325 lines
13 KiB
Python
325 lines
13 KiB
Python
import csv
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import io
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import json
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import math
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import os
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import random
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from threading import Thread
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import albumentations
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import cv2
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import gc
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import numpy as np
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import torch
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import torchvision.transforms as transforms
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from func_timeout import func_timeout, FunctionTimedOut
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from decord import VideoReader
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from PIL import Image
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from torch.utils.data import BatchSampler, Sampler
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from torch.utils.data.dataset import Dataset
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from contextlib import contextmanager
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VIDEO_READER_TIMEOUT = 20
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def get_random_mask(shape):
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f, c, h, w = shape
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if f != 1:
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mask_index = np.random.choice([0, 1, 2, 3, 4], p = [0.05, 0.3, 0.3, 0.3, 0.05]) # np.random.randint(0, 5)
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else:
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mask_index = np.random.choice([0, 1], p = [0.2, 0.8]) # np.random.randint(0, 2)
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mask = torch.zeros((f, 1, h, w), dtype=torch.uint8)
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if mask_index == 0:
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center_x = torch.randint(0, w, (1,)).item()
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center_y = torch.randint(0, h, (1,)).item()
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block_size_x = torch.randint(w // 4, w // 4 * 3, (1,)).item() # 方块的宽度范围
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block_size_y = torch.randint(h // 4, h // 4 * 3, (1,)).item() # 方块的高度范围
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start_x = max(center_x - block_size_x // 2, 0)
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end_x = min(center_x + block_size_x // 2, w)
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start_y = max(center_y - block_size_y // 2, 0)
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end_y = min(center_y + block_size_y // 2, h)
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mask[:, :, start_y:end_y, start_x:end_x] = 1
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elif mask_index == 1:
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mask[:, :, :, :] = 1
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elif mask_index == 2:
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mask_frame_index = np.random.randint(1, 5)
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mask[mask_frame_index:, :, :, :] = 1
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elif mask_index == 3:
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mask_frame_index = np.random.randint(1, 5)
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mask[mask_frame_index:-mask_frame_index, :, :, :] = 1
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elif mask_index == 4:
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center_x = torch.randint(0, w, (1,)).item()
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center_y = torch.randint(0, h, (1,)).item()
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block_size_x = torch.randint(w // 4, w // 4 * 3, (1,)).item() # 方块的宽度范围
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block_size_y = torch.randint(h // 4, h // 4 * 3, (1,)).item() # 方块的高度范围
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start_x = max(center_x - block_size_x // 2, 0)
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end_x = min(center_x + block_size_x // 2, w)
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start_y = max(center_y - block_size_y // 2, 0)
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end_y = min(center_y + block_size_y // 2, h)
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mask_frame_before = np.random.randint(0, f // 2)
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mask_frame_after = np.random.randint(f // 2, f)
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mask[mask_frame_before:mask_frame_after, :, start_y:end_y, start_x:end_x] = 1
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else:
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raise ValueError(f"The mask_index {mask_index} is not define")
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return mask
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class ImageVideoSampler(BatchSampler):
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"""A sampler wrapper for grouping images with similar aspect ratio into a same batch.
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Args:
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sampler (Sampler): Base sampler.
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dataset (Dataset): Dataset providing data information.
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batch_size (int): Size of mini-batch.
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drop_last (bool): If ``True``, the sampler will drop the last batch if
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its size would be less than ``batch_size``.
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aspect_ratios (dict): The predefined aspect ratios.
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"""
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def __init__(self,
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sampler: Sampler,
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dataset: Dataset,
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batch_size: int,
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drop_last: bool = False
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) -> None:
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if not isinstance(sampler, Sampler):
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raise TypeError('sampler should be an instance of ``Sampler``, '
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f'but got {sampler}')
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if not isinstance(batch_size, int) or batch_size <= 0:
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raise ValueError('batch_size should be a positive integer value, '
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f'but got batch_size={batch_size}')
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self.sampler = sampler
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self.dataset = dataset
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self.batch_size = batch_size
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self.drop_last = drop_last
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# buckets for each aspect ratio
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self.bucket = {'image':[], 'video':[]}
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def __iter__(self):
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for idx in self.sampler:
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content_type = self.dataset.dataset[idx].get('type', 'image')
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self.bucket[content_type].append(idx)
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# yield a batch of indices in the same aspect ratio group
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if len(self.bucket['video']) == self.batch_size:
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bucket = self.bucket['video']
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yield bucket[:]
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del bucket[:]
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elif len(self.bucket['image']) == self.batch_size:
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bucket = self.bucket['image']
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yield bucket[:]
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del bucket[:]
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@contextmanager
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def VideoReader_contextmanager(*args, **kwargs):
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vr = VideoReader(*args, **kwargs)
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try:
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yield vr
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finally:
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del vr
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gc.collect()
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def get_video_reader_batch(video_reader, batch_index):
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frames = video_reader.get_batch(batch_index).asnumpy()
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return frames
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def resize_frame(frame, target_short_side):
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h, w, _ = frame.shape
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if h < w:
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if target_short_side > h:
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return frame
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new_h = target_short_side
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new_w = int(target_short_side * w / h)
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else:
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if target_short_side > w:
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return frame
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new_w = target_short_side
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new_h = int(target_short_side * h / w)
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resized_frame = cv2.resize(frame, (new_w, new_h))
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return resized_frame
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class ImageVideoDataset(Dataset):
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def __init__(
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self,
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ann_path, data_root=None,
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video_sample_size=512, video_sample_stride=4, video_sample_n_frames=16,
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image_sample_size=512,
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video_repeat=0,
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text_drop_ratio=-1,
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enable_bucket=False,
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video_length_drop_start=0.1,
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video_length_drop_end=0.9,
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enable_inpaint=False,
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):
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# Loading annotations from files
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print(f"loading annotations from {ann_path} ...")
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if ann_path.endswith('.csv'):
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with open(ann_path, 'r') as csvfile:
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dataset = list(csv.DictReader(csvfile))
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elif ann_path.endswith('.json'):
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dataset = json.load(open(ann_path))
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self.data_root = data_root
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# It's used to balance num of images and videos.
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self.dataset = []
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for data in dataset:
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if data.get('type', 'image') != 'video':
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self.dataset.append(data)
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if video_repeat > 0:
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for _ in range(video_repeat):
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for data in dataset:
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if data.get('type', 'image') == 'video':
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self.dataset.append(data)
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del dataset
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self.length = len(self.dataset)
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print(f"data scale: {self.length}")
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# TODO: enable bucket training
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self.enable_bucket = enable_bucket
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self.text_drop_ratio = text_drop_ratio
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self.enable_inpaint = enable_inpaint
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self.video_length_drop_start = video_length_drop_start
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self.video_length_drop_end = video_length_drop_end
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# Video params
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self.video_sample_stride = video_sample_stride
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self.video_sample_n_frames = video_sample_n_frames
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self.video_sample_size = tuple(video_sample_size) if not isinstance(video_sample_size, int) else (video_sample_size, video_sample_size)
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self.video_transforms = transforms.Compose(
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[
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transforms.Resize(min(self.video_sample_size)),
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transforms.CenterCrop(self.video_sample_size),
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transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
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]
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)
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# Image params
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self.image_sample_size = tuple(image_sample_size) if not isinstance(image_sample_size, int) else (image_sample_size, image_sample_size)
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self.image_transforms = transforms.Compose([
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transforms.Resize(min(self.image_sample_size)),
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transforms.CenterCrop(self.image_sample_size),
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transforms.ToTensor(),
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transforms.Normalize([0.5, 0.5, 0.5],[0.5, 0.5, 0.5])
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])
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self.larger_side_of_image_and_video = max(min(self.image_sample_size), min(self.video_sample_size))
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def get_batch(self, idx):
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data_info = self.dataset[idx % len(self.dataset)]
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if data_info.get('type', 'image')=='video':
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video_id, text = data_info['file_path'], data_info['text']
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if self.data_root is None:
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video_dir = video_id
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else:
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video_dir = os.path.join(self.data_root, video_id)
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with VideoReader_contextmanager(video_dir, num_threads=2) as video_reader:
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min_sample_n_frames = min(
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self.video_sample_n_frames,
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int(len(video_reader) * (self.video_length_drop_end - self.video_length_drop_start) // self.video_sample_stride)
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)
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if min_sample_n_frames == 0:
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raise ValueError(f"No Frames in video.")
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video_length = int(self.video_length_drop_end * len(video_reader))
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clip_length = min(video_length, (min_sample_n_frames - 1) * self.video_sample_stride + 1)
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start_idx = random.randint(int(self.video_length_drop_start * video_length), video_length - clip_length) if video_length != clip_length else 0
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batch_index = np.linspace(start_idx, start_idx + clip_length - 1, min_sample_n_frames, dtype=int)
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try:
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sample_args = (video_reader, batch_index)
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pixel_values = func_timeout(
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VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
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)
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resized_frames = []
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for i in range(len(pixel_values)):
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frame = pixel_values[i]
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resized_frame = resize_frame(frame, self.larger_side_of_image_and_video)
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resized_frames.append(resized_frame)
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pixel_values = np.array(resized_frames)
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except FunctionTimedOut:
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raise ValueError(f"Read {idx} timeout.")
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except Exception as e:
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raise ValueError(f"Failed to extract frames from video. Error is {e}.")
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if not self.enable_bucket:
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pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous()
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pixel_values = pixel_values / 255.
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del video_reader
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else:
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pixel_values = pixel_values
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if not self.enable_bucket:
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pixel_values = self.video_transforms(pixel_values)
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# Random use no text generation
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if random.random() < self.text_drop_ratio:
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text = ''
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return pixel_values, text, 'video'
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else:
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image_path, text = data_info['file_path'], data_info['text']
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if self.data_root is not None:
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image_path = os.path.join(self.data_root, image_path)
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image = Image.open(image_path).convert('RGB')
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if not self.enable_bucket:
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image = self.image_transforms(image).unsqueeze(0)
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else:
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image = np.expand_dims(np.array(image), 0)
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if random.random() < self.text_drop_ratio:
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text = ''
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return image, text, 'image'
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def __len__(self):
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return self.length
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def __getitem__(self, idx):
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data_info = self.dataset[idx % len(self.dataset)]
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data_type = data_info.get('type', 'image')
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while True:
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sample = {}
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try:
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data_info_local = self.dataset[idx % len(self.dataset)]
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data_type_local = data_info_local.get('type', 'image')
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if data_type_local != data_type:
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raise ValueError("data_type_local != data_type")
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pixel_values, name, data_type = self.get_batch(idx)
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sample["pixel_values"] = pixel_values
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sample["text"] = name
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sample["data_type"] = data_type
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sample["idx"] = idx
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if len(sample) > 0:
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break
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except Exception as e:
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print(e, self.dataset[idx % len(self.dataset)])
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idx = random.randint(0, self.length-1)
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if self.enable_inpaint and not self.enable_bucket:
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mask = get_random_mask(pixel_values.size())
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mask_pixel_values = pixel_values * (1 - mask) + torch.ones_like(pixel_values) * -1 * mask
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sample["mask_pixel_values"] = mask_pixel_values
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sample["mask"] = mask
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clip_pixel_values = sample["pixel_values"][0].permute(1, 2, 0).contiguous()
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clip_pixel_values = (clip_pixel_values * 0.5 + 0.5) * 255
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sample["clip_pixel_values"] = clip_pixel_values
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ref_pixel_values = sample["pixel_values"][0].unsqueeze(0)
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if (mask == 1).all():
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ref_pixel_values = torch.ones_like(ref_pixel_values) * -1
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sample["ref_pixel_values"] = ref_pixel_values
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return sample
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