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import json
import torch
import cv2
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 numpy as np
import random, os
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,
root_path,
annotation_json,
tokenizer,
max_sequence_length: int = 226,
height: int = 480,
width: int = 640,
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__()
self.root_path = root_path
with open(annotation_json, '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_path = os.path.join(self.root_path, self.data_list[index]['clip_path'])
video_reader = decord.VideoReader(video_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]['caption']],
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
class AutoEncoderDataset(ImageVideoDataset):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def __getitem__(self, index):
while True:
try:
video_path = os.path.join(self.root_path, self.data_list[index]['clip_path'])
video_reader = decord.VideoReader(video_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_indice = [random.randint(1, video_num_frames - 1)] # random selects a frame from the video
frames = video_reader.get_batch(random_indice)
break
except Exception as e:
print("[WARN] Get problem when loading video: ", self.data_list[index]['clip_path'])
print(
"Error encounter during audio feature extraction: ", e,
)
index = random.randint(0, len(self.data_list) - 1)
continue
return frames
class LvisDataset(Dataset):
def __init__(
self,
root_path,
annotation_json,
height: int = 480,
width: int = 640,
random_flip: Optional[float] = None,
) -> None:
super().__init__()
self.root_path = root_path
with open(annotation_json, 'r') as f:
self.data_list = json.load(f)['images']
self.height = height
self.width = width
self.width = width
self.video_transforms = transforms.Compose(
[
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 __getitem__(self, index):
image_path = os.path.join(self.root_path, "unlabeled2017", self.data_list[index]['file_name'])
image = cv2.imread(image_path)
image = cv2.resize(image, (self.width, self.height))
image = self.video_transforms(torch.from_numpy(image).permute(2, 0, 1))
return image.contiguous()
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