Merge pull request #340 from wuyefeilin/dygraph

Update dataset,  add Pascal VOC and ADE20K

dygraph/core/train.py

@@ -117,7 +117,8 @@ def train(model,
                             avg_loss * nranks, lr, avg_train_batch_cost,
                             avg_train_reader_cost, eta))
                 if use_vdl:
-                    log_writer.add_scalar('Train/loss', avg_loss, num_steps)
+                    log_writer.add_scalar('Train/loss', avg_loss * nranks,
+                                          num_steps)
                     log_writer.add_scalar('Train/lr', lr, num_steps)
                     log_writer.add_scalar('Train/batch_cost',
                                           avg_train_batch_cost, num_steps)

dygraph/datasets/__init__.py

@@ -15,3 +15,12 @@
 from .dataset import Dataset
 from .optic_disc_seg import OpticDiscSeg
 from .cityscapes import Cityscapes
+from .voc import PascalVOC
+from .ade import ADE20K
+
+DATASETS = {
+    "OpticDiscSeg": OpticDiscSeg,
+    "Cityscapes": Cityscapes,
+    "PascalVOC": PascalVOC,
+    "ADE20K": ADE20K
+}

dygraph/datasets/ade.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
+
+import numpy as np
+from PIL import Image
+
+from .dataset import Dataset
+from utils.download import download_file_and_uncompress
+
+DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset')
+URL = "http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip"
+
+
+class ADE20K(Dataset):
+    """ADE20K dataset `http://sceneparsing.csail.mit.edu/`.
+    Args:
+        dataset_root: The dataset directory.
+        mode: Which part of dataset to use.. it is one of ('train', 'val'). Default: 'train'.
+        transforms: Transforms for image.
+        download: Whether to download dataset if `dataset_root` is None.
+    """
+
+    def __init__(self,
+                 dataset_root=None,
+                 mode='train',
+                 transforms=None,
+                 download=True):
+        self.dataset_root = dataset_root
+        self.transforms = transforms
+        self.mode = mode
+        self.file_list = list()
+        self.num_classes = 150
+
+        if mode.lower() not in ['train', 'val']:
+            raise Exception(
+                "`mode` should be one of ('train', 'val') in ADE20K dataset, but got {}."
+                .format(mode))
+
+        if self.transforms is None:
+            raise Exception("`transforms` is necessary, but it is None.")
+
+        if self.dataset_root is None:
+            if not download:
+                raise Exception(
+                    "`dataset_root` not set and auto download disabled.")
+            self.dataset_root = download_file_and_uncompress(
+                url=URL,
+                savepath=DATA_HOME,
+                extrapath=DATA_HOME,
+                extraname='ADEChallengeData2016')
+        elif not os.path.exists(self.dataset_root):
+            raise Exception('there is not `dataset_root`: {}.'.format(
+                self.dataset_root))
+
+        if mode == 'train':
+            img_dir = os.path.join(self.dataset_root, 'images/training')
+            grt_dir = os.path.join(self.dataset_root, 'annotations/training')
+        elif mode == 'val':
+            img_dir = os.path.join(self.dataset_root, 'images/validation')
+            grt_dir = os.path.join(self.dataset_root, 'annotations/validation')
+        img_files = os.listdir(img_dir)
+        grt_files = [i.replace('.jpg', '.png') for i in img_files]
+        for i in range(len(img_files)):
+            img_path = os.path.join(img_dir, img_files[i])
+            grt_path = os.path.join(grt_dir, grt_files[i])
+            self.file_list.append([img_path, grt_path])
+
+    def __getitem__(self, idx):
+        image_path, grt_path = self.file_list[idx]
+        if self.mode == 'test':
+            im, im_info, _ = self.transforms(im=image_path)
+            im = im[np.newaxis, ...]
+            return im, im_info, image_path
+        elif self.mode == 'val':
+            im, im_info, _ = self.transforms(im=image_path)
+            im = im[np.newaxis, ...]
+            label = np.asarray(Image.open(grt_path))
+            label = label - 1
+            label = label[np.newaxis, np.newaxis, :, :]
+            return im, im_info, label
+        else:
+            im, im_info, label = self.transforms(im=image_path, label=grt_path)
+            label = label - 1
+            return im, label

dygraph/datasets/cityscapes.py

-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# 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.
@@ -13,59 +13,62 @@
 # limitations under the License.
 
 import os
+import glob
 
 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/cityscapes.tar"
 
 
 class Cityscapes(Dataset):
-    def __init__(self,
-                 data_dir=None,
-                 transforms=None,
-                 mode='train',
-                 download=True):
-        self.data_dir = data_dir
+    """Cityscapes dataset `https://www.cityscapes-dataset.com/`.
+    The folder structure is as follow:
+    cityscapes
+    |
+    |--leftImg8bit
+    |  |--train
+    |  |--val
+    |  |--test
+    |
+    |--gtFine
+    |  |--train
+    |  |--val
+    |  |--test
+    Make sure there are **labelTrainIds.png in gtFine directory. If not, please run the conver_cityscapes.py in tools.
+
+    Args:
+        dataset_root: Cityscapes dataset directory.
+        mode: Which part of dataset to use. it is one of ('train', 'val', 'test'). Default: 'train'.
+        transforms: Transforms for image.
+    """
+
+    def __init__(self, dataset_root, transforms=None, mode='train'):
+        self.dataset_root = dataset_root
         self.transforms = transforms
         self.file_list = list()
         self.mode = mode
         self.num_classes = 19
 
-        if mode.lower() not in ['train', 'eval', 'test']:
+        if mode.lower() not in ['train', 'val', 'test']:
             raise Exception(
-                "mode should be 'train', 'eval' or 'test', but got {}.".format(
+                "mode should be 'train', 'val' 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)
+        img_dir = os.path.join(self.dataset_root, 'leftImg8bit')
+        grt_dir = os.path.join(self.dataset_root, 'gtFine')
+        if self.dataset_root is None or not os.path.isdir(
+                self.dataset_root) or not os.path.isdir(
+                    img_dir) or not os.path.isdir(grt_dir):
+            raise Exception(
+                "The dataset is not Found or the folder structure is nonconfoumance."
+            )
 
-        if mode == 'train':
-            file_list = os.path.join(self.data_dir, 'train.list')
-        elif mode == 'eval':
-            file_list = os.path.join(self.data_dir, 'val.list')
-        else:
-            file_list = os.path.join(self.data_dir, 'test.list')
+        grt_files = sorted(
+            glob.glob(
+                os.path.join(grt_dir, mode, '*', '*_gtFine_labelTrainIds.png')))
+        img_files = sorted(
+            glob.glob(os.path.join(img_dir, mode, '*', '*_leftImg8bit.png')))
 
-        with open(file_list, 'r') as f:
-            for line in f:
-                items = line.strip().split()
-                if len(items) != 2:
-                    if mode == 'train' or mode == 'eval':
-                        raise Exception(
-                            "File list format incorrect! It should be"
-                            " image_name label_name\\n")
-                    image_path = os.path.join(self.data_dir, items[0])
-                    grt_path = None
-                else:
-                    image_path = os.path.join(self.data_dir, items[0])
-                    grt_path = os.path.join(self.data_dir, items[1])
-                self.file_list.append([image_path, grt_path])
+        self.file_list = [[img_path, grt_path]
+                          for img_path, grt_path in zip(img_files, grt_files)]

dygraph/datasets/dataset.py

-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# 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.
@@ -20,53 +20,83 @@ from PIL import Image
 
 
 class Dataset(fluid.io.Dataset):
+    """Pass in a custom dataset that conforms to the format.
+
+    Args:
+        dataset_root: The dataset directory.
+        num_classes: Number of classes.
+        mode: which part of dataset to use. it is one of ('train', 'val', '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,
+                 dataset_root,
                  num_classes,
+                 mode='train',
                  train_list=None,
                  val_list=None,
                  test_list=None,
                  separator=' ',
-                 transforms=None,
-                 mode='train'):
-        self.data_dir = data_dir
+                 transforms=None):
+        self.dataset_root = dataset_root
         self.transforms = transforms
         self.file_list = list()
         self.mode = mode
         self.num_classes = num_classes
 
-        if mode.lower() not in ['train', 'eval', 'test']:
+        if mode.lower() not in ['train', 'val', 'test']:
             raise Exception(
-                "mode should be 'train', 'eval' or 'test', but got {}.".format(
+                "mode should be 'train', 'val' 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.dataset_root = dataset_root
+        if not os.path.exists(self.dataset_root):
+            raise Exception('there is not `dataset_root`: {}.'.format(
+                self.dataset_root))
 
-        self.data_dir = data_dir
         if mode == '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))
+                    '`train_list` is not found: {}'.format(train_list))
             else:
                 file_list = train_list
-        elif mode == 'eval':
+        elif mode == 'val':
             if val_list is None:
                 raise Exception(
-                    'When mode is "eval", val_list is need, but it is None.')
+                    'When `mode` is "val", `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))
+                raise Exception('`val_list` is not found: {}'.format(val_list))
             else:
                 file_list = val_list
         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))
+                raise Exception(
+                    '`test_list` is not found: {}'.format(test_list))
             else:
                 file_list = test_list
 
@@ -74,32 +104,32 @@ class Dataset(fluid.io.Dataset):
             for line in f:
                 items = line.strip().split(separator)
                 if len(items) != 2:
-                    if mode == 'train' or mode == 'eval':
+                    if mode == 'train' or mode == 'val':
                         raise Exception(
-                            "File list format incorrect! It should be"
+                            "File list format incorrect! In training or evaluation task it should be"
                             " image_name{}label_name\\n".format(separator))
-                    image_path = os.path.join(self.data_dir, items[0])
+                    image_path = os.path.join(self.dataset_root, items[0])
                     grt_path = None
                 else:
-                    image_path = os.path.join(self.data_dir, items[0])
-                    grt_path = os.path.join(self.data_dir, items[1])
+                    image_path = os.path.join(self.dataset_root, items[0])
+                    grt_path = os.path.join(self.dataset_root, items[1])
                 self.file_list.append([image_path, grt_path])
 
     def __getitem__(self, idx):
         image_path, grt_path = self.file_list[idx]
-        if self.mode == 'train':
-            im, im_info, label = self.transforms(im=image_path, label=grt_path)
-            return im, label
-        elif self.mode == 'eval':
+        if self.mode == 'test':
+            im, im_info, _ = self.transforms(im=image_path)
+            im = im[np.newaxis, ...]
+            return im, im_info, image_path
+        elif self.mode == 'val':
             im, im_info, _ = self.transforms(im=image_path)
             im = im[np.newaxis, ...]
             label = np.asarray(Image.open(grt_path))
             label = label[np.newaxis, np.newaxis, :, :]
             return im, im_info, label
-        if self.mode == 'test':
-            im, im_info, _ = self.transforms(im=image_path)
-            im = im[np.newaxis, ...]
-            return im, im_info, image_path
+        else:
+            im, im_info, label = self.transforms(im=image_path, label=grt_path)
+            return im, label
 
     def __len__(self):
         return len(self.file_list)

dygraph/datasets/optic_disc_seg.py

-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# 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.
@@ -23,49 +23,52 @@ URL = "https://paddleseg.bj.bcebos.com/dataset/optic_disc_seg.zip"
 
 class OpticDiscSeg(Dataset):
     def __init__(self,
-                 data_dir=None,
+                 dataset_root=None,
                  transforms=None,
                  mode='train',
                  download=True):
-        self.data_dir = data_dir
+        self.dataset_root = dataset_root
         self.transforms = transforms
         self.file_list = list()
         self.mode = mode
         self.num_classes = 2
 
-        if mode.lower() not in ['train', 'eval', 'test']:
+        if mode.lower() not in ['train', 'val', 'test']:
             raise Exception(
-                "mode should be 'train', 'eval' or 'test', but got {}.".format(
+                "`mode` should be 'train', 'val' 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 self.dataset_root is None:
             if not download:
-                raise Exception("data_file not set and auto download disabled.")
-            self.data_dir = download_file_and_uncompress(
+                raise Exception(
+                    "`data_root` not set and auto download disabled.")
+            self.dataset_root = download_file_and_uncompress(
                 url=URL, savepath=DATA_HOME, extrapath=DATA_HOME)
+        elif not os.path.exists(self.dataset_root):
+            raise Exception('there is not `dataset_root`: {}.'.format(
+                self.dataset_root))
 
         if mode == 'train':
-            file_list = os.path.join(self.data_dir, 'train_list.txt')
-        elif mode == 'eval':
-            file_list = os.path.join(self.data_dir, 'val_list.txt')
+            file_list = os.path.join(self.dataset_root, 'train_list.txt')
+        elif mode == 'val':
+            file_list = os.path.join(self.dataset_root, 'val_list.txt')
         else:
-            file_list = os.path.join(self.data_dir, 'test_list.txt')
+            file_list = os.path.join(self.dataset_root, 'test_list.txt')
 
         with open(file_list, 'r') as f:
             for line in f:
                 items = line.strip().split()
                 if len(items) != 2:
-                    if mode == 'train' or mode == 'eval':
+                    if mode == 'train' or mode == 'val':
                         raise Exception(
                             "File list format incorrect! It should be"
                             " image_name label_name\\n")
-                    image_path = os.path.join(self.data_dir, items[0])
+                    image_path = os.path.join(self.dataset_root, items[0])
                     grt_path = None
                 else:
-                    image_path = os.path.join(self.data_dir, items[0])
-                    grt_path = os.path.join(self.data_dir, items[1])
+                    image_path = os.path.join(self.dataset_root, items[0])
+                    grt_path = os.path.join(self.dataset_root, items[1])
                 self.file_list.append([image_path, grt_path])

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 = "http://host.robots.ox.ac.uk/pascal/VOC/voc2012/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:
+        dataset_root: The dataset directory.
+        mode: Which part of dataset to use.. it is one of ('train', 'val', 'test'). Default: 'train'.
+        transforms: Transforms for image.
+        download: Whether to download dataset if dataset_root is None.
+    """
+
+    def __init__(self,
+                 dataset_root=None,
+                 mode='train',
+                 transforms=None,
+                 download=True):
+        self.dataset_root = dataset_root
+        self.transforms = transforms
+        self.mode = mode
+        self.file_list = list()
+        self.num_classes = 21
+
+        if mode.lower() not in ['train', 'trainval', 'trainaug', 'val']:
+            raise Exception(
+                "`mode` should be one of ('train', 'trainval', 'trainaug', 'val') in PascalVOC dataset, but got {}."
+                .format(mode))
+
+        if self.transforms is None:
+            raise Exception("`transforms` is necessary, but it is None.")
+
+        if self.dataset_root is None:
+            if not download:
+                raise Exception(
+                    "`dataset_root` not set and auto download disabled.")
+            self.dataset_root = download_file_and_uncompress(
+                url=URL,
+                savepath=DATA_HOME,
+                extrapath=DATA_HOME,
+                extraname='VOCdevkit')
+        elif not os.path.exists(self.dataset_root):
+            raise Exception('there is not `dataset_root`: {}.'.format(
+                self.dataset_root))
+
+        image_set_dir = os.path.join(self.dataset_root, 'VOC2012', 'ImageSets',
+                                     'Segmentation')
+        if mode == 'train':
+            file_list = os.path.join(image_set_dir, 'train.txt')
+        elif mode == 'val':
+            file_list = os.path.join(image_set_dir, 'val.txt')
+        elif mode == 'trainval':
+            file_list = os.path.join(image_set_dir, 'trainval.txt')
+        elif mode == '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 `mode` 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.dataset_root, 'VOC2012', 'JPEGImages')
+        grt_dir = os.path.join(self.dataset_root, 'VOC2012',
+                               'SegmentationClass')
+        grt_dir_aug = os.path.join(self.dataset_root, '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 mode == '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_aug, ''.join([line,
+                                                                  '.png']))
+                    self.file_list.append([image_path, grt_path])

dygraph/infer.py

@@ -13,20 +13,13 @@
 # limitations under the License.
 
 import argparse
-import os
 
-from paddle.fluid.dygraph.base import to_variable
-import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid.dygraph.parallel import ParallelEnv
-import cv2
-import tqdm
 
-from datasets import OpticDiscSeg, Cityscapes
+from datasets import DATASETS
 import transforms as T
 from models import MODELS
-import utils
-import utils.logging as logging
 from utils import get_environ_info
 from core import infer
 
@@ -43,14 +36,20 @@ def parse_args():
         type=str,
         default='UNet')
 
-    # params of dataset
+    # params of infer
     parser.add_argument(
         '--dataset',
         dest='dataset',
-        help=
-        "The dataset you want to train, which is one of ('OpticDiscSeg', 'Cityscapes')",
+        help="The dataset you want to test, which is one of {}".format(
+            str(list(DATASETS.keys()))),
         type=str,
         default='OpticDiscSeg')
+    parser.add_argument(
+        '--dataset_root',
+        dest='dataset_root',
+        help="dataset root directory",
+        type=str,
+        default=None)
 
     # params of prediction
     parser.add_argument(
@@ -88,22 +87,21 @@ def main(args):
         if env_info['place'] == 'cuda' and fluid.is_compiled_with_cuda() \
         else fluid.CPUPlace()
 
-    if args.dataset.lower() == 'opticdiscseg':
-        dataset = OpticDiscSeg
-    elif args.dataset.lower() == 'cityscapes':
-        dataset = Cityscapes
-    else:
-        raise Exception(
-            "The --dataset set wrong. It should be one of ('OpticDiscSeg', 'Cityscapes')"
-        )
+    if args.dataset not in DATASETS:
+        raise Exception('`--dataset` is invalid. it should be one of {}'.format(
+            str(list(DATASETS.keys()))))
+    dataset = DATASETS[args.dataset]
 
     with fluid.dygraph.guard(places):
         test_transforms = T.Compose([T.Resize(args.input_size), T.Normalize()])
-        test_dataset = dataset(transforms=test_transforms, mode='test')
+        test_dataset = dataset(
+            dataset_root=args.dataset_root,
+            transforms=test_transforms,
+            mode='test')
 
         if args.model_name not in MODELS:
             raise Exception(
-                '--model_name is invalid. it should be one of {}'.format(
+                '`--model_name` is invalid. it should be one of {}'.format(
                     str(list(MODELS.keys()))))
         model = MODELS[args.model_name](num_classes=test_dataset.num_classes)
 

dygraph/models/hrnet.py

@@ -645,12 +645,14 @@ class FuseLayers(fluid.dygraph.Layer):
         residual_func_idx = 0
         for i in range(self._actual_ch):
             residual = input[i]
+            residual_shape = residual.shape[-2:]
             for j in range(len(self._in_channels)):
                 if j > i:
                     y = self.residual_func_list[residual_func_idx](input[j])
                     residual_func_idx += 1
 
-                    y = fluid.layers.resize_bilinear(input=y, scale=2**(j - i))
+                    y = fluid.layers.resize_bilinear(
+                        input=y, out_shape=residual_shape)
                     residual = fluid.layers.elementwise_add(
                         x=residual, y=y, act=None)
                 elif j < i:

dygraph/tools/conver_cityscapes.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: convert_cityscapes.py
+This file is based on https://github.com/mcordts/cityscapesScripts to generate **labelTrainIds.png for training.
+Before running, you should download the cityscapes form https://www.cityscapes-dataset.com/ and make the folder
+structure as follow:
+cityscapes
+|
+|--leftImg8bit
+|  |--train
+|  |--val
+|  |--test
+|
+|--gtFine
+|  |--train
+|  |--val
+|  |--test
+"""
+
+import os
+import argparse
+from multiprocessing import Pool, cpu_count
+import glob
+
+from cityscapesscripts.preparation.json2labelImg import json2labelImg
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Generate **labelTrainIds.png for training')
+    parser.add_argument(
+        '--cityscapes_path',
+        dest='cityscapes_path',
+        help='cityscapes path',
+        type=str)
+
+    parser.add_argument(
+        '--num_workers',
+        dest='num_workers',
+        help='How many processes are used for data conversion',
+        type=int,
+        default=cpu_count())
+    return parser.parse_args()
+
+
+def gen_labelTrainIds(json_file):
+    label_file = json_file.replace("_polygons.json", "_labelTrainIds.png")
+    json2labelImg(json_file, label_file, "trainIds")
+
+
+def main():
+    args = parse_args()
+    fine_path = os.path.join(args.cityscapes_path, 'gtFine')
+    json_files = glob.glob(os.path.join(fine_path, '*', '*', '*_polygons.json'))
+
+    print('generating **_labelTrainIds.png')
+    p = Pool(args.num_workers)
+    for f in json_files:
+        p.apply_async(gen_labelTrainIds, args=(f, ))
+    p.close()
+    p.join()
+
+
+if __name__ == '__main__':
+    main()

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=int,
+        default=cpu_count())
+    return parser.parse_args()
+
+
+def mat_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(
+            'There 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/SegmentationClassAug')
+    if not os.path.exists(save_dir):
+        os.mkdir(save_dir)
+    mat_file_list = os.listdir(sbd_cls_dir)
+    p = Pool(args.num_workers)
+    for f in tqdm.tqdm(mat_file_list):
+        p.apply_async(mat_to_png, args=(f, sbd_cls_dir, save_dir))
+    p.close()
+    p.join()
+
+
+if __name__ == '__main__':
+    main()

dygraph/train.py

@@ -13,21 +13,14 @@
 # limitations under the License.
 
 import argparse
-import os
 
 import paddle.fluid as fluid
 from paddle.fluid.dygraph.parallel import ParallelEnv
-from paddle.fluid.io import DataLoader
-from paddle.incubate.hapi.distributed import DistributedBatchSampler
 
-from datasets import OpticDiscSeg, Cityscapes
+from datasets import DATASETS
 import transforms as T
 from models import MODELS
-import utils.logging as logging
 from utils import get_environ_info
-from utils import load_pretrained_model
-from utils import resume
-from utils import Timer, calculate_eta
 from core import train
 
 
@@ -47,10 +40,16 @@ def parse_args():
     parser.add_argument(
         '--dataset',
         dest='dataset',
-        help=
-        "The dataset you want to train, which is one of ('OpticDiscSeg', 'Cityscapes')",
+        help="The dataset you want to train, which is one of {}".format(
+            str(list(DATASETS.keys()))),
         type=str,
         default='OpticDiscSeg')
+    parser.add_argument(
+        '--dataset_root',
+        dest='dataset_root',
+        help="dataset root directory",
+        type=str,
+        default=None)
 
     # params of training
     parser.add_argument(
@@ -134,14 +133,10 @@ def main(args):
         if env_info['place'] == 'cuda' and fluid.is_compiled_with_cuda() \
         else fluid.CPUPlace()
 
-    if args.dataset.lower() == 'opticdiscseg':
-        dataset = OpticDiscSeg
-    elif args.dataset.lower() == 'cityscapes':
-        dataset = Cityscapes
-    else:
-        raise Exception(
-            "The --dataset set wrong. It should be one of ('OpticDiscSeg', 'Cityscapes')"
-        )
+    if args.dataset not in DATASETS:
+        raise Exception('`--dataset` is invalid. it should be one of {}'.format(
+            str(list(DATASETS.keys()))))
+    dataset = DATASETS[args.dataset]
 
     with fluid.dygraph.guard(places):
         # Creat dataset reader
@@ -150,18 +145,24 @@ def main(args):
             T.RandomHorizontalFlip(),
             T.Normalize()
         ])
-        train_dataset = dataset(transforms=train_transforms, mode='train')
+        train_dataset = dataset(
+            dataset_root=args.dataset_root,
+            transforms=train_transforms,
+            mode='train')
 
         eval_dataset = None
         if args.do_eval:
             eval_transforms = T.Compose(
                 [T.Resize(args.input_size),
                  T.Normalize()])
-            eval_dataset = dataset(transforms=eval_transforms, mode='eval')
+            eval_dataset = dataset(
+                dataset_root=args.dataset_root,
+                transforms=eval_transforms,
+                mode='val')
 
         if args.model_name not in MODELS:
             raise Exception(
-                '--model_name is invalid. it should be one of {}'.format(
+                '`--model_name` is invalid. it should be one of {}'.format(
                     str(list(MODELS.keys()))))
         model = MODELS[args.model_name](num_classes=train_dataset.num_classes)
 

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

dygraph/utils/utils.py

@@ -88,6 +88,7 @@ def resume(model, optimizer, resume_model):
     if resume_model is not None:
         logging.info('Resume model from {}'.format(resume_model))
         if os.path.exists(resume_model):
+            resume_model = os.path.normpath(resume_model)
             ckpt_path = os.path.join(resume_model, 'model')
             para_state_dict, opti_state_dict = fluid.load_dygraph(ckpt_path)
             model.set_dict(para_state_dict)

dygraph/val.py

@@ -13,25 +13,14 @@
 # limitations under the License.
 
 import argparse
-import os
-import math
 
-import numpy as np
-import tqdm
-import cv2
-from paddle.fluid.dygraph.base import to_variable
 import paddle.fluid as fluid
 from paddle.fluid.dygraph.parallel import ParallelEnv
-from paddle.fluid.io import DataLoader
-from paddle.fluid.dataloader import BatchSampler
 
-from datasets import OpticDiscSeg, Cityscapes
+from datasets import DATASETS
 import transforms as T
 from models import MODELS
-import utils.logging as logging
 from utils import get_environ_info
-from utils import ConfusionMatrix
-from utils import Timer, calculate_eta
 from core import evaluate
 
 
@@ -51,10 +40,16 @@ def parse_args():
     parser.add_argument(
         '--dataset',
         dest='dataset',
-        help=
-        "The dataset you want to evaluation, which is one of ('OpticDiscSeg', 'Cityscapes')",
+        help="The dataset you want to evaluation, which is one of {}".format(
+            str(list(DATASETS.keys()))),
         type=str,
         default='OpticDiscSeg')
+    parser.add_argument(
+        '--dataset_root',
+        dest='dataset_root',
+        help="dataset root directory",
+        type=str,
+        default=None)
 
     # params of evaluate
     parser.add_argument(
@@ -80,22 +75,21 @@ def main(args):
         if env_info['place'] == 'cuda' and fluid.is_compiled_with_cuda() \
         else fluid.CPUPlace()
 
-    if args.dataset.lower() == 'opticdiscseg':
-        dataset = OpticDiscSeg
-    elif args.dataset.lower() == 'cityscapes':
-        dataset = Cityscapes
-    else:
-        raise Exception(
-            "The --dataset set wrong. It should be one of ('OpticDiscSeg', 'Cityscapes')"
-        )
+    if args.dataset not in DATASETS:
+        raise Exception('`--dataset` is invalid. it should be one of {}'.format(
+            str(list(DATASETS.keys()))))
+    dataset = DATASETS[args.dataset]
 
     with fluid.dygraph.guard(places):
         eval_transforms = T.Compose([T.Resize(args.input_size), T.Normalize()])
-        eval_dataset = dataset(transforms=eval_transforms, mode='eval')
+        eval_dataset = dataset(
+            dataset_root=args.dataset_root,
+            transforms=eval_transforms,
+            mode='val')
 
         if args.model_name not in MODELS:
             raise Exception(
-                '--model_name is invalid. it should be one of {}'.format(
+                '`--model_name` is invalid. it should be one of {}'.format(
                     str(list(MODELS.keys()))))
         model = MODELS[args.model_name](num_classes=eval_dataset.num_classes)