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voc2012.py
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voc2012.py
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# 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.
import io
import tarfile
import numpy as np
from PIL import Image
import paddle
from paddle.dataset.common import _check_exists_and_download
from paddle.io import Dataset
__all__ = []
VOC_URL = 'https://dataset.bj.bcebos.com/voc/VOCtrainval_11-May-2012.tar'
VOC_MD5 = '6cd6e144f989b92b3379bac3b3de84fd'
SET_FILE = 'VOCdevkit/VOC2012/ImageSets/Segmentation/{}.txt'
DATA_FILE = 'VOCdevkit/VOC2012/JPEGImages/{}.jpg'
LABEL_FILE = 'VOCdevkit/VOC2012/SegmentationClass/{}.png'
CACHE_DIR = 'voc2012'
MODE_FLAG_MAP = {'train': 'trainval', 'test': 'train', 'valid': "val"}
class VOC2012(Dataset):
"""
Implementation of `VOC2012 <http://host.robots.ox.ac.uk/pascal/VOC/voc2012/>`_ dataset.
Args:
data_file (str, optional): Path to data file, can be set None if
:attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/voc2012.
mode (str, optional): Either train or test mode. Default 'train'.
transform (Callable, 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, 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_vision_image_get_image_backend>`,
default backend is 'pil'. Default: None.
Returns:
:ref:`api_paddle_io_Dataset`. An instance of VOC2012 dataset.
Examples:
.. code-block:: python
import itertools
import paddle.vision.transforms as T
from paddle.vision.datasets import VOC2012
voc2012 = VOC2012()
print(len(voc2012))
# 2913
for i in range(5): # only show first 5 images
img, label = voc2012[i]
# do something with img and label
print(type(img), img.size)
# <class 'PIL.JpegImagePlugin.JpegImageFile'> (500, 281)
print(type(label), label.size)
# <class 'PIL.PngImagePlugin.PngImageFile'> (500, 281)
transform = T.Compose(
[
T.ToTensor(),
T.Normalize(
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5],
to_rgb=True,
),
]
)
voc2012_test = VOC2012(
mode="test",
transform=transform, # apply transform to every image
backend="cv2", # use OpenCV as image transform backend
)
print(len(voc2012_test))
# 1464
for img, label in itertools.islice(iter(voc2012_test), 5): # only show first 5 images
# do something with img and label
print(type(img), img.shape)
# <class 'paddle.Tensor'> [3, 281, 500]
print(type(label), label.shape)
# <class 'numpy.ndarray'> (281, 500)
"""
def __init__(
self,
data_file=None,
mode='train',
transform=None,
download=True,
backend=None,
):
assert mode.lower() in [
'train',
'valid',
'test',
], f"mode should be 'train', 'valid' or 'test', but got {mode}"
if backend is None:
backend = paddle.vision.get_image_backend()
if backend not in ['pil', 'cv2']:
raise ValueError(
"Expected backend are one of ['pil', 'cv2'], but got {}".format(
backend
)
)
self.backend = backend
self.flag = MODE_FLAG_MAP[mode.lower()]
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, VOC_URL, VOC_MD5, CACHE_DIR, download
)
self.transform = transform
# read dataset into memory
self._load_anno()
self.dtype = paddle.get_default_dtype()
def _load_anno(self):
self.name2mem = {}
self.data_tar = tarfile.open(self.data_file)
for ele in self.data_tar.getmembers():
self.name2mem[ele.name] = ele
set_file = SET_FILE.format(self.flag)
sets = self.data_tar.extractfile(self.name2mem[set_file])
self.data = []
self.labels = []
for line in sets:
line = line.strip()
data = DATA_FILE.format(line.decode('utf-8'))
label = LABEL_FILE.format(line.decode('utf-8'))
self.data.append(data)
self.labels.append(label)
def __getitem__(self, idx):
data_file = self.data[idx]
label_file = self.labels[idx]
data = self.data_tar.extractfile(self.name2mem[data_file]).read()
label = self.data_tar.extractfile(self.name2mem[label_file]).read()
data = Image.open(io.BytesIO(data))
label = Image.open(io.BytesIO(label))
if self.backend == 'cv2':
data = np.array(data)
label = np.array(label)
if self.transform is not None:
data = self.transform(data)
if self.backend == 'cv2':
return data.astype(self.dtype), label.astype(self.dtype)
return data, label
def __len__(self):
return len(self.data)
def __del__(self):
if self.data_tar:
self.data_tar.close()