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