wehub-resource-sync
315 lines
11 KiB
Python
315 lines
11 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import os
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import pickle
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import tarfile
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from functools import lru_cache
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from typing import TYPE_CHECKING, Any, Literal
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import numpy as np
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import numpy.typing as npt
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from PIL import Image
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import paddle
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from paddle.dataset.common import _check_exists_and_download, md5file
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from paddle.io import Dataset
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if TYPE_CHECKING:
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import numpy.typing as npt
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from paddle._typing.dtype_like import _DTypeLiteral
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from paddle.vision.transforms.transforms import _Transform
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from ..image import _ImageBackend, _ImageDataType
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_DatasetMode = Literal["train", "test"]
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__all__ = []
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URL_PREFIX = 'https://dataset.bj.bcebos.com/cifar/'
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CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz'
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CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
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CIFAR100_URL = URL_PREFIX + 'cifar-100-python.tar.gz'
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CIFAR100_MD5 = 'eb9058c3a382ffc7106e4002c42a8d85'
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MODE_FLAG_MAP = {
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'train10': 'data_batch',
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'test10': 'test_batch',
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'train100': 'train',
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'test100': 'test',
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}
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@lru_cache(maxsize=8)
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def _cached_md5file(path, _mtime_ns, _size):
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return md5file(path)
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def _check_local_cifar_md5(path, expected_md5):
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path = os.path.abspath(path)
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stat = os.stat(path)
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file_md5 = _cached_md5file(path, stat.st_mtime_ns, stat.st_size)
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if file_md5 != expected_md5:
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raise ValueError(
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"Loading unverified local CIFAR pickle archive is disabled. "
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f"Please use the official archive with MD5 {expected_md5}."
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)
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class Cifar10(Dataset[tuple["_ImageDataType", "npt.NDArray[Any]"]]):
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"""
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Implementation of `Cifar-10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_
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dataset, which has 10 categories.
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Args:
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data_file (str|None, optional): Path to data file, can be set None if
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:attr:`download` is True. Default None, default data path: ~/.cache/paddle/dataset/cifar
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mode (str, optional): Either train or test mode. Default 'train'.
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transform (Callable|None, optional): transform to perform on image, None for no transform. Default: None.
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download (bool, optional): download dataset automatically if :attr:`data_file` is None. Default True.
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backend (str|None, optional): Specifies which type of image to be returned:
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PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
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If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_paddle_vision_get_image_backend>`,
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default backend is 'pil'. Default: None.
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Returns:
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:ref:`api_paddle_io_Dataset`. An instance of Cifar10 dataset.
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Examples:
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.. code-block:: pycon
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>>> # doctest: +TIMEOUT(60)
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>>> import itertools
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>>> import paddle
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>>> import paddle.vision.transforms as T
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>>> from paddle.vision.datasets import Cifar10
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>>> cifar10 = Cifar10()
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>>> print(len(cifar10))
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50000
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>>> for i in range(5): # only show first 5 images
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... img, label = cifar10[i]
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... # do something with img and label
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... print(type(img), img.size, label)
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... # <class 'PIL.Image.Image'> (32, 32) 6
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>>> transform = T.Compose(
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... [
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... T.Resize(64),
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... T.ToTensor(),
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... T.Normalize(
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... mean=[0.5, 0.5, 0.5],
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... std=[0.5, 0.5, 0.5],
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... to_rgb=True,
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... ),
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... ]
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... )
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>>> cifar10_test = Cifar10(
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... mode="test",
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... transform=transform, # apply transform to every image
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... backend="cv2", # use OpenCV as image transform backend
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... )
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>>> print(len(cifar10_test))
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10000
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>>> for img, label in itertools.islice(iter(cifar10_test), 5): # only show first 5 images
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... # do something with img and label
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... assert isinstance(img, paddle.Tensor)
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... print(type(img), img.shape, label)
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... # <class 'paddle.Tensor'> [3, 64, 64] 3
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"""
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mode: _DatasetMode
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backend: _ImageBackend
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data_file: str | None
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transform: _Transform[Any, Any] | None
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dtype: _DTypeLiteral
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def __init__(
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self,
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data_file: str | None = None,
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mode: _DatasetMode = 'train',
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transform: _Transform[Any, Any] | None = None,
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download: bool = True,
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backend: _ImageBackend | None = None,
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) -> None:
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assert mode.lower() in [
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'train',
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'test',
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], f"mode.lower() should be 'train' or 'test', but got {mode}"
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self.mode = mode.lower()
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if backend is None:
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backend = paddle.vision.get_image_backend()
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if backend not in ['pil', 'cv2']:
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raise ValueError(
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f"Expected backend are one of ['pil', 'cv2'], but got {backend}"
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)
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self.backend = backend
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self._init_url_md5_flag()
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self.data_file = data_file
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if self.data_file is None:
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assert download, (
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"data_file is not set and downloading automatically is disabled"
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)
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self.data_file = _check_exists_and_download(
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data_file, self.data_url, self.data_md5, 'cifar', download
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)
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elif not os.path.exists(self.data_file):
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raise ValueError(
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f"Local CIFAR archive does not exist: {self.data_file}."
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)
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else:
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_check_local_cifar_md5(self.data_file, self.data_md5)
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self.transform = transform
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# read dataset into memory
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self._load_data()
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self.dtype = paddle.get_default_dtype()
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def _init_url_md5_flag(self):
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self.data_url = CIFAR10_URL
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self.data_md5 = CIFAR10_MD5
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self.flag = MODE_FLAG_MAP[self.mode + '10']
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def _load_data(self):
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self.data = []
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with tarfile.open(self.data_file, mode='r') as f:
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names = (
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each_item.name for each_item in f if self.flag in each_item.name
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)
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names = sorted(names)
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for name in names:
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batch = pickle.load(f.extractfile(name), encoding='bytes')
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data = batch[b'data']
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labels = batch.get(b'labels', batch.get(b'fine_labels', None))
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assert labels is not None
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for sample, label in zip(data, labels):
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self.data.append((sample, label))
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def __getitem__(self, idx: int) -> tuple[_ImageDataType, npt.NDArray[Any]]:
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image, label = self.data[idx]
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image = np.reshape(image, [3, 32, 32])
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image = image.transpose([1, 2, 0])
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if self.backend == 'pil':
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image = Image.fromarray(image.astype('uint8'))
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if self.transform is not None:
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image = self.transform(image)
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if self.backend == 'pil':
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return image, np.array(label).astype('int64')
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return image.astype(self.dtype), np.array(label).astype('int64')
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def __len__(self):
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return len(self.data)
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class Cifar100(Cifar10):
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"""
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Implementation of `Cifar-100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_
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dataset, which has 100 categories.
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Args:
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data_file (str|None, optional): path to data file, can be set None if
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:attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/cifar
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mode (str, optional): Either train or test mode. Default 'train'.
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transform (Callable|None, optional): transform to perform on image, None for no transform. Default: None.
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download (bool, optional): download dataset automatically if :attr:`data_file` is None. Default True.
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backend (str|None, optional): Specifies which type of image to be returned:
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PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
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If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_paddle_vision_get_image_backend>`,
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default backend is 'pil'. Default: None.
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Returns:
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:ref:`api_paddle_io_Dataset`. An instance of Cifar100 dataset.
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Examples:
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.. code-block:: pycon
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>>> # doctest: +TIMEOUT(60)
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>>> import itertools
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>>> import paddle
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>>> import paddle.vision.transforms as T
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>>> from paddle.vision.datasets import Cifar100
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>>> cifar100 = Cifar100()
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>>> print(len(cifar100))
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50000
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>>> for i in range(5): # only show first 5 images
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... img, label = cifar100[i]
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... # do something with img and label
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... print(type(img), img.size, label)
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... # <class 'PIL.Image.Image'> (32, 32) 19
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>>> transform = T.Compose(
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... [
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... T.Resize(64),
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... T.ToTensor(),
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... T.Normalize(
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... mean=[0.5, 0.5, 0.5],
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... std=[0.5, 0.5, 0.5],
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... to_rgb=True,
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... ),
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... ]
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... )
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>>> cifar100_test = Cifar100(
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... mode="test",
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... transform=transform, # apply transform to every image
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... backend="cv2", # use OpenCV as image transform backend
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... )
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>>> print(len(cifar100_test))
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10000
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>>> for img, label in itertools.islice(iter(cifar100_test), 5): # only show first 5 images
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... # do something with img and label
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... assert isinstance(img, paddle.Tensor)
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... print(type(img), img.shape, label)
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... # <class 'paddle.Tensor'> [3, 64, 64] 49
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"""
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def __init__(
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self,
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data_file: str | None = None,
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mode: _DatasetMode = 'train',
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transform: _Transform[Any, Any] | None = None,
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download: bool = True,
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backend: _ImageBackend | None = None,
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) -> None:
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super().__init__(data_file, mode, transform, download, backend)
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def _init_url_md5_flag(self):
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self.data_url = CIFAR100_URL
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self.data_md5 = CIFAR100_MD5
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self.flag = MODE_FLAG_MAP[self.mode + '100']
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