update dataset, add voc and voc aug

dygraph/datasets/__init__.py

@@ -15,3 +15,4 @@
 from .dataset import Dataset
 from .optic_disc_seg import OpticDiscSeg
 from .cityscapes import Cityscapes
+from .voc import PascalVoc

dygraph/datasets/cityscapes.py

@@ -39,9 +39,8 @@ class Cityscapes(Dataset):
                     mode))
 
         if self.transforms is None:
-            raise Exception("transform is necessary, but it is None.")
+            raise Exception("transforms is necessary, but it is None.")
 
-        self.data_dir = data_dir
         if self.data_dir is None:
             if not download:
                 raise Exception("data_file not set and auto download disabled.")

dygraph/datasets/dataset.py

@@ -20,43 +20,74 @@ from PIL import Image
 
 
 class Dataset(fluid.io.Dataset):
+    """Pass in a custom dataset that conforms to the format.
+
+    Args:
+        data_dir: The dataset directory.
+        num_classes: Number of classes.
+        image_set: Which part of dataset to use. Generally, image_set is of ('train', 'val', 'test'). Default: 'train'.
+        mode: Dataset usage. it is one of ('train', 'eva', 'test'). Default: 'train'.
+        train_list: The train dataset file. When image_set is 'train', train_list is necessary.
+            The contents of train_list file are as follow:
+            image1.jpg ground_truth1.png
+            image2.jpg ground_truth2.png
+        val_list: The evaluation dataset file. When image_set is 'val', val_list is necessary.
+            The contents is the same as train_list
+        test_list: The test dataset file. When image_set is 'test', test_list is necessary.
+            The annotation file is not necessary in test_list file.
+        separator: The separator of dataset list. Default: ' '.
+        transforms: Transforms for image.
+
+        Examples:
+            todo
+
+    """
+
     def __init__(self,
                  data_dir,
                  num_classes,
+                 image_set='train',
+                 mode='train',
                  train_list=None,
                  val_list=None,
                  test_list=None,
                  separator=' ',
-                 transforms=None,
-                 mode='train'):
+                 transforms=None):
         self.data_dir = data_dir
         self.transforms = transforms
         self.file_list = list()
         self.mode = mode
         self.num_classes = num_classes
 
+        if image_set.lower() not in ['train', 'val', 'test']:
+            raise Exception(
+                "image_set should be one of ('train', 'val', 'test'), but got {}."
+                .format(image_set))
+
         if mode.lower() not in ['train', 'eval', 'test']:
             raise Exception(
                 "mode should be 'train', 'eval' or 'test', but got {}.".format(
                     mode))
 
         if self.transforms is None:
-            raise Exception("transform is necessary, but it is None.")
+            raise Exception("transforms is necessary, but it is None.")
 
         self.data_dir = data_dir
-        if mode == 'train':
+        if image_set == 'train':
             if train_list is None:
                 raise Exception(
-                    'When mode is "train", train_list is need, but it is None.')
+                    'When mode is "train", train_list is necessary, but it is None.'
+                )
             elif not os.path.exists(train_list):
                 raise Exception(
                     'train_list is not found: {}'.format(train_list))
             else:
                 file_list = train_list
-        elif mode == 'eval':
+        elif image_set == 'eval':
             if val_list is None:
                 raise Exception(
-                    'When mode is "eval", val_list is need, but it is None.')
+                    'When mode is "eval", val_list is necessary, but it is None.'
+                )
             elif not os.path.exists(val_list):
                 raise Exception('val_list is not found: {}'.format(val_list))
             else:
@@ -64,7 +95,8 @@ class Dataset(fluid.io.Dataset):
         else:
             if test_list is None:
                 raise Exception(
-                    'When mode is "test", test_list is need, but it is None.')
+                    'When mode is "test", test_list is necessary, but it is None.'
+                )
             elif not os.path.exists(test_list):
                 raise Exception('test_list is not found: {}'.format(test_list))
             else:

dygraph/datasets/optic_disc_seg.py

@@ -25,6 +25,7 @@ class OpticDiscSeg(Dataset):
     def __init__(self,
                  data_dir=None,
                  transforms=None,
+                 image_set='train',
                  mode='train',
                  download=True):
         self.data_dir = data_dir
@@ -33,24 +34,28 @@ class OpticDiscSeg(Dataset):
         self.mode = mode
         self.num_classes = 2
 
+        if image_set.lower() not in ['train', 'val', 'test']:
+            raise Exception(
+                "image_set should be one of ('train', 'val', 'test'), but got {}."
+                .format(image_set))
+
         if mode.lower() not in ['train', 'eval', 'test']:
             raise Exception(
                 "mode should be 'train', 'eval' or 'test', but got {}.".format(
                     mode))
 
         if self.transforms is None:
-            raise Exception("transform is necessary, but it is None.")
+            raise Exception("transforms is necessary, but it is None.")
 
-        self.data_dir = data_dir
         if self.data_dir is None:
             if not download:
                 raise Exception("data_file not set and auto download disabled.")
             self.data_dir = download_file_and_uncompress(
                 url=URL, savepath=DATA_HOME, extrapath=DATA_HOME)
 
-        if mode == 'train':
+        if image_set == 'train':
             file_list = os.path.join(self.data_dir, 'train_list.txt')
-        elif mode == 'eval':
+        elif image_set == 'val':
             file_list = os.path.join(self.data_dir, 'val_list.txt')
         else:
             file_list = os.path.join(self.data_dir, 'test_list.txt')

dygraph/datasets/voc.py

+# 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 os
+from .dataset import Dataset
+from utils.download import download_file_and_uncompress
+
+DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset')
+URL = "https://paddleseg.bj.bcebos.com/dataset/VOCtrainval_11-May-2012.tar"
+
+
+class PascalVoc(Dataset):
+    """Pascal VOC dataset `http://host.robots.ox.ac.uk/pascal/VOC/`. If you want to augment the dataset,
+    please run the voc_augment.py in tools.
+    Args:
+        data_dir: The dataset directory.
+        image_set: Which part of dataset to use. Generally, image_set is of ('train', 'val', 'trainval', 'trainaug). Default: 'train'.
+        mode: Dataset usage. it is one of ('train', 'eva', 'test'). Default: 'train'.
+        transforms: Transforms for image.
+        download: Whether to download dataset if data_dir is None.
+    """
+
+    def __init__(self,
+                 data_dir=None,
+                 image_set='train',
+                 mode='train',
+                 transforms=None,
+                 download=False):
+        self.data_dir = data_dir
+        self.transforms = transforms
+        self.mode = mode
+        self.file_list = list()
+        self.num_classes = 21
+
+        if image_set.lower() not in ['train', 'val', 'trainval', 'trainaug']:
+            raise Exception(
+                "image_set should be one of ('train', 'val', 'trainval', 'trainaug'), but got {}."
+                .format(image_set))
+
+        if mode.lower() not in ['train', 'eval', 'test']:
+            raise Exception(
+                "mode should be 'train', 'eval' or 'test', but got {}.".format(
+                    mode))
+
+        if self.transforms is None:
+            raise Exception("transforms is necessary, but it is None.")
+
+        if self.data_dir is None:
+            if not download:
+                raise Exception("data_file not set and auto download disabled.")
+            self.data_dir = download_file_and_uncompress(
+                url=URL,
+                savepath=DATA_HOME,
+                extrapath=DATA_HOME,
+                extraname='VOCdevkit')
+            print(self.data_dir)
+
+        image_set_dir = os.path.join(self.data_dir, 'VOC2012', 'ImageSets',
+                                     'Segmentation')
+        if image_set == 'train':
+            file_list = os.path.join(image_set_dir, 'train.txt')
+        elif image_set == 'val':
+            file_list = os.path.join(image_set_dir, 'val.txt')
+        elif image_set == 'trainval':
+            file_list = os.path.join(image_set_dir, 'trainval.txt')
+        elif image_set == 'trainaug':
+            file_list = os.path.join(image_set_dir, 'train.txt')
+            file_list_aug = os.path.join(image_set_dir, 'aug.txt')
+
+            if not os.path.exists(file_list_aug):
+                raise Exception(
+                    "When image_set is 'trainaug', Pascal Voc dataset should be augmented, "
+                    "Please make sure voc_augment.py has been properly run when using this mode."
+                )
+
+        img_dir = os.path.join(self.data_dir, 'VOC2012', 'JPEGImages')
+        grt_dir = os.path.join(self.data_dir, 'VOC2012', 'SegmentationClass')
+        grt_dir_aug = os.path.join(self.data_dir, 'VOC2012',
+                                   'SegmentationClassAug')
+
+        with open(file_list, 'r') as f:
+            for line in f:
+                line = line.strip()
+                image_path = os.path.join(img_dir, ''.join([line, '.jpg']))
+                grt_path = os.path.join(grt_dir, ''.join([line, '.png']))
+                self.file_list.append([image_path, grt_path])
+        if image_set == 'trainaug':
+            with open(file_list_aug, 'r') as f:
+                for line in f:
+                    line = line.strip()
+                    image_path = os.path.join(img_dir, ''.join([line, '.jpg']))
+                    grt_path = os.path.join(grt_dir, ''.join([line, '.png']))
+                    self.file_list.append([image_path, grt_path])

dygraph/tools/voc_augment.py

+# 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.
+"""
+File: voc_augment.py
+
+This file use SBD(Semantic Boundaries Dataset) <http://home.bharathh.info/pubs/codes/SBD/download.html>
+to augment the Pascal VOC
+"""
+
+import os
+import argparse
+from multiprocessing import Pool, cpu_count
+
+import cv2
+import numpy as np
+from scipy.io import loadmat
+import tqdm
+
+from utils.download import download_file_and_uncompress
+
+DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset')
+URL = 'http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz'
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description=
+        'Convert SBD to Pascal Voc annotations to augment the train dataset of Pascal Voc'
+    )
+    parser.add_argument(
+        '--voc_path',
+        dest='voc_path',
+        help='pascal voc path',
+        type=str,
+        default=os.path.join(DATA_HOME + 'VOCdevkit'))
+
+    parser.add_argument(
+        '--num_workers',
+        dest='num_workers',
+        help='How many processes are used for data conversion',
+        type=str,
+        default=cpu_count())
+    return parser.parse_args()
+
+
+def conver_to_png(mat_file, sbd_cls_dir, save_dir):
+    mat_path = os.path.join(sbd_cls_dir, mat_file)
+    mat = loadmat(mat_path)
+    mask = mat['GTcls'][0]['Segmentation'][0].astype(np.uint8)
+    save_file = os.path.join(save_dir, mat_file.replace('mat', 'png'))
+    cv2.imwrite(save_file, mask)
+
+
+def main():
+    args = parse_args()
+    sbd_path = download_file_and_uncompress(
+        url=URL,
+        savepath=DATA_HOME,
+        extrapath=DATA_HOME,
+        extraname='benchmark_RELEASE')
+    with open(os.path.join(sbd_path, 'dataset/train.txt'), 'r') as f:
+        sbd_file_list = [line.strip() for line in f]
+    with open(os.path.join(sbd_path, 'dataset/val.txt'), 'r') as f:
+        sbd_file_list += [line.strip() for line in f]
+    if not os.path.exists(args.voc_path):
+        raise Exception(
+            'Ther is no voc_path: {}. Please ensure that the Pascal VOC dataset has been downloaded correctly'
+        )
+    with open(
+            os.path.join(args.voc_path,
+                         'VOC2012/ImageSets/Segmentation/trainval.txt',
+                         'r')) as f:
+        voc_file_list = [line.strip() for line in f]
+
+    aug_file_list = list(set(sbd_file_list) - set(voc_file_list))
+    with open(
+            os.path.join(args.voc_path,
+                         'VOC2012/ImageSets/Segmentation/aug.txt', 'w')) as f:
+        f.writelines(''.join(line, '\n') for line in aug_file_list)
+
+    sbd_cls_dir = os.path.join(sbd_path, 'dataset/cls')
+    save_dir = os.path.join(args.voc_path,
+                            'VOC2012/ImageSets/SegmentationClassAug')
+    mat_file_list = os.listdir(sbd_cls_dir)
+    p = Pool(args.num_workers)
+    for f in tqdm.tqdm(mat_file_list):
+        p.apply_async(conver_to_png, args=(f, sbd_cls_dir, save_dir))
+
+
+if __name__ == '__main__':
+    main()

dygraph/utils/download.py

@@ -85,8 +85,8 @@ def _uncompress_file(filepath, extrapath, delete_file, print_progress):
     for total_num, index, rootpath in handler(filepath, extrapath):
         if print_progress:
             done = int(50 * float(index) / total_num)
-            progress("[%-50s] %.2f%%" %
-                     ('=' * done, float(100 * index) / total_num))
+            progress(
+                "[%-50s] %.2f%%" % ('=' * done, float(100 * index) / total_num))
     if print_progress:
         progress("[%-50s] %.2f%%" % ('=' * 50, 100), end=True)
 
@@ -132,4 +132,4 @@ def download_file_and_uncompress(url,
                                         print_progress)
             savename = os.path.join(extrapath, savename)
         shutil.move(savename, extraname)
-    return savename
+    return extraname