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import csv
import io
import json
import math
import os
import random
from threading import Thread
import albumentations
import cv2
import gc
import numpy as np
import torch
import torchvision.transforms as transforms
from func_timeout import func_timeout, FunctionTimedOut
from decord import VideoReader
from PIL import Image
from torch.utils.data import BatchSampler, Sampler
from torch.utils.data.dataset import Dataset
from contextlib import contextmanager
VIDEO_READER_TIMEOUT = 20
class ImageVideoSampler(BatchSampler):
"""A sampler wrapper for grouping images with similar aspect ratio into a same batch.
Args:
sampler (Sampler): Base sampler.
dataset (Dataset): Dataset providing data information.
batch_size (int): Size of mini-batch.
drop_last (bool): If ``True``, the sampler will drop the last batch if
its size would be less than ``batch_size``.
aspect_ratios (dict): The predefined aspect ratios.
"""
def __init__(self,
sampler: Sampler,
dataset: Dataset,
batch_size: int,
drop_last: bool = False
) -> None:
if not isinstance(sampler, Sampler):
raise TypeError('sampler should be an instance of ``Sampler``, '
f'but got {sampler}')
if not isinstance(batch_size, int) or batch_size <= 0:
raise ValueError('batch_size should be a positive integer value, '
f'but got batch_size={batch_size}')
self.sampler = sampler
self.dataset = dataset
self.batch_size = batch_size
self.drop_last = drop_last
# buckets for each aspect ratio
self.bucket = {'image':[], 'video':[]}
def __iter__(self):
for idx in self.sampler:
content_type = self.dataset.dataset[idx].get('type', 'image')
self.bucket[content_type].append(idx)
# yield a batch of indices in the same aspect ratio group
if len(self.bucket['video']) == self.batch_size:
bucket = self.bucket['video']
yield bucket[:]
del bucket[:]
elif len(self.bucket['image']) == self.batch_size:
bucket = self.bucket['image']
yield bucket[:]
del bucket[:]
@contextmanager
def VideoReader_contextmanager(*args, **kwargs):
vr = VideoReader(*args, **kwargs)
try:
yield vr
finally:
del vr
gc.collect()
def get_video_reader_batch(video_reader, batch_index):
frames = video_reader.get_batch(batch_index).asnumpy()
return frames
class ImageVideoDataset(Dataset):
def __init__(
self,
ann_path, data_root=None,
video_sample_size=512, video_sample_stride=4, video_sample_n_frames=16,
image_sample_size=512,
video_repeat=0,
text_drop_ratio=0.001,
enable_bucket=False,
video_length_drop_start=0.1,
video_length_drop_end=0.9,
):
# Loading annotations from files
print(f"loading annotations from {ann_path} ...")
if ann_path.endswith('.csv'):
with open(ann_path, 'r') as csvfile:
dataset = list(csv.DictReader(csvfile))
elif ann_path.endswith('.json'):
dataset = json.load(open(ann_path))
self.data_root = data_root
# It's used to balance num of images and videos.
self.dataset = []
for data in dataset:
if data.get('type', 'image') != 'video':
self.dataset.append(data)
if video_repeat > 0:
for _ in range(video_repeat):
for data in dataset:
if data.get('type', 'image') == 'video':
self.dataset.append(data)
del dataset
self.length = len(self.dataset)
print(f"data scale: {self.length}")
# TODO: enable bucket training
self.enable_bucket = enable_bucket
self.text_drop_ratio = text_drop_ratio
self.video_length_drop_start = video_length_drop_start
self.video_length_drop_end = video_length_drop_end
# Video params
self.video_sample_stride = video_sample_stride
self.video_sample_n_frames = video_sample_n_frames
video_sample_size = tuple(video_sample_size) if not isinstance(video_sample_size, int) else (video_sample_size, video_sample_size)
self.video_transforms = transforms.Compose(
[
transforms.Resize(video_sample_size[0]),
transforms.CenterCrop(video_sample_size),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
]
)
# Image params
self.image_sample_size = tuple(image_sample_size) if not isinstance(image_sample_size, int) else (image_sample_size, image_sample_size)
self.image_transforms = transforms.Compose([
transforms.Resize(min(self.image_sample_size)),
transforms.CenterCrop(self.image_sample_size),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5],[0.5, 0.5, 0.5])
])
def get_batch(self, idx):
data_info = self.dataset[idx % len(self.dataset)]
if data_info.get('type', 'image')=='video':
video_id, text = data_info['file_path'], data_info['text']
if self.data_root is None:
video_dir = video_id
else:
video_dir = os.path.join(self.data_root, video_id)
with VideoReader_contextmanager(video_dir, num_threads=2) as video_reader:
min_sample_n_frames = min(
self.video_sample_n_frames,
int(len(video_reader) * (self.video_length_drop_end - self.video_length_drop_start))
)
if min_sample_n_frames == 0:
raise ValueError(f"No Frames in video.")
video_length = int(self.video_length_drop_end * len(video_reader))
clip_length = min(video_length, (min_sample_n_frames - 1) * self.video_sample_stride + 1)
start_idx = random.randint(int(self.video_length_drop_start * video_length), video_length - clip_length)
batch_index = np.linspace(start_idx, start_idx + clip_length - 1, min_sample_n_frames, dtype=int)
try:
sample_args = (video_reader, batch_index)
pixel_values = func_timeout(
VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
)
except FunctionTimedOut:
raise ValueError(f"Read {idx} timeout.")
except Exception as e:
raise ValueError(f"Failed to extract frames from video. Error is {e}.")
if not self.enable_bucket:
pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous()
pixel_values = pixel_values / 255.
del video_reader
else:
pixel_values = pixel_values
if not self.enable_bucket:
pixel_values = self.video_transforms(pixel_values)
# Random use no text generation
if random.random() < self.text_drop_ratio:
text = ''
return pixel_values, text, 'video'
else:
image_path, text = data_info['file_path'], data_info['text']
if self.data_root is not None:
image_path = os.path.join(self.data_root, image_path)
image = Image.open(image_path).convert('RGB')
if not self.enable_bucket:
image = self.image_transforms(image).unsqueeze(0)
else:
image = np.expand_dims(np.array(image), 0)
if random.random() < self.text_drop_ratio:
text = ''
return image, text, 'image'
def __len__(self):
return self.length
def __getitem__(self, idx):
data_info = self.dataset[idx % len(self.dataset)]
data_type = data_info.get('type', 'image')
while True:
sample = {}
try:
data_info_local = self.dataset[idx % len(self.dataset)]
data_type_local = data_info_local.get('type', 'image')
if data_type_local != data_type:
raise ValueError("data_type_local != data_type")
pixel_values, name, data_type = self.get_batch(idx)
sample["pixel_values"] = pixel_values
sample["text"] = name
sample["data_type"] = data_type
sample["idx"] = idx
if len(sample) > 0:
break
except Exception as e:
print(e, self.dataset[idx % len(self.dataset)])
idx = random.randint(0, self.length-1)
return sample
if __name__ == "__main__":
dataset = ImageVideoDataset(
ann_path="test.json"
)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=4, num_workers=16)
for idx, batch in enumerate(dataloader):
print(batch["pixel_values"].shape, len(batch["text"])) |