import os import numpy as np import torch from torchvision import transforms from random_erasing import RandomErasing import warnings from decord import VideoReader, cpu from torch.utils.data import Dataset import video_transforms as video_transforms import volume_transforms as volume_transforms class SSVideoClsDataset(Dataset): """Load your own video classification dataset.""" def __init__(self, anno_path, data_path, 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, args=None): self.anno_path = anno_path self.data_path = data_path 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.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 if VideoReader is None: raise ImportError("Unable to import `decord` which is required to read videos.") import pandas as pd cleaned = pd.read_csv(self.anno_path, header=None, delimiter=' ') self.dataset_samples = list(cleaned.values[:, 0]) 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] buffer = self.loadvideo_decord(sample, 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] buffer = self.loadvideo_decord(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.loadvideo_decord(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.loadvideo_decord(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.loadvideo_decord(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.loadvideo_decord(sample) 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 # 0/1 spatial_start = int(split_nb * spatial_step) if buffer.shape[1] >= buffer.shape[2]: buffer = buffer[temporal_start::2, \ spatial_start:spatial_start + self.short_side_size, :, :] else: buffer = buffer[temporal_start::2, \ :, 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 = ( [0.25, 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 loadvideo_decord(self, sample, sample_rate_scale=1): """Load video content using Decord""" fname = sample if not (os.path.exists(fname)): return [] # avoid hanging issue if os.path.getsize(fname) < 1 * 1024: print('SKIP: ', fname, " - ", os.path.getsize(fname)) return [] try: if self.keep_aspect_ratio: vr = VideoReader(fname, num_threads=1, ctx=cpu(0)) else: vr = VideoReader(fname, width=self.new_width, height=self.new_height, num_threads=1, ctx=cpu(0)) except: print("video cannot be loaded by decord: ", fname) return [] if self.mode == 'test': all_index = [] tick = len(vr) / float(self.num_segment) all_index = list(np.array([int(tick / 2.0 + tick * x) for x in range(self.num_segment)] + [int(tick * x) for x in range(self.num_segment)])) while len(all_index) < (self.num_segment * self.test_num_segment): all_index.append(all_index[-1]) all_index = list(np.sort(np.array(all_index))) vr.seek(0) buffer = vr.get_batch(all_index).asnumpy() return buffer # handle temporal segments average_duration = len(vr) // self.num_segment all_index = [] if average_duration > 0: all_index += list(np.multiply(list(range(self.num_segment)), average_duration) + np.random.randint(average_duration, size=self.num_segment)) elif len(vr) > self.num_segment: all_index += list(np.sort(np.random.randint(len(vr), size=self.num_segment))) else: all_index += list(np.zeros((self.num_segment,))) all_index = list(np.array(all_index)) vr.seek(0) buffer = vr.get_batch(all_index).asnumpy() 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