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import os, io, csv, math, random
import json
import numpy as np
from einops import rearrange
from decord import VideoReader
import torch
import torchvision.transforms as transforms
from torch.utils.data.dataset import Dataset
from diffusers.utils import logging
logger = logging.get_logger(__name__)
class WebVid10M(Dataset):
def __init__(
self,
json_path, video_folder=None,
sample_size=256, sample_stride=4, sample_n_frames=16,
is_image=False,
**kwargs,
):
logger.info(f"loading annotations from {json_path} ...")
with open(json_path, 'rb') as json_file:
json_list = list(json_file)
self.dataset = [json.loads(json_str) for json_str in json_list]
self.length = len(self.dataset)
logger.info(f"data scale: {self.length}")
self.video_folder = video_folder
self.sample_stride = sample_stride if isinstance(sample_stride, int) else tuple(sample_stride)
self.sample_n_frames = sample_n_frames
self.is_image = is_image
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], antialias=None),
transforms.CenterCrop(sample_size),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
])
def get_batch(self, idx):
video_dict = self.dataset[idx]
video_relative_path, name = video_dict['file'], video_dict['text']
if self.video_folder is not None:
if video_relative_path[0] == '/':
video_dir = os.path.join(self.video_folder, os.path.basename(video_relative_path))
else:
video_dir = os.path.join(self.video_folder, video_relative_path)
else:
video_dir = video_relative_path
video_reader = VideoReader(video_dir)
video_length = len(video_reader)
if not self.is_image:
if isinstance(self.sample_stride, int):
stride = self.sample_stride
elif isinstance(self.sample_stride, tuple):
stride = random.randint(self.sample_stride[0], self.sample_stride[1])
clip_length = min(video_length, (self.sample_n_frames - 1) * stride + 1)
start_idx = random.randint(0, video_length - clip_length)
batch_index = np.linspace(start_idx, start_idx + clip_length - 1, self.sample_n_frames, dtype=int)
else:
frame_difference = random.randint(2, self.sample_n_frames)
clip_length = min(video_length, (frame_difference - 1) * self.sample_stride + 1)
start_idx = random.randint(0, video_length - clip_length)
batch_index = [start_idx, start_idx + clip_length - 1]
pixel_values = torch.from_numpy(video_reader.get_batch(batch_index).asnumpy()).permute(0, 3, 1, 2).contiguous()
pixel_values = pixel_values / 255.
del video_reader
return pixel_values, name
def __len__(self):
return self.length
def __getitem__(self, idx):
while True:
try:
pixel_values, name = 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)
return sample
class Pexels(Dataset):
def __init__(
self,
json_path, caption_json_path, video_folder=None,
sample_size=256, sample_duration=1, sample_fps=8,
is_image=False,
**kwargs,
):
logger.info(f"loading captions from {caption_json_path} ...")
with open(caption_json_path, 'rb') as caption_json_file:
caption_json_list = list(caption_json_file)
self.caption_dict = {json.loads(json_str)['id']: json.loads(json_str)['text'] for json_str in caption_json_list}
logger.info(f"loading annotations from {json_path} ...")
with open(json_path, 'rb') as json_file:
json_list = list(json_file)
dataset = [json.loads(json_str) for json_str in json_list]
self.dataset = []
for data in dataset:
data['text'] = self.caption_dict[data['id']]
if data['height'] / data['width'] < 0.625:
self.dataset.append(data)
self.length = len(self.dataset)
logger.info(f"data scale: {self.length}")
self.video_folder = video_folder
self.sample_duration = sample_duration
self.sample_fps = sample_fps
self.sample_n_frames = sample_duration * sample_fps
self.is_image = is_image
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], antialias=None),
transforms.CenterCrop(sample_size),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
])
def get_batch(self, idx):
video_dict = self.dataset[idx]
video_relative_path, name = video_dict['file'], video_dict['text']
fps = video_dict['fps']
if self.video_folder is not None:
if video_relative_path[0] == '/':
video_dir = os.path.join(self.video_folder, os.path.basename(video_relative_path))
else:
video_dir = os.path.join(self.video_folder, video_relative_path)
else:
video_dir = video_relative_path
video_reader = VideoReader(video_dir)
video_length = len(video_reader)
if not self.is_image:
clip_length = min(video_length, math.ceil(fps * self.sample_duration))
start_idx = random.randint(0, video_length - clip_length)
batch_index = np.linspace(start_idx, start_idx + clip_length - 1, self.sample_n_frames, dtype=int)
else:
frame_difference = random.randint(2, self.sample_n_frames)
sample_stride = math.ceil((fps * self.sample_duration) / (self.sample_n_frames - 1) - 1)
clip_length = min(video_length, (frame_difference - 1) * sample_stride + 1)
start_idx = random.randint(0, video_length - clip_length)
batch_index = [start_idx, start_idx + clip_length - 1]
pixel_values = torch.from_numpy(video_reader.get_batch(batch_index).asnumpy()).permute(0, 3, 1, 2).contiguous()
pixel_values = pixel_values / 255.
del video_reader
return pixel_values, name
def __len__(self):
return self.length
def __getitem__(self, idx):
while True:
try:
pixel_values, name = 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)
return sample
class JointDataset(Dataset):
def __init__(
self,
webvid_config, pexels_config,
sample_size=256,
sample_duration=None, sample_fps=None, sample_stride=None, sample_n_frames=None,
is_image=False,
**kwargs,
):
assert (sample_duration is None and sample_fps is None) or (sample_duration is not None and sample_fps is not None), "sample_duration and sample_fps should be both None or not None"
if sample_duration is not None and sample_fps is not None:
assert sample_stride is None, "when sample_duration and sample_fps are not None, sample_stride should be None"
if sample_stride is not None:
assert sample_fps is None and sample_duration is None, "when sample_stride is not None, sample_duration and sample_fps should be both None"
self.dataset = []
if pexels_config.enable:
logger.info(f"loading pexels dataset")
logger.info(f"loading captions from {pexels_config.caption_json_path} ...")
with open(pexels_config.caption_json_path, 'rb') as caption_json_file:
caption_json_list = list(caption_json_file)
self.caption_dict = {json.loads(json_str)['id']: json.loads(json_str)['text'] for json_str in caption_json_list}
logger.info(f"loading annotations from {pexels_config.json_path} ...")
with open(pexels_config.json_path, 'rb') as json_file:
json_list = list(json_file)
dataset = [json.loads(json_str) for json_str in json_list]
for data in dataset:
data['text'] = self.caption_dict[data['id']]
data['dataset'] = 'pexels'
if data['height'] / data['width'] < 0.625:
self.dataset.append(data)
if webvid_config.enable:
logger.info(f"loading webvid dataset")
logger.info(f"loading annotations from {webvid_config.json_path} ...")
with open(webvid_config.json_path, 'rb') as json_file:
json_list = list(json_file)
dataset = [json.loads(json_str) for json_str in json_list]
for data in dataset:
data['dataset'] = 'webvid'
self.dataset.extend(dataset)
self.length = len(self.dataset)
logger.info(f"data scale: {self.length}")
self.pexels_folder = pexels_config.video_folder
self.webvid_folder = webvid_config.video_folder
self.sample_duration = sample_duration
self.sample_fps = sample_fps
self.sample_n_frames = sample_duration * sample_fps if sample_n_frames is None else sample_n_frames
self.sample_stride = sample_stride if (sample_stride is None) or (sample_stride is not None and isinstance(sample_stride, int)) else tuple(sample_stride)
self.is_image = is_image
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], antialias=None),
transforms.CenterCrop(sample_size),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
])
def get_batch(self, idx):
video_dict = self.dataset[idx]
video_relative_path, name = video_dict['file'], video_dict['text']
if video_dict['dataset'] == 'pexels':
video_folder = self.pexels_folder
elif video_dict['dataset'] == 'webvid':
video_folder = self.webvid_folder
else:
raise NotImplementedError
if video_folder is not None:
if video_relative_path[0] == '/':
video_dir = os.path.join(video_folder, os.path.basename(video_relative_path))
else:
video_dir = os.path.join(video_folder, video_relative_path)
else:
video_dir = video_relative_path
video_reader = VideoReader(video_dir)
video_length = len(video_reader)
stride = None
if not self.is_image:
if self.sample_duration is not None:
fps = video_dict['fps']
clip_length = min(video_length, math.ceil(fps * self.sample_duration))
elif self.sample_stride is not None:
if isinstance(self.sample_stride, int):
stride = self.sample_stride
elif isinstance(self.sample_stride, tuple):
stride = random.randint(self.sample_stride[0], self.sample_stride[1])
clip_length = min(video_length, (self.sample_n_frames - 1) * stride + 1)
start_idx = random.randint(0, video_length - clip_length)
batch_index = np.linspace(start_idx, start_idx + clip_length - 1, self.sample_n_frames, dtype=int)
else:
frame_difference = random.randint(2, self.sample_n_frames)
if self.sample_duration is not None:
fps = video_dict['fps']
sample_stride = math.ceil((fps * self.sample_duration) / (self.sample_n_frames - 1) - 1)
elif self.sample_stride is not None:
sample_stride = self.sample_stride
clip_length = min(video_length, (frame_difference - 1) * sample_stride + 1)
start_idx = random.randint(0, video_length - clip_length)
batch_index = [start_idx, start_idx + clip_length - 1]
pixel_values = torch.from_numpy(video_reader.get_batch(batch_index).asnumpy()).permute(0, 3, 1, 2).contiguous()
pixel_values = pixel_values / 255.
del video_reader
return pixel_values, name, stride
def __len__(self):
return self.length
def __getitem__(self, idx):
while True:
try:
pixel_values, name, stride = 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, stride=stride)
return sample
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