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# Copyright (c) OpenMMLab. All rights reserved.
import os
from typing import (Generic, Iterable, Iterator, List, Optional, Sequence,
Sized, TypeVar, Union)
import cv2
import numpy as np
import torch
from PIL import Image
from torch.utils.data import BatchSampler, Dataset, Sampler
ASPECT_RATIO_512 = {
'0.25': [256.0, 1024.0], '0.26': [256.0, 992.0], '0.27': [256.0, 960.0], '0.28': [256.0, 928.0],
'0.32': [288.0, 896.0], '0.33': [288.0, 864.0], '0.35': [288.0, 832.0], '0.4': [320.0, 800.0],
'0.42': [320.0, 768.0], '0.48': [352.0, 736.0], '0.5': [352.0, 704.0], '0.52': [352.0, 672.0],
'0.57': [384.0, 672.0], '0.6': [384.0, 640.0], '0.68': [416.0, 608.0], '0.72': [416.0, 576.0],
'0.78': [448.0, 576.0], '0.82': [448.0, 544.0], '0.88': [480.0, 544.0], '0.94': [480.0, 512.0],
'1.0': [512.0, 512.0], '1.07': [512.0, 480.0], '1.13': [544.0, 480.0], '1.21': [544.0, 448.0],
'1.29': [576.0, 448.0], '1.38': [576.0, 416.0], '1.46': [608.0, 416.0], '1.67': [640.0, 384.0],
'1.75': [672.0, 384.0], '2.0': [704.0, 352.0], '2.09': [736.0, 352.0], '2.4': [768.0, 320.0],
'2.5': [800.0, 320.0], '2.89': [832.0, 288.0], '3.0': [864.0, 288.0], '3.11': [896.0, 288.0],
'3.62': [928.0, 256.0], '3.75': [960.0, 256.0], '3.88': [992.0, 256.0], '4.0': [1024.0, 256.0]
}
ASPECT_RATIO_RANDOM_CROP_512 = {
'0.42': [320.0, 768.0], '0.5': [352.0, 704.0],
'0.57': [384.0, 672.0], '0.68': [416.0, 608.0], '0.78': [448.0, 576.0], '0.88': [480.0, 544.0],
'0.94': [480.0, 512.0], '1.0': [512.0, 512.0], '1.07': [512.0, 480.0],
'1.13': [544.0, 480.0], '1.29': [576.0, 448.0], '1.46': [608.0, 416.0], '1.75': [672.0, 384.0],
'2.0': [704.0, 352.0], '2.4': [768.0, 320.0]
}
ASPECT_RATIO_RANDOM_CROP_PROB = [
1, 2,
4, 4, 4, 4,
8, 8, 8,
4, 4, 4, 4,
2, 1
]
ASPECT_RATIO_RANDOM_CROP_PROB = np.array(ASPECT_RATIO_RANDOM_CROP_PROB) / sum(ASPECT_RATIO_RANDOM_CROP_PROB)
def get_closest_ratio(height: float, width: float, ratios: dict = ASPECT_RATIO_512):
aspect_ratio = height / width
closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - aspect_ratio))
return ratios[closest_ratio], float(closest_ratio)
def get_image_size_without_loading(path):
with Image.open(path) as img:
return img.size # (width, height)
class RandomSampler(Sampler[int]):
r"""Samples elements randomly. If without replacement, then sample from a shuffled dataset.
If with replacement, then user can specify :attr:`num_samples` to draw.
Args:
data_source (Dataset): dataset to sample from
replacement (bool): samples are drawn on-demand with replacement if ``True``, default=``False``
num_samples (int): number of samples to draw, default=`len(dataset)`.
generator (Generator): Generator used in sampling.
"""
data_source: Sized
replacement: bool
def __init__(self, data_source: Sized, replacement: bool = False,
num_samples: Optional[int] = None, generator=None) -> None:
self.data_source = data_source
self.replacement = replacement
self._num_samples = num_samples
self.generator = generator
self._pos_start = 0
if not isinstance(self.replacement, bool):
raise TypeError(f"replacement should be a boolean value, but got replacement={self.replacement}")
if not isinstance(self.num_samples, int) or self.num_samples <= 0:
raise ValueError(f"num_samples should be a positive integer value, but got num_samples={self.num_samples}")
@property
def num_samples(self) -> int:
# dataset size might change at runtime
if self._num_samples is None:
return len(self.data_source)
return self._num_samples
def __iter__(self) -> Iterator[int]:
n = len(self.data_source)
if self.generator is None:
seed = int(torch.empty((), dtype=torch.int64).random_().item())
generator = torch.Generator()
generator.manual_seed(seed)
else:
generator = self.generator
if self.replacement:
for _ in range(self.num_samples // 32):
yield from torch.randint(high=n, size=(32,), dtype=torch.int64, generator=generator).tolist()
yield from torch.randint(high=n, size=(self.num_samples % 32,), dtype=torch.int64, generator=generator).tolist()
else:
for _ in range(self.num_samples // n):
xx = torch.randperm(n, generator=generator).tolist()
if self._pos_start >= n:
self._pos_start = 0
print("xx top 10", xx[:10], self._pos_start)
for idx in range(self._pos_start, n):
yield xx[idx]
self._pos_start = (self._pos_start + 1) % n
self._pos_start = 0
yield from torch.randperm(n, generator=generator).tolist()[:self.num_samples % n]
def __len__(self) -> int:
return self.num_samples
class AspectRatioBatchImageSampler(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,
train_folder: str = None,
aspect_ratios: dict = ASPECT_RATIO_512,
drop_last: bool = False,
config=None,
**kwargs
) -> 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.train_folder = train_folder
self.batch_size = batch_size
self.aspect_ratios = aspect_ratios
self.drop_last = drop_last
self.config = config
# buckets for each aspect ratio
self._aspect_ratio_buckets = {ratio: [] for ratio in aspect_ratios}
# [str(k) for k, v in aspect_ratios]
self.current_available_bucket_keys = list(aspect_ratios.keys())
def __iter__(self):
for idx in self.sampler:
try:
image_dict = self.dataset[idx]
width, height = image_dict.get("width", None), image_dict.get("height", None)
if width is None or height is None:
image_id, name = image_dict['file_path'], image_dict['text']
if self.train_folder is None:
image_dir = image_id
else:
image_dir = os.path.join(self.train_folder, image_id)
width, height = get_image_size_without_loading(image_dir)
ratio = height / width # self.dataset[idx]
else:
height = int(height)
width = int(width)
ratio = height / width # self.dataset[idx]
except Exception as e:
print(e)
continue
# find the closest aspect ratio
closest_ratio = min(self.aspect_ratios.keys(), key=lambda r: abs(float(r) - ratio))
if closest_ratio not in self.current_available_bucket_keys:
continue
bucket = self._aspect_ratio_buckets[closest_ratio]
bucket.append(idx)
# yield a batch of indices in the same aspect ratio group
if len(bucket) == self.batch_size:
yield bucket[:]
del bucket[:]
class AspectRatioBatchSampler(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,
video_folder: str = None,
train_data_format: str = "webvid",
aspect_ratios: dict = ASPECT_RATIO_512,
drop_last: bool = False,
config=None,
**kwargs
) -> 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.video_folder = video_folder
self.train_data_format = train_data_format
self.batch_size = batch_size
self.aspect_ratios = aspect_ratios
self.drop_last = drop_last
self.config = config
# buckets for each aspect ratio
self._aspect_ratio_buckets = {ratio: [] for ratio in aspect_ratios}
# [str(k) for k, v in aspect_ratios]
self.current_available_bucket_keys = list(aspect_ratios.keys())
def __iter__(self):
for idx in self.sampler:
try:
video_dict = self.dataset[idx]
width, more = video_dict.get("width", None), video_dict.get("height", None)
if width is None or height is None:
if self.train_data_format == "normal":
video_id, name = video_dict['file_path'], video_dict['text']
if self.video_folder is None:
video_dir = video_id
else:
video_dir = os.path.join(self.video_folder, video_id)
else:
videoid, name, page_dir = video_dict['videoid'], video_dict['name'], video_dict['page_dir']
video_dir = os.path.join(self.video_folder, f"{videoid}.mp4")
cap = cv2.VideoCapture(video_dir)
# 获取视频尺寸
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) # 浮点数转换为整数
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 浮点数转换为整数
ratio = height / width # self.dataset[idx]
else:
height = int(height)
width = int(width)
ratio = height / width # self.dataset[idx]
except Exception as e:
print(e)
continue
# find the closest aspect ratio
closest_ratio = min(self.aspect_ratios.keys(), key=lambda r: abs(float(r) - ratio))
if closest_ratio not in self.current_available_bucket_keys:
continue
bucket = self._aspect_ratio_buckets[closest_ratio]
bucket.append(idx)
# yield a batch of indices in the same aspect ratio group
if len(bucket) == self.batch_size:
yield bucket[:]
del bucket[:]
class AspectRatioBatchImageVideoSampler(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,
train_folder: str = None,
aspect_ratios: dict = ASPECT_RATIO_512,
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.train_folder = train_folder
self.batch_size = batch_size
self.aspect_ratios = aspect_ratios
self.drop_last = drop_last
# buckets for each aspect ratio
self.current_available_bucket_keys = list(aspect_ratios.keys())
self.bucket = {
'image':{ratio: [] for ratio in aspect_ratios},
'video':{ratio: [] for ratio in aspect_ratios}
}
def __iter__(self):
for idx in self.sampler:
content_type = self.dataset[idx].get('type', 'image')
if content_type == 'image':
try:
image_dict = self.dataset[idx]
width, height = image_dict.get("width", None), image_dict.get("height", None)
if width is None or height is None:
image_id, name = image_dict['file_path'], image_dict['text']
if self.train_folder is None:
image_dir = image_id
else:
image_dir = os.path.join(self.train_folder, image_id)
width, height = get_image_size_without_loading(image_dir)
ratio = height / width # self.dataset[idx]
else:
height = int(height)
width = int(width)
ratio = height / width # self.dataset[idx]
except Exception as e:
print(e)
continue
# find the closest aspect ratio
closest_ratio = min(self.aspect_ratios.keys(), key=lambda r: abs(float(r) - ratio))
if closest_ratio not in self.current_available_bucket_keys:
continue
bucket = self.bucket['image'][closest_ratio]
bucket.append(idx)
# yield a batch of indices in the same aspect ratio group
if len(bucket) == self.batch_size:
yield bucket[:]
del bucket[:]
else:
try:
video_dict = self.dataset[idx]
width, height = video_dict.get("width", None), video_dict.get("height", None)
if width is None or height is None:
video_id, name = video_dict['file_path'], video_dict['text']
if self.train_folder is None:
video_dir = video_id
else:
video_dir = os.path.join(self.train_folder, video_id)
cap = cv2.VideoCapture(video_dir)
# 获取视频尺寸
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) # 浮点数转换为整数
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 浮点数转换为整数
ratio = height / width # self.dataset[idx]
else:
height = int(height)
width = int(width)
ratio = height / width # self.dataset[idx]
except Exception as e:
print(e)
continue
# find the closest aspect ratio
closest_ratio = min(self.aspect_ratios.keys(), key=lambda r: abs(float(r) - ratio))
if closest_ratio not in self.current_available_bucket_keys:
continue
bucket = self.bucket['video'][closest_ratio]
bucket.append(idx)
# yield a batch of indices in the same aspect ratio group
if len(bucket) == self.batch_size:
yield bucket[:]
del bucket[:] |