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import json
import torch
from typing import Any, Dict, List, Optional, Tuple
from torch.utils.data import DataLoader, Dataset
import torchvision.transforms as TT
from torchvision import transforms
from torchvision.transforms.functional import center_crop, resize
from torchvision.transforms import InterpolationMode
import random
try:
import decord
except ImportError:
raise ImportError(
"The `decord` package is required for loading the video dataset. Install with `pip install decord`"
)
decord.bridge.set_bridge("torch")
class ImageVideoDataset(Dataset):
def __init__(
self,
data_root,
tokenizer,
max_sequence_length: int = 226,
height: int = 480,
width: int = 720,
video_reshape_mode: str = "center",
fps: int = 8,
stripe: int = 2,
max_num_frames: int = 49,
skip_frames_start: int = 0,
skip_frames_end: int = 0,
random_flip: Optional[float] = None,
) -> None:
super().__init__()
with open(data_root, 'r') as f:
self.data_list = json.load(f)
self.tokenizer = tokenizer
self.max_sequence_length = max_sequence_length
self.height = height
self.width = width
self.video_reshape_mode = video_reshape_mode
self.fps = fps
self.max_num_frames = max_num_frames
self.skip_frames_start = skip_frames_start
self.skip_frames_end = skip_frames_end
self.stripe = stripe
self.video_transforms = transforms.Compose(
[
transforms.RandomHorizontalFlip(random_flip) if random_flip else transforms.Lambda(lambda x: x),
transforms.Lambda(lambda x: x / 255.0),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
]
)
def __len__(self):
return len(self.data_list)
def _resize_for_rectangle_crop(self, arr):
image_size = self.height, self.width
reshape_mode = self.video_reshape_mode
if arr.shape[3] / arr.shape[2] > image_size[1] / image_size[0]:
arr = resize(
arr,
size=[image_size[0], int(arr.shape[3] * image_size[0] / arr.shape[2])],
interpolation=InterpolationMode.BICUBIC,
)
else:
arr = resize(
arr,
size=[int(arr.shape[2] * image_size[1] / arr.shape[3]), image_size[1]],
interpolation=InterpolationMode.BICUBIC,
)
h, w = arr.shape[2], arr.shape[3]
arr = arr.squeeze(0)
delta_h = h - image_size[0]
delta_w = w - image_size[1]
if reshape_mode == "random" or reshape_mode == "none":
top = np.random.randint(0, delta_h + 1)
left = np.random.randint(0, delta_w + 1)
elif reshape_mode == "center":
top, left = delta_h // 2, delta_w // 2
else:
raise NotImplementedError
arr = TT.functional.crop(arr, top=top, left=left, height=image_size[0], width=image_size[1])
return arr
def __getitem__(self, index):
while True:
try:
video_reader = decord.VideoReader(self.data_list[index]['file_path'], width=self.width, height=self.height)
video_num_frames = len(video_reader)
# print(video_num_frames, video_reader.get_avg_fps())
if self.stripe * self.max_num_frames > video_num_frames:
stripe = 1
else:
stripe = self.stripe
random_range = video_num_frames - stripe * self.max_num_frames - 1
random_range = max(1, random_range)
start_frame = random.randint(1, random_range) if random_range > 0 else 1
indices = list(range(start_frame, start_frame + stripe * self.max_num_frames, stripe)) # (end_frame - start_frame) // self.max_num_frames))
frames = video_reader.get_batch(indices)
# Ensure that we don't go over the limit
frames = frames[: self.max_num_frames]
selected_num_frames = frames.shape[0]
# Choose first (4k + 1) frames as this is how many is required by the VAE
remainder = (3 + (selected_num_frames % 4)) % 4
if remainder != 0:
frames = frames[:-remainder]
selected_num_frames = frames.shape[0]
assert (selected_num_frames - 1) % 4 == 0
if selected_num_frames == self.max_num_frames:
break
else:
index = (index + 1) % len(self.data_list)
continue
except Exception as e:
index = (index + 1) % len(self.data_list)
print(video_num_frames, start_frame, indices)
print(
"Error encounter during audio feature extraction: ", e,
)
continue
# Training transforms
# frames = (frames - 127.5) / 127.5
frames = frames.permute(0, 3, 1, 2).contiguous() # [F, C, H, W]
frames = self._resize_for_rectangle_crop(frames)
frames = torch.stack([self.video_transforms(frame) for frame in frames], dim=0)
text_inputs = self.tokenizer(
[self.data_list[index]['text']],
padding="max_length",
max_length=self.max_sequence_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids[0]
return frames.contiguous(), text_input_ids
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