cuda.cpython-36m-x86_64-linux-gnu.so .
+```
+
 The code was tested on Unbuntu with Titan RTX. 
 
+## Data
 
-## Training on CIFAR-10:
+For generation, we use ShapeNet point cloud, which can be downloaded [here](https://github.com/stevenygd/PointFlow).
+
+For completion, we use ShapeNet rendering provided by [GenRe](https://github.com/xiumingzhang/GenRe-ShapeHD).
+We provide script `convert_cam_params.py` to process the provided data.
+
+For training the model on shape completion, we need camera parameters for each view
+which are not directly available. To obtain these, simply run 
+```bash
+$ python convert_cam_params.py --dataroot DATA_DIR --mitsuba_xml_root XML_DIR
+```
+which will create `..._cam_params.npz` in each provided data folder for each view.
+
+## Pretrained models
+Pretrained models can be downloaded [here](https://drive.google.com/drive/folders/1Q7aSaTr6lqmo8qx80nIm1j28mOHAHGiM?usp=sharing).
+
+## Training:
 
 ```bash
-$ python train_cifar.py
+$ python train_generation.py --category car|chair|airplane
 ```
 
 Please refer to the python file for optimal training parameters.
 
+## Testing:
+
+```bash
+$ python train_generation.py --category car|chair|airplane --model MODEL_PATH
+```
+
 ## 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"/>
+  <img src="assets/cifar_gen.png" width="300"/>
+  <img src="assets/lsun_gen.png" width="300"/>
 </p>
 
 

convert_cam_params.py

@@ -0,0 +1,123 @@
+
+from glob import glob
+import re
+import argparse
+import numpy as np
+from pathlib import Path
+import os
+
+def raw_camparam_from_xml(path, pose="lookAt"):
+    import xml.etree.ElementTree as ET
+    tree = ET.parse(path)
+    elm = tree.find("./sensor/transform/" + pose)
+    camparam = elm.attrib
+    origin = np.fromstring(camparam['origin'], dtype=np.float32, sep=',')
+    target = np.fromstring(camparam['target'], dtype=np.float32, sep=',')
+    up = np.fromstring(camparam['up'], dtype=np.float32, sep=',')
+    height = int(
+        tree.find("./sensor/film/integer[@name='height']").attrib['value'])
+    width = int(
+        tree.find("./sensor/film/integer[@name='width']").attrib['value'])
+
+    camparam = dict()
+    camparam['origin'] = origin
+    camparam['up'] = up
+    camparam['target'] = target
+    camparam['height'] = height
+    camparam['width'] = width
+    return camparam
+
+def get_cam_pos(origin, target, up):
+    inward = origin - target
+    right = np.cross(up, inward)
+    up = np.cross(inward, right)
+    rx = np.cross(up, inward)
+    ry = np.array(up)
+    rz = np.array(inward)
+    rx /= np.linalg.norm(rx)
+    ry /= np.linalg.norm(ry)
+    rz /= np.linalg.norm(rz)
+
+    rot = np.stack([
+        rx,
+        ry,
+        -rz
+    ], axis=0)
+
+
+    aff = np.concatenate([
+        np.eye(3), -origin[:,None]
+    ], axis=1)
+
+
+    ext = np.matmul(rot, aff)
+
+    result = np.concatenate(
+        [ext, np.array([[0,0,0,1]])], axis=0
+    )
+
+
+
+    return result
+
+
+
+def convert_cam_params_all_views(datapoint_dir, dataroot, camera_param_dir):
+    depths = sorted(glob(os.path.join(datapoint_dir, '*depth.png')))
+    cam_ext = ['_'.join(re.sub(dataroot.strip('/'), camera_param_dir.strip('/'), f).split('_')[:-1])+'.xml' for f in depths]
+
+
+    for i, (f, pth) in enumerate(zip(cam_ext, depths)):
+        if not os.path.exists(f):
+            continue
+        params=raw_camparam_from_xml(f)
+        origin, target, up, width, height = params['origin'], params['target'], params['up'],\
+                                            params['width'], params['height']
+
+        ext_matrix = get_cam_pos(origin, target, up)
+
+        #####
+        diag = (0.036 ** 2 + 0.024 ** 2) ** 0.5
+        focal_length = 0.05
+        res = [480, 480]
+        h_relative = (res[1] / res[0])
+        sensor_width = np.sqrt(diag ** 2 / (1 + h_relative ** 2))
+        pix_size = sensor_width / res[0]
+
+        K = np.array([
+            [focal_length / pix_size, 0, (sensor_width / pix_size - 1) / 2],
+            [0, -focal_length / pix_size, (sensor_width * (res[1] / res[0]) / pix_size - 1) / 2],
+            [0, 0, 1]
+        ])
+
+        np.savez(pth.split('depth.png')[0]+ 'cam_params.npz', extr=ext_matrix, intr=K)
+
+
+def main(opt):
+    dataroot_dir = Path(opt.dataroot)
+
+    leaf_subdirs = []
+
+    for dirpath, dirnames, filenames in os.walk(dataroot_dir):
+        if (not dirnames) and opt.mitsuba_xml_root not in dirpath:
+            leaf_subdirs.append(dirpath)
+
+
+
+    for k, dir_ in enumerate(leaf_subdirs):
+        print('Processing dir {}/{}: {}'.format(k, len(leaf_subdirs), dir_))
+
+        convert_cam_params_all_views(dir_, opt.dataroot, opt.mitsuba_xml_root)
+
+
+
+
+
+if __name__ == '__main__':
+    args = argparse.ArgumentParser()
+    args.add_argument('--dataroot', type=str, default='GenReData/')
+    args.add_argument('--mitsuba_xml_root', type=str, default='GenReData/genre-xml_v2')
+
+    opt = args.parse_args()
+
+    main(opt)

datasets/partnet.py

@@ -5,9 +5,7 @@ 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
@@ -181,33 +179,3 @@ class GANdatasetPartNet(Dataset):
 
 
 
-
-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

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

@@ -1,825 +0,0 @@
-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

@@ -1,825 +0,0 @@
-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

@@ -1,822 +0,0 @@
-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

@@ -1,660 +0,0 @@
-
-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

@@ -1,706 +0,0 @@
-
-
-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

@@ -1,681 +0,0 @@
-
-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

@@ -1,753 +0,0 @@
-
-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_partnet_chair.py

@@ -1,599 +0,0 @@
-
-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

@@ -1,599 +0,0 @@
-
-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

@@ -1,681 +0,0 @@
-
-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

@@ -1,764 +0,0 @@
-
-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)

shapenet/test_car.py

@@ -1,905 +0,0 @@
-import torch
-from pprint import pprint
-from metrics.evaluation_metrics import jsd_between_point_cloud_sets as JSD
-from metrics.evaluation_metrics import compute_all_metrics, EMD_CD
-
-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 utils.mitsuba_renderer import write_to_xml_batch
-from model.pvcnn_generation import PVCNN2Base
-
-from tqdm import tqdm
-
-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):
-        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)
-
-        # 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)
-
-
-        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(data)
-            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(data)
-        else:
-            raise NotImplementedError(self.model_var_type)
-
-        if self.model_mean_type == 'eps':
-            x_recon = self._predict_xstart_from_eps(data, t=t, eps=model_output)
-
-            if clip_denoised:
-                x_recon = torch.clamp(x_recon, -.5, .5)
-
-            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data, t=t)
-        else:
-            raise NotImplementedError(self.loss_type)
-
-
-        assert model_mean.shape == x_recon.shape == data.shape
-        assert model_variance.shape == model_log_variance.shape == data.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, use_var=True):
-        """
-        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=data.shape, dtype=data.dtype, device=data.device)
-        assert noise.shape == data.shape
-        # no noise when t == 0
-        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(data.shape) - 1))
-
-        sample = model_mean
-        if use_var:
-            sample = sample + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
-        assert sample.shape == pred_xstart.shape
-        return (sample, pred_xstart) if return_pred_xstart else sample
-
-
-    def p_sample_loop(self, denoise_fn, shape, device,
-                      noise_fn=torch.randn, constrain_fn=lambda x, t:x,
-                      clip_denoised=True, max_timestep=None, keep_running=False):
-        """
-        Generate samples
-        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
-
-        """
-        if max_timestep is None:
-            final_time = self.num_timesteps
-        else:
-            final_time = max_timestep
-
-        assert isinstance(shape, (tuple, list))
-        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
-        for t in reversed(range(0, final_time if not keep_running else len(self.betas))):
-            img_t = constrain_fn(img_t, t)
-            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).detach()
-
-
-        assert img_t.shape == shape
-        return img_t
-
-    def reconstruct(self, x0, t, denoise_fn, noise_fn=torch.randn, constrain_fn=lambda x, t:x):
-
-        assert t >= 1
-
-        t_vec = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.device).fill_(t-1)
-        encoding = self.q_sample(x0, t_vec)
-
-        img_t = encoding
-
-        for k in reversed(range(0,t)):
-            img_t = constrain_fn(img_t, k)
-            t_ = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.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, use_var=True).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, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
-                 voxel_resolution_multiplier=1):
-        super().__init__(
-            num_classes=num_classes, 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)
-
-        self.model = PVCNN2(num_classes=args.nc, 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)
-
-        assert out.shape == torch.Size([B, D, N])
-        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, shape, device, noise_fn=torch.randn, constrain_fn=lambda x, t:x,
-                    clip_denoised=False, max_timestep=None,
-                    keep_running=False):
-        return self.diffusion.p_sample_loop(self._denoise, shape=shape, device=device, noise_fn=noise_fn,
-                                            constrain_fn=constrain_fn,
-                                            clip_denoised=clip_denoised, max_timestep=max_timestep,
-                                            keep_running=keep_running)
-
-    def reconstruct(self, x0, t, constrain_fn=lambda x, t:x):
-
-        return self.diffusion.reconstruct(x0, t, self._denoise, constrain_fn=constrain_fn)
-
-    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_constrain_function(ground_truth, mask, eps, num_steps=1):
-    '''
-
-    :param target_shape_constraint: target voxels
-    :return: constrained x
-    '''
-    # eps_all = list(reversed(np.linspace(0,np.float_power(eps, 1/2), 500)**2))
-    eps_all = list(reversed(np.linspace(0, np.sqrt(eps), 1000)**2 ))
-    def constrain_fn(x, t):
-        eps_ =  eps_all[t] if (t<1000) else 0
-        for _ in range(num_steps):
-            x  = x - eps_ * ((x - ground_truth) * mask)
-
-
-        return x
-    return constrain_fn
-
-
-#############################################################################
-
-def get_dataset(dataroot, npoints,category,use_mask=False):
-    tr_dataset = ShapeNet15kPointClouds(root_dir=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, use_mask = use_mask)
-    te_dataset = ShapeNet15kPointClouds(root_dir=dataroot,
-        categories=category, split='val',
-        tr_sample_size=npoints,
-        te_sample_size=npoints,
-        scale=1.,
-        normalize_per_shape=False,
-        normalize_std_per_axis=False,
-        all_points_mean=tr_dataset.all_points_mean,
-        all_points_std=tr_dataset.all_points_std,
-        use_mask=use_mask
-    )
-    return tr_dataset, te_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=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, 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_Multiview_Points(root_pc=pc_dataroot, root_mesh=mesh_root,
-                                            cache=os.path.join(mesh_root, '../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 get_mvr_dataset_v2(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_gen(opt, ref_pcs, logger):
-
-    if ref_pcs is None:
-        _, test_dataset = get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=False)
-        test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
-                                                      shuffle=False, num_workers=int(opt.workers), drop_last=False)
-        ref = []
-        for data in tqdm(test_dataloader, total=len(test_dataloader), desc='Generating Samples'):
-            x = data['test_points']
-            m, s = data['mean'].float(), data['std'].float()
-
-            ref.append(x*s + m)
-
-        ref_pcs = torch.cat(ref, dim=0).contiguous()
-
-    logger.info("Loading sample path: %s"
-      % (opt.eval_path))
-    sample_pcs = torch.load(opt.eval_path).contiguous()
-
-    logger.info("Generation sample size:%s reference size: %s"
-          % (sample_pcs.size(), ref_pcs.size()))
-
-
-    # Compute metrics
-    results = compute_all_metrics(sample_pcs, ref_pcs, opt.batch_size)
-    results = {k: (v.cpu().detach().item()
-                   if not isinstance(v, float) else v) for k, v in results.items()}
-
-    pprint(results)
-    logger.info(results)
-
-    jsd = JSD(sample_pcs.numpy(), ref_pcs.numpy())
-    pprint('JSD: {}'.format(jsd))
-    logger.info('JSD: {}'.format(jsd))
-
-def evaluate_recon(opt, netE, save_dir, logger):
-    test_dataset = get_mvr_dataset(opt.dataroot, '/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2',
-                                      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'):
-
-        randind = i%24
-        gt_all = data['test_points'][:,randind:randind+1]
-        x_all = data['sv_points'][:,randind:randind+1]
-        mask_all= data['masks'][:,randind:randind+1]
-        img_all = data['image'][:,randind:randind+1]
-
-
-        B,V,N,C = x_all.shape
-
-        x = x_all.reshape(B*V,N,C).transpose(1,2).contiguous()
-        mask = mask_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()
-
-        # visualize_pointcloud_batch(os.path.join(save_dir, 'y_%03d.png'%i), x.transpose(1,2) * s + m, None, None, None)
-        # for t in [10]:
-        # recon = netE.reconstruct2(x.cuda(), mask.cuda(), get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-        #                                                               opt.constrain_steps)).detach().cpu()
-
-        recon = netE.gen_samples(x.shape, 'cuda',
-                                      constrain_fn=get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-                                                                          opt.constrain_steps),
-                                      clip_denoised=False).detach().cpu()
-
-        recon = recon.transpose(1, 2).contiguous()
-        x = x.transpose(1, 2).contiguous()
-
-
-        # for d in zip(list(data['test_points'].reshape(B*V,N,C)), list(recon), list(x), list(torch.zeros_like(x))):
-        #     write_to_xml_batch(os.path.join(save_dir, 'x_%03d'%k), (torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
-        #     visualize_pointcloud_batch(os.path.join(save_dir, 'x_%03d.png'%k), torch.stack(d, dim=0)* s[0:1] + m[0:1], None, None, None)
-        #
-        #     export_to_pc_batch(os.path.join(save_dir, 'x_ply_%03d'%k),(torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
-        #
-        #     k+=1
-
-        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() for key, val in results.items()})
-    logger.info({key: val.mean() for key, val in results.items()})
-
-    results['pc'] = sample_pcs
-    torch.save(results, os.path.join(save_dir, 'ours_results.pth'))
-
-    #
-    # results = compute_all_metrics(sample_pcs, ref_pcs, opt.batch_size)
-    #
-    # results = {k: (v.cpu().detach().item()
-    #                if not isinstance(v, float) else v) for k, v in results.items()}
-    # pprint(results)
-    # logger.info(results)
-def evaluate_recon_mvr(opt, netE, save_dir, logger):
-    test_dataset = get_mvr_dataset_v2(opt.dataroot, '/viscam/u/alexzhou907/01DATA/shapenet/shapenet_mit_preprocessed',
-                                      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']
-        mask_all= data['masks']
-        img_all = data['image']
-
-
-        B,V,N,C = x_all.shape
-        gt_all = gt_all[:,None,:,:].expand(-1, V, -1,-1)
-
-        # visualize_pointcloud_batch(os.path.join(save_dir, 'y_%03d.png'%i), x.transpose(1,2) * s + m, None, None, None)
-        # for t in [10]:
-        # recon = netE.reconstruct2(x.cuda(), mask.cuda(), get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-        #                                                               opt.constrain_steps)).detach().cpu()
-
-        cd_res = []
-        recon_res = []
-        for p in range(5):
-            x = x_all.reshape(B * V, N, C).transpose(1, 2).contiguous()
-            mask = mask_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.shape, 'cuda',
-                                          constrain_fn=get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-                                                                              opt.constrain_steps),
-                                          clip_denoised=False).detach().cpu()
-
-            recon = recon.transpose(1, 2).contiguous()
-            x = x.transpose(1, 2).contiguous()
-
-            cd = (((recon - x)**2)*mask.transpose(1, 2).contiguous()).sum(dim=(1,2))
-
-            cd_res.append(cd)
-            recon_res.append(recon)
-
-        cd_res = torch.stack(cd_res, dim=0)
-        recon_res = torch.stack(recon_res, dim=0)
-        _, argmin = torch.min(cd_res, 0)
-        recon = recon_res[argmin,torch.arange(0,argmin.shape[0])]
-
-        # for d in zip(list(data['test_points'].reshape(B*V,N,C)), list(recon), list(x), list(torch.zeros_like(x))):
-        #     write_to_xml_batch(os.path.join(save_dir, 'x_%03d'%k), (torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
-        #     visualize_pointcloud_batch(os.path.join(save_dir, 'x_%03d.png'%k), torch.stack(d, dim=0)* s[0:1] + m[0:1], None, None, None)
-        #
-        #     export_to_pc_batch(os.path.join(save_dir, 'x_ply_%03d'%k),(torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
-        #
-        #     k+=1
-
-        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'))
-
-    #
-    # results = compute_all_metrics(sample_pcs, ref_pcs, opt.batch_size)
-    #
-    # results = {k: (v.cpu().detach().item()
-    #                if not isinstance(v, float) else v) for k, v in results.items()}
-    # pprint(results)
-    # logger.info(results)
-
-
-
-def generate(netE, opt, logger):
-
-    _, test_dataset = get_dataset(opt.dataroot, opt.npoints, opt.classes)
-
-    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
-                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
-
-    with torch.no_grad():
-
-        samples = []
-        ref = []
-
-        for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Generating Samples'):
-
-            x = data['test_points'].transpose(1,2)
-            m, s = data['mean'].float(), data['std'].float()
-
-            gen = netE.gen_samples(x.shape,
-                                       'cuda', clip_denoised=False).detach().cpu()
-
-            gen = gen.transpose(1,2).contiguous()
-            x = x.transpose(1,2).contiguous()
-
-
-
-            gen = gen * s + m
-            x = x * s + m
-            samples.append(gen)
-            ref.append(x)
-
-            visualize_pointcloud_batch(os.path.join(str(Path(opt.eval_path).parent), 'x.png'), gen[:64], None,
-                                       None, None)
-
-            write_to_xml_batch(os.path.join(str(Path(opt.eval_path).parent), 'xml_samples_%03d'%i), gen[:min(gen.shape[0], 40)].numpy(), default_color='b')
-
-        samples = torch.cat(samples, dim=0)
-        ref = torch.cat(ref, dim=0)
-
-        torch.save(samples, opt.eval_path)
-
-
-
-    return ref
-
-
-def generate_multimodal(opt, netE, save_dir, logger):
-    test_dataset = get_svr_dataset(opt.dataroot, '/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2',
-                                      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)
-
-    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']
-        mask_all= data['masks']
-        img_all = data['image']
-
-
-        # visualize_pointcloud_batch(os.path.join(save_dir, 'y_%03d.png'%i), x.transpose(1,2) * s + m, None, None, None)
-        # for t in [10]:
-        # recon = netE.reconstruct2(x.cuda(), mask.cuda(), get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-        #                                                               opt.constrain_steps)).detach().cpu()
-
-        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()
-            mask = mask_all.transpose(1, 2).contiguous()
-            img = img_all
-
-            m, s = data['mean'].float(), data['std'].float()
-
-            recon = netE.gen_samples(x.shape, 'cuda',
-                                          constrain_fn=get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-                                                                              opt.constrain_steps),
-                                          clip_denoised=False).detach().cpu()
-
-            recon = recon.transpose(1, 2).contiguous()
-            x = x.transpose(1, 2).contiguous()
-
-
-            for d in 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.png'%k), im, cmap='gray')
-                write_to_xml_batch(os.path.join(save_dir, 'x_%03d'%k, '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'%k, 'mode_%03d'%v),(torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
-
-        k+=1
-
-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]) )):
-            #'/viscam/u/alexzhou907/research/diffusion/shapenet/output/66_res32_pc_chair_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do/2020-10-04-01-02-45/epoch_1399.pth'
-            #'/viscam/u/alexzhou907/research/diffusion/shapenet/output/67_res32_pc_car_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do/2020-10-04-01-03-38/epoch_2799.pth'
-            opt.netE = '/viscam/u/alexzhou907/research/diffusion/shapenet/output/67_res32_pc_car_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do/2020-10-04-01-03-38/epoch_2799.pth'#ckpt
-            logger.info("Resume Path:%s" % opt.netE)
-
-            resumed_param = torch.load(opt.netE)
-            netE.load_state_dict(resumed_param['model_state'])
-
-
-            ref = None
-            if opt.generate:
-                epoch = int(os.path.basename(ckpt).split('.')[0].split('_')[-1])
-                opt.eval_path = os.path.join(outf_syn, 'epoch_{}_samples.pth'.format(epoch))
-                Path(opt.eval_path).parent.mkdir(parents=True, exist_ok=True)
-                ref=generate(netE, opt, logger)
-            if opt.eval_gen:
-                # Evaluate generation
-                evaluate_gen(opt, ref, logger)
-
-            if opt.eval_recon:
-                # Evaluate generation
-                evaluate_recon(opt, netE, outf_syn, logger)
-
-            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)
-
-            exit()
-
-def parse_args():
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataroot', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
-    parser.add_argument('--classes', default=['car'])
-
-    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('--generate',default=True)
-    parser.add_argument('--eval_gen', default=True)
-
-    parser.add_argument('--nc', default=3)
-    parser.add_argument('--npoints', default=2048)
-    '''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='',required=True, help="path to netE (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()
-    set_seed(opt)
-
-    main(opt)
-
-    # results in /viscam/u/alexzhou907/research/diffusion/shapenet/output/test_chair

shapenet/test_chair.py

@@ -1,911 +0,0 @@
-import torch
-from pprint import pprint
-from metrics.evaluation_metrics import jsd_between_point_cloud_sets as JSD
-from metrics.evaluation_metrics import compute_all_metrics, EMD_CD
-
-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 utils.mitsuba_renderer import write_to_xml_batch
-from model.pvcnn_generation import PVCNN2Base
-
-from tqdm import tqdm
-
-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):
-        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)
-
-        # 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)
-
-
-        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(data)
-            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(data)
-        else:
-            raise NotImplementedError(self.model_var_type)
-
-        if self.model_mean_type == 'eps':
-            x_recon = self._predict_xstart_from_eps(data, t=t, eps=model_output)
-
-            if clip_denoised:
-                x_recon = torch.clamp(x_recon, -.5, .5)
-
-            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data, t=t)
-        else:
-            raise NotImplementedError(self.loss_type)
-
-
-        assert model_mean.shape == x_recon.shape == data.shape
-        assert model_variance.shape == model_log_variance.shape == data.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, use_var=True):
-        """
-        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=data.shape, dtype=data.dtype, device=data.device)
-        assert noise.shape == data.shape
-        # no noise when t == 0
-        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(data.shape) - 1))
-
-        sample = model_mean
-        if use_var:
-            sample = sample + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
-        assert sample.shape == pred_xstart.shape
-        return (sample, pred_xstart) if return_pred_xstart else sample
-
-
-    def p_sample_loop(self, denoise_fn, shape, device,
-                      noise_fn=torch.randn, constrain_fn=lambda x, t:x,
-                      clip_denoised=True, max_timestep=None, keep_running=False):
-        """
-        Generate samples
-        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
-
-        """
-        if max_timestep is None:
-            final_time = self.num_timesteps
-        else:
-            final_time = max_timestep
-
-        assert isinstance(shape, (tuple, list))
-        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
-        for t in reversed(range(0, final_time if not keep_running else len(self.betas))):
-            img_t = constrain_fn(img_t, t)
-            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).detach()
-
-
-        assert img_t.shape == shape
-        return img_t
-
-    def reconstruct(self, x0, t, denoise_fn, noise_fn=torch.randn, constrain_fn=lambda x, t:x):
-
-        assert t >= 1
-
-        t_vec = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.device).fill_(t-1)
-        encoding = self.q_sample(x0, t_vec)
-
-        img_t = encoding
-
-        for k in reversed(range(0,t)):
-            img_t = constrain_fn(img_t, k)
-            t_ = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.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, use_var=True).detach()
-
-
-        return img_t
-
-    def interpolate(self, x0, x1, t, lamb, denoise_fn, noise_fn=torch.randn, constrain_fn=lambda x, t:x):
-
-        assert t >= 1
-
-        t_vec = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.device).fill_(t-1)
-        encoding0 = self.q_sample(x0, t_vec)
-        encoding1 = self.q_sample(x1, t_vec)
-
-        enc = encoding0 * lamb + (1-lamb) * encoding1
-
-        img_t = enc
-
-        for k in reversed(range(0,t)):
-            img_t = constrain_fn(img_t, k)
-            t_ = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.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, use_var=True).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, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
-                 voxel_resolution_multiplier=1):
-        super().__init__(
-            num_classes=num_classes, 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)
-
-        self.model = PVCNN2(num_classes=args.nc, 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)
-
-        assert out.shape == torch.Size([B, D, N])
-        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, shape, device, noise_fn=torch.randn, constrain_fn=lambda x, t:x,
-                    clip_denoised=False, max_timestep=None,
-                    keep_running=False):
-        return self.diffusion.p_sample_loop(self._denoise, shape=shape, device=device, noise_fn=noise_fn,
-                                            constrain_fn=constrain_fn,
-                                            clip_denoised=clip_denoised, max_timestep=max_timestep,
-                                            keep_running=keep_running)
-
-    def reconstruct(self, x0, t, constrain_fn=lambda x, t:x):
-
-        return self.diffusion.reconstruct(x0, t, self._denoise, constrain_fn=constrain_fn)
-
-    def interpolate(self, x0, x1, t, lamb, constrain_fn=lambda x, t:x):
-
-        return self.diffusion.interpolate(x0, x1, t, lamb, self._denoise, constrain_fn=constrain_fn)
-
-    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_constrain_function(ground_truth, mask, eps, num_steps=1):
-    '''
-
-    :param target_shape_constraint: target voxels
-    :return: constrained x
-    '''
-    # eps_all = list(reversed(np.linspace(0,np.float_power(eps, 1/2), 500)**2))
-    eps_all = list(reversed(np.linspace(0, np.sqrt(eps), 1000)**2 ))
-    def constrain_fn(x, t):
-        eps_ =  eps_all[t] if (t<1000) else 0
-        for _ in range(num_steps):
-            x  = x - eps_ * ((x - ground_truth) * mask)
-
-
-        return x
-    return constrain_fn
-
-
-#############################################################################
-
-def get_dataset(dataroot, npoints,category,use_mask=False):
-    tr_dataset = ShapeNet15kPointClouds(root_dir=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, use_mask = use_mask)
-    te_dataset = ShapeNet15kPointClouds(root_dir=dataroot,
-        categories=category, split='val',
-        tr_sample_size=npoints,
-        te_sample_size=npoints,
-        scale=1.,
-        normalize_per_shape=False,
-        normalize_std_per_axis=False,
-        all_points_mean=tr_dataset.all_points_mean,
-        all_points_std=tr_dataset.all_points_std,
-        use_mask=use_mask
-    )
-    return tr_dataset, te_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, 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_Multiview_Points(root_pc=pc_dataroot, root_mesh=mesh_root,
-                                            cache=os.path.join(mesh_root, '../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 get_mvr_dataset_v2(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_gen(opt, ref_pcs, logger):
-
-    if ref_pcs is None:
-        _, test_dataset = get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=False)
-        test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
-                                                      shuffle=False, num_workers=int(opt.workers), drop_last=False)
-        ref = []
-        for data in tqdm(test_dataloader, total=len(test_dataloader), desc='Generating Samples'):
-            x = data['test_points']
-            m, s = data['mean'].float(), data['std'].float()
-
-            ref.append(x*s + m)
-
-        ref_pcs = torch.cat(ref, dim=0).contiguous()
-
-    logger.info("Loading sample path: %s"
-      % (opt.eval_path))
-    sample_pcs = torch.load(opt.eval_path).contiguous()
-
-    logger.info("Generation sample size:%s reference size: %s"
-          % (sample_pcs.size(), ref_pcs.size()))
-
-
-    # Compute metrics
-    # results = compute_all_metrics(sample_pcs, ref_pcs, opt.batch_size)
-    # results = {k: (v.cpu().detach().item()
-    #                if not isinstance(v, float) else v) for k, v in results.items()}
-    #
-    # pprint(results)
-    # logger.info(results)
-
-    jsd = JSD(sample_pcs.numpy(), ref_pcs.numpy())
-    pprint('JSD: {}'.format(jsd))
-    logger.info('JSD: {}'.format(jsd))
-
-def evaluate_recon(opt, netE, save_dir, logger):
-    test_dataset = get_mvr_dataset(opt.dataroot, '/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2',
-                                      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'):
-
-        randind = i%24
-        gt_all = data['test_points'][:,randind:randind+1]
-        x_all = data['sv_points'][:,randind:randind+1]
-        mask_all= data['masks'][:,randind:randind+1]
-        img_all = data['image'][:,randind:randind+1]
-
-
-        B,V,N,C = x_all.shape
-
-        gt = gt_all.reshape(B*V,N,C).transpose(1,2).contiguous()
-        x = x_all.reshape(B*V,N,C).transpose(1,2).contiguous()
-        mask = mask_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()
-
-        # visualize_pointcloud_batch(os.path.join(save_dir, 'y_%03d.png'%i), x.transpose(1,2) * s + m, None, None, None)
-        # for t in [10]:
-        # t_vec = torch.empty(gt.shape[0], dtype=torch.int64, device='cuda').fill_(80)
-        # recon = netE.diffusion.q_sample(gt.cuda(), t_vec).detach().cpu()
-        recon = netE.gen_samples(x.shape, 'cuda',
-                                      constrain_fn=get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-                                                                          opt.constrain_steps),
-                                      clip_denoised=False).detach().cpu()
-
-        # recon = recon.transpose(1, 2).contiguous()
-        # x = x.transpose(1, 2).contiguous()
-        # gt = gt.transpose(1, 2).contiguous()
-        # write_to_xml_batch(os.path.join(save_dir, 'intermediate_%03d' % i),
-        #                    (recon.detach().cpu() * s[0].squeeze() + m[0].squeeze()).numpy())
-        # write_to_xml_batch(os.path.join(save_dir, 'x_%03d' % i),
-        #                    (gt.detach().cpu() * s[0].squeeze() + m[0].squeeze()).numpy())
-        # write_to_xml_batch(os.path.join(save_dir, 'noise_%03d' % i),
-        #                    (torch.randn_like(gt).detach().cpu() * s[0].squeeze() + m[0].squeeze()).numpy())
-        # for d in zip(list(data['test_points'].reshape(B*V,N,C)), list(recon), list(x), list(torch.zeros_like(x))):
-        #     write_to_xml_batch(os.path.join(save_dir, 'x_%03d'%k), (torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
-        #     visualize_pointcloud_batch(os.path.join(save_dir, 'x_%03d.png'%k), torch.stack(d, dim=0)* s[0:1] + m[0:1], None, None, None)
-        #
-        #     k+=1
-
-        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'))
-
-    #
-    # results = compute_all_metrics(sample_pcs, ref_pcs, opt.batch_size)
-    #
-    # results = {k: (v.cpu().detach().item()
-    #                if not isinstance(v, float) else v) for k, v in results.items()}
-    # pprint(results)
-    # logger.info(results)
-
-def evaluate_recon_mvr(opt, netE, save_dir, logger):
-    test_dataset = get_mvr_dataset_v2(opt.dataroot, '/viscam/u/alexzhou907/01DATA/shapenet/shapenet_mit_preprocessed',
-                                      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']
-        mask_all= data['masks']
-        # 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()
-        mask = mask_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.shape, 'cuda',
-                                      constrain_fn=get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-                                                                          opt.constrain_steps),
-                                      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'))
-
-    #
-    # results = compute_all_metrics(sample_pcs, ref_pcs, opt.batch_size)
-    #
-    # results = {k: (v.cpu().detach().item()
-    #                if not isinstance(v, float) else v) for k, v in results.items()}
-    # pprint(results)
-    # logger.info(results)
-
-    del ref_pcs, masked
-
-
-def generate(netE, opt, logger):
-
-    _, test_dataset = get_dataset(opt.dataroot, opt.npoints, opt.classes)
-
-    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
-                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
-
-    with torch.no_grad():
-
-        samples = []
-        ref = []
-
-        for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Generating Samples'):
-
-            x = data['test_points'].transpose(1,2)
-            m, s = data['mean'].float(), data['std'].float()
-
-            gen = netE.gen_samples(x.shape,
-                                       'cuda', clip_denoised=False).detach().cpu()
-
-            gen = gen.transpose(1,2).contiguous()
-            x = x.transpose(1,2).contiguous()
-
-
-
-            gen = gen * s + m
-            x = x * s + m
-            samples.append(gen)
-            ref.append(x)
-
-            visualize_pointcloud_batch(os.path.join(str(Path(opt.eval_path).parent), 'x.png'), gen[:64], None,
-                                       None, None)
-
-            write_to_xml_batch(os.path.join(str(Path(opt.eval_path).parent), 'xml_samples_%03d'%i), gen[:min(gen.shape[0], 40)].numpy(), cat='chair')
-
-        samples = torch.cat(samples, dim=0)
-        ref = torch.cat(ref, dim=0)
-
-        torch.save(samples, opt.eval_path)
-
-
-
-    return ref
-
-
-
-def generate_multimodal(opt, netE, save_dir, logger):
-    test_dataset = get_svr_dataset(opt.dataroot, '/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2',
-                                      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']
-        mask_all= data['masks']
-        img_all = data['image']
-
-
-        # visualize_pointcloud_batch(os.path.join(save_dir, 'y_%03d.png'%i), x.transpose(1,2) * s + m, None, None, None)
-        # for t in [10]:
-        # recon = netE.reconstruct2(x.cuda(), mask.cuda(), get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-        #                                                               opt.constrain_steps)).detach().cpu()
-
-        for v in range(10):
-            x = x_all.transpose(1, 2).contiguous()
-            mask = mask_all.transpose(1, 2).contiguous()
-            img = img_all
-
-            m, s = data['mean'].float(), data['std'].float()
-
-            recon = netE.gen_samples(x.shape, 'cuda',
-                                          constrain_fn=get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-                                                                              opt.constrain_steps),
-                                          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]) )):
-            #'/viscam/u/alexzhou907/research/diffusion/shapenet/output/66_res32_pc_chair_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do/2020-10-04-01-02-45/epoch_1399.pth'
-            #'/viscam/u/alexzhou907/research/diffusion/shapenet/output/67_res32_pc_car_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do/2020-10-04-01-03-38/epoch_2799.pth'
-            opt.netE = '/viscam/u/alexzhou907/research/diffusion/shapenet/output/66_res32_pc_chair_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do_best/2020-10-16-12-23-44/epoch_1799.pth'#ckpt
-            logger.info("Resume Path:%s" % opt.netE)
-
-            resumed_param = torch.load(opt.netE)
-            netE.load_state_dict(resumed_param['model_state'])
-
-
-            ref = None
-            if opt.generate:
-                epoch = int(os.path.basename(ckpt).split('.')[0].split('_')[-1])
-                opt.eval_path = os.path.join(outf_syn, 'epoch_{}_samples.pth'.format(epoch))
-                Path(opt.eval_path).parent.mkdir(parents=True, exist_ok=True)
-                ref=generate(netE, opt, logger)
-            if opt.eval_gen:
-                # Evaluate generation
-                evaluate_gen(opt, ref, logger)
-
-            if opt.eval_recon:
-                # Evaluate generation
-                evaluate_recon(opt, netE, outf_syn, logger)
-
-            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)
-
-
-
-            exit()
-
-def parse_args():
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataroot', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', 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('--generate',default=True)
-    parser.add_argument('--eval_gen', default=False)
-    parser.add_argument('--eval_recon', default=False)
-    parser.add_argument('--eval_recon_mvr', default=False)
-    parser.add_argument('--generate_multimodal', default=False)
-
-    parser.add_argument('--nc', default=3)
-    parser.add_argument('--npoints', default=2048)
-    '''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=.051)
-    parser.add_argument('--constrain_steps', type=int, default=1)
-
-    parser.add_argument('--ckpt_dir', default='/viscam/u/alexzhou907/research/diffusion/shapenet/output/66_res32_pc_chair_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do_best/2020-10-16-12-23-44/', help="path to netE (to continue training)")
-
-    '''eval'''
-
-    parser.add_argument('--eval_path',
-                        default='/viscam/u/alexzhou907/research/diffusion/shapenet/output/test_chair/2020-10-18-13-46-21/syn/epoch_1699_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)
-
-    # results in /viscam/u/alexzhou907/research/diffusion/shapenet/output/test_chair/2020-10-18-13-46-21

shapenet/test_plane.py

@@ -1,925 +0,0 @@
-import torch
-import functools
-from pprint import pprint
-from metrics.evaluation_metrics import jsd_between_point_cloud_sets as JSD
-from metrics.evaluation_metrics import compute_all_metrics, EMD_CD, distChamfer
-
-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 utils.mitsuba_renderer import write_to_xml_batch
-from model.pvcnn_generation import PVCNN2Base
-
-from tqdm import tqdm
-
-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):
-        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)
-
-        # 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)
-
-
-        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(data)
-            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(data)
-        else:
-            raise NotImplementedError(self.model_var_type)
-
-        if self.model_mean_type == 'eps':
-            x_recon = self._predict_xstart_from_eps(data, t=t, eps=model_output)
-
-            if clip_denoised:
-                x_recon = torch.clamp(x_recon, -.5, .5)
-
-            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data, t=t)
-        else:
-            raise NotImplementedError(self.loss_type)
-
-
-        assert model_mean.shape == x_recon.shape == data.shape
-        assert model_variance.shape == model_log_variance.shape == data.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, use_var=True):
-        """
-        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=data.shape, dtype=data.dtype, device=data.device)
-        assert noise.shape == data.shape
-        # no noise when t == 0
-        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(data.shape) - 1))
-
-        sample = model_mean
-        if use_var:
-            sample = sample + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
-        assert sample.shape == pred_xstart.shape
-        return (sample, pred_xstart) if return_pred_xstart else sample
-
-
-    def p_sample_loop(self, denoise_fn, shape, device,
-                      noise_fn=torch.randn, constrain_fn=lambda x, t:x,
-                      clip_denoised=True, max_timestep=None, keep_running=False):
-        """
-        Generate samples
-        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
-
-        """
-        if max_timestep is None:
-            final_time = self.num_timesteps
-        else:
-            final_time = max_timestep
-
-        assert isinstance(shape, (tuple, list))
-        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
-        for t in reversed(range(0, final_time if not keep_running else len(self.betas))):
-            img_t = constrain_fn(img_t, t)
-            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).detach()
-
-
-        assert img_t.shape == shape
-        return img_t
-
-    def reconstruct(self, x0, t, denoise_fn, noise_fn=torch.randn, constrain_fn=lambda x, t:x):
-
-        assert t >= 1
-
-        t_vec = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.device).fill_(t-1)
-        encoding = self.q_sample(x0, t_vec)
-
-        img_t = encoding
-
-        for k in reversed(range(0,t)):
-            img_t = constrain_fn(img_t, k)
-            t_ = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.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, use_var=True).detach()
-
-
-        return img_t
-
-    def reconstruct2(self, x0, mask, denoise_fn, noise_fn=torch.randn, constrain_fn=lambda forward, x, t:x):
-        z = noise_fn(size=x0.shape, dtype=torch.float, device=x0.device)
-
-        for _ in range(10):
-            img_t = z
-            outputs =[None for _ in range(len(self.betas))]
-            for t in reversed(range(0, len(self.betas))):
-
-                t_ = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.device).fill_(t)
-                outputs[t] = img_t.detach().cpu().clone()
-
-                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, use_var=True).detach()
-
-
-            img_t = torch.autograd.Variable(img_t.data, requires_grad=True)
-
-            dist = ((img_t - x0) ** 2 * mask).sum(dim=0).mean()
-            grad = torch.autograd.grad(dist, [img_t])[0].detach()
-
-            print('Dist', dist.detach().cpu().item())
-
-            for t in (range(0, len(outputs))):
-
-                t_ = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.device).fill_(t)
-
-                x = outputs[t].to(x0).requires_grad_()
-
-                y = self.p_sample(denoise_fn=denoise_fn, data=x, t=t_, noise_fn=noise_fn,
-                                  clip_denoised=False, return_pred_xstart=False, use_var=True)
-
-                grad = torch.autograd.grad(y, [x], grad_outputs=grad)[0]
-
-
-            z = x.detach().to(x0) - 0.1 * grad.detach()
-
-        img_t = z
-        for t in reversed(range(0, len(self.betas))):
-
-            t_ = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.device).fill_(t)
-
-            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, use_var=True).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, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
-#                  voxel_resolution_multiplier=1):
-#         super().__init__(
-#             num_classes=num_classes, 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 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, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
-                 voxel_resolution_multiplier=1):
-        super().__init__(
-            num_classes=num_classes, 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)
-
-        self.model = PVCNN2(num_classes=args.nc, 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)
-
-        assert out.shape == torch.Size([B, D, N])
-        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, shape, device, noise_fn=torch.randn, constrain_fn=lambda x, t:x,
-                    clip_denoised=False, max_timestep=None,
-                    keep_running=False):
-        return self.diffusion.p_sample_loop(self._denoise, shape=shape, device=device, noise_fn=noise_fn,
-                                            constrain_fn=constrain_fn,
-                                            clip_denoised=clip_denoised, max_timestep=max_timestep,
-                                            keep_running=keep_running)
-
-    def reconstruct(self, x0, t, constrain_fn=lambda x, t:x):
-
-        return self.diffusion.reconstruct(x0, t, self._denoise, constrain_fn=constrain_fn)
-
-    def reconstruct2(self, x0, mask, constrain_fn):
-
-        return self.diffusion.reconstruct2(x0, mask, self._denoise, constrain_fn=constrain_fn)
-
-    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_constrain_function(ground_truth, mask, eps, num_steps=1):
-    '''
-
-    :param target_shape_constraint: target voxels
-    :return: constrained x
-    '''
-    # eps_all = list(reversed(np.linspace(0,np.float_power(eps, 1/2), 500)**2))
-    eps_all = list(reversed(np.linspace(0, np.sqrt(eps), 500)**2 ))
-    def constrain_fn(x, t):
-        eps_ =  eps_all[t] if (t<500) else 0
-        for _ in range(num_steps):
-            x  = x - eps_ * ((x - ground_truth) * mask)
-
-
-        return x
-
-    # mask_single = mask[0, :, 0]
-    # num = mask_single.sum().int().item()
-    def constrain_fn2(forward, x, t):
-
-        x = torch.autograd.Variable(x.data, requires_grad=True)
-        y = forward(x)
-
-
-
-        dist = ((y - ground_truth)**2 * mask).sum(dim=0).mean()
-        grad = torch.autograd.grad(dist, [x], retain_graph=True)[0]
-        x  = x - eps * (grad)
-
-        print('Dist', dist.detach().cpu().item())
-
-        return x
-    return constrain_fn
-
-
-
-#############################################################################
-
-def get_dataset(dataroot, npoints,category,use_mask=False):
-    tr_dataset = ShapeNet15kPointClouds(root_dir=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, use_mask = use_mask)
-    te_dataset = ShapeNet15kPointClouds(root_dir=dataroot,
-        categories=category, split='val',
-        tr_sample_size=npoints,
-        te_sample_size=npoints,
-        scale=1.,
-        normalize_per_shape=False,
-        normalize_std_per_axis=False,
-        all_points_mean=tr_dataset.all_points_mean,
-        all_points_std=tr_dataset.all_points_std,
-        use_mask=use_mask
-    )
-    return tr_dataset, te_dataset
-
-def get_mvr_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_Multiview_Points(root_pc=pc_dataroot, root_mesh=mesh_root,
-                                            cache=os.path.join(mesh_root, '../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 get_mvr_dataset_v2(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_gen(opt, ref_pcs, logger):
-
-    if ref_pcs is None:
-        _, test_dataset = get_dataset(opt.dataroot, opt.npoints, opt.classes, use_mask=False)
-        test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
-                                                      shuffle=False, num_workers=int(opt.workers), drop_last=False)
-        ref = []
-        for data in tqdm(test_dataloader, total=len(test_dataloader), desc='Generating Samples'):
-            x = data['test_points']
-            m, s = data['mean'].float(), data['std'].float()
-
-            ref.append(x*s + m)
-
-        ref_pcs = torch.cat(ref, dim=0).contiguous()
-
-    logger.info("Loading sample path: %s"
-      % (opt.eval_path))
-    sample_pcs = torch.load(opt.eval_path).contiguous()
-
-    logger.info("Generation sample size:%s reference size: %s"
-          % (sample_pcs.size(), ref_pcs.size()))
-
-
-    # Compute metrics
-    # results = compute_all_metrics(sample_pcs, ref_pcs, opt.batch_size)
-    # results = {k: (v.cpu().detach().item()
-    #                if not isinstance(v, float) else v) for k, v in results.items()}
-    #
-    # pprint(results)
-    # logger.info(results)
-
-    jsd = JSD(sample_pcs.numpy(), ref_pcs.numpy())
-    pprint('JSD: {}'.format(jsd))
-    logger.info('JSD: {}'.format(jsd))
-
-
-def evaluate_recon(opt, netE, save_dir, logger):
-    test_dataset = get_mvr_dataset(opt.dataroot, '/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2',
-                                      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'):
-
-        randind = i%24
-        gt_all = data['test_points'][:,randind:randind+1]
-        x_all = data['sv_points'][:,randind:randind+1]
-        mask_all= data['masks'][:,randind:randind+1]
-        img_all = data['image'][:,randind:randind+1]
-
-
-        B,V,N,C = x_all.shape
-
-        x = x_all.reshape(B*V,N,C).transpose(1,2).contiguous()
-        mask = mask_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()
-
-        # visualize_pointcloud_batch(os.path.join(save_dir, 'y_%03d.png'%i), x.transpose(1,2) * s + m, None, None, None)
-        # for t in [10]:
-        # recon = netE.reconstruct2(x.cuda(), mask.cuda(), get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-        #                                                               opt.constrain_steps)).detach().cpu()
-
-        recon = netE.gen_samples(x.shape, 'cuda',
-                                      constrain_fn=get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-                                                                          opt.constrain_steps),
-                                      clip_denoised=False).detach().cpu()
-
-        recon = recon.transpose(1, 2).contiguous()
-        x = x.transpose(1, 2).contiguous()
-
-
-        # for d in zip(list(data['test_points'].reshape(B*V,N,C)), list(recon), list(x), list(torch.zeros_like(x))):
-        #     write_to_xml_batch(os.path.join(save_dir, 'x_%03d'%k), (torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
-        #     visualize_pointcloud_batch(os.path.join(save_dir, 'x_%03d.png'%k), torch.stack(d, dim=0)* s[0:1] + m[0:1], None, None, None)
-        #
-        #     export_to_pc_batch(os.path.join(save_dir, 'x_ply_%03d'%k),(torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
-        #
-        #     k+=1
-
-        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() for key, val in results.items()})
-    logger.info({key: val.mean() for key, val in results.items()})
-
-    results['pc'] = sample_pcs
-    torch.save(results, os.path.join(save_dir, 'ours_results.pth'))
-
-    #
-    # results = compute_all_metrics(sample_pcs, ref_pcs, opt.batch_size)
-    #
-    # results = {k: (v.cpu().detach().item()
-    #                if not isinstance(v, float) else v) for k, v in results.items()}
-    # pprint(results)
-    # logger.info(results)
-
-
-
-def evaluate_recon_mvr(opt, netE, save_dir, logger):
-    test_dataset = get_mvr_dataset_v2(opt.dataroot, '/viscam/u/alexzhou907/01DATA/shapenet/shapenet_mit_preprocessed',
-                                      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']
-        mask_all= data['masks']
-        img_all = data['image']
-
-
-        B,V,N,C = x_all.shape
-        gt_all = gt_all[:,None,:,:].expand(-1, V, -1,-1)
-
-        # visualize_pointcloud_batch(os.path.join(save_dir, 'y_%03d.png'%i), x.transpose(1,2) * s + m, None, None, None)
-        # for t in [10]:
-        # recon = netE.reconstruct2(x.cuda(), mask.cuda(), get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-        #                                                               opt.constrain_steps)).detach().cpu()
-
-        cd_res = []
-        recon_res = []
-        for p in range(5):
-            x = x_all.reshape(B * V, N, C).transpose(1, 2).contiguous()
-            mask = mask_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.shape, 'cuda',
-                                          constrain_fn=get_constrain_function(x.cuda(), mask.cuda(), opt.constrain_eps,
-                                                                              opt.constrain_steps),
-                                          clip_denoised=False).detach().cpu()
-
-            recon = recon.transpose(1, 2).contiguous()
-            x = x.transpose(1, 2).contiguous()
-
-            cd = (((recon - x)**2)*mask.transpose(1, 2).contiguous()).sum(dim=(1,2))
-
-            cd_res.append(cd)
-            recon_res.append(recon)
-
-        cd_res = torch.stack(cd_res, dim=0)
-        recon_res = torch.stack(recon_res, dim=0)
-        _, argmin = torch.min(cd_res, 0)
-        recon = recon_res[argmin,torch.arange(0,argmin.shape[0])]
-
-        # for d in zip(list(data['test_points'].reshape(B*V,N,C)), list(recon), list(x), list(torch.zeros_like(x))):
-        #     write_to_xml_batch(os.path.join(save_dir, 'x_%03d'%k), (torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
-        #     visualize_pointcloud_batch(os.path.join(save_dir, 'x_%03d.png'%k), torch.stack(d, dim=0)* s[0:1] + m[0:1], None, None, None)
-        #
-        #     export_to_pc_batch(os.path.join(save_dir, 'x_ply_%03d'%k),(torch.stack(d[:-1], dim=0)* s[0:1] + m[0:1]).numpy())
-        #
-        #     k+=1
-
-        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'))
-
-    #
-    # results = compute_all_metrics(sample_pcs, ref_pcs, opt.batch_size)
-    #
-    # results = {k: (v.cpu().detach().item()
-    #                if not isinstance(v, float) else v) for k, v in results.items()}
-    # pprint(results)
-    # logger.info(results)
-
-
-
-def generate(netE, opt, logger):
-
-    _, test_dataset = get_dataset(opt.dataroot, opt.npoints, opt.classes)
-
-    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
-                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)
-
-    with torch.no_grad():
-
-        samples = []
-        ref = []
-
-        for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Generating Samples'):
-
-            x = data['test_points'].transpose(1,2)
-            m, s = data['mean'].float(), data['std'].float()
-
-            gen = netE.gen_samples(x.shape,
-                                       'cuda', clip_denoised=False).detach().cpu()
-
-            gen = gen.transpose(1,2).contiguous()
-            x = x.transpose(1,2).contiguous()
-
-
-
-            gen = gen * s + m
-            x = x * s + m
-            samples.append(gen)
-            ref.append(x)
-
-            # visualize_pointcloud_batch(os.path.join(str(Path(opt.eval_path).parent), 'x_%03d.png'%i), gen[:64], None,
-            #                            None, None)
-            # export_to_pc_batch(os.path.join(str(Path(opt.eval_path).parent), 'ply_%03d'%i),
-            #                    gen[:64].numpy())
-            write_to_xml_batch(os.path.join(str(Path(opt.eval_path).parent), 'xml_samples_%03d'%i), gen[:min(gen.shape[0], 40)].numpy(), cat='airplane')
-
-        samples = torch.cat(samples, dim=0)
-        ref = torch.cat(ref, dim=0)
-
-        torch.save(samples, opt.eval_path)
-
-
-
-    return ref
-
-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]) )):
-            #'/viscam/u/alexzhou907/research/diffusion/shapenet/output/66_res32_pc_chair_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do/2020-10-04-01-02-45/epoch_1399.pth'
-            #'/viscam/u/alexzhou907/research/diffusion/shapenet/output/67_res32_pc_car_mse_fs_dattng_2e2-1e4_linbeta_0wd_0.1do/2020-10-04-01-03-38/epoch_2799.pth'
-            # opt.netE = '/viscam/u/alexzhou907/research/diffusion/shapenet/output/71_res32_pc_plane_mse_fs_dattng_8e3-1e5_w0.1beta_0wd_0.1do_best/2020-10-13-13-33-53/epoch_2899.pth'#ckpt
-            opt.netE = '/viscam/u/alexzhou907/research/diffusion/shapenet/output/71_res32_pc_plane_mse_fs_dattng_8e3-1e5_w0.1beta_0wd_0.1do/2020-10-07-13-26-10/epoch_2299.pth'
-            logger.info("Resume Path:%s" % opt.netE)
-
-            resumed_param = torch.load(opt.netE)
-            netE.load_state_dict(resumed_param['model_state'])
-
-
-            ref = None
-            if opt.generate:
-                epoch = int(os.path.basename(ckpt).split('.')[0].split('_')[-1])
-                opt.eval_path = os.path.join(outf_syn, 'epoch_{}_samples.pth'.format(epoch))
-                Path(opt.eval_path).parent.mkdir(parents=True, exist_ok=True)
-                ref=generate(netE, opt, logger)
-            if opt.eval_gen:
-                # Evaluate generation
-                evaluate_gen(opt, ref, logger)
-
-            if opt.eval_recon:
-                # Evaluate generation
-                evaluate_recon(opt, netE, outf_syn, logger)
-
-            if opt.eval_recon_mvr:
-                # Evaluate generation
-                evaluate_recon_mvr(opt, netE, outf_syn, logger)
-
-
-def parse_args():
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataroot', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
-    parser.add_argument('--classes', default=['airplane'])
-
-    parser.add_argument('--batch_size', type=int, default=20, 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('--generate',default=False)
-    parser.add_argument('--eval_gen', default=True)
-    parser.add_argument('--eval_recon', default=False)
-    parser.add_argument('--eval_recon_mvr', default=False)
-
-    parser.add_argument('--nc', default=3)
-    parser.add_argument('--npoints', default=2048)
-    '''model'''
-    parser.add_argument('--beta_start', default=0.00001)
-    parser.add_argument('--beta_end', default=0.008)
-    parser.add_argument('--schedule_type', default='warm0.1')
-    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=.1)
-    parser.add_argument('--constrain_steps', type=int, default=1)
-
-    parser.add_argument('--ckpt_dir', default='/viscam/u/alexzhou907/research/diffusion/shapenet/output/71_res32_pc_plane_mse_fs_dattng_8e3-1e5_w0.1beta_0wd_0.1do_best/2020-10-13-13-33-53', help="path to netE (to continue training)")
-
-    '''eval'''
-
-    parser.add_argument('--eval_path',
-                        default='/viscam/u/alexzhou907/research/diffusion/shapenet/output/test_plane/2020-10-18-13-49-20/syn/epoch_2499_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)

test_completion.py

@@ -7,9 +7,7 @@ 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
@@ -579,9 +577,8 @@ def main(opt):
 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('--dataroot_pc', default='ShapeNetCore.v2.PC15k/')
+    parser.add_argument('--dataroot_sv', default='GenReData/')
     parser.add_argument('--category', default='chair')
 
     parser.add_argument('--batch_size', type=int, default=50, help='input batch size')

test_generation.py

@@ -4,16 +4,13 @@ from metrics.evaluation_metrics import jsd_between_point_cloud_sets as JSD
 from metrics.evaluation_metrics import compute_all_metrics, EMD_CD
 
 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 utils.mitsuba_renderer import write_to_xml_batch
 from model.pvcnn_generation import PVCNN2Base
 
 from tqdm import tqdm
@@ -271,6 +268,8 @@ class PVCNN2(PVCNN2Base):
             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__()
@@ -534,8 +533,8 @@ def main(opt):
 def parse_args():
 
     parser = argparse.ArgumentParser()
-    parser.add_argument('--dataroot', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
-    parser.add_argument('--category', default='car')
+    parser.add_argument('--dataroot', default='ShapeNetCore.v2.PC15k/')
+    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')
@@ -585,5 +584,3 @@ if __name__ == '__main__':
     set_seed(opt)
 
     main(opt)
-
-    # results in /viscam/u/alexzhou907/research/diffusion/shapenet/output/test_chair

train_completion.py

@@ -4,7 +4,6 @@ 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 *
@@ -559,7 +558,7 @@ def train(gpu, opt, output_dir, noises_init):
 
 
     ''' data '''
-    train_dataset = get_dataset(opt.dataroot_pc, opt.dataroot_sv, opt.npoints, opt.svpoints,opt.classes)
+    train_dataset = get_dataset(opt.dataroot_pc, opt.dataroot_sv, opt.npoints, opt.svpoints,opt.category)
     dataloader, _, train_sampler, _ = get_dataloader(opt, train_dataset, None)
 
 
@@ -609,10 +608,6 @@ def train(gpu, opt, output_dir, noises_init):
     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):
 
@@ -754,7 +749,7 @@ def main():
 
     ''' workaround '''
 
-    train_dataset = get_dataset(opt.dataroot_pc, opt.dataroot_sv, opt.npoints, opt.svpoints,opt.classes)
+    train_dataset = get_dataset(opt.dataroot_pc, opt.dataroot_sv, opt.npoints, opt.svpoints,opt.category)
     noises_init = torch.randn(len(train_dataset), opt.npoints-opt.svpoints, opt.nc)
 
     if opt.dist_url == "env://" and opt.world_size == -1:
@@ -772,9 +767,8 @@ def main():
 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('--dataroot_pc', default='ShapeNetCore.v2.PC15k/')
+    parser.add_argument('--dataroot_sv', default='GenReData/')
     parser.add_argument('--category', default='chair')
 
     parser.add_argument('--bs', type=int, default=48, help='input batch size')

train_generation.py

@@ -4,7 +4,6 @@ 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 *
@@ -787,10 +786,10 @@ def main():
 def parse_args():
 
     parser = argparse.ArgumentParser()
-    parser.add_argument('--dataroot', default='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
+    parser.add_argument('--dataroot', default='ShapeNetCore.v2.PC15k/')
     parser.add_argument('--category', default='chair')
 
-    parser.add_argument('--bs', type=int, default=48, help='input batch size')
+    parser.add_argument('--bs', type=int, default=16, 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')
 

utils/binvox_rw.py

@@ -1,266 +0,0 @@
-#  Copyright (C) 2012 Daniel Maturana
-#  This file is part of binvox-rw-py.
-#
-#  binvox-rw-py is free software: you can redistribute it and/or modify
-#  it under the terms of the GNU General Public License as published by
-#  the Free Software Foundation, either version 3 of the License, or
-#  (at your option) any later version.
-#
-#  binvox-rw-py is distributed in the hope that it will be useful,
-#  but WITHOUT ANY WARRANTY; without even the implied warranty of
-#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-#  GNU General Public License for more details.
-#
-#  You should have received a copy of the GNU General Public License
-#  along with binvox-rw-py. If not, see <http://www.gnu.org/licenses/>.
-#
-
-"""
-Binvox to Numpy and back.
->>> import numpy as np
->>> import binvox_rw
->>> with open('chair.binvox', 'rb') as f:
-...     m1 = binvox_rw.read_as_3d_array(f)
-...
->>> m1.dims
-[32, 32, 32]
->>> m1.scale
-41.133000000000003
->>> m1.translate
-[0.0, 0.0, 0.0]
->>> with open('chair_out.binvox', 'wb') as f:
-...     m1.write(f)
-...
->>> with open('chair_out.binvox', 'rb') as f:
-...     m2 = binvox_rw.read_as_3d_array(f)
-...
->>> m1.dims==m2.dims
-True
->>> m1.scale==m2.scale
-True
->>> m1.translate==m2.translate
-True
->>> np.all(m1.data==m2.data)
-True
->>> with open('chair.binvox', 'rb') as f:
-...     md = binvox_rw.read_as_3d_array(f)
-...
->>> with open('chair.binvox', 'rb') as f:
-...     ms = binvox_rw.read_as_coord_array(f)
-...
->>> data_ds = binvox_rw.dense_to_sparse(md.data)
->>> data_sd = binvox_rw.sparse_to_dense(ms.data, 32)
->>> np.all(data_sd==md.data)
-True
->>> # the ordering of elements returned by numpy.nonzero changes with axis
->>> # ordering, so to compare for equality we first lexically sort the voxels.
->>> np.all(ms.data[:, np.lexsort(ms.data)] == data_ds[:, np.lexsort(data_ds)])
-True
-"""
-
-import numpy as np
-
-class Voxels(object):
-    """ Holds a binvox model.
-    data is either a three-dimensional numpy boolean array (dense representation)
-    or a two-dimensional numpy float array (coordinate representation).
-    dims, translate and scale are the model metadata.
-    dims are the voxel dimensions, e.g. [32, 32, 32] for a 32x32x32 model.
-    scale and translate relate the voxels to the original model coordinates.
-    To translate voxel coordinates i, j, k to original coordinates x, y, z:
-    x_n = (i+.5)/dims[0]
-    y_n = (j+.5)/dims[1]
-    z_n = (k+.5)/dims[2]
-    x = scale*x_n + translate[0]
-    y = scale*y_n + translate[1]
-    z = scale*z_n + translate[2]
-    """
-
-    def __init__(self, data, dims, translate, scale, axis_order):
-        self.data = data
-        self.dims = dims
-        self.translate = translate
-        self.scale = scale
-        assert (axis_order in ('xzy', 'xyz'))
-        self.axis_order = axis_order
-
-    def clone(self):
-        data = self.data.copy()
-        dims = self.dims[:]
-        translate = self.translate[:]
-        return Voxels(data, dims, translate, self.scale, self.axis_order)
-
-    def write(self, fp):
-        write(self, fp)
-
-def read_header(fp):
-    """ Read binvox header. Mostly meant for internal use.
-    """
-    line = fp.readline().strip()
-    if not line.startswith(b'#binvox'):
-        raise IOError('Not a binvox file')
-    dims = list(map(int, fp.readline().strip().split(b' ')[1:]))
-    translate = list(map(float, fp.readline().strip().split(b' ')[1:]))
-    scale = list(map(float, fp.readline().strip().split(b' ')[1:]))[0]
-    line = fp.readline()
-    return dims, translate, scale
-
-def read_as_3d_array(fp, fix_coords=True):
-    """ Read binary binvox format as array.
-    Returns the model with accompanying metadata.
-    Voxels are stored in a three-dimensional numpy array, which is simple and
-    direct, but may use a lot of memory for large models. (Storage requirements
-    are 8*(d^3) bytes, where d is the dimensions of the binvox model. Numpy
-    boolean arrays use a byte per element).
-    Doesn't do any checks on input except for the '#binvox' line.
-    """
-    dims, translate, scale = read_header(fp)
-    raw_data = np.frombuffer(fp.read(), dtype=np.uint8)
-    # if just using reshape() on the raw data:
-    # indexing the array as array[i,j,k], the indices map into the
-    # coords as:
-    # i -> x
-    # j -> z
-    # k -> y
-    # if fix_coords is true, then data is rearranged so that
-    # mapping is
-    # i -> x
-    # j -> y
-    # k -> z
-    values, counts = raw_data[::2], raw_data[1::2]
-    data = np.repeat(values, counts).astype(np.bool)
-    data = data.reshape(dims)
-    if fix_coords:
-        # xzy to xyz TODO the right thing
-        data = np.transpose(data, (0, 2, 1))
-        axis_order = 'xyz'
-    else:
-        axis_order = 'xzy'
-    return Voxels(data, dims, translate, scale, axis_order)
-
-def read_as_coord_array(fp, fix_coords=True):
-    """ Read binary binvox format as coordinates.
-    Returns binvox model with voxels in a "coordinate" representation, i.e.  an
-    3 x N array where N is the number of nonzero voxels. Each column
-    corresponds to a nonzero voxel and the 3 rows are the (x, z, y) coordinates
-    of the voxel.  (The odd ordering is due to the way binvox format lays out
-    data).  Note that coordinates refer to the binvox voxels, without any
-    scaling or translation.
-    Use this to save memory if your model is very sparse (mostly empty).
-    Doesn't do any checks on input except for the '#binvox' line.
-    """
-    dims, translate, scale = read_header(fp)
-    raw_data = np.frombuffer(fp.read(), dtype=np.uint8)
-
-    values, counts = raw_data[::2], raw_data[1::2]
-
-    sz = np.prod(dims)
-    index, end_index = 0, 0
-    end_indices = np.cumsum(counts)
-    indices = np.concatenate(([0], end_indices[:-1])).astype(end_indices.dtype)
-
-    values = values.astype(np.bool)
-    indices = indices[values]
-    end_indices = end_indices[values]
-
-    nz_voxels = []
-    for index, end_index in zip(indices, end_indices):
-        nz_voxels.extend(range(index, end_index))
-    nz_voxels = np.array(nz_voxels)
-    # TODO are these dims correct?
-    # according to docs,
-    # index = x * wxh + z * width + y; // wxh = width * height = d * d
-
-    x = nz_voxels / (dims[0]*dims[1])
-    zwpy = nz_voxels % (dims[0]*dims[1]) # z*w + y
-    z = zwpy / dims[0]
-    y = zwpy % dims[0]
-    if fix_coords:
-        data = np.vstack((x, y, z))
-        axis_order = 'xyz'
-    else:
-        data = np.vstack((x, z, y))
-        axis_order = 'xzy'
-
-    #return Voxels(data, dims, translate, scale, axis_order)
-    return Voxels(np.ascontiguousarray(data), dims, translate, scale, axis_order)
-
-def dense_to_sparse(voxel_data, dtype=np.int):
-    """ From dense representation to sparse (coordinate) representation.
-    No coordinate reordering.
-    """
-    if voxel_data.ndim!=3:
-        raise ValueError('voxel_data is wrong shape; should be 3D array.')
-    return np.asarray(np.nonzero(voxel_data), dtype)
-
-def sparse_to_dense(voxel_data, dims, dtype=np.bool):
-    if voxel_data.ndim!=2 or voxel_data.shape[0]!=3:
-        raise ValueError('voxel_data is wrong shape; should be 3xN array.')
-    if np.isscalar(dims):
-        dims = [dims]*3
-    dims = np.atleast_2d(dims).T
-    # truncate to integers
-    xyz = voxel_data.astype(np.int)
-    # discard voxels that fall outside dims
-    valid_ix = ~np.any((xyz < 0) | (xyz >= dims), 0)
-    xyz = xyz[:,valid_ix]
-    out = np.zeros(dims.flatten(), dtype=dtype)
-    out[tuple(xyz)] = True
-    return out
-
-#def get_linear_index(x, y, z, dims):
-    #""" Assuming xzy order. (y increasing fastest.
-    #TODO ensure this is right when dims are not all same
-    #"""
-    #return x*(dims[1]*dims[2]) + z*dims[1] + y
-
-def write(voxel_model, fp):
-    """ Write binary binvox format.
-    Note that when saving a model in sparse (coordinate) format, it is first
-    converted to dense format.
-    Doesn't check if the model is 'sane'.
-    """
-    if voxel_model.data.ndim==2:
-        # TODO avoid conversion to dense
-        dense_voxel_data = sparse_to_dense(voxel_model.data, voxel_model.dims)
-    else:
-        dense_voxel_data = voxel_model.data
-
-    fp.write('#binvox 1\n')
-    fp.write('dim '+' '.join(map(str, voxel_model.dims))+'\n')
-    fp.write('translate '+' '.join(map(str, voxel_model.translate))+'\n')
-    fp.write('scale '+str(voxel_model.scale)+'\n')
-    fp.write('data\n')
-    if not voxel_model.axis_order in ('xzy', 'xyz'):
-        raise ValueError('Unsupported voxel model axis order')
-
-    if voxel_model.axis_order=='xzy':
-        voxels_flat = dense_voxel_data.flatten()
-    elif voxel_model.axis_order=='xyz':
-        voxels_flat = np.transpose(dense_voxel_data, (0, 2, 1)).flatten()
-
-    # keep a sort of state machine for writing run length encoding
-    state = voxels_flat[0]
-    ctr = 0
-    for c in voxels_flat:
-        if c==state:
-            ctr += 1
-            # if ctr hits max, dump
-            if ctr==255:
-                fp.write(chr(state))
-                fp.write(chr(ctr))
-                ctr = 0
-        else:
-            # if switch state, dump
-            fp.write(chr(state))
-            fp.write(chr(ctr))
-            state = c
-            ctr = 1
-    # flush out remainders
-    if ctr > 0:
-        fp.write(chr(state))
-        fp.write(chr(ctr))
-
-if __name__ == '__main__':
-    import doctest
-    doctest.testmod()

utils/conversion.py

@@ -1,46 +0,0 @@
-
-from skimage import measure
-import numpy as np
-
-
-def get_mesh(tsdf_vol, color_vol, threshold=0, vol_max=.5, vol_min=-.5):
-    """Compute a mesh from the voxel volume using marching cubes.
-    """
-    vol_origin = vol_min
-    voxel_size = (vol_max - vol_min) / tsdf_vol.shape[-1]
-
-    # Marching cubes
-    verts, faces, norms, vals = measure.marching_cubes_lewiner(tsdf_vol, level=threshold)
-    verts_ind = np.round(verts).astype(int)
-    verts = verts * voxel_size + vol_origin  # voxel grid coordinates to world coordinates
-
-    # Get vertex colors
-    if color_vol is None:
-        return verts, faces, norms
-    colors = color_vol[:, verts_ind[:, 0], verts_ind[:, 1], verts_ind[:, 2]].T
-
-    return verts, faces, norms, colors
-
-
-def get_point_cloud(tsdf_vol, color_vol, vol_max=0.5, vol_min=-0.5):
-    vol_origin = vol_min
-    voxel_size = (vol_max - vol_min) / tsdf_vol.shape[-1]
-    # Marching cubes
-    verts = measure.marching_cubes_lewiner(tsdf_vol, level=0)[0]
-    verts_ind = np.round(verts).astype(int)
-    verts = verts * voxel_size + vol_origin
-
-    # Get vertex colors
-    colors = color_vol[:, verts_ind[:, 0], verts_ind[:, 1], verts_ind[:, 2]].T
-
-    pc = np.hstack([verts, colors])
-
-    return pc
-
-def sparse_to_dense_voxel(coords, feats, res):
-    coords = coords.astype('int64', copy=False)
-    a = np.zeros((res, res, res), dtype=feats.dtype)
-
-    a[coords[:,0],coords[:,1],coords[:,2] ] = feats[:,0].astype(a.dtype, copy=False)
-
-    return a

utils/mitsuba_renderer.py

@@ -1,146 +0,0 @@
-import numpy as np
-from pathlib import Path
-import os
-
-
-def standardize_bbox(pcl, points_per_object, scale=None):
-    pt_indices = np.random.choice(pcl.shape[0], points_per_object, replace=False)
-    np.random.shuffle(pt_indices)
-    pcl = pcl[pt_indices]  # n by 3
-    mins = np.amin(pcl, axis=0)
-    maxs = np.amax(pcl, axis=0)
-    center = (mins + maxs) / 2.
-    if scale is None:
-        scale = np.amax(maxs - mins)
-    result = ((pcl - center) / scale).astype(np.float32)  # [-0.5, 0.5]
-    return result
-
-
-xml_head = \
-    """
-    <scene version="0.6.0">
-        <integrator type="path">
-            <integer name="maxDepth" value="-1"/>
-        </integrator>
-        <sensor type="perspective">
-            <float name="farClip" value="100"/>
-            <float name="nearClip" value="0.1"/>
-            <transform name="toWorld">
-                <lookat origin="3,3,3" target="0,0,0" up="0,0,1"/>
-            </transform>
-            <float name="fov" value="{}"/>
-
-            <sampler type="ldsampler">
-                <integer name="sampleCount" value="256"/>
-            </sampler>
-            <film type="hdrfilm">
-                <integer name="width" value="256"/>
-                <integer name="height" value="256"/>
-                <rfilter type="gaussian"/>
-                <boolean name="banner" value="false"/>
-            </film>
-        </sensor>
-
-        <bsdf type="roughplastic" id="surfaceMaterial">
-            <string name="distribution" value="ggx"/>
-            <float name="alpha" value="0.05"/>
-            <float name="intIOR" value="1.46"/>
-            <rgb name="diffuseReflectance" value="1,1,1"/> <!-- default 0.5 -->
-        </bsdf>
-
-    """
-
-xml_ball_segment = \
-    """
-        <shape type="sphere">
-            <float name="radius" value="{}"/>
-            <transform name="toWorld">
-                <translate x="{}" y="{}" z="{}"/>
-            </transform>
-            <bsdf type="diffuse">
-                <rgb name="reflectance" value="{},{},{}"/>
-            </bsdf>
-        </shape>
-    """
-
-xml_tail = \
-    """
-        <shape type="rectangle">
-            <bsdf type="diffuse">
-                <rgb name="reflectance" value="1"/>
-            </bsdf>
-            <transform name="toWorld">
-                <scale x="100" y="100" z="1"/>
-                <translate x="0" y="0" z="{}"/>
-            </transform>
-        </shape>
-    	<shape type="sphere">
-            <transform name="toWorld">
-                <scale x="10" y="10" z="1"/>
-                <lookat origin="2,0,18" target="0,0,0" up="0,0,1"/>
-            </transform>
-            <emitter type="area">
-            	<rgb name="radiance" value="5"/>
-            </emitter>
-        </shape>
-        <shape type="sphere">
-            <transform name="toWorld">
-                <scale x="10" y="10" z="1"/>
-                <lookat origin="-30,0,18" target="-100,0,0" up="0,0,1"/>
-            </transform>
-            <emitter type="area">
-            	<rgb name="radiance" value="5"/>
-            </emitter>
-        </shape>
-    </scene>
-    """
-
-
-def colormap_fn(x, y, z):
-    vec = np.array([x, y, z])
-    vec = np.clip(vec, 0.001, 1.0)
-    norm = np.sqrt(np.sum(vec ** 2))
-    vec /= norm
-    return [vec[0], vec[1], vec[2]]
-
-
-color_dict = {'r': [163, 102, 96], 'g': [20, 130, 3],
-              'o': [145, 128, 47], 'b': [91, 102, 112], 'p':[133,111,139], 'br':[111,92,81]}
-
-color_map = {'airplane': 'r', 'chair': 'o', 'car': 'b', 'table': 'p', 'lamp':'br'}
-fov_map = {'airplane': 12, 'chair': 16, 'car':15,  'table': 13, 'lamp':13}
-radius_map = {'airplane': 0.02, 'chair': 0.035, 'car': 0.01, 'table':0.035, 'lamp':0.035}
-
-def write_to_xml_batch(dir, pcl_batch, filenames=None, color_batch=None, cat='airplane'):
-    default_color = color_map[cat]
-    Path(dir).mkdir(parents=True, exist_ok=True)
-    if filenames is not None:
-        assert len(filenames) == pcl_batch.shape[0]
-    # mins = np.amin(pcl_batch, axis=(0,1))
-    # maxs = np.amax(pcl_batch, axis=(0,1))
-    # scale = 1; print(np.amax(maxs - mins))
-
-    for k, pcl in enumerate(pcl_batch):
-        xml_segments = [xml_head.format(fov_map[cat])]
-        pcl = standardize_bbox(pcl, pcl.shape[0])
-        pcl = pcl[:, [2, 0, 1]]
-        pcl[:, 0] *= -1
-        pcl[:, 2] += 0.0125
-        for i in range(pcl.shape[0]):
-            if color_batch is not None:
-                color = color_batch[k, i]
-            else:
-                color = np.array(color_dict[default_color]) / 255
-            # color = colormap_fn(pcl[i,0]+0.5,pcl[i,1]+0.5,pcl[i,2]+0.5-0.0125)
-            xml_segments.append(xml_ball_segment.format(radius_map[cat], pcl[i, 0], pcl[i, 1], pcl[i, 2], *color))
-        xml_segments.append(
-            xml_tail.format(pcl[:, 2].min()))
-
-        xml_content = str.join('', xml_segments)
-
-        if filenames is None:
-            fn = 'sample_{}.xml'.format(k)
-        else:
-            fn = filenames[k]
-        with open(os.path.join(dir, fn), 'w') as f:
-            f.write(xml_content)

utils/mitsuba_renderer2.py

@@ -1,170 +0,0 @@
-import numpy as np
-from pathlib import Path
-import os
-
-
-def standardize_bbox(pcl, points_per_object):
-    pt_indices = np.random.choice(pcl.shape[0], points_per_object, replace=False)
-    np.random.shuffle(pt_indices)
-    pcl = pcl[pt_indices]  # n by 3
-    mins = np.amin(pcl, axis=0)
-    maxs = np.amax(pcl, axis=0)
-    center = (mins + maxs) / 2.
-    scale = np.amax(maxs - mins)
-    result = ((pcl - center) / scale).astype(np.float32)  # [-0.5, 0.5]
-    return result
-
-
-xml_head = \
-    """
-    <scene version="0.6.0">
-        <integrator type="path">
-            <integer name="maxDepth" value="-1"/>
-        </integrator>
-        <sensor type="perspective">
-            <float name="farClip" value="100"/>
-            <float name="nearClip" value="0.1"/>
-            <transform name="toWorld">
-                <lookat origin="{},{},{}" target="0,0,0" up="0,0,1"/>
-            </transform>
-            <float name="fov" value="20"/>
-
-            <sampler type="ldsampler">
-                <integer name="sampleCount" value="256"/>
-            </sampler>
-            <film type="hdrfilm">
-                <integer name="width" value="480"/>
-                <integer name="height" value="480"/>
-                <rfilter type="gaussian"/>
-                <boolean name="banner" value="false"/>
-            </film>
-        </sensor>
-
-        <bsdf type="roughplastic" id="surfaceMaterial">
-            <string name="distribution" value="ggx"/>
-            <float name="alpha" value="0.05"/>
-            <float name="intIOR" value="1.46"/>
-            <rgb name="diffuseReflectance" value="1,1,1"/> <!-- default 0.5 -->
-        </bsdf>
-
-    """
-
-xml_ball_segment = \
-    """
-        <shape type="sphere">
-            <float name="radius" value="{}"/>
-            <transform name="toWorld">
-                <translate x="{}" y="{}" z="{}"/>
-            </transform>
-            <bsdf type="diffuse">
-                <rgb name="reflectance" value="{},{},{}"/>
-            </bsdf>
-        </shape>
-    """
-
-xml_tail = \
-    """
-        <shape type="rectangle">
-            <bsdf type="diffuse">
-                <rgb name="reflectance" value="1"/>
-            </bsdf>
-            <transform name="toWorld">
-                <scale x="100" y="100" z="1"/>
-                <translate x="0" y="0" z="{}"/>
-            </transform>
-        </shape>
-    	<shape type="sphere">
-            <transform name="toWorld">
-                <scale x="10" y="10" z="1"/>
-                <lookat origin="2,0,18" target="0,0,0" up="0,0,1"/>
-            </transform>
-            <emitter type="area">
-            	<rgb name="radiance" value="5"/>
-            </emitter>
-        </shape>
-        <shape type="sphere">
-            <transform name="toWorld">
-                <scale x="10" y="10" z="1"/>
-                <lookat origin="-30,0,18" target="-100,0,0" up="0,0,1"/>
-            </transform>
-            <emitter type="area">
-            	<rgb name="radiance" value="5"/>
-            </emitter>
-        </shape>
-    </scene>
-    """
-
-
-def colormap_fn(x, y, z):
-    vec = np.array([x, y, z])
-    vec = np.clip(vec, 0.001, 1.0)
-    norm = np.sqrt(np.sum(vec ** 2))
-    vec /= norm
-    return [vec[0], vec[1], vec[2]]
-
-
-color_dict = {'r': [163, 102, 96], 'p': [133,111,139], 'g': [20, 130, 3],
-              'o': [145, 128, 47], 'b': [91, 102, 112]}
-
-color_map = {'airplane': 'r', 'chair': 'o', 'car': 'b', 'table': 'p'}
-fov_map = {'airplane': 12, 'chair': 15, 'car':12, 'table':12}
-radius_map = {'airplane': 0.0175, 'chair': 0.035, 'car': 0.025, 'table': 0.02}
-
-def write_to_xml_batch(dir, pcl_batch, color_batch=None, cat='airplane', elev=15, azim=45, radius=np.sqrt(18)):
-    elev_rad = elev * np.pi / 180
-    azim_rad = azim * np.pi / 180
-
-    x = radius * np.cos(elev_rad)*np.cos(azim_rad)
-    y = radius * np.cos(elev_rad)*np.sin(azim_rad)
-    z = radius * np.sin(elev_rad)
-
-    default_color = color_map[cat]
-    Path(dir).mkdir(parents=True, exist_ok=True)
-    for k, pcl in enumerate(pcl_batch):
-        xml_segments = [xml_head.format(x,y,z)]
-        pcl = standardize_bbox(pcl, pcl.shape[0])
-        pcl = pcl[:, [2, 0, 1]]
-        pcl[:, 0] *= -1
-        pcl[:, 2] += 0.0125
-        for i in range(pcl.shape[0]):
-            if color_batch is not None:
-                color = color_batch[k, i]
-            else:
-                color = np.array(color_dict[default_color]) / 255
-            # color = colormap_fn(pcl[i,0]+0.5,pcl[i,1]+0.5,pcl[i,2]+0.5-0.0125)
-            xml_segments.append(xml_ball_segment.format(0.0175, pcl[i, 0], pcl[i, 1], pcl[i, 2], *color))
-        xml_segments.append(
-            xml_tail.format(pcl[:, 2].min()))
-
-        xml_content = str.join('', xml_segments)
-
-        with open(os.path.join(dir, 'sample_{}.xml'.format(k)), 'w') as f:
-            f.write(xml_content)
-
-def write_to_xml(file, pcl, cat='airplane', elev=15, azim=45, radius=np.sqrt(18)):
-    assert pcl.ndim == 2
-    elev_rad = elev * np.pi / 180
-    azim_rad = azim * np.pi / 180
-
-    x = radius * np.cos(elev_rad)*np.cos(azim_rad)
-    y = radius * np.cos(elev_rad)*np.sin(azim_rad)
-    z = radius * np.sin(elev_rad)
-
-    default_color = color_map[cat]
-
-    xml_segments = [xml_head.format(x,y,z)]
-    pcl = standardize_bbox(pcl, pcl.shape[0])
-    pcl = pcl[:, [2, 0, 1]]
-    pcl[:, 0] *= -1
-    pcl[:, 2] += 0.0125
-    for i in range(pcl.shape[0]):
-        color = np.array(color_dict[default_color]) / 255
-        # color = colormap_fn(pcl[i,0]+0.5,pcl[i,1]+0.5,pcl[i,2]+0.5-0.0125)
-        xml_segments.append(xml_ball_segment.format(radius_map[cat], pcl[i, 0], pcl[i, 1], pcl[i, 2], *color))
-    xml_segments.append(
-        xml_tail.format(pcl[:, 2].min()))
-
-    xml_content = str.join('', xml_segments)
-
-    with open(file, 'w') as f:
-        f.write(xml_content)

utils/xml_from_mesh.py

@@ -1,86 +0,0 @@
-import sys
-
-sys.path.append('..')
-import argparse
-import os
-import numpy as np
-import trimesh
-import glob
-from joblib import Parallel, delayed
-import re
-from utils.mitsuba_renderer import write_to_xml_batch
-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]
-    '''
-    N = rotation_matrix([1, 0, 0], 3* np.pi / 4).transpose()
-    # M = rotation_matrix([0, 1, 0], -np.pi / 2).transpose()
-
-
-    v, f = vertices.dot(N), faces
-    return v, f
-
-def as_mesh(scene_or_mesh):
-    if isinstance(scene_or_mesh, trimesh.Scene):
-        mesh = trimesh.util.concatenate([
-            trimesh.Trimesh(vertices=m.vertices, faces=m.faces)
-            for m in scene_or_mesh.geometry.values()])
-    else:
-        mesh = scene_or_mesh
-    return mesh
-def process_one(shape_dir, cat):
-    pc_paths = glob.glob(os.path.join(shape_dir, "*.obj"))
-    pc_paths = sorted(pc_paths)
-
-    xml_paths = [] #[re.sub('.ply', '.xml', os.path.basename(pth)) for pth in pc_paths]
-
-    gen_pcs = []
-    for path in pc_paths:
-        sample_mesh = trimesh.load(path, force='mesh')
-        v, f = rotate(sample_mesh.vertices,sample_mesh.faces)
-        mesh = trimesh.Trimesh(v, f)
-        sample_pts = trimesh.sample.sample_surface(mesh, 2048)[0]
-        gen_pcs.append(sample_pts)
-        xml_paths.append(re.sub('.obj', '.xml', os.path.basename(path)))
-
-
-
-    gen_pcs = np.stack(gen_pcs, axis=0)
-    write_to_xml_batch(os.path.dirname(pc_paths[0]), gen_pcs, xml_paths, cat=cat)
-
-
-def process(args):
-    shape_names = [n for n in sorted(os.listdir(args.src)) if
-                   os.path.isdir(os.path.join(args.src, n)) and not n.startswith('x')]
-
-    all_shape_dir = [os.path.join(args.src, name) for name in shape_names]
-
-    Parallel(n_jobs=10, verbose=2)(delayed(process_one)(path) for path in all_shape_dir)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--src", type=str)
-    parser.add_argument("--cat", type=str)
-    args = parser.parse_args()
-
-    process_one(args.src, args.cat)
-
-
-if __name__ == '__main__':
-    main()

utils/xml_from_ply.py

@@ -1,54 +0,0 @@
-import sys
-
-sys.path.append('..')
-import argparse
-import os
-import numpy as np
-import trimesh
-import glob
-from joblib import Parallel, delayed
-import re
-from utils.mitsuba_renderer import write_to_xml_batch
-
-
-def process_one(shape_dir):
-    pc_paths = glob.glob(os.path.join(shape_dir, "fake*.ply"))
-    pc_paths = sorted(pc_paths)
-
-    xml_paths = [re.sub('.ply', '.xml', os.path.basename(pth)) for pth in pc_paths]
-
-    gen_pcs = []
-    for path in pc_paths:
-        sample_pts = trimesh.load(path)
-        sample_pts = np.array(sample_pts.vertices)
-        gen_pcs.append(sample_pts)
-
-    raw_pc = np.array(trimesh.load(os.path.join(shape_dir, "raw.ply")).vertices)
-    raw_pc = np.concatenate([raw_pc, np.tile(raw_pc[0:1], (gen_pcs[0].shape[0]-raw_pc.shape[0],1))])
-
-    gen_pcs.append(raw_pc)
-    gen_pcs = np.stack(gen_pcs, axis=0)
-    xml_paths.append('raw.xml')
-
-    write_to_xml_batch(os.path.dirname(pc_paths[0]), gen_pcs, xml_paths)
-
-
-def process(args):
-    shape_names = [n for n in sorted(os.listdir(args.src)) if
-                   os.path.isdir(os.path.join(args.src, n)) and not n.startswith('x')]
-
-    all_shape_dir = [os.path.join(args.src, name) for name in shape_names]
-
-    Parallel(n_jobs=10, verbose=2)(delayed(process_one)(path) for path in all_shape_dir)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--src", type=str)
-    args = parser.parse_args()
-
-    process_one(args)
-
-
-if __name__ == '__main__':
-    main()