| | import os, csv, random |
| | import numpy as np |
| | from decord import VideoReader |
| | import torch |
| | import torchvision.transforms as transforms |
| | from torch.utils.data.dataset import Dataset |
| |
|
| |
|
| | class ChronoMagic(Dataset): |
| | def __init__( |
| | self, |
| | csv_path, video_folder, |
| | sample_size=512, sample_stride=4, sample_n_frames=16, |
| | is_image=False, |
| | is_uniform=True, |
| | ): |
| | with open(csv_path, 'r') as csvfile: |
| | self.dataset = list(csv.DictReader(csvfile)) |
| | self.length = len(self.dataset) |
| |
|
| | self.video_folder = video_folder |
| | self.sample_stride = sample_stride |
| | self.sample_n_frames = sample_n_frames |
| | self.is_image = is_image |
| | self.is_uniform = is_uniform |
| | |
| | sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size) |
| | self.pixel_transforms = transforms.Compose([ |
| | transforms.RandomHorizontalFlip(), |
| | transforms.Resize(sample_size[0], interpolation=transforms.InterpolationMode.BICUBIC), |
| | transforms.CenterCrop(sample_size), |
| | transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), |
| | ]) |
| |
|
| | def _get_frame_indices_adjusted(self, video_length, n_frames): |
| | indices = list(range(video_length)) |
| | additional_frames_needed = n_frames - video_length |
| | |
| | repeat_indices = [] |
| | for i in range(additional_frames_needed): |
| | index_to_repeat = i % video_length |
| | repeat_indices.append(indices[index_to_repeat]) |
| | |
| | all_indices = indices + repeat_indices |
| | all_indices.sort() |
| |
|
| | return all_indices |
| |
|
| | def _generate_frame_indices(self, video_length, n_frames, sample_stride, is_transmit): |
| | prob_execute_original = 1 if int(is_transmit) == 0 else 0 |
| |
|
| | |
| | if random.random() < prob_execute_original: |
| | if video_length <= n_frames: |
| | return self._get_frame_indices_adjusted(video_length, n_frames) |
| | else: |
| | interval = (video_length - 1) / (n_frames - 1) |
| | indices = [int(round(i * interval)) for i in range(n_frames)] |
| | indices[-1] = video_length - 1 |
| | return indices |
| | else: |
| | if video_length <= n_frames: |
| | return self._get_frame_indices_adjusted(video_length, n_frames) |
| | else: |
| | clip_length = min(video_length, (n_frames - 1) * sample_stride + 1) |
| | start_idx = random.randint(0, video_length - clip_length) |
| | return np.linspace(start_idx, start_idx + clip_length - 1, n_frames, dtype=int).tolist() |
| | |
| | def get_batch(self, idx): |
| | video_dict = self.dataset[idx] |
| | videoid, name, is_transmit = video_dict['videoid'], video_dict['name'], video_dict['is_transmit'] |
| | |
| | video_dir = os.path.join(self.video_folder, f"{videoid}.mp4") |
| | video_reader = VideoReader(video_dir, num_threads=0) |
| | video_length = len(video_reader) |
| |
|
| | batch_index = self._generate_frame_indices(video_length, self.sample_n_frames, self.sample_stride, is_transmit) if not self.is_image else [random.randint(0, video_length - 1)] |
| |
|
| | pixel_values = torch.from_numpy(video_reader.get_batch(batch_index).asnumpy()).permute(0, 3, 1, 2) / 255. |
| | del video_reader |
| |
|
| | if self.is_image: |
| | pixel_values = pixel_values[0] |
| | |
| | return pixel_values, name, videoid |
| |
|
| | def __len__(self): |
| | return self.length |
| |
|
| | def __getitem__(self, idx): |
| | while True: |
| | try: |
| | pixel_values, name, videoid = self.get_batch(idx) |
| | break |
| |
|
| | except Exception as e: |
| | idx = random.randint(0, self.length-1) |
| |
|
| | pixel_values = self.pixel_transforms(pixel_values) |
| | sample = dict(pixel_values=pixel_values, text=name, id=videoid) |
| | return sample |
