chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,314 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import pickle
|
||||
import tarfile
|
||||
from functools import lru_cache
|
||||
from typing import TYPE_CHECKING, Any, Literal
|
||||
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
from PIL import Image
|
||||
|
||||
import paddle
|
||||
from paddle.dataset.common import _check_exists_and_download, md5file
|
||||
from paddle.io import Dataset
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import numpy.typing as npt
|
||||
|
||||
from paddle._typing.dtype_like import _DTypeLiteral
|
||||
from paddle.vision.transforms.transforms import _Transform
|
||||
|
||||
from ..image import _ImageBackend, _ImageDataType
|
||||
|
||||
_DatasetMode = Literal["train", "test"]
|
||||
|
||||
__all__ = []
|
||||
|
||||
URL_PREFIX = 'https://dataset.bj.bcebos.com/cifar/'
|
||||
CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz'
|
||||
CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
|
||||
CIFAR100_URL = URL_PREFIX + 'cifar-100-python.tar.gz'
|
||||
CIFAR100_MD5 = 'eb9058c3a382ffc7106e4002c42a8d85'
|
||||
|
||||
MODE_FLAG_MAP = {
|
||||
'train10': 'data_batch',
|
||||
'test10': 'test_batch',
|
||||
'train100': 'train',
|
||||
'test100': 'test',
|
||||
}
|
||||
|
||||
|
||||
@lru_cache(maxsize=8)
|
||||
def _cached_md5file(path, _mtime_ns, _size):
|
||||
return md5file(path)
|
||||
|
||||
|
||||
def _check_local_cifar_md5(path, expected_md5):
|
||||
path = os.path.abspath(path)
|
||||
stat = os.stat(path)
|
||||
file_md5 = _cached_md5file(path, stat.st_mtime_ns, stat.st_size)
|
||||
if file_md5 != expected_md5:
|
||||
raise ValueError(
|
||||
"Loading unverified local CIFAR pickle archive is disabled. "
|
||||
f"Please use the official archive with MD5 {expected_md5}."
|
||||
)
|
||||
|
||||
|
||||
class Cifar10(Dataset[tuple["_ImageDataType", "npt.NDArray[Any]"]]):
|
||||
"""
|
||||
Implementation of `Cifar-10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_
|
||||
dataset, which has 10 categories.
|
||||
|
||||
Args:
|
||||
data_file (str|None, optional): Path to data file, can be set None if
|
||||
:attr:`download` is True. Default None, default data path: ~/.cache/paddle/dataset/cifar
|
||||
mode (str, optional): Either train or test mode. Default 'train'.
|
||||
transform (Callable|None, optional): transform to perform on image, None for no transform. Default: None.
|
||||
download (bool, optional): download dataset automatically if :attr:`data_file` is None. Default True.
|
||||
backend (str|None, optional): Specifies which type of image to be returned:
|
||||
PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
|
||||
If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_paddle_vision_get_image_backend>`,
|
||||
default backend is 'pil'. Default: None.
|
||||
|
||||
Returns:
|
||||
:ref:`api_paddle_io_Dataset`. An instance of Cifar10 dataset.
|
||||
|
||||
Examples:
|
||||
|
||||
.. code-block:: pycon
|
||||
|
||||
>>> # doctest: +TIMEOUT(60)
|
||||
>>> import itertools
|
||||
>>> import paddle
|
||||
>>> import paddle.vision.transforms as T
|
||||
>>> from paddle.vision.datasets import Cifar10
|
||||
|
||||
>>> cifar10 = Cifar10()
|
||||
>>> print(len(cifar10))
|
||||
50000
|
||||
|
||||
>>> for i in range(5): # only show first 5 images
|
||||
... img, label = cifar10[i]
|
||||
... # do something with img and label
|
||||
... print(type(img), img.size, label)
|
||||
... # <class 'PIL.Image.Image'> (32, 32) 6
|
||||
|
||||
|
||||
>>> transform = T.Compose(
|
||||
... [
|
||||
... T.Resize(64),
|
||||
... T.ToTensor(),
|
||||
... T.Normalize(
|
||||
... mean=[0.5, 0.5, 0.5],
|
||||
... std=[0.5, 0.5, 0.5],
|
||||
... to_rgb=True,
|
||||
... ),
|
||||
... ]
|
||||
... )
|
||||
>>> cifar10_test = Cifar10(
|
||||
... mode="test",
|
||||
... transform=transform, # apply transform to every image
|
||||
... backend="cv2", # use OpenCV as image transform backend
|
||||
... )
|
||||
>>> print(len(cifar10_test))
|
||||
10000
|
||||
|
||||
>>> for img, label in itertools.islice(iter(cifar10_test), 5): # only show first 5 images
|
||||
... # do something with img and label
|
||||
... assert isinstance(img, paddle.Tensor)
|
||||
... print(type(img), img.shape, label)
|
||||
... # <class 'paddle.Tensor'> [3, 64, 64] 3
|
||||
|
||||
"""
|
||||
|
||||
mode: _DatasetMode
|
||||
backend: _ImageBackend
|
||||
data_file: str | None
|
||||
transform: _Transform[Any, Any] | None
|
||||
dtype: _DTypeLiteral
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
data_file: str | None = None,
|
||||
mode: _DatasetMode = 'train',
|
||||
transform: _Transform[Any, Any] | None = None,
|
||||
download: bool = True,
|
||||
backend: _ImageBackend | None = None,
|
||||
) -> None:
|
||||
assert mode.lower() in [
|
||||
'train',
|
||||
'test',
|
||||
], f"mode.lower() should be 'train' or 'test', but got {mode}"
|
||||
self.mode = mode.lower()
|
||||
|
||||
if backend is None:
|
||||
backend = paddle.vision.get_image_backend()
|
||||
if backend not in ['pil', 'cv2']:
|
||||
raise ValueError(
|
||||
f"Expected backend are one of ['pil', 'cv2'], but got {backend}"
|
||||
)
|
||||
self.backend = backend
|
||||
|
||||
self._init_url_md5_flag()
|
||||
|
||||
self.data_file = data_file
|
||||
if self.data_file is None:
|
||||
assert download, (
|
||||
"data_file is not set and downloading automatically is disabled"
|
||||
)
|
||||
self.data_file = _check_exists_and_download(
|
||||
data_file, self.data_url, self.data_md5, 'cifar', download
|
||||
)
|
||||
elif not os.path.exists(self.data_file):
|
||||
raise ValueError(
|
||||
f"Local CIFAR archive does not exist: {self.data_file}."
|
||||
)
|
||||
else:
|
||||
_check_local_cifar_md5(self.data_file, self.data_md5)
|
||||
|
||||
self.transform = transform
|
||||
|
||||
# read dataset into memory
|
||||
self._load_data()
|
||||
|
||||
self.dtype = paddle.get_default_dtype()
|
||||
|
||||
def _init_url_md5_flag(self):
|
||||
self.data_url = CIFAR10_URL
|
||||
self.data_md5 = CIFAR10_MD5
|
||||
self.flag = MODE_FLAG_MAP[self.mode + '10']
|
||||
|
||||
def _load_data(self):
|
||||
self.data = []
|
||||
with tarfile.open(self.data_file, mode='r') as f:
|
||||
names = (
|
||||
each_item.name for each_item in f if self.flag in each_item.name
|
||||
)
|
||||
|
||||
names = sorted(names)
|
||||
|
||||
for name in names:
|
||||
batch = pickle.load(f.extractfile(name), encoding='bytes')
|
||||
|
||||
data = batch[b'data']
|
||||
labels = batch.get(b'labels', batch.get(b'fine_labels', None))
|
||||
assert labels is not None
|
||||
for sample, label in zip(data, labels):
|
||||
self.data.append((sample, label))
|
||||
|
||||
def __getitem__(self, idx: int) -> tuple[_ImageDataType, npt.NDArray[Any]]:
|
||||
image, label = self.data[idx]
|
||||
image = np.reshape(image, [3, 32, 32])
|
||||
image = image.transpose([1, 2, 0])
|
||||
|
||||
if self.backend == 'pil':
|
||||
image = Image.fromarray(image.astype('uint8'))
|
||||
if self.transform is not None:
|
||||
image = self.transform(image)
|
||||
|
||||
if self.backend == 'pil':
|
||||
return image, np.array(label).astype('int64')
|
||||
|
||||
return image.astype(self.dtype), np.array(label).astype('int64')
|
||||
|
||||
def __len__(self):
|
||||
return len(self.data)
|
||||
|
||||
|
||||
class Cifar100(Cifar10):
|
||||
"""
|
||||
Implementation of `Cifar-100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_
|
||||
dataset, which has 100 categories.
|
||||
|
||||
Args:
|
||||
data_file (str|None, optional): path to data file, can be set None if
|
||||
:attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/cifar
|
||||
mode (str, optional): Either train or test mode. Default 'train'.
|
||||
transform (Callable|None, optional): transform to perform on image, None for no transform. Default: None.
|
||||
download (bool, optional): download dataset automatically if :attr:`data_file` is None. Default True.
|
||||
backend (str|None, optional): Specifies which type of image to be returned:
|
||||
PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
|
||||
If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_paddle_vision_get_image_backend>`,
|
||||
default backend is 'pil'. Default: None.
|
||||
|
||||
Returns:
|
||||
:ref:`api_paddle_io_Dataset`. An instance of Cifar100 dataset.
|
||||
|
||||
Examples:
|
||||
|
||||
.. code-block:: pycon
|
||||
|
||||
>>> # doctest: +TIMEOUT(60)
|
||||
>>> import itertools
|
||||
>>> import paddle
|
||||
>>> import paddle.vision.transforms as T
|
||||
>>> from paddle.vision.datasets import Cifar100
|
||||
|
||||
>>> cifar100 = Cifar100()
|
||||
>>> print(len(cifar100))
|
||||
50000
|
||||
|
||||
>>> for i in range(5): # only show first 5 images
|
||||
... img, label = cifar100[i]
|
||||
... # do something with img and label
|
||||
... print(type(img), img.size, label)
|
||||
... # <class 'PIL.Image.Image'> (32, 32) 19
|
||||
|
||||
|
||||
>>> transform = T.Compose(
|
||||
... [
|
||||
... T.Resize(64),
|
||||
... T.ToTensor(),
|
||||
... T.Normalize(
|
||||
... mean=[0.5, 0.5, 0.5],
|
||||
... std=[0.5, 0.5, 0.5],
|
||||
... to_rgb=True,
|
||||
... ),
|
||||
... ]
|
||||
... )
|
||||
|
||||
>>> cifar100_test = Cifar100(
|
||||
... mode="test",
|
||||
... transform=transform, # apply transform to every image
|
||||
... backend="cv2", # use OpenCV as image transform backend
|
||||
... )
|
||||
>>> print(len(cifar100_test))
|
||||
10000
|
||||
|
||||
>>> for img, label in itertools.islice(iter(cifar100_test), 5): # only show first 5 images
|
||||
... # do something with img and label
|
||||
... assert isinstance(img, paddle.Tensor)
|
||||
... print(type(img), img.shape, label)
|
||||
... # <class 'paddle.Tensor'> [3, 64, 64] 49
|
||||
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
data_file: str | None = None,
|
||||
mode: _DatasetMode = 'train',
|
||||
transform: _Transform[Any, Any] | None = None,
|
||||
download: bool = True,
|
||||
backend: _ImageBackend | None = None,
|
||||
) -> None:
|
||||
super().__init__(data_file, mode, transform, download, backend)
|
||||
|
||||
def _init_url_md5_flag(self):
|
||||
self.data_url = CIFAR100_URL
|
||||
self.data_md5 = CIFAR100_MD5
|
||||
self.flag = MODE_FLAG_MAP[self.mode + '100']
|
||||
Reference in New Issue
Block a user