# pylint: disable=line-too-long,too-many-lines,missing-docstring import os import warnings import numpy as np import pandas as pd import torch from torch.utils.data import Dataset from torchvision import transforms from . import video_transforms, volume_transforms from .loader import get_image_loader, get_video_loader from .random_erasing import RandomErasing class VideoClsDataset(Dataset): """Load your own video classification dataset.""" def __init__(self, anno_path, data_root='', mode='train', clip_len=8, frame_sample_rate=2, crop_size=224, short_side_size=256, new_height=256, new_width=340, keep_aspect_ratio=True, num_segment=1, num_crop=1, test_num_segment=10, test_num_crop=3, sparse_sample=False, args=None): self.anno_path = anno_path self.data_root = data_root self.mode = mode self.clip_len = clip_len self.frame_sample_rate = frame_sample_rate self.crop_size = crop_size self.short_side_size = short_side_size self.new_height = new_height self.new_width = new_width self.keep_aspect_ratio = keep_aspect_ratio self.num_segment = num_segment self.test_num_segment = test_num_segment self.num_crop = num_crop self.test_num_crop = test_num_crop self.sparse_sample = sparse_sample self.args = args self.aug = False self.rand_erase = False if self.mode in ['train']: self.aug = True if self.args.reprob > 0: self.rand_erase = True self.video_loader = get_video_loader() cleaned = pd.read_csv(self.anno_path, header=None, delimiter=' ') self.dataset_samples = list( cleaned[0].apply(lambda row: os.path.join(self.data_root, row))) self.label_array = list(cleaned.values[:, 1]) if (mode == 'train'): pass elif (mode == 'validation'): self.data_transform = video_transforms.Compose([ video_transforms.Resize( self.short_side_size, interpolation='bilinear'), video_transforms.CenterCrop( size=(self.crop_size, self.crop_size)), volume_transforms.ClipToTensor(), video_transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) elif mode == 'test': self.data_resize = video_transforms.Compose([ video_transforms.Resize( size=(short_side_size), interpolation='bilinear') ]) self.data_transform = video_transforms.Compose([ volume_transforms.ClipToTensor(), video_transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) self.test_seg = [] self.test_dataset = [] self.test_label_array = [] for ck in range(self.test_num_segment): for cp in range(self.test_num_crop): for idx in range(len(self.label_array)): sample_label = self.label_array[idx] self.test_label_array.append(sample_label) self.test_dataset.append(self.dataset_samples[idx]) self.test_seg.append((ck, cp)) def __getitem__(self, index): if self.mode == 'train': args = self.args scale_t = 1 sample = self.dataset_samples[index] # T H W C buffer = self.load_video(sample, sample_rate_scale=scale_t) if len(buffer) == 0: while len(buffer) == 0: warnings.warn( "video {} not correctly loaded during training".format( sample)) index = np.random.randint(self.__len__()) sample = self.dataset_samples[index] buffer = self.load_video(sample, sample_rate_scale=scale_t) if args.num_sample > 1: frame_list = [] label_list = [] index_list = [] for _ in range(args.num_sample): new_frames = self._aug_frame(buffer, args) label = self.label_array[index] frame_list.append(new_frames) label_list.append(label) index_list.append(index) return frame_list, label_list, index_list, {} else: buffer = self._aug_frame(buffer, args) return buffer, self.label_array[index], index, {} elif self.mode == 'validation': sample = self.dataset_samples[index] buffer = self.load_video(sample) if len(buffer) == 0: while len(buffer) == 0: warnings.warn( "video {} not correctly loaded during validation". format(sample)) index = np.random.randint(self.__len__()) sample = self.dataset_samples[index] buffer = self.load_video(sample) buffer = self.data_transform(buffer) return buffer, self.label_array[index], sample.split( "/")[-1].split(".")[0] elif self.mode == 'test': sample = self.test_dataset[index] chunk_nb, split_nb = self.test_seg[index] buffer = self.load_video(sample) while len(buffer) == 0: warnings.warn( "video {}, temporal {}, spatial {} not found during testing" .format(str(self.test_dataset[index]), chunk_nb, split_nb)) index = np.random.randint(self.__len__()) sample = self.test_dataset[index] chunk_nb, split_nb = self.test_seg[index] buffer = self.load_video(sample) buffer = self.data_resize(buffer) if isinstance(buffer, list): buffer = np.stack(buffer, 0) if self.sparse_sample: spatial_step = 1.0 * (max(buffer.shape[1], buffer.shape[2]) - self.short_side_size) / ( self.test_num_crop - 1) temporal_start = chunk_nb spatial_start = int(split_nb * spatial_step) if buffer.shape[1] >= buffer.shape[2]: buffer = buffer[temporal_start::self.test_num_segment, spatial_start:spatial_start + self.short_side_size, :, :] else: buffer = buffer[temporal_start::self.test_num_segment, :, spatial_start:spatial_start + self.short_side_size, :] else: spatial_step = 1.0 * (max(buffer.shape[1], buffer.shape[2]) - self.short_side_size) / ( self.test_num_crop - 1) temporal_step = max( 1.0 * (buffer.shape[0] - self.clip_len) / (self.test_num_segment - 1), 0) temporal_start = int(chunk_nb * temporal_step) spatial_start = int(split_nb * spatial_step) if buffer.shape[1] >= buffer.shape[2]: buffer = buffer[temporal_start:temporal_start + self.clip_len, spatial_start:spatial_start + self.short_side_size, :, :] else: buffer = buffer[temporal_start:temporal_start + self.clip_len, :, spatial_start:spatial_start + self.short_side_size, :] buffer = self.data_transform(buffer) return buffer, self.test_label_array[index], sample.split( "/")[-1].split(".")[0], chunk_nb, split_nb else: raise NameError('mode {} unkown'.format(self.mode)) def _aug_frame(self, buffer, args): aug_transform = video_transforms.create_random_augment( input_size=(self.crop_size, self.crop_size), auto_augment=args.aa, interpolation=args.train_interpolation, ) buffer = [transforms.ToPILImage()(frame) for frame in buffer] buffer = aug_transform(buffer) buffer = [transforms.ToTensor()(img) for img in buffer] buffer = torch.stack(buffer) # T C H W buffer = buffer.permute(0, 2, 3, 1) # T H W C # T H W C buffer = tensor_normalize(buffer, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # T H W C -> C T H W. buffer = buffer.permute(3, 0, 1, 2) # Perform data augmentation. scl, asp = ( [0.08, 1.0], [0.75, 1.3333], ) buffer = spatial_sampling( buffer, spatial_idx=-1, min_scale=256, max_scale=320, # crop_size=224, crop_size=args.input_size, random_horizontal_flip=False if args.data_set == 'SSV2' else True, inverse_uniform_sampling=False, aspect_ratio=asp, scale=scl, motion_shift=False) if self.rand_erase: erase_transform = RandomErasing( args.reprob, mode=args.remode, max_count=args.recount, num_splits=args.recount, device="cpu", ) buffer = buffer.permute(1, 0, 2, 3) # C T H W -> T C H W buffer = erase_transform(buffer) buffer = buffer.permute(1, 0, 2, 3) # T C H W -> C T H W return buffer def load_video(self, sample, sample_rate_scale=1): fname = sample try: vr = self.video_loader(fname) except Exception as e: print(f"Failed to load video from {fname} with error {e}!") return [] length = len(vr) if self.mode == 'test': if self.sparse_sample: tick = length / float(self.num_segment) all_index = [] for t_seg in range(self.test_num_segment): tmp_index = [ int(t_seg * tick / self.test_num_segment + tick * x) for x in range(self.num_segment) ] all_index.extend(tmp_index) all_index = list(np.sort(np.array(all_index))) else: all_index = [ x for x in range(0, length, self.frame_sample_rate) ] while len(all_index) < self.clip_len: all_index.append(all_index[-1]) vr.seek(0) buffer = vr.get_batch(all_index).asnumpy() return buffer # handle temporal segments converted_len = int(self.clip_len * self.frame_sample_rate) seg_len = length // self.num_segment all_index = [] for i in range(self.num_segment): if seg_len <= converted_len: index = np.linspace( 0, seg_len, num=seg_len // self.frame_sample_rate) index = np.concatenate( (index, np.ones(self.clip_len - seg_len // self.frame_sample_rate) * seg_len)) index = np.clip(index, 0, seg_len - 1).astype(np.int64) else: if self.mode == 'validation': end_idx = (converted_len + seg_len) // 2 else: end_idx = np.random.randint(converted_len, seg_len) str_idx = end_idx - converted_len index = np.linspace(str_idx, end_idx, num=self.clip_len) index = np.clip(index, str_idx, end_idx - 1).astype(np.int64) index = index + i * seg_len all_index.extend(list(index)) all_index = all_index[::int(sample_rate_scale)] vr.seek(0) buffer = vr.get_batch(all_index).asnumpy() return buffer def __len__(self): # return 200 if self.mode != 'test': return len(self.dataset_samples) else: return len(self.test_dataset) class RawFrameClsDataset(Dataset): """Load your own raw frame classification dataset.""" def __init__(self, anno_path, data_root, mode='train', clip_len=8, crop_size=224, short_side_size=256, new_height=256, new_width=340, keep_aspect_ratio=True, num_segment=1, num_crop=1, test_num_segment=10, test_num_crop=3, filename_tmpl='img_{:05}.jpg', start_idx=1, args=None): self.anno_path = anno_path self.data_root = data_root self.mode = mode self.clip_len = clip_len self.crop_size = crop_size self.short_side_size = short_side_size self.new_height = new_height self.new_width = new_width self.keep_aspect_ratio = keep_aspect_ratio self.num_segment = num_segment self.test_num_segment = test_num_segment self.num_crop = num_crop self.test_num_crop = test_num_crop self.filename_tmpl = filename_tmpl self.start_idx = start_idx self.args = args self.aug = False self.rand_erase = False if self.mode in ['train']: self.aug = True if self.args.reprob > 0: self.rand_erase = True self.image_loader = get_image_loader() cleaned = pd.read_csv(self.anno_path, header=None, delimiter=' ') self.dataset_samples = list( cleaned[0].apply(lambda row: os.path.join(self.data_root, row))) self.total_frames = list(cleaned.values[:, 1]) self.label_array = list(cleaned.values[:, -1]) if (mode == 'train'): pass elif (mode == 'validation'): self.data_transform = video_transforms.Compose([ video_transforms.Resize( self.short_side_size, interpolation='bilinear'), video_transforms.CenterCrop( size=(self.crop_size, self.crop_size)), volume_transforms.ClipToTensor(), video_transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) elif mode == 'test': self.data_resize = video_transforms.Compose([ video_transforms.Resize( size=(short_side_size), interpolation='bilinear') ]) self.data_transform = video_transforms.Compose([ volume_transforms.ClipToTensor(), video_transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) self.test_seg = [] self.test_dataset = [] self.test_total_frames = [] self.test_label_array = [] for ck in range(self.test_num_segment): for cp in range(self.test_num_crop): for idx in range(len(self.label_array)): self.test_seg.append((ck, cp)) self.test_dataset.append(self.dataset_samples[idx]) self.test_total_frames.append(self.total_frames[idx]) self.test_label_array.append(self.label_array[idx]) def __getitem__(self, index): if self.mode == 'train': args = self.args scale_t = 1 sample = self.dataset_samples[index] total_frame = self.total_frames[index] buffer = self.load_frame( sample, total_frame, sample_rate_scale=scale_t) # T H W C if len(buffer) == 0: while len(buffer) == 0: warnings.warn( "video {} not correctly loaded during training".format( sample)) index = np.random.randint(self.__len__()) sample = self.dataset_samples[index] total_frame = self.total_frames[index] buffer = self.load_frame( sample, total_frame, sample_rate_scale=scale_t) if args.num_sample > 1: frame_list = [] label_list = [] index_list = [] for _ in range(args.num_sample): new_frames = self._aug_frame(buffer, args) label = self.label_array[index] frame_list.append(new_frames) label_list.append(label) index_list.append(index) return frame_list, label_list, index_list, {} else: buffer = self._aug_frame(buffer, args) return buffer, self.label_array[index], index, {} elif self.mode == 'validation': sample = self.dataset_samples[index] total_frame = self.total_frames[index] buffer = self.load_frame(sample, total_frame) if len(buffer) == 0: while len(buffer) == 0: warnings.warn( "video {} not correctly loaded during validation". format(sample)) index = np.random.randint(self.__len__()) sample = self.dataset_samples[index] buffer = self.load_frame(sample, total_frame) buffer = self.data_transform(buffer) return buffer, self.label_array[index], sample.split( "/")[-1].split(".")[0] elif self.mode == 'test': sample = self.test_dataset[index] total_frame = self.test_total_frames[index] chunk_nb, split_nb = self.test_seg[index] buffer = self.load_frame(sample, total_frame) while len(buffer) == 0: warnings.warn( "video {}, temporal {}, spatial {} not found during testing" .format(str(self.test_dataset[index]), chunk_nb, split_nb)) index = np.random.randint(self.__len__()) sample = self.test_dataset[index] total_frame = self.test_total_frames[index] chunk_nb, split_nb = self.test_seg[index] buffer = self.load_frame(sample, total_frame) buffer = self.data_resize(buffer) if isinstance(buffer, list): buffer = np.stack(buffer, 0) spatial_step = 1.0 * (max(buffer.shape[1], buffer.shape[2]) - self.short_side_size) / ( self.test_num_crop - 1) temporal_start = chunk_nb spatial_start = int(split_nb * spatial_step) if buffer.shape[1] >= buffer.shape[2]: buffer = buffer[temporal_start::self.test_num_segment, spatial_start:spatial_start + self.short_side_size, :, :] else: buffer = buffer[temporal_start::self.test_num_segment, :, spatial_start:spatial_start + self.short_side_size, :] buffer = self.data_transform(buffer) return buffer, self.test_label_array[index], sample.split( "/")[-1].split(".")[0], chunk_nb, split_nb else: raise NameError('mode {} unkown'.format(self.mode)) def _aug_frame(self, buffer, args): aug_transform = video_transforms.create_random_augment( input_size=(self.crop_size, self.crop_size), auto_augment=args.aa, interpolation=args.train_interpolation, ) buffer = [transforms.ToPILImage()(frame) for frame in buffer] buffer = aug_transform(buffer) buffer = [transforms.ToTensor()(img) for img in buffer] buffer = torch.stack(buffer) # T C H W buffer = buffer.permute(0, 2, 3, 1) # T H W C # T H W C buffer = tensor_normalize(buffer, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # T H W C -> C T H W. buffer = buffer.permute(3, 0, 1, 2) # Perform data augmentation. scl, asp = ( [0.08, 1.0], [0.75, 1.3333], ) buffer = spatial_sampling( buffer, spatial_idx=-1, min_scale=256, max_scale=320, crop_size=self.crop_size, random_horizontal_flip=False if args.data_set == 'SSV2' else True, inverse_uniform_sampling=False, aspect_ratio=asp, scale=scl, motion_shift=False) if self.rand_erase: erase_transform = RandomErasing( args.reprob, mode=args.remode, max_count=args.recount, num_splits=args.recount, device="cpu", ) buffer = buffer.permute(1, 0, 2, 3) buffer = erase_transform(buffer) buffer = buffer.permute(1, 0, 2, 3) return buffer def load_frame(self, sample, num_frames, sample_rate_scale=1): """Load video content using Decord""" fname = sample if self.mode == 'test': tick = num_frames / float(self.num_segment) all_index = [] for t_seg in range(self.test_num_segment): tmp_index = [ int(t_seg * tick / self.test_num_segment + tick * x) for x in range(self.num_segment) ] all_index.extend(tmp_index) all_index = list(np.sort(np.array(all_index) + self.start_idx)) imgs = [] for idx in all_index: frame_fname = os.path.join(fname, self.filename_tmpl.format(idx)) img = self.image_loader(frame_fname) imgs.append(img) buffer = np.array(imgs) return buffer # handle temporal segments average_duration = num_frames // self.num_segment all_index = [] if average_duration > 0: if self.mode == 'validation': all_index = list( np.multiply( list(range(self.num_segment)), average_duration) + np.ones(self.num_segment, dtype=int) * (average_duration // 2)) else: all_index = list( np.multiply( list(range(self.num_segment)), average_duration) + np.random.randint(average_duration, size=self.num_segment)) elif num_frames > self.num_segment: if self.mode == 'validation': all_index = list(range(self.num_segment)) else: all_index = list( np.sort( np.random.randint(num_frames, size=self.num_segment))) else: all_index = [0] * (self.num_segment - num_frames) + list( range(num_frames)) all_index = list(np.array(all_index) + self.start_idx) imgs = [] for idx in all_index: frame_fname = os.path.join(fname, self.filename_tmpl.format(idx)) img = self.image_loader(frame_fname) imgs.append(img) buffer = np.array(imgs) return buffer def __len__(self): if self.mode != 'test': return len(self.dataset_samples) else: return len(self.test_dataset) def spatial_sampling( frames, spatial_idx=-1, min_scale=256, max_scale=320, crop_size=224, random_horizontal_flip=True, inverse_uniform_sampling=False, aspect_ratio=None, scale=None, motion_shift=False, ): """ Perform spatial sampling on the given video frames. If spatial_idx is -1, perform random scale, random crop, and random flip on the given frames. If spatial_idx is 0, 1, or 2, perform spatial uniform sampling with the given spatial_idx. Args: frames (tensor): frames of images sampled from the video. The dimension is `num frames` x `height` x `width` x `channel`. spatial_idx (int): if -1, perform random spatial sampling. If 0, 1, or 2, perform left, center, right crop if width is larger than height, and perform top, center, buttom crop if height is larger than width. min_scale (int): the minimal size of scaling. max_scale (int): the maximal size of scaling. crop_size (int): the size of height and width used to crop the frames. inverse_uniform_sampling (bool): if True, sample uniformly in [1 / max_scale, 1 / min_scale] and take a reciprocal to get the scale. If False, take a uniform sample from [min_scale, max_scale]. aspect_ratio (list): Aspect ratio range for resizing. scale (list): Scale range for resizing. motion_shift (bool): Whether to apply motion shift for resizing. Returns: frames (tensor): spatially sampled frames. """ assert spatial_idx in [-1, 0, 1, 2] if spatial_idx == -1: if aspect_ratio is None and scale is None: frames, _ = video_transforms.random_short_side_scale_jitter( images=frames, min_size=min_scale, max_size=max_scale, inverse_uniform_sampling=inverse_uniform_sampling, ) frames, _ = video_transforms.random_crop(frames, crop_size) else: transform_func = ( video_transforms.random_resized_crop_with_shift if motion_shift else video_transforms.random_resized_crop) frames = transform_func( images=frames, target_height=crop_size, target_width=crop_size, scale=scale, ratio=aspect_ratio, ) if random_horizontal_flip: frames, _ = video_transforms.horizontal_flip(0.5, frames) else: # The testing is deterministic and no jitter should be performed. # min_scale, max_scale, and crop_size are expect to be the same. assert len({min_scale, max_scale, crop_size}) == 1 frames, _ = video_transforms.random_short_side_scale_jitter( frames, min_scale, max_scale) frames, _ = video_transforms.uniform_crop(frames, crop_size, spatial_idx) return frames def tensor_normalize(tensor, mean, std): """ Normalize a given tensor by subtracting the mean and dividing the std. Args: tensor (tensor): tensor to normalize. mean (tensor or list): mean value to subtract. std (tensor or list): std to divide. """ if tensor.dtype == torch.uint8: tensor = tensor.float() tensor = tensor / 255.0 if type(mean) == list: mean = torch.tensor(mean) if type(std) == list: std = torch.tensor(std) tensor = tensor - mean tensor = tensor / std return tensor