405 lines
16 KiB
Python
405 lines
16 KiB
Python
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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
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""" copied and modified from https://github.com/stevenygd/PointFlow/blob/master/datasets.py """
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import os
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import open3d as o3d
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import time
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import torch
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import numpy as np
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from loguru import logger
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from torch.utils.data import Dataset
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from torch.utils import data
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import random
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import tqdm
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from datasets.data_path import get_path
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OVERFIT = 0
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# taken from https://github.com/optas/latent_3d_points/blob/
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# 8e8f29f8124ed5fc59439e8551ba7ef7567c9a37/src/in_out.py
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synsetid_to_cate = {
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'02691156': 'airplane',
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'02773838': 'bag',
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'02801938': 'basket',
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'02808440': 'bathtub',
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'02818832': 'bed',
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'02828884': 'bench',
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'02876657': 'bottle',
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'02880940': 'bowl',
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'02924116': 'bus',
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'02933112': 'cabinet',
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'02747177': 'can',
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'02942699': 'camera',
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'02954340': 'cap',
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'02958343': 'car',
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'03001627': 'chair',
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'03046257': 'clock',
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'03207941': 'dishwasher',
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'03211117': 'monitor',
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'04379243': 'table',
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'04401088': 'telephone',
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'02946921': 'tin_can',
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'04460130': 'tower',
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'04468005': 'train',
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'03085013': 'keyboard',
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'03261776': 'earphone',
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'03325088': 'faucet',
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'03337140': 'file',
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'03467517': 'guitar',
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'03513137': 'helmet',
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'03593526': 'jar',
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'03624134': 'knife',
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'03636649': 'lamp',
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'03642806': 'laptop',
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'03691459': 'speaker',
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'03710193': 'mailbox',
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'03759954': 'microphone',
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'03761084': 'microwave',
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'03790512': 'motorcycle',
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'03797390': 'mug',
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'03928116': 'piano',
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'03938244': 'pillow',
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'03948459': 'pistol',
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'03991062': 'pot',
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'04004475': 'printer',
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'04074963': 'remote_control',
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'04090263': 'rifle',
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'04099429': 'rocket',
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'04225987': 'skateboard',
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'04256520': 'sofa',
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'04330267': 'stove',
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'04530566': 'vessel',
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'04554684': 'washer',
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'02992529': 'cellphone',
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'02843684': 'birdhouse',
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'02871439': 'bookshelf',
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# '02858304': 'boat', no boat in our dataset, merged into vessels
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# '02834778': 'bicycle', not in our taxonomy
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}
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cate_to_synsetid = {v: k for k, v in synsetid_to_cate.items()}
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class ShapeNet15kPointClouds(Dataset):
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def __init__(self,
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categories=['airplane'],
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tr_sample_size=10000,
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te_sample_size=10000,
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split='train',
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scale=1.,
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normalize_per_shape=False,
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normalize_shape_box=False,
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random_subsample=False,
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sample_with_replacement=1,
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normalize_std_per_axis=False,
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normalize_global=False,
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recenter_per_shape=False,
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all_points_mean=None,
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all_points_std=None,
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input_dim=3,
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):
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self.normalize_shape_box = normalize_shape_box
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root_dir = get_path('pointflow')
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self.root_dir = root_dir
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logger.info('[DATA] cat: {}, split: {}, full path: {}; norm global={}, norm-box={}',
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categories, split, self.root_dir, normalize_global, normalize_shape_box)
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self.split = split
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assert self.split in ['train', 'test', 'val']
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self.tr_sample_size = tr_sample_size
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self.te_sample_size = te_sample_size
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if type(categories) is str:
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categories = [categories]
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self.cates = categories
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if 'all' in categories:
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self.synset_ids = list(cate_to_synsetid.values())
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else:
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self.synset_ids = [cate_to_synsetid[c] for c in self.cates]
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subdirs = self.synset_ids
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# assert 'v2' in root_dir, "Only supporting v2 right now."
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self.gravity_axis = 1
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self.display_axis_order = [0, 2, 1]
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self.root_dir = root_dir
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self.split = split
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self.in_tr_sample_size = tr_sample_size
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self.in_te_sample_size = te_sample_size
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self.subdirs = subdirs
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self.scale = scale
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self.random_subsample = random_subsample
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self.sample_with_replacement = sample_with_replacement
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self.input_dim = input_dim
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self.all_cate_mids = []
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self.cate_idx_lst = []
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self.all_points = []
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tic = time.time()
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for cate_idx, subd in enumerate(self.subdirs):
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# NOTE: [subd] here is synset id
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sub_path = os.path.join(root_dir, subd, self.split)
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if not os.path.isdir(sub_path):
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print("Directory missing : %s " % (sub_path))
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raise ValueError('check the data path')
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continue
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if True:
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all_mids = []
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assert(os.path.exists(sub_path)), f'path missing: {sub_path}'
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for x in os.listdir(sub_path):
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if not x.endswith('.npy'):
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continue
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all_mids.append(os.path.join(self.split, x[:-len('.npy')]))
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logger.info('[DATA] number of file [{}] under: {} ',
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len(os.listdir(sub_path)), sub_path)
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# NOTE: [mid] contains the split: i.e. "train/<mid>"
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# or "val/<mid>" or "test/<mid>"
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all_mids = sorted(all_mids)
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for mid in all_mids:
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# obj_fname = os.path.join(sub_path, x)
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obj_fname = os.path.join(root_dir, subd, mid + ".npy")
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point_cloud = np.load(obj_fname) # (15k, 3)
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self.all_points.append(point_cloud[np.newaxis, ...])
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self.cate_idx_lst.append(cate_idx)
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self.all_cate_mids.append((subd, mid))
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logger.info('[DATA] Load data time: {:.1f}s | dir: {} | '
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'sample_with_replacement: {}; num points: {}', time.time() - tic, self.subdirs,
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self.sample_with_replacement, len(self.all_points))
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# Shuffle the index deterministically (based on the number of examples)
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self.shuffle_idx = list(range(len(self.all_points)))
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random.Random(38383).shuffle(self.shuffle_idx)
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self.cate_idx_lst = [self.cate_idx_lst[i] for i in self.shuffle_idx]
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self.all_points = [self.all_points[i] for i in self.shuffle_idx]
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self.all_cate_mids = [self.all_cate_mids[i] for i in self.shuffle_idx]
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# Normalization
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self.all_points = np.concatenate(self.all_points) # (N, 15000, 3)
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self.normalize_per_shape = normalize_per_shape
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self.normalize_std_per_axis = normalize_std_per_axis
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self.recenter_per_shape = recenter_per_shape
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if self.normalize_shape_box: # per shape normalization
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B, N = self.all_points.shape[:2]
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self.all_points_mean = ( # B,1,3
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(np.amax(self.all_points, axis=1)).reshape(B, 1, input_dim) +
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(np.amin(self.all_points, axis=1)).reshape(B, 1, input_dim)) / 2
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self.all_points_std = np.amax( # B,1,1
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((np.amax(self.all_points, axis=1)).reshape(B, 1, input_dim) -
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(np.amin(self.all_points, axis=1)).reshape(B, 1, input_dim)),
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axis=-1).reshape(B, 1, 1) / 2
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elif self.normalize_per_shape: # per shape normalization
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B, N = self.all_points.shape[:2]
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self.all_points_mean = self.all_points.mean(axis=1).reshape(
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B, 1, input_dim)
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logger.info('all_points shape: {}. mean over axis=1',
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self.all_points.shape)
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if normalize_std_per_axis:
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self.all_points_std = self.all_points.reshape(
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B, N, -1).std(axis=1).reshape(B, 1, input_dim)
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else:
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self.all_points_std = self.all_points.reshape(
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B, -1).std(axis=1).reshape(B, 1, 1)
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elif all_points_mean is not None and all_points_std is not None and not self.recenter_per_shape:
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# using loaded dataset stats
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self.all_points_mean = all_points_mean
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self.all_points_std = all_points_std
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elif self.recenter_per_shape: # per shape center
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# TODO: bounding box scale at the large dim and center
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B, N = self.all_points.shape[:2]
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self.all_points_mean = (
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(np.amax(self.all_points, axis=1)).reshape(B, 1, input_dim) +
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(np.amin(self.all_points, axis=1)).reshape(B, 1,
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input_dim)) / 2
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self.all_points_std = np.amax(
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((np.amax(self.all_points, axis=1)).reshape(B, 1, input_dim) -
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(np.amin(self.all_points, axis=1)).reshape(B, 1, input_dim)),
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axis=-1).reshape(B, 1, 1) / 2
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# else: # normalize across the dataset
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elif normalize_global: # normalize across the dataset
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self.all_points_mean = self.all_points.reshape(
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-1, input_dim).mean(axis=0).reshape(1, 1, input_dim)
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if normalize_std_per_axis:
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self.all_points_std = self.all_points.reshape(
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-1, input_dim).std(axis=0).reshape(1, 1, input_dim)
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else:
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self.all_points_std = self.all_points.reshape(-1).std(
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axis=0).reshape(1, 1, 1)
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logger.info('[DATA] normalize_global: mean={}, std={}',
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self.all_points_mean.reshape(-1),
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self.all_points_std.reshape(-1))
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else:
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raise NotImplementedError('No Normalization')
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self.all_points = (self.all_points - self.all_points_mean) / \
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self.all_points_std
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logger.info('[DATA] shape={}, all_points_mean:={}, std={}, max={:.3f}, min={:.3f}; num-pts={}',
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self.all_points.shape,
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self.all_points_mean.shape, self.all_points_std.shape,
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self.all_points.max(), self.all_points.min(), tr_sample_size)
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if OVERFIT:
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self.all_points = self.all_points[:40]
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# TODO: why do we need this??
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self.train_points = self.all_points[:, :min(
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10000, self.all_points.shape[1])]
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self.tr_sample_size = min(10000, tr_sample_size)
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self.te_sample_size = min(5000, te_sample_size)
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assert self.scale == 1, "Scale (!= 1) is deprecated"
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# Default display axis order
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self.display_axis_order = [0, 1, 2]
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def get_pc_stats(self, idx):
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if self.recenter_per_shape:
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m = self.all_points_mean[idx].reshape(1, self.input_dim)
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s = self.all_points_std[idx].reshape(1, -1)
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return m, s
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if self.normalize_per_shape or self.normalize_shape_box:
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m = self.all_points_mean[idx].reshape(1, self.input_dim)
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s = self.all_points_std[idx].reshape(1, -1)
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return m, s
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return self.all_points_mean.reshape(1, -1), \
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self.all_points_std.reshape(1, -1)
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def renormalize(self, mean, std):
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self.all_points = self.all_points * self.all_points_std + \
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self.all_points_mean
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self.all_points_mean = mean
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self.all_points_std = std
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self.all_points = (self.all_points - self.all_points_mean) / \
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self.all_points_std
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self.train_points = self.all_points[:, :min(
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10000, self.all_points.shape[1])]
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## self.test_points = self.all_points[:, 10000:]
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def __len__(self):
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return len(self.train_points)
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def __getitem__(self, idx):
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output = {}
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tr_out = self.train_points[idx]
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if self.random_subsample and self.sample_with_replacement:
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tr_idxs = np.random.choice(tr_out.shape[0], self.tr_sample_size)
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elif self.random_subsample and not self.sample_with_replacement:
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tr_idxs = np.random.permutation(
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np.arange(tr_out.shape[0]))[:self.tr_sample_size]
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else:
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tr_idxs = np.arange(self.tr_sample_size)
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tr_out = torch.from_numpy(tr_out[tr_idxs, :]).float()
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m, s = self.get_pc_stats(idx)
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cate_idx = self.cate_idx_lst[idx]
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sid, mid = self.all_cate_mids[idx]
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input_pts = tr_out
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output.update(
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{
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'idx': idx,
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'select_idx': tr_idxs,
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'tr_points': tr_out,
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'input_pts': input_pts,
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'mean': m,
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'std': s,
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'cate_idx': cate_idx,
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'sid': sid,
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'mid': mid,
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'display_axis_order': self.display_axis_order
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})
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return output
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def init_np_seed(worker_id):
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seed = torch.initial_seed()
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np.random.seed(seed % 4294967296)
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def get_datasets(cfg, args):
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"""
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cfg: config.data sub part
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"""
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if OVERFIT:
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random_subsample = 0
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else:
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random_subsample = cfg.random_subsample
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logger.info(f'get_datasets: tr_sample_size={cfg.tr_max_sample_points}, '
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f' te_sample_size={cfg.te_max_sample_points}; '
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f' random_subsample={random_subsample}'
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f' normalize_global={cfg.normalize_global}'
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f' normalize_std_per_axix={cfg.normalize_std_per_axis}'
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f' normalize_per_shape={cfg.normalize_per_shape}'
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f' recenter_per_shape={cfg.recenter_per_shape}'
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)
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kwargs = {}
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|
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tr_dataset = ShapeNet15kPointClouds(
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categories=cfg.cates,
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|
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split='train',
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|
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tr_sample_size=cfg.tr_max_sample_points,
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|
|
te_sample_size=cfg.te_max_sample_points,
|
||
|
|
sample_with_replacement=cfg.sample_with_replacement,
|
||
|
|
scale=cfg.dataset_scale, # root_dir=cfg.data_dir,
|
||
|
|
normalize_shape_box=cfg.normalize_shape_box,
|
||
|
|
normalize_per_shape=cfg.normalize_per_shape,
|
||
|
|
normalize_std_per_axis=cfg.normalize_std_per_axis,
|
||
|
|
normalize_global=cfg.normalize_global,
|
||
|
|
recenter_per_shape=cfg.recenter_per_shape,
|
||
|
|
random_subsample=random_subsample,
|
||
|
|
**kwargs)
|
||
|
|
|
||
|
|
eval_split = getattr(args, "eval_split", "val")
|
||
|
|
# te_dataset has random_subsample as False, therefore not using sample_with_replacement
|
||
|
|
te_dataset = ShapeNet15kPointClouds(
|
||
|
|
categories=cfg.cates,
|
||
|
|
split=eval_split,
|
||
|
|
tr_sample_size=cfg.tr_max_sample_points,
|
||
|
|
te_sample_size=cfg.te_max_sample_points,
|
||
|
|
scale=cfg.dataset_scale, # root_dir=cfg.data_dir,
|
||
|
|
normalize_shape_box=cfg.normalize_shape_box,
|
||
|
|
normalize_per_shape=cfg.normalize_per_shape,
|
||
|
|
normalize_std_per_axis=cfg.normalize_std_per_axis,
|
||
|
|
normalize_global=cfg.normalize_global,
|
||
|
|
recenter_per_shape=cfg.recenter_per_shape,
|
||
|
|
all_points_mean=tr_dataset.all_points_mean,
|
||
|
|
all_points_std=tr_dataset.all_points_std,
|
||
|
|
)
|
||
|
|
return tr_dataset, te_dataset
|
||
|
|
|
||
|
|
|
||
|
|
def get_data_loaders(cfg, args):
|
||
|
|
tr_dataset, te_dataset = get_datasets(cfg, args)
|
||
|
|
kwargs = {}
|
||
|
|
if args.distributed:
|
||
|
|
kwargs['sampler'] = data.distributed.DistributedSampler(
|
||
|
|
tr_dataset, shuffle=True)
|
||
|
|
else:
|
||
|
|
kwargs['shuffle'] = True
|
||
|
|
if args.eval_trainnll:
|
||
|
|
kwargs['shuffle'] = False
|
||
|
|
train_loader = data.DataLoader(dataset=tr_dataset,
|
||
|
|
batch_size=cfg.batch_size,
|
||
|
|
num_workers=cfg.num_workers,
|
||
|
|
drop_last=cfg.train_drop_last == 1,
|
||
|
|
pin_memory=False, **kwargs)
|
||
|
|
test_loader = data.DataLoader(dataset=te_dataset,
|
||
|
|
batch_size=cfg.batch_size_test,
|
||
|
|
shuffle=False,
|
||
|
|
num_workers=cfg.num_workers,
|
||
|
|
pin_memory=False,
|
||
|
|
drop_last=False,
|
||
|
|
)
|
||
|
|
logger.info(
|
||
|
|
f'[Batch Size] train={cfg.batch_size}, test={cfg.batch_size_test}; drop-last={cfg.train_drop_last}')
|
||
|
|
loaders = {
|
||
|
|
"test_loader": test_loader,
|
||
|
|
'train_loader': train_loader,
|
||
|
|
}
|
||
|
|
return loaders
|