Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleSeg into develop

README.md

@@ -35,7 +35,7 @@ PaddleSeg是基于[PaddlePaddle](https://www.paddlepaddle.org.cn)开发的端到
 
 - **高性能**
 
-PaddleSeg支持多进程I/O、多卡并行、跨卡Batch Norm同步等训练加速策略，结合飞桨核心框架的显存优化功能，可大幅度减少分割模型的显存开销，让开发者更低成本、更高效地完成图像分割训练。
+PaddleSeg支持多进程I/O、多卡并行等训练加速策略，结合飞桨核心框架的显存优化功能，可大幅度减少分割模型的显存开销，让开发者更低成本、更高效地完成图像分割训练。
 
 - **工业级部署**
 

contrib/ACE2P/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.

contrib/ACE2P/config.py

-# -*- coding: utf-8 -*-
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 utils.util import AttrDict, merge_cfg_from_args, get_arguments
 import os
 
@@ -19,10 +33,10 @@ cfg.class_num = 20
 # 均值, 图像预处理减去的均值
 cfg.MEAN = 0.406, 0.456, 0.485
 # 标准差，图像预处理除以标准差
-cfg.STD =  0.225, 0.224, 0.229
+cfg.STD = 0.225, 0.224, 0.229
 
 # 多尺度预测时图像尺寸
-cfg.multi_scales = (377,377), (473,473), (567,567)
+cfg.multi_scales = (377, 377), (473, 473), (567, 567)
 # 多尺度预测时图像是否水平翻转
 cfg.flip = True
 

contrib/ACE2P/download_ACE2P.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/ACE2P/infer.py

-# -*- coding: utf-8 -*-
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 cv2
 import numpy as np
@@ -12,18 +26,19 @@ config = importlib.import_module('config')
 cfg = getattr(config, 'cfg')
 
 # paddle垃圾回收策略FLAG，ACE2P模型较大，当显存不够时建议开启
-os.environ['FLAGS_eager_delete_tensor_gb']='0.0'
+os.environ['FLAGS_eager_delete_tensor_gb'] = '0.0'
 
 import paddle.fluid as fluid
 
+
 # 预测数据集类
 class TestDataSet():
     def __init__(self):
-        self.data_dir = cfg.data_dir 
+        self.data_dir = cfg.data_dir
         self.data_list_file = cfg.data_list_file
         self.data_list = self.get_data_list()
         self.data_num = len(self.data_list)
-    
+
     def get_data_list(self):
         # 获取预测图像路径列表
         data_list = []
@@ -56,10 +71,10 @@ class TestDataSet():
         img_path = self.data_list[index]
         img = cv2.imread(img_path, cv2.IMREAD_COLOR)
         if img is None:
-            return img, img,img_path, None
+            return img, img, img_path, None
 
         img_name = img_path.split(os.sep)[-1]
-        name_prefix = img_name.replace('.'+img_name.split('.')[-1],'')
+        name_prefix = img_name.replace('.' + img_name.split('.')[-1], '')
         img_shape = img.shape[:2]
         img_process = self.preprocess(img)
 
@@ -90,39 +105,44 @@ def infer():
         if image is None:
             print(im_name, 'is None')
             continue
-        
+
         # 预测
         if cfg.example == 'ACE2P':
             # ACE2P模型使用多尺度预测
             reader = importlib.import_module('reader')
             multi_scale_test = getattr(reader, 'multi_scale_test')
-            parsing, logits = multi_scale_test(exe, test_prog, feed_name, fetch_list, image, im_shape)
+            parsing, logits = multi_scale_test(exe, test_prog, feed_name,
+                                               fetch_list, image, im_shape)
         else:
             # HumanSeg,RoadLine模型单尺度预测
-            result = exe.run(program=test_prog, feed={feed_name[0]: image}, fetch_list=fetch_list)
+            result = exe.run(
+                program=test_prog,
+                feed={feed_name[0]: image},
+                fetch_list=fetch_list)
             parsing = np.argmax(result[0][0], axis=0)
             parsing = cv2.resize(parsing.astype(np.uint8), im_shape[::-1])
-        
+
         # 预测结果保存
         result_path = os.path.join(cfg.vis_dir, im_name + '.png')
         if cfg.example == 'HumanSeg':
-            logits = result[0][0][1]*255
+            logits = result[0][0][1] * 255
             logits = cv2.resize(logits, im_shape[::-1])
             ret, logits = cv2.threshold(logits, thresh, 0, cv2.THRESH_TOZERO)
-            logits = 255 *(logits - thresh)/(255 - thresh)
+            logits = 255 * (logits - thresh) / (255 - thresh)
             # 将分割结果添加到alpha通道
-            rgba = np.concatenate((ori_img, np.expand_dims(logits, axis=2)), axis=2)
+            rgba = np.concatenate((ori_img, np.expand_dims(logits, axis=2)),
+                                  axis=2)
             cv2.imwrite(result_path, rgba)
-        else: 
+        else:
             output_im = PILImage.fromarray(np.asarray(parsing, dtype=np.uint8))
             output_im.putpalette(palette)
             output_im.save(result_path)
 
         if (idx + 1) % 100 == 0:
             print('%d  processd' % (idx + 1))
-            
-    print('%d  processd done' % (idx + 1))   
-    
+
+    print('%d  processd done' % (idx + 1))
+
     return 0
 
 

contrib/ACE2P/reader.py

-# -*- coding: utf-8 -*- 
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 numpy as np
 import paddle.fluid as fluid
 from config import cfg
 import cv2
 
+
 def get_affine_points(src_shape, dst_shape, rot_grad=0):
     # 获取图像和仿射后图像的三组对应点坐标
     # 三组点为仿射变换后图像的中心点, [w/2,0], [0,0]，及对应原始图像的点
@@ -23,7 +38,7 @@ def get_affine_points(src_shape, dst_shape, rot_grad=0):
 
     # 原始图像三组点
     points = [[0, 0]] * 3
-    points[0] = (np.array([w, h]) - 1) * 0.5 
+    points[0] = (np.array([w, h]) - 1) * 0.5
     points[1] = points[0] + 0.5 * affine_shape[0] * np.array([sin_v, -cos_v])
     points[2] = points[1] - 0.5 * affine_shape[1] * np.array([cos_v, sin_v])
 
@@ -34,6 +49,7 @@ def get_affine_points(src_shape, dst_shape, rot_grad=0):
 
     return points, points_trans
 
+
 def preprocess(im):
     # ACE2P模型数据预处理
     im_shape = im.shape[:2]
@@ -42,13 +58,10 @@ def preprocess(im):
         # 获取图像和仿射变换后图像的对应点坐标
         points, points_trans = get_affine_points(im_shape, scale)
         # 根据对应点集获得仿射矩阵
-        trans = cv2.getAffineTransform(np.float32(points),
-                                       np.float32(points_trans))
+        trans = cv2.getAffineTransform(
+            np.float32(points), np.float32(points_trans))
         # 根据仿射矩阵对图像进行仿射
-        input = cv2.warpAffine(im,
-                               trans,
-                               scale[::-1],
-                               flags=cv2.INTER_LINEAR)
+        input = cv2.warpAffine(im, trans, scale[::-1], flags=cv2.INTER_LINEAR)
 
         # 减均值测，除以方差，转换数据格式为NCHW
         input = input.astype(np.float32)
@@ -66,19 +79,20 @@ def preprocess(im):
     return input_images
 
 
-def multi_scale_test(exe, test_prog, feed_name, fetch_list,
-                        input_ims, im_shape):
-    
+def multi_scale_test(exe, test_prog, feed_name, fetch_list, input_ims,
+                     im_shape):
+
     # 由于部分类别分左右部位, flipped_idx为其水平翻转后对应的标签
     flipped_idx = (15, 14, 17, 16, 19, 18)
     ms_outputs = []
-    
+
     # 多尺度预测
     for idx, scale in enumerate(cfg.multi_scales):
         input_im = input_ims[idx]
-        parsing_output = exe.run(program=test_prog,
-                                 feed={feed_name[0]: input_im},
-                                 fetch_list=fetch_list)
+        parsing_output = exe.run(
+            program=test_prog,
+            feed={feed_name[0]: input_im},
+            fetch_list=fetch_list)
         output = parsing_output[0][0]
         if cfg.flip:
             # 若水平翻转，对部分类别进行翻转，与原始预测结果取均值
@@ -92,7 +106,8 @@ def multi_scale_test(exe, test_prog, feed_name, fetch_list,
         # 仿射变换回图像原始尺寸
         points, points_trans = get_affine_points(im_shape, scale)
         M = cv2.getAffineTransform(np.float32(points_trans), np.float32(points))
-        logits_result = cv2.warpAffine(output, M, im_shape[::-1], flags=cv2.INTER_LINEAR)
+        logits_result = cv2.warpAffine(
+            output, M, im_shape[::-1], flags=cv2.INTER_LINEAR)
         ms_outputs.append(logits_result)
 
     # 多尺度预测结果求均值，求预测概率最大的类别
@@ -100,4 +115,3 @@ def multi_scale_test(exe, test_prog, feed_name, fetch_list,
     ms_fused_parsing_output = np.mean(ms_fused_parsing_output, axis=0)
     parsing = np.argmax(ms_fused_parsing_output, axis=2)
     return parsing, ms_fused_parsing_output
-

contrib/ACE2P/utils/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.

contrib/ACE2P/utils/palette.py

@@ -7,6 +7,7 @@
 ## This source code is licensed under the MIT-style license found in the
 ## LICENSE file in the root directory of this source tree
 ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

contrib/ACE2P/utils/util.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 division
 from __future__ import print_function
 from __future__ import unicode_literals
 import argparse
 import os
 
+
 def get_arguments():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--use_gpu",
-                        action="store_true",
-                        help="Use gpu or cpu to test.")
-    parser.add_argument('--example',
-                        type=str,
-                        help='RoadLine, HumanSeg or ACE2P')
-                        
+    parser.add_argument(
+        "--use_gpu", action="store_true", help="Use gpu or cpu to test.")
+    parser.add_argument(
+        '--example', type=str, help='RoadLine, HumanSeg or ACE2P')
+
     return parser.parse_args()
 
 
@@ -34,6 +48,7 @@ class AttrDict(dict):
         else:
             self[name] = value
 
+
 def merge_cfg_from_args(args, cfg):
     """Merge config keys, values in args into the global config."""
     for k, v in vars(args).items():
@@ -44,4 +59,3 @@ def merge_cfg_from_args(args, cfg):
             value = v
         if value is not None:
             cfg[k] = value
-

contrib/HumanSeg/datasets/__init__.py

-#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/HumanSeg/datasets/dataset.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/HumanSeg/datasets/shared_queue/__init__.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/HumanSeg/datasets/shared_queue/queue.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/HumanSeg/datasets/shared_queue/sharedmemory.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,9 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# utils for memory management which is allocated on sharedmemory,
-#    note that these structures may not be thread-safe
-
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

contrib/HumanSeg/export.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 models
 import argparse
 

contrib/HumanSeg/infer.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 argparse
 import os
 import os.path as osp

contrib/HumanSeg/models/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 .humanseg import HumanSegMobile
 from .humanseg import HumanSegServer
 from .humanseg import HumanSegLite

contrib/HumanSeg/models/humanseg.py

-#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/HumanSeg/models/load_model.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/HumanSeg/nets/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 .backbone import mobilenet_v2
 from .backbone import xception
 from .deeplabv3p import DeepLabv3p

contrib/HumanSeg/nets/backbone/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 .mobilenet_v2 import MobileNetV2
 from .xception import Xception

contrib/HumanSeg/nets/backbone/mobilenet_v2.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -10,6 +11,7 @@
 # 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 absolute_import
 from __future__ import division

contrib/HumanSeg/nets/backbone/xception.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/HumanSeg/nets/deeplabv3p.py

 # coding: utf8
-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/HumanSeg/nets/hrnet.py

 # coding: utf8
-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/HumanSeg/nets/libs.py

 # coding: utf8
-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/HumanSeg/nets/seg_modules.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/HumanSeg/nets/shufflenet_slim.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 absolute_import
 from __future__ import division
 from __future__ import print_function

contrib/HumanSeg/pretrained_weights/download_pretrained_weights.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/HumanSeg/quant_offline.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 argparse
 from datasets.dataset import Dataset
 import transforms

contrib/HumanSeg/quant_online.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 argparse
 from datasets.dataset import Dataset
 from models import HumanSegMobile, HumanSegLite, HumanSegServer

contrib/HumanSeg/train.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 argparse
 from datasets.dataset import Dataset
 from models import HumanSegMobile, HumanSegLite, HumanSegServer

contrib/HumanSeg/transforms/__init__.py

-#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/HumanSeg/transforms/functional.py

-#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/HumanSeg/transforms/transforms.py

-#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/HumanSeg/utils/__init__.py

-#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/HumanSeg/utils/humanseg_postprocess.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 numpy as np
 import cv2
 import os

contrib/HumanSeg/utils/logging.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/HumanSeg/utils/metrics.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/HumanSeg/utils/post_quantization.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/HumanSeg/utils/utils.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -205,11 +206,9 @@ def load_pretrained_weights(exe, main_prog, weights_dir, fuse_bn=False):
         vars_to_load.append(var)
         logging.debug("Weight {} will be load".format(var.name))
 
-    fluid.io.load_vars(
-        executor=exe,
-        dirname=weights_dir,
-        main_program=main_prog,
-        vars=vars_to_load)
+    params_dict = fluid.io.load_program_state(
+        weights_dir, var_list=vars_to_load)
+    fluid.io.set_program_state(main_prog, params_dict)
     if len(vars_to_load) == 0:
         logging.warning(
             "There is no pretrain weights loaded, maybe you should check you pretrain model!"

contrib/HumanSeg/val.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 argparse
 from datasets.dataset import Dataset
 import transforms

contrib/HumanSeg/video_infer.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 argparse
 import os
 import os.path as osp

contrib/LaneNet/data_aug.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -21,6 +21,7 @@ from models.model_builder import ModelPhase
 from pdseg.data_aug import get_random_scale, randomly_scale_image_and_label, random_rotation, \
     rand_scale_aspect, hsv_color_jitter, rand_crop
 
+
 def resize(img, grt=None, grt_instance=None, mode=ModelPhase.TRAIN):
     """
     改变图像及标签图像尺寸
@@ -44,7 +45,8 @@ def resize(img, grt=None, grt_instance=None, mode=ModelPhase.TRAIN):
         if grt is not None:
             grt = cv2.resize(grt, target_size, interpolation=cv2.INTER_NEAREST)
         if grt_instance is not None:
-            grt_instance = cv2.resize(grt_instance, target_size, interpolation=cv2.INTER_NEAREST)
+            grt_instance = cv2.resize(
+                grt_instance, target_size, interpolation=cv2.INTER_NEAREST)
     elif cfg.AUG.AUG_METHOD == 'stepscaling':
         if mode == ModelPhase.TRAIN:
             min_scale_factor = cfg.AUG.MIN_SCALE_FACTOR

contrib/LaneNet/dataset/download_tusimple.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/LaneNet/eval.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -122,7 +122,10 @@ def evaluate(cfg, ckpt_dir=None, use_gpu=False, use_mpio=False, **kwargs):
 
     if ckpt_dir is not None:
         print('load test model:', ckpt_dir)
-        fluid.io.load_params(exe, ckpt_dir, main_program=test_prog)
+        try:
+            fluid.load(test_prog, os.path.join(ckpt_dir, 'model'), exe)
+        except:
+            fluid.io.load_params(exe, ckpt_dir, main_program=test_prog)
 
     # Use streaming confusion matrix to calculate mean_iou
     np.set_printoptions(

contrib/LaneNet/loss.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/LaneNet/models/__init__.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/LaneNet/models/model_builder.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/LaneNet/models/modeling/lanenet.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -18,7 +18,6 @@ from __future__ import print_function
 
 import paddle.fluid as fluid
 
-
 from utils.config import cfg
 from pdseg.models.libs.model_libs import scope, name_scope
 from pdseg.models.libs.model_libs import bn, bn_relu, relu
@@ -86,7 +85,12 @@ def bottleneck(inputs,
         with scope('down_sample'):
             inputs_shape = inputs.shape
             with scope('main_max_pool'):
-                net_main = fluid.layers.conv2d(inputs, inputs_shape[1], filter_size=3, stride=2, padding='SAME')
+                net_main = fluid.layers.conv2d(
+                    inputs,
+                    inputs_shape[1],
+                    filter_size=3,
+                    stride=2,
+                    padding='SAME')
 
             #First get the difference in depth to pad, then pad with zeros only on the last dimension.
             depth_to_pad = abs(inputs_shape[1] - output_depth)
@@ -95,12 +99,16 @@ def bottleneck(inputs,
                 net_main = fluid.layers.pad(net_main, paddings=paddings)
 
             with scope('block1'):
-                net = conv(inputs, reduced_depth, [2, 2], stride=2, padding='same')
+                net = conv(
+                    inputs, reduced_depth, [2, 2], stride=2, padding='same')
                 net = bn(net)
                 net = prelu(net, decoder=decoder)
 
             with scope('block2'):
-                net = conv(net, reduced_depth, [filter_size, filter_size], padding='same')
+                net = conv(
+                    net,
+                    reduced_depth, [filter_size, filter_size],
+                    padding='same')
                 net = bn(net)
                 net = prelu(net, decoder=decoder)
 
@@ -137,13 +145,18 @@ def bottleneck(inputs,
 
             # Second conv block --- apply dilated convolution here
             with scope('block2'):
-                net = conv(net, reduced_depth, filter_size, padding='SAME', dilation=dilation_rate)
+                net = conv(
+                    net,
+                    reduced_depth,
+                    filter_size,
+                    padding='SAME',
+                    dilation=dilation_rate)
                 net = bn(net)
                 net = prelu(net, decoder=decoder)
 
             # Final projection with 1x1 kernel (Expansion)
             with scope('block3'):
-                net = conv(net, output_depth, [1,1])
+                net = conv(net, output_depth, [1, 1])
                 net = bn(net)
                 net = prelu(net, decoder=decoder)
 
@@ -172,9 +185,11 @@ def bottleneck(inputs,
             # Second conv block --- apply asymmetric conv here
             with scope('block2'):
                 with scope('asymmetric_conv2a'):
-                    net = conv(net, reduced_depth, [filter_size, 1], padding='same')
+                    net = conv(
+                        net, reduced_depth, [filter_size, 1], padding='same')
                 with scope('asymmetric_conv2b'):
-                    net = conv(net, reduced_depth, [1, filter_size], padding='same')
+                    net = conv(
+                        net, reduced_depth, [1, filter_size], padding='same')
                 net = bn(net)
                 net = prelu(net, decoder=decoder)
 
@@ -211,7 +226,8 @@ def bottleneck(inputs,
             with scope('unpool'):
                 net_unpool = conv(inputs, output_depth, [1, 1])
                 net_unpool = bn(net_unpool)
-                net_unpool = fluid.layers.resize_bilinear(net_unpool, out_shape=output_shape[2:])
+                net_unpool = fluid.layers.resize_bilinear(
+                    net_unpool, out_shape=output_shape[2:])
 
             # First 1x1 projection to reduce depth
             with scope('block1'):
@@ -220,7 +236,12 @@ def bottleneck(inputs,
                 net = prelu(net, decoder=decoder)
 
             with scope('block2'):
-                net = deconv(net, reduced_depth, filter_size=filter_size, stride=2, padding='same')
+                net = deconv(
+                    net,
+                    reduced_depth,
+                    filter_size=filter_size,
+                    stride=2,
+                    padding='same')
                 net = bn(net)
                 net = prelu(net, decoder=decoder)
 
@@ -253,7 +274,10 @@ def bottleneck(inputs,
 
             # Second conv block
             with scope('block2'):
-                net = conv(net, reduced_depth, [filter_size, filter_size], padding='same')
+                net = conv(
+                    net,
+                    reduced_depth, [filter_size, filter_size],
+                    padding='same')
                 net = bn(net)
                 net = prelu(net, decoder=decoder)
 
@@ -281,17 +305,33 @@ def ENet_stage1(inputs, name_scope='stage1_block'):
               = bottleneck(inputs, output_depth=64, filter_size=3, regularizer_prob=0.01, type=DOWNSAMPLING,
                            name_scope='bottleneck1_0')
         with scope('bottleneck1_1'):
-            net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.01,
-                             name_scope='bottleneck1_1')
+            net = bottleneck(
+                net,
+                output_depth=64,
+                filter_size=3,
+                regularizer_prob=0.01,
+                name_scope='bottleneck1_1')
         with scope('bottleneck1_2'):
-            net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.01,
-                             name_scope='bottleneck1_2')
+            net = bottleneck(
+                net,
+                output_depth=64,
+                filter_size=3,
+                regularizer_prob=0.01,
+                name_scope='bottleneck1_2')
         with scope('bottleneck1_3'):
-            net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.01,
-                             name_scope='bottleneck1_3')
+            net = bottleneck(
+                net,
+                output_depth=64,
+                filter_size=3,
+                regularizer_prob=0.01,
+                name_scope='bottleneck1_3')
         with scope('bottleneck1_4'):
-            net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.01,
-                             name_scope='bottleneck1_4')
+            net = bottleneck(
+                net,
+                output_depth=64,
+                filter_size=3,
+                regularizer_prob=0.01,
+                name_scope='bottleneck1_4')
     return net, inputs_shape_1
 
 
@@ -302,17 +342,38 @@ def ENet_stage2(inputs, name_scope='stage2_block'):
                        name_scope='bottleneck2_0')
         for i in range(2):
             with scope('bottleneck2_{}'.format(str(4 * i + 1))):
-                net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1,
-                                 name_scope='bottleneck2_{}'.format(str(4 * i + 1)))
+                net = bottleneck(
+                    net,
+                    output_depth=128,
+                    filter_size=3,
+                    regularizer_prob=0.1,
+                    name_scope='bottleneck2_{}'.format(str(4 * i + 1)))
             with scope('bottleneck2_{}'.format(str(4 * i + 2))):
-                net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DILATED, dilation_rate=(2 ** (2*i+1)),
-                                 name_scope='bottleneck2_{}'.format(str(4 * i + 2)))
+                net = bottleneck(
+                    net,
+                    output_depth=128,
+                    filter_size=3,
+                    regularizer_prob=0.1,
+                    type=DILATED,
+                    dilation_rate=(2**(2 * i + 1)),
+                    name_scope='bottleneck2_{}'.format(str(4 * i + 2)))
             with scope('bottleneck2_{}'.format(str(4 * i + 3))):
-                net = bottleneck(net, output_depth=128, filter_size=5, regularizer_prob=0.1, type=ASYMMETRIC,
-                                 name_scope='bottleneck2_{}'.format(str(4 * i + 3)))
+                net = bottleneck(
+                    net,
+                    output_depth=128,
+                    filter_size=5,
+                    regularizer_prob=0.1,
+                    type=ASYMMETRIC,
+                    name_scope='bottleneck2_{}'.format(str(4 * i + 3)))
             with scope('bottleneck2_{}'.format(str(4 * i + 4))):
-                net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DILATED, dilation_rate=(2 ** (2*i+2)),
-                                 name_scope='bottleneck2_{}'.format(str(4 * i + 4)))
+                net = bottleneck(
+                    net,
+                    output_depth=128,
+                    filter_size=3,
+                    regularizer_prob=0.1,
+                    type=DILATED,
+                    dilation_rate=(2**(2 * i + 2)),
+                    name_scope='bottleneck2_{}'.format(str(4 * i + 4)))
     return net, inputs_shape_2
 
 
@@ -320,52 +381,106 @@ def ENet_stage3(inputs, name_scope='stage3_block'):
     with scope(name_scope):
         for i in range(2):
             with scope('bottleneck3_{}'.format(str(4 * i + 0))):
-                net = bottleneck(inputs, output_depth=128, filter_size=3, regularizer_prob=0.1,
-                                 name_scope='bottleneck3_{}'.format(str(4 * i + 0)))
+                net = bottleneck(
+                    inputs,
+                    output_depth=128,
+                    filter_size=3,
+                    regularizer_prob=0.1,
+                    name_scope='bottleneck3_{}'.format(str(4 * i + 0)))
             with scope('bottleneck3_{}'.format(str(4 * i + 1))):
-                net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DILATED, dilation_rate=(2 ** (2*i+1)),
-                                 name_scope='bottleneck3_{}'.format(str(4 * i + 1)))
+                net = bottleneck(
+                    net,
+                    output_depth=128,
+                    filter_size=3,
+                    regularizer_prob=0.1,
+                    type=DILATED,
+                    dilation_rate=(2**(2 * i + 1)),
+                    name_scope='bottleneck3_{}'.format(str(4 * i + 1)))
             with scope('bottleneck3_{}'.format(str(4 * i + 2))):
-                net = bottleneck(net, output_depth=128, filter_size=5, regularizer_prob=0.1, type=ASYMMETRIC,
-                                 name_scope='bottleneck3_{}'.format(str(4 * i + 2)))
+                net = bottleneck(
+                    net,
+                    output_depth=128,
+                    filter_size=5,
+                    regularizer_prob=0.1,
+                    type=ASYMMETRIC,
+                    name_scope='bottleneck3_{}'.format(str(4 * i + 2)))
             with scope('bottleneck3_{}'.format(str(4 * i + 3))):
-                net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DILATED, dilation_rate=(2 ** (2*i+2)),
-                                 name_scope='bottleneck3_{}'.format(str(4 * i + 3)))
+                net = bottleneck(
+                    net,
+                    output_depth=128,
+                    filter_size=3,
+                    regularizer_prob=0.1,
+                    type=DILATED,
+                    dilation_rate=(2**(2 * i + 2)),
+                    name_scope='bottleneck3_{}'.format(str(4 * i + 3)))
     return net
 
 
-def ENet_stage4(inputs, inputs_shape, connect_tensor,
-                skip_connections=True, name_scope='stage4_block'):
+def ENet_stage4(inputs,
+                inputs_shape,
+                connect_tensor,
+                skip_connections=True,
+                name_scope='stage4_block'):
     with scope(name_scope):
         with scope('bottleneck4_0'):
-            net = bottleneck(inputs, output_depth=64, filter_size=3, regularizer_prob=0.1,
-                             type=UPSAMPLING, decoder=True, output_shape=inputs_shape,
-                             name_scope='bottleneck4_0')
+            net = bottleneck(
+                inputs,
+                output_depth=64,
+                filter_size=3,
+                regularizer_prob=0.1,
+                type=UPSAMPLING,
+                decoder=True,
+                output_shape=inputs_shape,
+                name_scope='bottleneck4_0')
 
         if skip_connections:
             net = fluid.layers.elementwise_add(net, connect_tensor)
         with scope('bottleneck4_1'):
-            net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.1, decoder=True,
-                             name_scope='bottleneck4_1')
+            net = bottleneck(
+                net,
+                output_depth=64,
+                filter_size=3,
+                regularizer_prob=0.1,
+                decoder=True,
+                name_scope='bottleneck4_1')
         with scope('bottleneck4_2'):
-            net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.1, decoder=True,
-                             name_scope='bottleneck4_2')
+            net = bottleneck(
+                net,
+                output_depth=64,
+                filter_size=3,
+                regularizer_prob=0.1,
+                decoder=True,
+                name_scope='bottleneck4_2')
 
     return net
 
 
-def ENet_stage5(inputs, inputs_shape, connect_tensor, skip_connections=True,
+def ENet_stage5(inputs,
+                inputs_shape,
+                connect_tensor,
+                skip_connections=True,
                 name_scope='stage5_block'):
     with scope(name_scope):
-        net = bottleneck(inputs, output_depth=16, filter_size=3, regularizer_prob=0.1, type=UPSAMPLING,
-                         decoder=True, output_shape=inputs_shape,
-                         name_scope='bottleneck5_0')
+        net = bottleneck(
+            inputs,
+            output_depth=16,
+            filter_size=3,
+            regularizer_prob=0.1,
+            type=UPSAMPLING,
+            decoder=True,
+            output_shape=inputs_shape,
+            name_scope='bottleneck5_0')
 
         if skip_connections:
             net = fluid.layers.elementwise_add(net, connect_tensor)
         with scope('bottleneck5_1'):
-            net = bottleneck(net, output_depth=16, filter_size=3, regularizer_prob=0.1, decoder=True,
-                             name_scope='bottleneck5_1')
+            net = bottleneck(
+                net,
+                output_depth=16,
+                filter_size=3,
+                regularizer_prob=0.1,
+                decoder=True,
+                name_scope='bottleneck5_1')
     return net
 
 
@@ -378,14 +493,16 @@ def decoder(input, num_classes):
             segStage3 = ENet_stage3(stage2)
             segStage4 = ENet_stage4(segStage3, inputs_shape_2, stage1)
             segStage5 = ENet_stage5(segStage4, inputs_shape_1, initial)
-            segLogits = deconv(segStage5, num_classes, filter_size=2, stride=2, padding='SAME')
+            segLogits = deconv(
+                segStage5, num_classes, filter_size=2, stride=2, padding='SAME')
 
         # Embedding branch
         with scope('LaneNetEm'):
             emStage3 = ENet_stage3(stage2)
             emStage4 = ENet_stage4(emStage3, inputs_shape_2, stage1)
             emStage5 = ENet_stage5(emStage4, inputs_shape_1, initial)
-            emLogits = deconv(emStage5, 4, filter_size=2, stride=2, padding='SAME')
+            emLogits = deconv(
+                emStage5, 4, filter_size=2, stride=2, padding='SAME')
 
     elif 'vgg' in cfg.MODEL.LANENET.BACKBONE:
         encoder_list = ['pool5', 'pool4', 'pool3']
@@ -396,14 +513,16 @@ def decoder(input, num_classes):
         encoder_list = encoder_list[1:]
         for i in range(len(encoder_list)):
             with scope('deconv_{:d}'.format(i + 1)):
-                deconv_out = deconv(score, 64, filter_size=4, stride=2, padding='SAME')
+                deconv_out = deconv(
+                    score, 64, filter_size=4, stride=2, padding='SAME')
             input_tensor = input[encoder_list[i]]
             with scope('score_{:d}'.format(i + 1)):
                 score = conv(input_tensor, 64, 1)
             score = fluid.layers.elementwise_add(deconv_out, score)
 
         with scope('deconv_final'):
-            emLogits = deconv(score, 64, filter_size=16, stride=8, padding='SAME')
+            emLogits = deconv(
+                score, 64, filter_size=16, stride=8, padding='SAME')
         with scope('score_final'):
             segLogits = conv(emLogits, num_classes, 1)
         emLogits = relu(conv(emLogits, 4, 1))
@@ -415,7 +534,8 @@ def encoder(input):
         model = vgg_backbone(layers=16)
         #output = model.net(input)
 
-        _, encode_feature_dict = model.net(input, end_points=13, decode_points=[7, 10, 13])
+        _, encode_feature_dict = model.net(
+            input, end_points=13, decode_points=[7, 10, 13])
         output = {}
         output['pool3'] = encode_feature_dict[7]
         output['pool4'] = encode_feature_dict[10]
@@ -427,8 +547,9 @@ def encoder(input):
             stage2, inputs_shape_2 = ENet_stage2(stage1)
             output = (initial, stage1, stage2, inputs_shape_1, inputs_shape_2)
     else:
-        raise Exception("LaneNet expect enet and vgg backbone, but received {}".
-                        format(cfg.MODEL.LANENET.BACKBONE))
+        raise Exception(
+            "LaneNet expect enet and vgg backbone, but received {}".format(
+                cfg.MODEL.LANENET.BACKBONE))
     return output
 
 

contrib/LaneNet/reader.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -58,7 +58,8 @@ class LaneNetDataset():
         if self.shuffle and cfg.NUM_TRAINERS > 1:
             np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines)
             num_lines = len(self.all_lines) // cfg.NUM_TRAINERS
-            self.lines = self.all_lines[num_lines * cfg.TRAINER_ID: num_lines * (cfg.TRAINER_ID + 1)]
+            self.lines = self.all_lines[num_lines * cfg.TRAINER_ID:num_lines *
+                                        (cfg.TRAINER_ID + 1)]
             self.shuffle_seed += 1
         elif self.shuffle:
             np.random.shuffle(self.lines)
@@ -86,7 +87,8 @@ class LaneNetDataset():
         if self.shuffle and cfg.NUM_TRAINERS > 1:
             np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines)
             num_lines = len(self.all_lines) // self.num_trainers
-            self.lines = self.all_lines[num_lines * self.trainer_id: num_lines * (self.trainer_id + 1)]
+            self.lines = self.all_lines[num_lines * self.trainer_id:num_lines *
+                                        (self.trainer_id + 1)]
             self.shuffle_seed += 1
         elif self.shuffle:
             np.random.shuffle(self.lines)
@@ -118,7 +120,8 @@ class LaneNetDataset():
         def batch_reader(is_test=False, drop_last=drop_last):
             if is_test:
                 imgs, grts, grts_instance, img_names, valid_shapes, org_shapes = [], [], [], [], [], []
-                for img, grt, grt_instance, img_name, valid_shape, org_shape in reader():
+                for img, grt, grt_instance, img_name, valid_shape, org_shape in reader(
+                ):
                     imgs.append(img)
                     grts.append(grt)
                     grts_instance.append(grt_instance)
@@ -126,14 +129,15 @@ class LaneNetDataset():
                     valid_shapes.append(valid_shape)
                     org_shapes.append(org_shape)
                     if len(imgs) == batch_size:
-                        yield np.array(imgs), np.array(
-                            grts), np.array(grts_instance), img_names, np.array(valid_shapes), np.array(
-                                org_shapes)
+                        yield np.array(imgs), np.array(grts), np.array(
+                            grts_instance), img_names, np.array(
+                                valid_shapes), np.array(org_shapes)
                         imgs, grts, grts_instance, img_names, valid_shapes, org_shapes = [], [], [], [], [], []
 
                 if not drop_last and len(imgs) > 0:
-                    yield np.array(imgs), np.array(grts), np.array(grts_instance), img_names, np.array(
-                        valid_shapes), np.array(org_shapes)
+                    yield np.array(imgs), np.array(grts), np.array(
+                        grts_instance), img_names, np.array(
+                            valid_shapes), np.array(org_shapes)
             else:
                 imgs, labs, labs_instance, ignore = [], [], [], []
                 bs = 0
@@ -144,12 +148,14 @@ class LaneNetDataset():
                     ignore.append(ig)
                     bs += 1
                     if bs == batch_size:
-                        yield np.array(imgs), np.array(labs), np.array(labs_instance), np.array(ignore)
+                        yield np.array(imgs), np.array(labs), np.array(
+                            labs_instance), np.array(ignore)
                         bs = 0
                         imgs, labs, labs_instance, ignore = [], [], [], []
 
                 if not drop_last and bs > 0:
-                    yield np.array(imgs), np.array(labs), np.array(labs_instance), np.array(ignore)
+                    yield np.array(imgs), np.array(labs), np.array(
+                        labs_instance), np.array(ignore)
 
         return batch_reader(is_test, drop_last)
 
@@ -299,10 +305,12 @@ class LaneNetDataset():
 
             img, grt = aug.rand_crop(img, grt, mode=mode)
         elif ModelPhase.is_eval(mode):
-            img, grt, grt_instance = aug.resize(img, grt, grt_instance, mode=mode)
+            img, grt, grt_instance = aug.resize(
+                img, grt, grt_instance, mode=mode)
         elif ModelPhase.is_visual(mode):
             ori_img = img.copy()
-            img, grt, grt_instance = aug.resize(img, grt, grt_instance, mode=mode)
+            img, grt, grt_instance = aug.resize(
+                img, grt, grt_instance, mode=mode)
             valid_shape = [img.shape[0], img.shape[1]]
         else:
             raise ValueError("Dataset mode={} Error!".format(mode))

contrib/LaneNet/train.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -40,10 +40,10 @@ from pdseg.utils.timer import Timer, calculate_eta
 from reader import LaneNetDataset
 from models.model_builder import build_model
 from models.model_builder import ModelPhase
-from models.model_builder import parse_shape_from_file
 from eval import evaluate
 from vis import visualize
 from utils import dist_utils
+from utils.load_model_utils import load_pretrained_weights
 
 
 def parse_args():
@@ -101,37 +101,6 @@ def parse_args():
     return parser.parse_args()
 
 
-def save_vars(executor, dirname, program=None, vars=None):
-    """
-    Temporary resolution for Win save variables compatability.
-    Will fix in PaddlePaddle v1.5.2
-    """
-
-    save_program = fluid.Program()
-    save_block = save_program.global_block()
-
-    for each_var in vars:
-        # NOTE: don't save the variable which type is RAW
-        if each_var.type == fluid.core.VarDesc.VarType.RAW:
-            continue
-        new_var = save_block.create_var(
-            name=each_var.name,
-            shape=each_var.shape,
-            dtype=each_var.dtype,
-            type=each_var.type,
-            lod_level=each_var.lod_level,
-            persistable=True)
-        file_path = os.path.join(dirname, new_var.name)
-        file_path = os.path.normpath(file_path)
-        save_block.append_op(
-            type='save',
-            inputs={'X': [new_var]},
-            outputs={},
-            attrs={'file_path': file_path})
-
-    executor.run(save_program)
-
-
 def save_checkpoint(exe, program, ckpt_name):
     """
     Save checkpoint for evaluation or resume training
@@ -141,29 +110,22 @@ def save_checkpoint(exe, program, ckpt_name):
     if not os.path.isdir(ckpt_dir):
         os.makedirs(ckpt_dir)
 
-    save_vars(
-        exe,
-        ckpt_dir,
-        program,
-        vars=list(filter(fluid.io.is_persistable, program.list_vars())))
+    fluid.save(program, os.path.join(ckpt_dir, 'model'))
 
     return ckpt_dir
 
 
 def load_checkpoint(exe, program):
     """
-    Load checkpoiont from pretrained model directory for resume training
+    Load checkpoiont for resuming training
     """
-
-    print('Resume model training from:', cfg.TRAIN.RESUME_MODEL_DIR)
-    if not os.path.exists(cfg.TRAIN.RESUME_MODEL_DIR):
-        raise ValueError("TRAIN.PRETRAIN_MODEL {} not exist!".format(
-            cfg.TRAIN.RESUME_MODEL_DIR))
-
-    fluid.io.load_persistables(
-        exe, cfg.TRAIN.RESUME_MODEL_DIR, main_program=program)
-
     model_path = cfg.TRAIN.RESUME_MODEL_DIR
+    print('Resume model training from:', model_path)
+    if not os.path.exists(model_path):
+        raise ValueError(
+            "TRAIN.PRETRAIN_MODEL {} not exist!".format(model_path))
+    fluid.load(program, os.path.join(model_path, 'model'), exe)
+
     # Check is path ended by path spearator
     if model_path[-1] == os.sep:
         model_path = model_path[0:-1]
@@ -178,7 +140,6 @@ def load_checkpoint(exe, program):
     else:
         raise ValueError("Resume model path is not valid!")
     print("Model checkpoint loaded successfully!")
-
     return begin_epoch
 
 
@@ -271,44 +232,7 @@ def train(cfg):
         begin_epoch = load_checkpoint(exe, train_prog)
     # Load pretrained model
     elif os.path.exists(cfg.TRAIN.PRETRAINED_MODEL_DIR):
-        print_info('Pretrained model dir: ', cfg.TRAIN.PRETRAINED_MODEL_DIR)
-        load_vars = []
-        load_fail_vars = []
-
-        def var_shape_matched(var, shape):
-            """
-            Check whehter persitable variable shape is match with current network
-            """
-            var_exist = os.path.exists(
-                os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
-            if var_exist:
-                var_shape = parse_shape_from_file(
-                    os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
-                if var_shape != shape:
-                    print(var.name, var_shape, shape)
-                return var_shape == shape
-            return False
-
-        for x in train_prog.list_vars():
-            if isinstance(x, fluid.framework.Parameter):
-                shape = tuple(fluid.global_scope().find_var(
-                    x.name).get_tensor().shape())
-                if var_shape_matched(x, shape):
-                    load_vars.append(x)
-                else:
-                    load_fail_vars.append(x)
-
-        fluid.io.load_vars(
-            exe, dirname=cfg.TRAIN.PRETRAINED_MODEL_DIR, vars=load_vars)
-        for var in load_vars:
-            print_info("Parameter[{}] loaded sucessfully!".format(var.name))
-        for var in load_fail_vars:
-            print_info(
-                "Parameter[{}] don't exist or shape does not match current network, skip"
-                " to load it.".format(var.name))
-        print_info("{}/{} pretrained parameters loaded successfully!".format(
-            len(load_vars),
-            len(load_vars) + len(load_fail_vars)))
+        load_pretrained_weights(exe, train_prog, cfg.TRAIN.PRETRAINED_MODEL_DIR)
     else:
         print_info(
             'Pretrained model dir {} not exists, training from scratch...'.
@@ -393,8 +317,7 @@ def train(cfg):
                              avg_emb_loss, avg_acc, avg_fp, avg_fn, speed,
                              calculate_eta(all_step - step, speed)))
                     if args.use_vdl:
-                        log_writer.add_scalar('Train/loss', avg_loss,
-                                              step)
+                        log_writer.add_scalar('Train/loss', avg_loss, step)
                         log_writer.add_scalar('Train/lr', lr[0], step)
                         log_writer.add_scalar('Train/speed', speed, step)
                     sys.stdout.flush()
@@ -423,8 +346,7 @@ def train(cfg):
                     use_gpu=args.use_gpu,
                     use_mpio=args.use_mpio)
                 if args.use_vdl:
-                    log_writer.add_scalar('Evaluate/accuracy', accuracy,
-                                          step)
+                    log_writer.add_scalar('Evaluate/accuracy', accuracy, step)
                     log_writer.add_scalar('Evaluate/fp', fp, step)
                     log_writer.add_scalar('Evaluate/fn', fn, step)
 

contrib/LaneNet/utils/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.

contrib/LaneNet/utils/config.py

-# -*- coding: utf-8 -*-
-#   Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,
@@ -80,8 +80,8 @@ cfg.DATASET.DATA_DIM = 3
 cfg.DATASET.SEPARATOR = ' '
 # 忽略的像素标签值, 默认为255，一般无需改动
 cfg.DATASET.IGNORE_INDEX = 255
-# 数据增强是图像的padding值 
-cfg.DATASET.PADDING_VALUE = [127.5,127.5,127.5]
+# 数据增强是图像的padding值
+cfg.DATASET.PADDING_VALUE = [127.5, 127.5, 127.5]
 
 ########################### 数据增强配置 ######################################
 # 图像镜像左右翻转

contrib/LaneNet/utils/dist_utils.py

-#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-#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
+# 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.
+# 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 absolute_import
 from __future__ import division

contrib/LaneNet/utils/generate_tusimple_dataset.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
 """
 generate tusimple training dataset
 """
@@ -14,12 +28,16 @@ import numpy as np
 
 def init_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--src_dir', type=str, help='The origin path of unzipped tusimple dataset')
+    parser.add_argument(
+        '--src_dir',
+        type=str,
+        help='The origin path of unzipped tusimple dataset')
 
     return parser.parse_args()
 
 
-def process_json_file(json_file_path, src_dir, ori_dst_dir, binary_dst_dir, instance_dst_dir):
+def process_json_file(json_file_path, src_dir, ori_dst_dir, binary_dst_dir,
+                      instance_dst_dir):
 
     assert ops.exists(json_file_path), '{:s} not exist'.format(json_file_path)
 
@@ -39,11 +57,14 @@ def process_json_file(json_file_path, src_dir, ori_dst_dir, binary_dst_dir, inst
             h_samples = info_dict['h_samples']
             lanes = info_dict['lanes']
 
-            image_name_new = '{:s}.png'.format('{:d}'.format(line_index + image_nums).zfill(4))
+            image_name_new = '{:s}.png'.format(
+                '{:d}'.format(line_index + image_nums).zfill(4))
 
             src_image = cv2.imread(image_path, cv2.IMREAD_COLOR)
-            dst_binary_image = np.zeros([src_image.shape[0], src_image.shape[1]], np.uint8)
-            dst_instance_image = np.zeros([src_image.shape[0], src_image.shape[1]], np.uint8)
+            dst_binary_image = np.zeros(
+                [src_image.shape[0], src_image.shape[1]], np.uint8)
+            dst_instance_image = np.zeros(
+                [src_image.shape[0], src_image.shape[1]], np.uint8)
 
             for lane_index, lane in enumerate(lanes):
                 assert len(h_samples) == len(lane)
@@ -62,13 +83,23 @@ def process_json_file(json_file_path, src_dir, ori_dst_dir, binary_dst_dir, inst
                 lane_pts = np.vstack((lane_x, lane_y)).transpose()
                 lane_pts = np.array([lane_pts], np.int64)
 
-                cv2.polylines(dst_binary_image, lane_pts, isClosed=False,
-                              color=255, thickness=5)
-                cv2.polylines(dst_instance_image, lane_pts, isClosed=False,
-                              color=lane_index * 50 + 20, thickness=5)
-
-            dst_binary_image_path = ops.join(src_dir, binary_dst_dir, image_name_new)
-            dst_instance_image_path = ops.join(src_dir, instance_dst_dir, image_name_new)
+                cv2.polylines(
+                    dst_binary_image,
+                    lane_pts,
+                    isClosed=False,
+                    color=255,
+                    thickness=5)
+                cv2.polylines(
+                    dst_instance_image,
+                    lane_pts,
+                    isClosed=False,
+                    color=lane_index * 50 + 20,
+                    thickness=5)
+
+            dst_binary_image_path = ops.join(src_dir, binary_dst_dir,
+                                             image_name_new)
+            dst_instance_image_path = ops.join(src_dir, instance_dst_dir,
+                                               image_name_new)
             dst_rgb_image_path = ops.join(src_dir, ori_dst_dir, image_name_new)
 
             cv2.imwrite(dst_binary_image_path, dst_binary_image)
@@ -78,7 +109,12 @@ def process_json_file(json_file_path, src_dir, ori_dst_dir, binary_dst_dir, inst
             print('Process {:s} success'.format(image_name))
 
 
-def gen_sample(src_dir, b_gt_image_dir, i_gt_image_dir, image_dir, phase='train', split=False):
+def gen_sample(src_dir,
+               b_gt_image_dir,
+               i_gt_image_dir,
+               image_dir,
+               phase='train',
+               split=False):
 
     label_list = []
     with open('{:s}/{}ing/{}.txt'.format(src_dir, phase, phase), 'w') as file:
@@ -92,7 +128,8 @@ def gen_sample(src_dir, b_gt_image_dir, i_gt_image_dir, image_dir, phase='train'
             image_path = ops.join(image_dir, image_name)
 
             assert ops.exists(image_path), '{:s} not exist'.format(image_path)
-            assert ops.exists(instance_gt_image_path), '{:s} not exist'.format(instance_gt_image_path)
+            assert ops.exists(instance_gt_image_path), '{:s} not exist'.format(
+                instance_gt_image_path)
 
             b_gt_image = cv2.imread(binary_gt_image_path, cv2.IMREAD_COLOR)
             i_gt_image = cv2.imread(instance_gt_image_path, cv2.IMREAD_COLOR)
@@ -102,7 +139,8 @@ def gen_sample(src_dir, b_gt_image_dir, i_gt_image_dir, image_dir, phase='train'
                 print('image: {:s} corrupt'.format(image_name))
                 continue
             else:
-                info = '{:s} {:s} {:s}'.format(image_path, binary_gt_image_path, instance_gt_image_path)
+                info = '{:s} {:s} {:s}'.format(image_path, binary_gt_image_path,
+                                               instance_gt_image_path)
                 file.write(info + '\n')
                 label_list.append(info)
     if phase == 'train' and split:
@@ -110,10 +148,12 @@ def gen_sample(src_dir, b_gt_image_dir, i_gt_image_dir, image_dir, phase='train'
         val_list_len = len(label_list) // 10
         val_label_list = label_list[:val_list_len]
         train_label_list = label_list[val_list_len:]
-        with open('{:s}/{}ing/train_part.txt'.format(src_dir, phase, phase), 'w') as file:
+        with open('{:s}/{}ing/train_part.txt'.format(src_dir, phase, phase),
+                  'w') as file:
             for info in train_label_list:
                 file.write(info + '\n')
-        with open('{:s}/{}ing/val_part.txt'.format(src_dir, phase, phase), 'w') as file:
+        with open('{:s}/{}ing/val_part.txt'.format(src_dir, phase, phase),
+                  'w') as file:
             for info in val_label_list:
                 file.write(info + '\n')
     return
@@ -130,12 +170,14 @@ def process_tusimple_dataset(src_dir):
     for json_label_path in glob.glob('{:s}/label*.json'.format(src_dir)):
         json_label_name = ops.split(json_label_path)[1]
 
-        shutil.copyfile(json_label_path, ops.join(traing_folder_path, json_label_name))
+        shutil.copyfile(json_label_path,
+                        ops.join(traing_folder_path, json_label_name))
 
     for json_label_path in glob.glob('{:s}/test_label.json'.format(src_dir)):
         json_label_name = ops.split(json_label_path)[1]
 
-        shutil.copyfile(json_label_path, ops.join(testing_folder_path, json_label_name))
+        shutil.copyfile(json_label_path,
+                        ops.join(testing_folder_path, json_label_name))
 
     train_gt_image_dir = ops.join('training', 'gt_image')
     train_gt_binary_dir = ops.join('training', 'gt_binary_image')
@@ -154,9 +196,11 @@ def process_tusimple_dataset(src_dir):
     os.makedirs(os.path.join(src_dir, test_gt_instance_dir), exist_ok=True)
 
     for json_label_path in glob.glob('{:s}/*.json'.format(traing_folder_path)):
-        process_json_file(json_label_path, src_dir, train_gt_image_dir, train_gt_binary_dir, train_gt_instance_dir)
+        process_json_file(json_label_path, src_dir, train_gt_image_dir,
+                          train_gt_binary_dir, train_gt_instance_dir)
 
-    gen_sample(src_dir, train_gt_binary_dir, train_gt_instance_dir, train_gt_image_dir, 'train', True)
+    gen_sample(src_dir, train_gt_binary_dir, train_gt_instance_dir,
+               train_gt_image_dir, 'train', True)
 
 
 if __name__ == '__main__':

contrib/LaneNet/utils/lanenet_postprocess.py

-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
+
 # this code heavily base on https://github.com/MaybeShewill-CV/lanenet-lane-detection/blob/master/lanenet_model/lanenet_postprocess.py
 """
 LaneNet model post process
@@ -22,12 +35,14 @@ def _morphological_process(image, kernel_size=5):
     :return:
     """
     if len(image.shape) == 3:
-        raise ValueError('Binary segmentation result image should be a single channel image')
+        raise ValueError(
+            'Binary segmentation result image should be a single channel image')
 
     if image.dtype is not np.uint8:
         image = np.array(image, np.uint8)
 
-    kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size))
+    kernel = cv2.getStructuringElement(
+        shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size))
 
     # close operation fille hole
     closing = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=1)
@@ -46,13 +61,15 @@ def _connect_components_analysis(image):
     else:
         gray_image = image
 
-    return cv2.connectedComponentsWithStats(gray_image, connectivity=8, ltype=cv2.CV_32S)
+    return cv2.connectedComponentsWithStats(
+        gray_image, connectivity=8, ltype=cv2.CV_32S)
 
 
 class _LaneFeat(object):
     """
 
     """
+
     def __init__(self, feat, coord, class_id=-1):
         """
         lane feat object
@@ -108,18 +125,21 @@ class _LaneNetCluster(object):
     """
      Instance segmentation result cluster
     """
+
     def __init__(self):
         """
 
         """
-        self._color_map = [np.array([255, 0, 0]),
-                           np.array([0, 255, 0]),
-                           np.array([0, 0, 255]),
-                           np.array([125, 125, 0]),
-                           np.array([0, 125, 125]),
-                           np.array([125, 0, 125]),
-                           np.array([50, 100, 50]),
-                           np.array([100, 50, 100])]
+        self._color_map = [
+            np.array([255, 0, 0]),
+            np.array([0, 255, 0]),
+            np.array([0, 0, 255]),
+            np.array([125, 125, 0]),
+            np.array([0, 125, 125]),
+            np.array([125, 0, 125]),
+            np.array([50, 100, 50]),
+            np.array([100, 50, 100])
+        ]
 
     @staticmethod
     def _embedding_feats_dbscan_cluster(embedding_image_feats):
@@ -186,15 +206,16 @@ class _LaneNetCluster(object):
         # get embedding feats and coords
         get_lane_embedding_feats_result = self._get_lane_embedding_feats(
             binary_seg_ret=binary_seg_result,
-            instance_seg_ret=instance_seg_result
-        )
+            instance_seg_ret=instance_seg_result)
 
         # dbscan cluster
         dbscan_cluster_result = self._embedding_feats_dbscan_cluster(
-            embedding_image_feats=get_lane_embedding_feats_result['lane_embedding_feats']
-        )
+            embedding_image_feats=get_lane_embedding_feats_result[
+                'lane_embedding_feats'])
 
-        mask = np.zeros(shape=[binary_seg_result.shape[0], binary_seg_result.shape[1], 3], dtype=np.uint8)
+        mask = np.zeros(
+            shape=[binary_seg_result.shape[0], binary_seg_result.shape[1], 3],
+            dtype=np.uint8)
         db_labels = dbscan_cluster_result['db_labels']
         unique_labels = dbscan_cluster_result['unique_labels']
         coord = get_lane_embedding_feats_result['lane_coordinates']
@@ -219,11 +240,13 @@ class LaneNetPostProcessor(object):
     """
     lanenet post process for lane generation
     """
+
     def __init__(self, ipm_remap_file_path='./utils/tusimple_ipm_remap.yml'):
         """
         convert front car view to bird view
         """
-        assert ops.exists(ipm_remap_file_path), '{:s} not exist'.format(ipm_remap_file_path)
+        assert ops.exists(ipm_remap_file_path), '{:s} not exist'.format(
+            ipm_remap_file_path)
 
         self._cluster = _LaneNetCluster()
         self._ipm_remap_file_path = ipm_remap_file_path
@@ -232,14 +255,16 @@ class LaneNetPostProcessor(object):
         self._remap_to_ipm_x = remap_file_load_ret['remap_to_ipm_x']
         self._remap_to_ipm_y = remap_file_load_ret['remap_to_ipm_y']
 
-        self._color_map = [np.array([255, 0, 0]),
-                           np.array([0, 255, 0]),
-                           np.array([0, 0, 255]),
-                           np.array([125, 125, 0]),
-                           np.array([0, 125, 125]),
-                           np.array([125, 0, 125]),
-                           np.array([50, 100, 50]),
-                           np.array([100, 50, 100])]
+        self._color_map = [
+            np.array([255, 0, 0]),
+            np.array([0, 255, 0]),
+            np.array([0, 0, 255]),
+            np.array([125, 125, 0]),
+            np.array([0, 125, 125]),
+            np.array([125, 0, 125]),
+            np.array([50, 100, 50]),
+            np.array([100, 50, 100])
+        ]
 
     def _load_remap_matrix(self):
         fs = cv2.FileStorage(self._ipm_remap_file_path, cv2.FILE_STORAGE_READ)
@@ -256,15 +281,20 @@ class LaneNetPostProcessor(object):
 
         return ret
 
-    def postprocess(self, binary_seg_result, instance_seg_result=None,
-                    min_area_threshold=100, source_image=None,
+    def postprocess(self,
+                    binary_seg_result,
+                    instance_seg_result=None,
+                    min_area_threshold=100,
+                    source_image=None,
                     data_source='tusimple'):
 
         # convert binary_seg_result
         binary_seg_result = np.array(binary_seg_result * 255, dtype=np.uint8)
         # apply image morphology operation to fill in the hold and reduce the small area
-        morphological_ret = _morphological_process(binary_seg_result, kernel_size=5)
-        connect_components_analysis_ret = _connect_components_analysis(image=morphological_ret)
+        morphological_ret = _morphological_process(
+            binary_seg_result, kernel_size=5)
+        connect_components_analysis_ret = _connect_components_analysis(
+            image=morphological_ret)
 
         labels = connect_components_analysis_ret[1]
         stats = connect_components_analysis_ret[2]
@@ -276,8 +306,7 @@ class LaneNetPostProcessor(object):
         # apply embedding features cluster
         mask_image, lane_coords = self._cluster.apply_lane_feats_cluster(
             binary_seg_result=morphological_ret,
-            instance_seg_result=instance_seg_result
-        )
+            instance_seg_result=instance_seg_result)
 
         if mask_image is None:
             return {
@@ -292,15 +321,15 @@ class LaneNetPostProcessor(object):
         for lane_index, coords in enumerate(lane_coords):
             if data_source == 'tusimple':
                 tmp_mask = np.zeros(shape=(720, 1280), dtype=np.uint8)
-                tmp_mask[tuple((np.int_(coords[:, 1] * 720 / 256), np.int_(coords[:, 0] * 1280 / 512)))] = 255
+                tmp_mask[tuple((np.int_(coords[:, 1] * 720 / 256),
+                                np.int_(coords[:, 0] * 1280 / 512)))] = 255
             else:
                 raise ValueError('Wrong data source now only support tusimple')
             tmp_ipm_mask = cv2.remap(
                 tmp_mask,
                 self._remap_to_ipm_x,
                 self._remap_to_ipm_y,
-                interpolation=cv2.INTER_NEAREST
-            )
+                interpolation=cv2.INTER_NEAREST)
             nonzero_y = np.array(tmp_ipm_mask.nonzero()[0])
             nonzero_x = np.array(tmp_ipm_mask.nonzero()[1])
 
@@ -309,16 +338,19 @@ class LaneNetPostProcessor(object):
 
             [ipm_image_height, ipm_image_width] = tmp_ipm_mask.shape
             plot_y = np.linspace(10, ipm_image_height, ipm_image_height - 10)
-            fit_x = fit_param[0] * plot_y ** 2 + fit_param[1] * plot_y + fit_param[2]
+            fit_x = fit_param[0] * plot_y**2 + fit_param[
+                1] * plot_y + fit_param[2]
 
             lane_pts = []
             for index in range(0, plot_y.shape[0], 5):
                 src_x = self._remap_to_ipm_x[
-                    int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))]
+                    int(plot_y[index]),
+                    int(np.clip(fit_x[index], 0, ipm_image_width - 1))]
                 if src_x <= 0:
                     continue
                 src_y = self._remap_to_ipm_y[
-                    int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))]
+                    int(plot_y[index]),
+                    int(np.clip(fit_x[index], 0, ipm_image_width - 1))]
                 src_y = src_y if src_y > 0 else 0
 
                 lane_pts.append([src_x, src_y])
@@ -366,8 +398,10 @@ class LaneNetPostProcessor(object):
                     continue
 
                 lane_color = self._color_map[index].tolist()
-                cv2.circle(source_image, (int(interpolation_src_pt_x),
-                                          int(interpolation_src_pt_y)), 5, lane_color, -1)
+                cv2.circle(
+                    source_image,
+                    (int(interpolation_src_pt_x), int(interpolation_src_pt_y)),
+                    5, lane_color, -1)
         ret = {
             'mask_image': mask_image,
             'fit_params': fit_params,

contrib/LaneNet/utils/load_model_utils.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 os.path as osp
+
+import six
+import numpy as np
+
+
+def parse_param_file(param_file, return_shape=True):
+    from paddle.fluid.proto.framework_pb2 import VarType
+    f = open(param_file, 'rb')
+    version = np.fromstring(f.read(4), dtype='int32')
+    lod_level = np.fromstring(f.read(8), dtype='int64')
+    for i in range(int(lod_level)):
+        _size = np.fromstring(f.read(8), dtype='int64')
+        _ = f.read(_size)
+    version = np.fromstring(f.read(4), dtype='int32')
+    tensor_desc = VarType.TensorDesc()
+    tensor_desc_size = np.fromstring(f.read(4), dtype='int32')
+    tensor_desc.ParseFromString(f.read(int(tensor_desc_size)))
+    tensor_shape = tuple(tensor_desc.dims)
+    if return_shape:
+        f.close()
+        return tuple(tensor_desc.dims)
+    if tensor_desc.data_type != 5:
+        raise Exception(
+            "Unexpected data type while parse {}".format(param_file))
+    data_size = 4
+    for i in range(len(tensor_shape)):
+        data_size *= tensor_shape[i]
+    weight = np.fromstring(f.read(data_size), dtype='float32')
+    f.close()
+    return np.reshape(weight, tensor_shape)
+
+
+def load_pdparams(exe, main_prog, model_dir):
+    import paddle.fluid as fluid
+    from paddle.fluid.proto.framework_pb2 import VarType
+    from paddle.fluid.framework import Program
+
+    vars_to_load = list()
+    vars_not_load = list()
+    import pickle
+    with open(osp.join(model_dir, 'model.pdparams'), 'rb') as f:
+        params_dict = pickle.load(f) if six.PY2 else pickle.load(
+            f, encoding='latin1')
+    unused_vars = list()
+    for var in main_prog.list_vars():
+        if not isinstance(var, fluid.framework.Parameter):
+            continue
+        if var.name not in params_dict:
+            print("{} is not in saved model".format(var.name))
+            vars_not_load.append(var.name)
+            continue
+        if var.shape != params_dict[var.name].shape:
+            unused_vars.append(var.name)
+            vars_not_load.append(var.name)
+            print(
+                "[SKIP] Shape of pretrained weight {} doesn't match.(Pretrained: {}, Actual: {})"
+                .format(var.name, params_dict[var.name].shape, var.shape))
+            continue
+        vars_to_load.append(var)
+    for var_name in unused_vars:
+        del params_dict[var_name]
+    fluid.io.set_program_state(main_prog, params_dict)
+
+    if len(vars_to_load) == 0:
+        print(
+            "There is no pretrain weights loaded, maybe you should check you pretrain model!"
+        )
+    else:
+        print("There are {}/{} varaibles in {} are loaded.".format(
+            len(vars_to_load),
+            len(vars_to_load) + len(vars_not_load), model_dir))
+
+
+def load_pretrained_weights(exe, main_prog, weights_dir):
+    if not osp.exists(weights_dir):
+        raise Exception("Path {} not exists.".format(weights_dir))
+    if osp.exists(osp.join(weights_dir, "model.pdparams")):
+        return load_pdparams(exe, main_prog, weights_dir)
+    import paddle.fluid as fluid
+    vars_to_load = list()
+    vars_not_load = list()
+    for var in main_prog.list_vars():
+        if not isinstance(var, fluid.framework.Parameter):
+            continue
+        if not osp.exists(osp.join(weights_dir, var.name)):
+            print("[SKIP] Pretrained weight {}/{} doesn't exist".format(
+                weights_dir, var.name))
+            vars_not_load.append(var)
+            continue
+        pretrained_shape = parse_param_file(osp.join(weights_dir, var.name))
+        actual_shape = tuple(var.shape)
+        if pretrained_shape != actual_shape:
+            print(
+                "[SKIP] Shape of pretrained weight {}/{} doesn't match.(Pretrained: {}, Actual: {})"
+                .format(weights_dir, var.name, pretrained_shape, actual_shape))
+            vars_not_load.append(var)
+            continue
+        vars_to_load.append(var)
+    params_dict = fluid.io.load_program_state(
+        weights_dir, var_list=vars_to_load)
+    fluid.io.set_program_state(main_prog, params_dict)
+    if len(vars_to_load) == 0:
+        print(
+            "There is no pretrain weights loaded, maybe you should check you pretrain model!"
+        )
+    else:
+        print("There are {}/{} varaibles in {} are loaded.".format(
+            len(vars_to_load),
+            len(vars_to_load) + len(vars_not_load), weights_dir))

contrib/LaneNet/vis.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -45,6 +45,7 @@ from models.model_builder import ModelPhase
 from utils import lanenet_postprocess
 import matplotlib.pyplot as plt
 
+
 def parse_args():
     parser = argparse.ArgumentParser(description='PaddeSeg visualization tools')
     parser.add_argument(
@@ -106,7 +107,6 @@ def minmax_scale(input_arr):
     return output_arr
 
 
-
 def visualize(cfg,
               vis_file_list=None,
               use_gpu=False,
@@ -119,7 +119,6 @@ def visualize(cfg,
     if vis_file_list is None:
         vis_file_list = cfg.DATASET.TEST_FILE_LIST
 
-
     dataset = LaneNetDataset(
         file_list=vis_file_list,
         mode=ModelPhase.VISUAL,
@@ -139,7 +138,12 @@ def visualize(cfg,
 
     ckpt_dir = cfg.TEST.TEST_MODEL if not ckpt_dir else ckpt_dir
 
-    fluid.io.load_params(exe, ckpt_dir, main_program=test_prog)
+    if ckpt_dir is not None:
+        print('load test model:', ckpt_dir)
+        try:
+            fluid.load(test_prog, os.path.join(ckpt_dir, 'model'), exe)
+        except:
+            fluid.io.load_params(exe, ckpt_dir, main_program=test_prog)
 
     save_dir = os.path.join(vis_dir, 'visual_results')
     makedirs(save_dir)
@@ -161,22 +165,26 @@ def visualize(cfg,
 
         for i in range(num_imgs):
             gt_image = org_imgs[i]
-            binary_seg_image, instance_seg_image = segLogits[i].squeeze(-1), emLogits[i].transpose((1,2,0))
+            binary_seg_image, instance_seg_image = segLogits[i].squeeze(
+                -1), emLogits[i].transpose((1, 2, 0))
 
             postprocess_result = postprocessor.postprocess(
                 binary_seg_result=binary_seg_image,
                 instance_seg_result=instance_seg_image,
-                source_image=gt_image
-            )
-            pred_binary_fn = os.path.join(save_dir, to_png_fn(img_names[i], name='_pred_binary'))
-            pred_lane_fn = os.path.join(save_dir, to_png_fn(img_names[i], name='_pred_lane'))
-            pred_instance_fn = os.path.join(save_dir, to_png_fn(img_names[i], name='_pred_instance'))
+                source_image=gt_image)
+            pred_binary_fn = os.path.join(
+                save_dir, to_png_fn(img_names[i], name='_pred_binary'))
+            pred_lane_fn = os.path.join(
+                save_dir, to_png_fn(img_names[i], name='_pred_lane'))
+            pred_instance_fn = os.path.join(
+                save_dir, to_png_fn(img_names[i], name='_pred_instance'))
             dirname = os.path.dirname(pred_binary_fn)
 
             makedirs(dirname)
             mask_image = postprocess_result['mask_image']
             for i in range(4):
-                instance_seg_image[:, :, i] = minmax_scale(instance_seg_image[:, :, i])
+                instance_seg_image[:, :, i] = minmax_scale(
+                    instance_seg_image[:, :, i])
             embedding_image = np.array(instance_seg_image).astype(np.uint8)
 
             plt.figure('mask_image')
@@ -189,13 +197,13 @@ def visualize(cfg,
             plt.imshow(binary_seg_image * 255, cmap='gray')
             plt.show()
 
-            cv2.imwrite(pred_binary_fn, np.array(binary_seg_image * 255).astype(np.uint8))
+            cv2.imwrite(pred_binary_fn,
+                        np.array(binary_seg_image * 255).astype(np.uint8))
             cv2.imwrite(pred_lane_fn, postprocess_result['source_image'])
             cv2.imwrite(pred_instance_fn, mask_image)
             print(pred_lane_fn, 'saved!')
 
 
-
 if __name__ == '__main__':
     args = parse_args()
     if args.cfg_file is not None:

contrib/MechanicalIndustryMeter/download_mini_mechanical_industry_meter.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/MechanicalIndustryMeter/download_unet_mechanical_industry_meter.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,
@@ -23,7 +24,8 @@ from test_utils import download_file_and_uncompress
 
 if __name__ == "__main__":
     download_file_and_uncompress(
-        url='https://paddleseg.bj.bcebos.com/models/unet_mechanical_industry_meter.tar',
+        url=
+        'https://paddleseg.bj.bcebos.com/models/unet_mechanical_industry_meter.tar',
         savepath=LOCAL_PATH,
         extrapath=LOCAL_PATH)
 

contrib/RemoteSensing/__init__.py

-#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/RemoteSensing/models/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 .load_model import *
 from .unet import *
 from .hrnet import *

contrib/RemoteSensing/models/base.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-#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
+# 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.
+# 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 absolute_import
 import paddle.fluid as fluid

contrib/RemoteSensing/models/load_model.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/models/unet.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-#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
+# 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.
+# 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 absolute_import
 import numpy as np

contrib/RemoteSensing/nets/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 .unet import UNet
 from .hrnet import HRNet

contrib/RemoteSensing/nets/libs.py

 # coding: utf8
-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/nets/loss.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/nets/unet.py

 # coding: utf8
-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/predict_demo.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 os.path as osp
 import sys

contrib/RemoteSensing/readers/__init__.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/readers/base.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/readers/reader.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/tools/create_dataset_list.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/tools/split_dataset_list.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/train_demo.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.path as osp
 import argparse
 import transforms.transforms as T

contrib/RemoteSensing/transforms/__init__.py

-#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/RemoteSensing/transforms/ops.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/transforms/transforms.py

 # coding: utf8
-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/utils/__init__.py

-#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/RemoteSensing/utils/logging.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/utils/metrics.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

contrib/RemoteSensing/utils/pretrain_weights.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.path as osp
 
 

contrib/RemoteSensing/utils/utils.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -201,11 +202,9 @@ def load_pretrain_weights(exe, main_prog, weights_dir, fuse_bn=False):
         vars_to_load.append(var)
         logging.debug("Weight {} will be load".format(var.name))
 
-    fluid.io.load_vars(
-        executor=exe,
-        dirname=weights_dir,
-        main_program=main_prog,
-        vars=vars_to_load)
+    params_dict = fluid.io.load_program_state(
+        weights_dir, var_list=vars_to_load)
+    fluid.io.set_program_state(main_prog, params_dict)
     if len(vars_to_load) == 0:
         logging.warning(
             "There is no pretrain weights loaded, maybe you should check you pretrain model!"

contrib/RoadLine/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.

contrib/RoadLine/config.py

-# -*- coding: utf-8 -*-
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 utils.util import AttrDict, merge_cfg_from_args, get_arguments
 import os
 
@@ -6,20 +20,20 @@ args = get_arguments()
 cfg = AttrDict()
 
 # 待预测图像所在路径
-cfg.data_dir = os.path.join(args.example , "data", "test_images")
+cfg.data_dir = os.path.join(args.example, "data", "test_images")
 # 待预测图像名称列表
-cfg.data_list_file = os.path.join(args.example , "data", "test.txt")
+cfg.data_list_file = os.path.join(args.example, "data", "test.txt")
 # 模型加载路径
-cfg.model_path = os.path.join(args.example , "model")
+cfg.model_path = os.path.join(args.example, "model")
 # 预测结果保存路径
-cfg.vis_dir = os.path.join(args.example , "result")
+cfg.vis_dir = os.path.join(args.example, "result")
 
 # 预测类别数
 cfg.class_num = 2
 # 均值, 图像预处理减去的均值
 cfg.MEAN = 127.5, 127.5, 127.5
 # 标准差，图像预处理除以标准差
-cfg.STD =  127.5, 127.5, 127.5
+cfg.STD = 127.5, 127.5, 127.5
 # 待预测图像输入尺寸
 cfg.input_size = 1536, 576
 

contrib/RoadLine/download_RoadLine.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

contrib/RoadLine/infer.py

-# -*- coding: utf-8 -*-
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 cv2
 import numpy as np
@@ -12,18 +26,19 @@ config = importlib.import_module('config')
 cfg = getattr(config, 'cfg')
 
 # paddle垃圾回收策略FLAG，ACE2P模型较大，当显存不够时建议开启
-os.environ['FLAGS_eager_delete_tensor_gb']='0.0'
+os.environ['FLAGS_eager_delete_tensor_gb'] = '0.0'
 
 import paddle.fluid as fluid
 
+
 # 预测数据集类
 class TestDataSet():
     def __init__(self):
-        self.data_dir = cfg.data_dir 
+        self.data_dir = cfg.data_dir
         self.data_list_file = cfg.data_list_file
         self.data_list = self.get_data_list()
         self.data_num = len(self.data_list)
-    
+
     def get_data_list(self):
         # 获取预测图像路径列表
         data_list = []
@@ -40,7 +55,7 @@ class TestDataSet():
     def preprocess(self, img):
         # 图像预处理
         if cfg.example == 'ACE2P':
-            reader = importlib.import_module(args.example+'.reader')
+            reader = importlib.import_module(args.example + '.reader')
             ACE2P_preprocess = getattr(reader, 'preprocess')
             img = ACE2P_preprocess(img)
         else:
@@ -56,10 +71,10 @@ class TestDataSet():
         img_path = self.data_list[index]
         img = cv2.imread(img_path, cv2.IMREAD_COLOR)
         if img is None:
-            return img, img,img_path, None
+            return img, img, img_path, None
 
         img_name = img_path.split(os.sep)[-1]
-        name_prefix = img_name.replace('.'+img_name.split('.')[-1],'')
+        name_prefix = img_name.replace('.' + img_name.split('.')[-1], '')
         img_shape = img.shape[:2]
         img_process = self.preprocess(img)
 
@@ -90,39 +105,44 @@ def infer():
         if image is None:
             print(im_name, 'is None')
             continue
-        
+
         # 预测
         if cfg.example == 'ACE2P':
             # ACE2P模型使用多尺度预测
-            reader = importlib.import_module(args.example+'.reader')
+            reader = importlib.import_module(args.example + '.reader')
             multi_scale_test = getattr(reader, 'multi_scale_test')
-            parsing, logits = multi_scale_test(exe, test_prog, feed_name, fetch_list, image, im_shape)
+            parsing, logits = multi_scale_test(exe, test_prog, feed_name,
+                                               fetch_list, image, im_shape)
         else:
             # HumanSeg,RoadLine模型单尺度预测
-            result = exe.run(program=test_prog, feed={feed_name[0]: image}, fetch_list=fetch_list)
+            result = exe.run(
+                program=test_prog,
+                feed={feed_name[0]: image},
+                fetch_list=fetch_list)
             parsing = np.argmax(result[0][0], axis=0)
             parsing = cv2.resize(parsing.astype(np.uint8), im_shape[::-1])
-        
+
         # 预测结果保存
         result_path = os.path.join(cfg.vis_dir, im_name + '.png')
         if cfg.example == 'HumanSeg':
-            logits = result[0][0][1]*255
+            logits = result[0][0][1] * 255
             logits = cv2.resize(logits, im_shape[::-1])
             ret, logits = cv2.threshold(logits, thresh, 0, cv2.THRESH_TOZERO)
-            logits = 255 *(logits - thresh)/(255 - thresh)
+            logits = 255 * (logits - thresh) / (255 - thresh)
             # 将分割结果添加到alpha通道
-            rgba = np.concatenate((ori_img, np.expand_dims(logits, axis=2)), axis=2)
+            rgba = np.concatenate((ori_img, np.expand_dims(logits, axis=2)),
+                                  axis=2)
             cv2.imwrite(result_path, rgba)
-        else: 
+        else:
             output_im = PILImage.fromarray(np.asarray(parsing, dtype=np.uint8))
             output_im.putpalette(palette)
             output_im.save(result_path)
 
         if (idx + 1) % 100 == 0:
             print('%d  processd' % (idx + 1))
-            
-    print('%d  processd done' % (idx + 1))   
-    
+
+    print('%d  processd done' % (idx + 1))
+
     return 0
 
 

contrib/RoadLine/utils/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.

contrib/RoadLine/utils/palette.py

-##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-## Created by: RainbowSecret
-## Microsoft Research
-## yuyua@microsoft.com
-## Copyright (c) 2018
-##
-## This source code is licensed under the MIT-style license found in the
-## LICENSE file in the root directory of this source tree
-##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 absolute_import
 from __future__ import division
 from __future__ import print_function

contrib/RoadLine/utils/util.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 division
 from __future__ import print_function
 from __future__ import unicode_literals
 import argparse
 import os
 
+
 def get_arguments():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--use_gpu",
-                        action="store_true",
-                        help="Use gpu or cpu to test.")
-    parser.add_argument('--example',
-                        type=str,
-                        help='RoadLine, HumanSeg or ACE2P')
-                        
+    parser.add_argument(
+        "--use_gpu", action="store_true", help="Use gpu or cpu to test.")
+    parser.add_argument(
+        '--example', type=str, help='RoadLine, HumanSeg or ACE2P')
+
     return parser.parse_args()
 
 
@@ -34,6 +48,7 @@ class AttrDict(dict):
         else:
             self[name] = value
 
+
 def merge_cfg_from_args(args, cfg):
     """Merge config keys, values in args into the global config."""
     for k, v in vars(args).items():
@@ -44,4 +59,3 @@ def merge_cfg_from_args(args, cfg):
             value = v
         if value is not None:
             cfg[k] = value
-

dataset/convert_voc2012.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,
@@ -20,6 +21,8 @@ from PIL import Image
 import glob
 
 LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+
+
 def remove_colormap(filename):
     gray_anno = np.array(Image.open(filename))
     return gray_anno
@@ -30,6 +33,7 @@ def save_annotation(annotation, filename):
     annotation = Image.fromarray(annotation)
     annotation.save(filename)
 
+
 def convert_list(origin_file, seg_file, output_folder):
     with open(seg_file, 'w') as fid_seg:
         with open(origin_file) as fid_ori:
@@ -43,6 +47,7 @@ def convert_list(origin_file, seg_file, output_folder):
                 new_line = ' '.join([img_name, anno_name])
                 fid_seg.write(new_line + "\n")
 
+
 if __name__ == "__main__":
     pascal_root = "./VOCtrainval_11-May-2012/VOC2012"
     pascal_root = os.path.join(LOCAL_PATH, pascal_root)
@@ -54,7 +59,7 @@ if __name__ == "__main__":
 
     # 标注图转换后存储目录
     output_folder = os.path.join(pascal_root, "SegmentationClassAug")
-    
+
     print("annotation convert and file list convert")
     if not os.path.exists(os.path.join(LOCAL_PATH, output_folder)):
         os.mkdir(os.path.join(LOCAL_PATH, output_folder))
@@ -67,5 +72,5 @@ if __name__ == "__main__":
 
     convert_list(train_path, train_path.replace('txt', 'list'), output_folder)
     convert_list(val_path, val_path.replace('txt', 'list'), output_folder)
-    convert_list(trainval_path, trainval_path.replace('txt', 'list'), output_folder)
-
+    convert_list(trainval_path, trainval_path.replace('txt', 'list'),
+                 output_folder)

dataset/download_and_convert_voc2012.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,
@@ -28,12 +29,12 @@ from convert_voc2012 import remove_colormap
 from convert_voc2012 import save_annotation
 
 
-
 def download_VOC_dataset(savepath, extrapath):
     url = "https://paddleseg.bj.bcebos.com/dataset/VOCtrainval_11-May-2012.tar"
     download_file_and_uncompress(
         url=url, savepath=savepath, extrapath=extrapath)
 
+
 if __name__ == "__main__":
     download_VOC_dataset(LOCAL_PATH, LOCAL_PATH)
     print("Dataset download finish!")
@@ -45,10 +46,10 @@ if __name__ == "__main__":
     train_path = os.path.join(txt_folder, "train.txt")
     val_path = os.path.join(txt_folder, "val.txt")
     trainval_path = os.path.join(txt_folder, "trainval.txt")
-    
+
     # 标注图转换后存储目录
     output_folder = os.path.join(pascal_root, "SegmentationClassAug")
-    
+
     print("annotation convert and file list convert")
     if not os.path.exists(output_folder):
         os.mkdir(output_folder)
@@ -61,5 +62,5 @@ if __name__ == "__main__":
 
     convert_list(train_path, train_path.replace('txt', 'list'), output_folder)
     convert_list(val_path, val_path.replace('txt', 'list'), output_folder)
-    convert_list(trainval_path, trainval_path.replace('txt', 'list'), output_folder)
-
+    convert_list(trainval_path, trainval_path.replace('txt', 'list'),
+                 output_folder)

dataset/download_cityscapes.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

dataset/download_mini_deepglobe_road_extraction.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

dataset/download_optic.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

dataset/download_pet.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

deploy/python/infer.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -29,9 +29,9 @@ from concurrent.futures import ThreadPoolExecutor, as_completed
 
 gflags.DEFINE_string("conf", default="", help="Configuration File Path")
 gflags.DEFINE_string("input_dir", default="", help="Directory of Input Images")
-gflags.DEFINE_boolean("use_pr", default=False, help="Use optimized model")
 gflags.DEFINE_string("trt_mode", default="", help="Use optimized model")
-gflags.DEFINE_string("ext", default=".jpeg|.jpg", help="Input Image File Extensions")
+gflags.DEFINE_string(
+    "ext", default=".jpeg|.jpg", help="Input Image File Extensions")
 gflags.FLAGS = gflags.FLAGS
 
 
@@ -103,6 +103,9 @@ class DeployConfig:
             self.batch_size = deploy_conf["BATCH_SIZE"]
             # 9. channels
             self.channels = deploy_conf["CHANNELS"]
+            # 10. use_pr
+            self.use_pr = deploy_conf["USE_PR"]
+
 
 
 class ImageReader:
@@ -257,23 +260,24 @@ class Predictor:
         # record starting time point
         total_start = time.time()
         batch_size = self.config.batch_size
+        use_pr = self.config.use_pr
         for i in range(0, len(images), batch_size):
             real_batch_size = batch_size
             if i + batch_size >= len(images):
                 real_batch_size = len(images) - i
             reader_start = time.time()
             img_datas = self.image_reader.process(images[i:i + real_batch_size],
-                                                  gflags.FLAGS.use_pr)
+                                                  use_pr)
             input_data = np.concatenate([item[1] for item in img_datas])
             input_data = self.create_tensor(
-                input_data, real_batch_size, use_pr=gflags.FLAGS.use_pr)
+                input_data, real_batch_size, use_pr=use_pr)
             reader_end = time.time()
             infer_start = time.time()
             output_data = self.predictor.run(input_data)[0]
             infer_end = time.time()
             output_data = output_data.as_ndarray()
             post_start = time.time()
-            self.output_result(img_datas, output_data, gflags.FLAGS.use_pr)
+            self.output_result(img_datas, output_data, use_pr)
             post_end = time.time()
             reader_time += (reader_end - reader_start)
             infer_time += (infer_end - infer_start)

pdseg/__init__.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,4 +14,4 @@
 # limitations under the License.
 import models
 import utils
-from . import tools
+from . import tools
\ No newline at end of file

pdseg/check.py

 # coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 absolute_import
 from __future__ import division
@@ -427,12 +440,17 @@ def max_img_size_statistics():
     logger.info("max width and max height of images are ({},{})".format(
         max_width, max_height))
 
+
 def num_classes_loss_matching_check():
     loss_type = cfg.SOLVER.LOSS
     num_classes = cfg.DATASET.NUM_CLASSES
-    if num_classes > 2 and (("dice_loss" in loss_type) or ("bce_loss" in loss_type)):
-        logger.info(error_print("loss check."
-            " Dice loss and bce loss is only applicable to binary classfication"))
+    if num_classes > 2 and (("dice_loss" in loss_type) or
+                            ("bce_loss" in loss_type)):
+        logger.info(
+            error_print(
+                "loss check."
+                " Dice loss and bce loss is only applicable to binary classfication"
+            ))
     else:
         logger.info(correct_print("loss check"))
 

pdseg/data_aug.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -362,7 +362,7 @@ def hsv_color_jitter(crop_img,
         saturation_jitter_ratio > 0 or \
         contrast_jitter_ratio > 0:
         crop_img = random_jitter(crop_img, saturation_jitter_ratio,
-                      brightness_jitter_ratio, contrast_jitter_ratio)
+                                 brightness_jitter_ratio, contrast_jitter_ratio)
     return crop_img
 
 
@@ -391,7 +391,7 @@ def rand_crop(crop_img, crop_seg, mode=ModelPhase.TRAIN):
         crop_width = cfg.EVAL_CROP_SIZE[0]
         crop_height = cfg.EVAL_CROP_SIZE[1]
 
-    if not ModelPhase.is_train(mode): 
+    if not ModelPhase.is_train(mode):
         if (crop_height < img_height or crop_width < img_width):
             raise Exception(
                 "Crop size({},{}) must large than img size({},{}) when in EvalPhase."

pdseg/data_utils.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
 """
 This code is based on https://github.com/fchollet/keras/blob/master/keras/utils/data_utils.py
 """
@@ -14,10 +28,10 @@ except ImportError:
 
 class GeneratorEnqueuer(object):
     """
-    Multiple generators 
+    Multiple generators
 
     Args:
-        generators: 
+        generators:
         wait_time (float): time to sleep in-between calls to `put()`.
     """
 

pdseg/eval.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -22,13 +22,9 @@ import os
 os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0"
 
 import sys
-import time
 import argparse
-import functools
 import pprint
-import cv2
 import numpy as np
-import paddle
 import paddle.fluid as fluid
 
 from utils.config import cfg
@@ -116,7 +112,10 @@ def evaluate(cfg, ckpt_dir=None, use_gpu=False, use_mpio=False, **kwargs):
 
     if ckpt_dir is not None:
         print('load test model:', ckpt_dir)
-        fluid.io.load_params(exe, ckpt_dir, main_program=test_prog)
+        try:
+            fluid.load(test_prog, os.path.join(ckpt_dir, 'model'), exe)
+        except:
+            fluid.io.load_params(exe, ckpt_dir, main_program=test_prog)
 
     # Use streaming confusion matrix to calculate mean_iou
     np.set_printoptions(

pdseg/export_model.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -49,6 +49,7 @@ def parse_args():
         sys.exit(1)
     return parser.parse_args()
 
+
 def export_inference_config():
     deploy_cfg = '''DEPLOY:
         USE_GPU : 1
@@ -66,9 +67,8 @@ def export_inference_config():
         PREDICTOR_MODE : "ANALYSIS"
         BATCH_SIZE : 1
     ''' % (cfg.FREEZE.SAVE_DIR, cfg.FREEZE.MODEL_FILENAME,
-            cfg.FREEZE.PARAMS_FILENAME, cfg.EVAL_CROP_SIZE,
-            cfg.MEAN, cfg.STD, cfg.DATASET.IMAGE_TYPE,
-            cfg.DATASET.NUM_CLASSES, len(cfg.STD))
+           cfg.FREEZE.PARAMS_FILENAME, cfg.EVAL_CROP_SIZE, cfg.MEAN, cfg.STD,
+           cfg.DATASET.IMAGE_TYPE, cfg.DATASET.NUM_CLASSES, len(cfg.STD))
     if not os.path.exists(cfg.FREEZE.SAVE_DIR):
         os.mkdir(cfg.FREEZE.SAVE_DIR)
     yaml_path = os.path.join(cfg.FREEZE.SAVE_DIR, 'deploy.yaml')
@@ -94,7 +94,13 @@ def export_inference_model(args):
     infer_prog = infer_prog.clone(for_test=True)
 
     if os.path.exists(cfg.TEST.TEST_MODEL):
-        fluid.io.load_params(exe, cfg.TEST.TEST_MODEL, main_program=infer_prog)
+        print('load test model:', cfg.TEST.TEST_MODEL)
+        try:
+            fluid.load(infer_prog, os.path.join(cfg.TEST.TEST_MODEL, 'model'),
+                       exe)
+        except:
+            fluid.io.load_params(
+                exe, cfg.TEST.TEST_MODEL, main_program=infer_prog)
     else:
         print("TEST.TEST_MODEL diretory is empty!")
         exit(-1)

pdseg/loss.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/lovasz_losses.py

-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/metrics.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/models/__init__.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/models/backbone/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.

pdseg/models/backbone/mobilenet_v2.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/models/backbone/resnet.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -141,7 +141,7 @@ class ResNet():
                     else:
                         conv_name = "res" + str(block + 2) + chr(97 + i)
                     dilation_rate = get_dilated_rate(dilation_dict, block)
-                    
+
                     conv = self.bottleneck_block(
                         input=conv,
                         num_filters=int(num_filters[block] * self.scale),
@@ -215,11 +215,11 @@ class ResNet():
                       groups=1,
                       act=None,
                       name=None):
-   
+
         if self.stem == 'pspnet':
-            bias_attr=ParamAttr(name=name + "_biases")
+            bias_attr = ParamAttr(name=name + "_biases")
         else:
-            bias_attr=False
+            bias_attr = False
 
         conv = fluid.layers.conv2d(
             input=input,
@@ -238,13 +238,15 @@ class ResNet():
             bn_name = "bn_" + name
         else:
             bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(input=conv,
-                                       act=act,
-                                       name=bn_name + '.output.1',
-                                       param_attr=ParamAttr(name=bn_name + '_scale'),
-                                       bias_attr=ParamAttr(bn_name + '_offset'),
-                                       moving_mean_name=bn_name + '_mean',
-                                       moving_variance_name=bn_name + '_variance', )
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            name=bn_name + '.output.1',
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance',
+        )
 
     def shortcut(self, input, ch_out, stride, is_first, name):
         ch_in = input.shape[1]
@@ -258,7 +260,7 @@ class ResNet():
             strides = [1, stride]
         else:
             strides = [stride, 1]
-        
+
         conv0 = self.conv_bn_layer(
             input=input,
             num_filters=num_filters,

pdseg/models/backbone/vgg.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -55,7 +55,8 @@ class VGGNet():
         channels = [64, 128, 256, 512, 512]
         conv = input
         for i in range(len(nums)):
-            conv = self.conv_block(conv, channels[i], nums[i], name="conv" + str(i + 1) + "_")
+            conv = self.conv_block(
+                conv, channels[i], nums[i], name="conv" + str(i + 1) + "_")
             layers_count += nums[i]
             if check_points(layers_count, decode_points):
                 short_cuts[layers_count] = conv

pdseg/models/backbone/xception.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/models/libs/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.

pdseg/models/libs/model_libs.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -197,4 +197,4 @@ def conv_bn_layer(input,
     if if_act:
         return fluid.layers.relu6(bn)
     else:
-        return bn
+        return bn
\ No newline at end of file

pdseg/models/model_builder.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/models/modeling/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.

pdseg/models/modeling/deeplab.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/models/modeling/fast_scnn.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/models/modeling/hrnet.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -25,7 +25,14 @@ from paddle.fluid.param_attr import ParamAttr
 from utils.config import cfg
 
 
-def conv_bn_layer(input, filter_size, num_filters, stride=1, padding=1, num_groups=1, if_act=True, name=None):
+def conv_bn_layer(input,
+                  filter_size,
+                  num_filters,
+                  stride=1,
+                  padding=1,
+                  num_groups=1,
+                  if_act=True,
+                  name=None):
     conv = fluid.layers.conv2d(
         input=input,
         num_filters=num_filters,
@@ -37,37 +44,74 @@ def conv_bn_layer(input, filter_size, num_filters, stride=1, padding=1, num_grou
         param_attr=ParamAttr(initializer=MSRA(), name=name + '_weights'),
         bias_attr=False)
     bn_name = name + '_bn'
-    bn = fluid.layers.batch_norm(input=conv,
-                                 param_attr=ParamAttr(name=bn_name + "_scale",
-                                                      initializer=fluid.initializer.Constant(1.0)),
-                                 bias_attr=ParamAttr(name=bn_name + "_offset",
-                                                     initializer=fluid.initializer.Constant(0.0)),
-                                 moving_mean_name=bn_name + '_mean',
-                                 moving_variance_name=bn_name + '_variance')
+    bn = fluid.layers.batch_norm(
+        input=conv,
+        param_attr=ParamAttr(
+            name=bn_name + "_scale",
+            initializer=fluid.initializer.Constant(1.0)),
+        bias_attr=ParamAttr(
+            name=bn_name + "_offset",
+            initializer=fluid.initializer.Constant(0.0)),
+        moving_mean_name=bn_name + '_mean',
+        moving_variance_name=bn_name + '_variance')
     if if_act:
         bn = fluid.layers.relu(bn)
     return bn
 
+
 def basic_block(input, num_filters, stride=1, downsample=False, name=None):
     residual = input
-    conv = conv_bn_layer(input=input, filter_size=3, num_filters=num_filters, stride=stride, name=name + '_conv1')
-    conv = conv_bn_layer(input=conv, filter_size=3, num_filters=num_filters, if_act=False, name=name + '_conv2')
+    conv = conv_bn_layer(
+        input=input,
+        filter_size=3,
+        num_filters=num_filters,
+        stride=stride,
+        name=name + '_conv1')
+    conv = conv_bn_layer(
+        input=conv,
+        filter_size=3,
+        num_filters=num_filters,
+        if_act=False,
+        name=name + '_conv2')
     if downsample:
-        residual = conv_bn_layer(input=input, filter_size=1, num_filters=num_filters, if_act=False,
-                                      name=name + '_downsample')
+        residual = conv_bn_layer(
+            input=input,
+            filter_size=1,
+            num_filters=num_filters,
+            if_act=False,
+            name=name + '_downsample')
     return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
 
+
 def bottleneck_block(input, num_filters, stride=1, downsample=False, name=None):
     residual = input
-    conv = conv_bn_layer(input=input, filter_size=1, num_filters=num_filters, name=name + '_conv1')
-    conv = conv_bn_layer(input=conv, filter_size=3, num_filters=num_filters, stride=stride, name=name + '_conv2')
-    conv = conv_bn_layer(input=conv, filter_size=1, num_filters=num_filters * 4, if_act=False,
-                              name=name + '_conv3')
+    conv = conv_bn_layer(
+        input=input,
+        filter_size=1,
+        num_filters=num_filters,
+        name=name + '_conv1')
+    conv = conv_bn_layer(
+        input=conv,
+        filter_size=3,
+        num_filters=num_filters,
+        stride=stride,
+        name=name + '_conv2')
+    conv = conv_bn_layer(
+        input=conv,
+        filter_size=1,
+        num_filters=num_filters * 4,
+        if_act=False,
+        name=name + '_conv3')
     if downsample:
-        residual = conv_bn_layer(input=input, filter_size=1, num_filters=num_filters * 4, if_act=False,
-                                      name=name + '_downsample')
+        residual = conv_bn_layer(
+            input=input,
+            filter_size=1,
+            num_filters=num_filters * 4,
+            if_act=False,
+            name=name + '_downsample')
     return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
 
+
 def fuse_layers(x, channels, multi_scale_output=True, name=None):
     out = []
     for i in range(len(channels) if multi_scale_output else 1):
@@ -77,40 +121,64 @@ def fuse_layers(x, channels, multi_scale_output=True, name=None):
         height = shape[-2]
         for j in range(len(channels)):
             if j > i:
-                y = conv_bn_layer(x[j], filter_size=1, num_filters=channels[i], if_act=False,
-                                       name=name + '_layer_' + str(i + 1) + '_' + str(j + 1))
-                y = fluid.layers.resize_bilinear(input=y, out_shape=[height, width])
-                residual = fluid.layers.elementwise_add(x=residual, y=y, act=None)
+                y = conv_bn_layer(
+                    x[j],
+                    filter_size=1,
+                    num_filters=channels[i],
+                    if_act=False,
+                    name=name + '_layer_' + str(i + 1) + '_' + str(j + 1))
+                y = fluid.layers.resize_bilinear(
+                    input=y, out_shape=[height, width])
+                residual = fluid.layers.elementwise_add(
+                    x=residual, y=y, act=None)
             elif j < i:
                 y = x[j]
                 for k in range(i - j):
                     if k == i - j - 1:
-                        y = conv_bn_layer(y, filter_size=3, num_filters=channels[i], stride=2, if_act=False,
-                                               name=name + '_layer_' + str(i + 1) + '_' + str(j + 1) + '_' + str(k + 1))
+                        y = conv_bn_layer(
+                            y,
+                            filter_size=3,
+                            num_filters=channels[i],
+                            stride=2,
+                            if_act=False,
+                            name=name + '_layer_' + str(i + 1) + '_' +
+                            str(j + 1) + '_' + str(k + 1))
                     else:
-                        y = conv_bn_layer(y, filter_size=3, num_filters=channels[j], stride=2,
-                                               name=name + '_layer_' + str(i + 1) + '_' + str(j + 1) + '_' + str(k + 1))
-                residual = fluid.layers.elementwise_add(x=residual, y=y, act=None)
+                        y = conv_bn_layer(
+                            y,
+                            filter_size=3,
+                            num_filters=channels[j],
+                            stride=2,
+                            name=name + '_layer_' + str(i + 1) + '_' +
+                            str(j + 1) + '_' + str(k + 1))
+                residual = fluid.layers.elementwise_add(
+                    x=residual, y=y, act=None)
 
         residual = fluid.layers.relu(residual)
         out.append(residual)
     return out
 
+
 def branches(x, block_num, channels, name=None):
     out = []
     for i in range(len(channels)):
         residual = x[i]
         for j in range(block_num):
-            residual = basic_block(residual, channels[i],
-                                        name=name + '_branch_layer_' + str(i + 1) + '_' + str(j + 1))
+            residual = basic_block(
+                residual,
+                channels[i],
+                name=name + '_branch_layer_' + str(i + 1) + '_' + str(j + 1))
         out.append(residual)
     return out
 
+
 def high_resolution_module(x, channels, multi_scale_output=True, name=None):
     residual = branches(x, 4, channels, name=name)
-    out = fuse_layers(residual, channels, multi_scale_output=multi_scale_output, name=name)
+    out = fuse_layers(
+        residual, channels, multi_scale_output=multi_scale_output, name=name)
     return out
 
+
 def transition_layer(x, in_channels, out_channels, name=None):
     num_in = len(in_channels)
     num_out = len(out_channels)
@@ -118,46 +186,76 @@ def transition_layer(x, in_channels, out_channels, name=None):
     for i in range(num_out):
         if i < num_in:
             if in_channels[i] != out_channels[i]:
-                residual = conv_bn_layer(x[i], filter_size=3, num_filters=out_channels[i],
-                                              name=name + '_layer_' + str(i + 1))
+                residual = conv_bn_layer(
+                    x[i],
+                    filter_size=3,
+                    num_filters=out_channels[i],
+                    name=name + '_layer_' + str(i + 1))
                 out.append(residual)
             else:
                 out.append(x[i])
         else:
-            residual = conv_bn_layer(x[-1], filter_size=3, num_filters=out_channels[i], stride=2,
-                                          name=name + '_layer_' + str(i + 1))
+            residual = conv_bn_layer(
+                x[-1],
+                filter_size=3,
+                num_filters=out_channels[i],
+                stride=2,
+                name=name + '_layer_' + str(i + 1))
             out.append(residual)
     return out
 
+
 def stage(x, num_modules, channels, multi_scale_output=True, name=None):
     out = x
     for i in range(num_modules):
         if i == num_modules - 1 and multi_scale_output == False:
-            out = high_resolution_module(out, channels, multi_scale_output=False, name=name + '_' + str(i + 1))
+            out = high_resolution_module(
+                out,
+                channels,
+                multi_scale_output=False,
+                name=name + '_' + str(i + 1))
         else:
-            out = high_resolution_module(out, channels, name=name + '_' + str(i + 1))
+            out = high_resolution_module(
+                out, channels, name=name + '_' + str(i + 1))
 
     return out
 
+
 def layer1(input, name=None):
     conv = input
     for i in range(4):
-        conv = bottleneck_block(conv, num_filters=64, downsample=True if i == 0 else False,
-                                     name=name + '_' + str(i + 1))
+        conv = bottleneck_block(
+            conv,
+            num_filters=64,
+            downsample=True if i == 0 else False,
+            name=name + '_' + str(i + 1))
     return conv
 
+
 def high_resolution_net(input, num_classes):
-    
+
     channels_2 = cfg.MODEL.HRNET.STAGE2.NUM_CHANNELS
     channels_3 = cfg.MODEL.HRNET.STAGE3.NUM_CHANNELS
     channels_4 = cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS
-    
+
     num_modules_2 = cfg.MODEL.HRNET.STAGE2.NUM_MODULES
     num_modules_3 = cfg.MODEL.HRNET.STAGE3.NUM_MODULES
     num_modules_4 = cfg.MODEL.HRNET.STAGE4.NUM_MODULES
 
-    x = conv_bn_layer(input=input, filter_size=3, num_filters=64, stride=2, if_act=True, name='layer1_1')
-    x = conv_bn_layer(input=x, filter_size=3, num_filters=64, stride=2, if_act=True, name='layer1_2')
+    x = conv_bn_layer(
+        input=input,
+        filter_size=3,
+        num_filters=64,
+        stride=2,
+        if_act=True,
+        name='layer1_1')
+    x = conv_bn_layer(
+        input=x,
+        filter_size=3,
+        num_filters=64,
+        stride=2,
+        if_act=True,
+        name='layer1_2')
 
     la1 = layer1(x, name='layer2')
     tr1 = transition_layer([la1], [256], channels_2, name='tr1')
@@ -170,18 +268,21 @@ def high_resolution_net(input, num_classes):
     # upsample
     shape = st4[0].shape
     height, width = shape[-2], shape[-1]
-    st4[1] = fluid.layers.resize_bilinear(
-        st4[1], out_shape=[height, width])
-    st4[2] = fluid.layers.resize_bilinear(
-        st4[2], out_shape=[height, width])
-    st4[3] = fluid.layers.resize_bilinear(
-        st4[3], out_shape=[height, width])
+    st4[1] = fluid.layers.resize_bilinear(st4[1], out_shape=[height, width])
+    st4[2] = fluid.layers.resize_bilinear(st4[2], out_shape=[height, width])
+    st4[3] = fluid.layers.resize_bilinear(st4[3], out_shape=[height, width])
 
     out = fluid.layers.concat(st4, axis=1)
     last_channels = sum(channels_4)
 
-    out = conv_bn_layer(input=out, filter_size=1, num_filters=last_channels, stride=1, if_act=True, name='conv-2')
-    out= fluid.layers.conv2d(
+    out = conv_bn_layer(
+        input=out,
+        filter_size=1,
+        num_filters=last_channels,
+        stride=1,
+        if_act=True,
+        name='conv-2')
+    out = fluid.layers.conv2d(
         input=out,
         num_filters=num_classes,
         filter_size=1,
@@ -193,7 +294,6 @@ def high_resolution_net(input, num_classes):
 
     out = fluid.layers.resize_bilinear(out, input.shape[2:])
 
-
     return out
 
 
@@ -201,6 +301,7 @@ def hrnet(input, num_classes):
     logit = high_resolution_net(input, num_classes)
     return logit
 
+
 if __name__ == '__main__':
     image_shape = [-1, 3, 769, 769]
     image = fluid.data(name='image', shape=image_shape, dtype='float32')

pdseg/models/modeling/icnet.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/models/modeling/pspnet.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -24,6 +24,7 @@ from models.libs.model_libs import avg_pool, conv, bn
 from models.backbone.resnet import ResNet as resnet_backbone
 from utils.config import cfg
 
+
 def get_logit_interp(input, num_classes, out_shape, name="logit"):
     # 根据类别数决定最后一层卷积输出, 并插值回原始尺寸
     param_attr = fluid.ParamAttr(
@@ -33,16 +34,15 @@ def get_logit_interp(input, num_classes, out_shape, name="logit"):
         initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.01))
 
     with scope(name):
-        logit = conv(input,
-                    num_classes,
-                    filter_size=1,
-                    param_attr=param_attr,
-                    bias_attr=True,
-                    name=name+'_conv')
+        logit = conv(
+            input,
+            num_classes,
+            filter_size=1,
+            param_attr=param_attr,
+            bias_attr=True,
+            name=name + '_conv')
         logit_interp = fluid.layers.resize_bilinear(
-                    logit, 
-                    out_shape=out_shape,
-                    name=name+'_interp') 
+            logit, out_shape=out_shape, name=name + '_interp')
     return logit_interp
 
 
@@ -51,40 +51,44 @@ def psp_module(input, out_features):
     # 输入：backbone输出的特征
     # 输出：对输入进行不同尺度pooling, 卷积操作后插值回原始尺寸，并concat
     #       最后进行一个卷积及BN操作
-    
+
     cat_layers = []
-    sizes = (1,2,3,6)
+    sizes = (1, 2, 3, 6)
     for size in sizes:
         psp_name = "psp" + str(size)
         with scope(psp_name):
-            pool = fluid.layers.adaptive_pool2d(input, 
-                    pool_size=[size, size], 
-                    pool_type='avg', 
-                    name=psp_name+'_adapool')
-            data = conv(pool, out_features, 
-                    filter_size=1,
-                    bias_attr=True, 
-                    name= psp_name + '_conv')
+            pool = fluid.layers.adaptive_pool2d(
+                input,
+                pool_size=[size, size],
+                pool_type='avg',
+                name=psp_name + '_adapool')
+            data = conv(
+                pool,
+                out_features,
+                filter_size=1,
+                bias_attr=True,
+                name=psp_name + '_conv')
             data_bn = bn(data, act='relu')
-            interp = fluid.layers.resize_bilinear(data_bn, 
-                    out_shape=input.shape[2:], 
-                    name=psp_name+'_interp') 
+            interp = fluid.layers.resize_bilinear(
+                data_bn, out_shape=input.shape[2:], name=psp_name + '_interp')
         cat_layers.append(interp)
     cat_layers = [input] + cat_layers[::-1]
     cat = fluid.layers.concat(cat_layers, axis=1, name='psp_cat')
 
     psp_end_name = "psp_end"
     with scope(psp_end_name):
-        data = conv(cat, 
-                    out_features, 
-                    filter_size=3,
-                    padding=1, 
-                    bias_attr=True,
-                    name=psp_end_name)
+        data = conv(
+            cat,
+            out_features,
+            filter_size=3,
+            padding=1,
+            bias_attr=True,
+            name=psp_end_name)
         out = bn(data, act='relu')
 
     return out
 
+
 def resnet(input):
     # PSPNET backbone: resnet, 默认resnet50
     # end_points: resnet终止层数
@@ -92,14 +96,14 @@ def resnet(input):
     scale = cfg.MODEL.PSPNET.DEPTH_MULTIPLIER
     layers = cfg.MODEL.PSPNET.LAYERS
     end_points = layers - 1
-    dilation_dict = {2:2, 3:4}
+    dilation_dict = {2: 2, 3: 4}
     model = resnet_backbone(layers, scale, stem='pspnet')
-    data, _ = model.net(input, 
-                        end_points=end_points, 
-                        dilation_dict=dilation_dict)
+    data, _ = model.net(
+        input, end_points=end_points, dilation_dict=dilation_dict)
 
     return data
 
+
 def pspnet(input, num_classes):
     # Backbone: ResNet
     res = resnet(input)
@@ -109,4 +113,3 @@ def pspnet(input, num_classes):
     # 根据类别数决定最后一层卷积输出, 并插值回原始尺寸
     logit = get_logit_interp(dropout, num_classes, input.shape[2:])
     return logit
-

pdseg/models/modeling/unet.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/reader.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -71,7 +71,8 @@ class SegDataset(object):
         if self.shuffle and cfg.NUM_TRAINERS > 1:
             np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines)
             num_lines = len(self.all_lines) // cfg.NUM_TRAINERS
-            self.lines = self.all_lines[num_lines * cfg.TRAINER_ID: num_lines * (cfg.TRAINER_ID + 1)]
+            self.lines = self.all_lines[num_lines * cfg.TRAINER_ID:num_lines *
+                                        (cfg.TRAINER_ID + 1)]
             self.shuffle_seed += 1
         elif self.shuffle:
             np.random.shuffle(self.lines)
@@ -99,7 +100,8 @@ class SegDataset(object):
         if self.shuffle and cfg.NUM_TRAINERS > 1:
             np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines)
             num_lines = len(self.all_lines) // cfg.NUM_TRAINERS
-            self.lines = self.all_lines[num_lines * cfg.TRAINER_ID: num_lines * (cfg.TRAINER_ID + 1)]
+            self.lines = self.all_lines[num_lines * cfg.TRAINER_ID:num_lines *
+                                        (cfg.TRAINER_ID + 1)]
             self.shuffle_seed += 1
         elif self.shuffle:
             np.random.shuffle(self.lines)

pdseg/solver.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/tools/__init__.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

pdseg/tools/create_dataset_list.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -21,55 +21,48 @@ import warnings
 
 def parse_args():
     parser = argparse.ArgumentParser(
-        description='PaddleSeg generate file list on cityscapes or your customized dataset.')
-    parser.add_argument(
-        'dataset_root',
-        help='dataset root directory',
-        type=str
+        description=
+        'PaddleSeg generate file list on cityscapes or your customized dataset.'
     )
+    parser.add_argument('dataset_root', help='dataset root directory', type=str)
     parser.add_argument(
         '--type',
         help='dataset type: \n'
-             '- cityscapes \n'
-             '- custom(default)',
+        '- cityscapes \n'
+        '- custom(default)',
         default="custom",
-        type=str
-    )
+        type=str)
     parser.add_argument(
         '--separator',
         dest='separator',
         help='file list separator',
         default="|",
-        type=str
-    )
+        type=str)
     parser.add_argument(
         '--folder',
         help='the folder names of images and labels',
         type=str,
         nargs=2,
-        default=['images', 'annotations']
-    )
+        default=['images', 'annotations'])
     parser.add_argument(
         '--second_folder',
-        help='the second-level folder names of train set, validation set, test set',
+        help=
+        'the second-level folder names of train set, validation set, test set',
         type=str,
         nargs='*',
-        default=['train', 'val', 'test']
-    )
+        default=['train', 'val', 'test'])
     parser.add_argument(
         '--format',
         help='data format of images and labels, e.g. jpg or png.',
         type=str,
         nargs=2,
-        default=['jpg', 'png']
-    )
+        default=['jpg', 'png'])
     parser.add_argument(
         '--postfix',
         help='postfix of images or labels',
         type=str,
         nargs=2,
-        default=['', '']
-    )
+        default=['', ''])
 
     return parser.parse_args()
 
@@ -120,15 +113,17 @@ def generate_list(args):
         num_images = len(image_files)
 
         if not label_files:
-            label_dir = os.path.join(dataset_root, args.folder[1], dataset_split)
+            label_dir = os.path.join(dataset_root, args.folder[1],
+                                     dataset_split)
             warnings.warn("No labels in {} !!!".format(label_dir))
         num_label = len(label_files)
 
         if num_images != num_label and num_label > 0:
-            raise Exception("Number of images = {}    number of labels = {} \n"
-                            "Either number of images is equal to number of labels, "
-                            "or number of labels is equal to 0.\n"
-                            "Please check your dataset!".format(num_images, num_label))
+            raise Exception(
+                "Number of images = {}    number of labels = {} \n"
+                "Either number of images is equal to number of labels, "
+                "or number of labels is equal to 0.\n"
+                "Please check your dataset!".format(num_images, num_label))
 
         file_list = os.path.join(dataset_root, dataset_split + '.txt')
         with open(file_list, "w") as f:

pdseg/tools/gray2pseudo_color.py

-# -*- coding: utf-8 -*-
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 print_function
 
 import argparse
@@ -11,16 +25,12 @@ from PIL import Image
 
 def parse_args():
     parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    parser.add_argument('dir_or_file',
-                        help='input gray label directory or file list path')
-    parser.add_argument('output_dir',
-                        help='output colorful label directory')
-    parser.add_argument('--dataset_dir',
-                        help='dataset directory')
-    parser.add_argument('--file_separator',
-                        help='file list separator')
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument(
+        'dir_or_file', help='input gray label directory or file list path')
+    parser.add_argument('output_dir', help='output colorful label directory')
+    parser.add_argument('--dataset_dir', help='dataset directory')
+    parser.add_argument('--file_separator', help='file list separator')
     return parser.parse_args()
 
 

pdseg/tools/jingling2seg.py

-#!/usr/bin/env python
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 print_function
 

pdseg/tools/labelme2seg.py

-#!/usr/bin/env python
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 print_function
 

pdseg/train.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -26,9 +26,7 @@ import argparse
 import pprint
 import random
 import shutil
-import functools
 
-import paddle
 import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid import profiler
@@ -39,10 +37,10 @@ from metrics import ConfusionMatrix
 from reader import SegDataset
 from models.model_builder import build_model
 from models.model_builder import ModelPhase
-from models.model_builder import parse_shape_from_file
 from eval import evaluate
 from vis import visualize
 from utils import dist_utils
+from utils.load_model_utils import load_pretrained_weights
 
 
 def parse_args():
@@ -118,38 +116,7 @@ def parse_args():
     return parser.parse_args()
 
 
-def save_vars(executor, dirname, program=None, vars=None):
-    """
-    Temporary resolution for Win save variables compatability.
-    Will fix in PaddlePaddle v1.5.2
-    """
-
-    save_program = fluid.Program()
-    save_block = save_program.global_block()
-
-    for each_var in vars:
-        # NOTE: don't save the variable which type is RAW
-        if each_var.type == fluid.core.VarDesc.VarType.RAW:
-            continue
-        new_var = save_block.create_var(
-            name=each_var.name,
-            shape=each_var.shape,
-            dtype=each_var.dtype,
-            type=each_var.type,
-            lod_level=each_var.lod_level,
-            persistable=True)
-        file_path = os.path.join(dirname, new_var.name)
-        file_path = os.path.normpath(file_path)
-        save_block.append_op(
-            type='save',
-            inputs={'X': [new_var]},
-            outputs={},
-            attrs={'file_path': file_path})
-
-    executor.run(save_program)
-
-
-def save_checkpoint(exe, program, ckpt_name):
+def save_checkpoint(program, ckpt_name):
     """
     Save checkpoint for evaluation or resume training
     """
@@ -158,29 +125,22 @@ def save_checkpoint(exe, program, ckpt_name):
     if not os.path.isdir(ckpt_dir):
         os.makedirs(ckpt_dir)
 
-    save_vars(
-        exe,
-        ckpt_dir,
-        program,
-        vars=list(filter(fluid.io.is_persistable, program.list_vars())))
+    fluid.save(program, os.path.join(ckpt_dir, 'model'))
 
     return ckpt_dir
 
 
 def load_checkpoint(exe, program):
     """
-    Load checkpoiont from pretrained model directory for resume training
+    Load checkpoiont for resuming training
     """
-
-    print('Resume model training from:', cfg.TRAIN.RESUME_MODEL_DIR)
-    if not os.path.exists(cfg.TRAIN.RESUME_MODEL_DIR):
-        raise ValueError("TRAIN.PRETRAIN_MODEL {} not exist!".format(
-            cfg.TRAIN.RESUME_MODEL_DIR))
-
-    fluid.io.load_persistables(
-        exe, cfg.TRAIN.RESUME_MODEL_DIR, main_program=program)
-
     model_path = cfg.TRAIN.RESUME_MODEL_DIR
+    print('Resume model training from:', model_path)
+    if not os.path.exists(model_path):
+        raise ValueError(
+            "TRAIN.PRETRAIN_MODEL {} not exist!".format(model_path))
+    fluid.load(program, os.path.join(model_path, 'model'), exe)
+
     # Check is path ended by path spearator
     if model_path[-1] == os.sep:
         model_path = model_path[0:-1]
@@ -195,7 +155,6 @@ def load_checkpoint(exe, program):
     else:
         raise ValueError("Resume model path is not valid!")
     print("Model checkpoint loaded successfully!")
-
     return begin_epoch
 
 
@@ -247,8 +206,6 @@ def train(cfg):
                 yield item[0], item[1], item[2]
 
     # Get device environment
-    # places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places()
-    # place = places[0]
     gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
     place = fluid.CUDAPlace(gpu_id) if args.use_gpu else fluid.CPUPlace()
     places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places()
@@ -304,42 +261,7 @@ def train(cfg):
         begin_epoch = load_checkpoint(exe, train_prog)
     # Load pretrained model
     elif os.path.exists(cfg.TRAIN.PRETRAINED_MODEL_DIR):
-        print_info('Pretrained model dir: ', cfg.TRAIN.PRETRAINED_MODEL_DIR)
-        load_vars = []
-        load_fail_vars = []
-
-        def var_shape_matched(var, shape):
-            """
-            Check whehter persitable variable shape is match with current network
-            """
-            var_exist = os.path.exists(
-                os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
-            if var_exist:
-                var_shape = parse_shape_from_file(
-                    os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
-                return var_shape == shape
-            return False
-
-        for x in train_prog.list_vars():
-            if isinstance(x, fluid.framework.Parameter):
-                shape = tuple(fluid.global_scope().find_var(
-                    x.name).get_tensor().shape())
-                if var_shape_matched(x, shape):
-                    load_vars.append(x)
-                else:
-                    load_fail_vars.append(x)
-
-        fluid.io.load_vars(
-            exe, dirname=cfg.TRAIN.PRETRAINED_MODEL_DIR, vars=load_vars)
-        for var in load_vars:
-            print_info("Parameter[{}] loaded sucessfully!".format(var.name))
-        for var in load_fail_vars:
-            print_info(
-                "Parameter[{}] don't exist or shape does not match current network, skip"
-                " to load it.".format(var.name))
-        print_info("{}/{} pretrained parameters loaded successfully!".format(
-            len(load_vars),
-            len(load_vars) + len(load_fail_vars)))
+        load_pretrained_weights(exe, train_prog, cfg.TRAIN.PRETRAINED_MODEL_DIR)
     else:
         print_info(
             'Pretrained model dir {} not exists, training from scratch...'.
@@ -418,12 +340,9 @@ def train(cfg):
                                                   step)
                             log_writer.add_scalar('Train/mean_acc', mean_acc,
                                                   step)
-                            log_writer.add_scalar('Train/loss', avg_loss,
-                                                  step)
-                            log_writer.add_scalar('Train/lr', lr[0],
-                                                  step)
-                            log_writer.add_scalar('Train/step/sec', speed,
-                                                  step)
+                            log_writer.add_scalar('Train/loss', avg_loss, step)
+                            log_writer.add_scalar('Train/lr', lr[0], step)
+                            log_writer.add_scalar('Train/step/sec', speed, step)
                         sys.stdout.flush()
                         avg_loss = 0.0
                         cm.zero_matrix()
@@ -445,12 +364,9 @@ def train(cfg):
                         ).format(epoch, step, lr[0], avg_loss, speed,
                                  calculate_eta(all_step - step, speed)))
                         if args.use_vdl:
-                            log_writer.add_scalar('Train/loss', avg_loss,
-                                                  step)
-                            log_writer.add_scalar('Train/lr', lr[0],
-                                                  step)
-                            log_writer.add_scalar('Train/speed', speed,
-                                                  step)
+                            log_writer.add_scalar('Train/loss', avg_loss, step)
+                            log_writer.add_scalar('Train/lr', lr[0], step)
+                            log_writer.add_scalar('Train/speed', speed, step)
                         sys.stdout.flush()
                         avg_loss = 0.0
                         timer.restart()
@@ -470,7 +386,7 @@ def train(cfg):
 
         if (epoch % cfg.TRAIN.SNAPSHOT_EPOCH == 0
                 or epoch == cfg.SOLVER.NUM_EPOCHS) and cfg.TRAINER_ID == 0:
-            ckpt_dir = save_checkpoint(exe, train_prog, epoch)
+            ckpt_dir = save_checkpoint(train_prog, epoch)
 
             if args.do_eval:
                 print("Evaluation start")
@@ -480,10 +396,8 @@ def train(cfg):
                     use_gpu=args.use_gpu,
                     use_mpio=args.use_mpio)
                 if args.use_vdl:
-                    log_writer.add_scalar('Evaluate/mean_iou', mean_iou,
-                                          step)
-                    log_writer.add_scalar('Evaluate/mean_acc', mean_acc,
-                                          step)
+                    log_writer.add_scalar('Evaluate/mean_iou', mean_iou, step)
+                    log_writer.add_scalar('Evaluate/mean_acc', mean_acc, step)
 
                 if mean_iou > best_mIoU:
                     best_mIoU = mean_iou
@@ -505,7 +419,7 @@ def train(cfg):
 
     # save final model
     if cfg.TRAINER_ID == 0:
-        save_checkpoint(exe, train_prog, 'final')
+        save_checkpoint(train_prog, 'final')
 
 
 def main(args):

pdseg/utils/__init__.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.

pdseg/utils/collect.py

-#   Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,
@@ -98,7 +99,7 @@ class SegConfig(dict):
                 'DATASET.IMAGE_TYPE config error, only support `rgb`, `gray` and `rgba`'
             )
         if self.MEAN is not None:
-            self.DATASET.PADDING_VALUE = [x*255.0 for x in self.MEAN]
+            self.DATASET.PADDING_VALUE = [x * 255.0 for x in self.MEAN]
 
         if not self.TRAIN_CROP_SIZE:
             raise ValueError(
@@ -111,9 +112,12 @@ class SegConfig(dict):
             )
 
         # Ensure file list is use UTF-8 encoding
-        train_sets = codecs.open(self.DATASET.TRAIN_FILE_LIST, 'r', 'utf-8').readlines()
-        val_sets = codecs.open(self.DATASET.VAL_FILE_LIST, 'r', 'utf-8').readlines()
-        test_sets = codecs.open(self.DATASET.TEST_FILE_LIST, 'r', 'utf-8').readlines()
+        train_sets = codecs.open(self.DATASET.TRAIN_FILE_LIST, 'r',
+                                 'utf-8').readlines()
+        val_sets = codecs.open(self.DATASET.VAL_FILE_LIST, 'r',
+                               'utf-8').readlines()
+        test_sets = codecs.open(self.DATASET.TEST_FILE_LIST, 'r',
+                                'utf-8').readlines()
         self.DATASET.TRAIN_TOTAL_IMAGES = len(train_sets)
         self.DATASET.VAL_TOTAL_IMAGES = len(val_sets)
         self.DATASET.TEST_TOTAL_IMAGES = len(test_sets)
@@ -122,12 +126,13 @@ class SegConfig(dict):
                 len(self.MODEL.MULTI_LOSS_WEIGHT) != 3:
             self.MODEL.MULTI_LOSS_WEIGHT = [1.0, 0.4, 0.16]
 
-        if self.AUG.AUG_METHOD not in ['unpadding', 'stepscaling', 'rangescaling']:
+        if self.AUG.AUG_METHOD not in [
+                'unpadding', 'stepscaling', 'rangescaling'
+        ]:
             raise ValueError(
                 'AUG.AUG_METHOD config error, only support `unpadding`, `unpadding` and `rangescaling`'
             )
 
-
     def update_from_list(self, config_list):
         if len(config_list) % 2 != 0:
             raise ValueError(

pdseg/utils/config.py

-# -*- coding: utf-8 -*-
-#   Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

pdseg/utils/dist_utils.py

-#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 #Licensed under the Apache License, Version 2.0 (the "License");
 #you may not use this file except in compliance with the License.

pdseg/utils/fp16_utils.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 paddle import fluid
 
+
 def load_fp16_vars(executor, dirname, program):
     load_dirname = os.path.normpath(dirname)
 
@@ -28,4 +44,4 @@ def load_fp16_vars(executor, dirname, program):
                 'load_as_fp16': var.dtype == fluid.core.VarDesc.VarType.FP16
             })
 
-    executor.run(load_prog)
+    executor.run(load_prog)
\ No newline at end of file

pdseg/utils/load_model_utils.py

+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 os.path as osp
+
+import six
+import numpy as np
+
+
+def parse_param_file(param_file, return_shape=True):
+    from paddle.fluid.proto.framework_pb2 import VarType
+    f = open(param_file, 'rb')
+    version = np.fromstring(f.read(4), dtype='int32')
+    lod_level = np.fromstring(f.read(8), dtype='int64')
+    for i in range(int(lod_level)):
+        _size = np.fromstring(f.read(8), dtype='int64')
+        _ = f.read(_size)
+    version = np.fromstring(f.read(4), dtype='int32')
+    tensor_desc = VarType.TensorDesc()
+    tensor_desc_size = np.fromstring(f.read(4), dtype='int32')
+    tensor_desc.ParseFromString(f.read(int(tensor_desc_size)))
+    tensor_shape = tuple(tensor_desc.dims)
+    if return_shape:
+        f.close()
+        return tuple(tensor_desc.dims)
+    if tensor_desc.data_type != 5:
+        raise Exception(
+            "Unexpected data type while parse {}".format(param_file))
+    data_size = 4
+    for i in range(len(tensor_shape)):
+        data_size *= tensor_shape[i]
+    weight = np.fromstring(f.read(data_size), dtype='float32')
+    f.close()
+    return np.reshape(weight, tensor_shape)
+
+
+def load_pdparams(exe, main_prog, model_dir):
+    import paddle.fluid as fluid
+    from paddle.fluid.proto.framework_pb2 import VarType
+    from paddle.fluid.framework import Program
+
+    vars_to_load = list()
+    vars_not_load = list()
+    import pickle
+    with open(osp.join(model_dir, 'model.pdparams'), 'rb') as f:
+        params_dict = pickle.load(f) if six.PY2 else pickle.load(
+            f, encoding='latin1')
+    unused_vars = list()
+    for var in main_prog.list_vars():
+        if not isinstance(var, fluid.framework.Parameter):
+            continue
+        if var.name not in params_dict:
+            print("{} is not in saved model".format(var.name))
+            vars_not_load.append(var.name)
+            continue
+        if var.shape != params_dict[var.name].shape:
+            unused_vars.append(var.name)
+            vars_not_load.append(var.name)
+            print(
+                "[SKIP] Shape of pretrained weight {} doesn't match.(Pretrained: {}, Actual: {})"
+                .format(var.name, params_dict[var.name].shape, var.shape))
+            continue
+        vars_to_load.append(var)
+    for var_name in unused_vars:
+        del params_dict[var_name]
+    fluid.io.set_program_state(main_prog, params_dict)
+
+    if len(vars_to_load) == 0:
+        print(
+            "There is no pretrain weights loaded, maybe you should check you pretrain model!"
+        )
+    else:
+        print("There are {}/{} varaibles in {} are loaded.".format(
+            len(vars_to_load),
+            len(vars_to_load) + len(vars_not_load), model_dir))
+
+
+def load_pretrained_weights(exe, main_prog, weights_dir):
+    if not osp.exists(weights_dir):
+        raise Exception("Path {} not exists.".format(weights_dir))
+    if osp.exists(osp.join(weights_dir, "model.pdparams")):
+        return load_pdparams(exe, main_prog, weights_dir)
+    import paddle.fluid as fluid
+    vars_to_load = list()
+    vars_not_load = list()
+    for var in main_prog.list_vars():
+        if not isinstance(var, fluid.framework.Parameter):
+            continue
+        if not osp.exists(osp.join(weights_dir, var.name)):
+            print("[SKIP] Pretrained weight {}/{} doesn't exist".format(
+                weights_dir, var.name))
+            vars_not_load.append(var)
+            continue
+        pretrained_shape = parse_param_file(osp.join(weights_dir, var.name))
+        actual_shape = tuple(var.shape)
+        if pretrained_shape != actual_shape:
+            print(
+                "[SKIP] Shape of pretrained weight {}/{} doesn't match.(Pretrained: {}, Actual: {})"
+                .format(weights_dir, var.name, pretrained_shape, actual_shape))
+            vars_not_load.append(var)
+            continue
+        vars_to_load.append(var)
+
+    params_dict = fluid.io.load_program_state(
+        weights_dir, var_list=vars_to_load)
+    fluid.io.set_program_state(main_prog, params_dict)
+
+    if len(vars_to_load) == 0:
+        print(
+            "There is no pretrain weights loaded, maybe you should check you pretrain model!"
+        )
+    else:
+        print("There are {}/{} varaibles in {} are loaded.".format(
+            len(vars_to_load),
+            len(vars_to_load) + len(vars_not_load), weights_dir))

pdseg/utils/timer.py

-#   Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

pdseg/vis.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -115,7 +115,12 @@ def visualize(cfg,
 
     ckpt_dir = cfg.TEST.TEST_MODEL if not ckpt_dir else ckpt_dir
 
-    fluid.io.load_params(exe, ckpt_dir, main_program=test_prog)
+    if ckpt_dir is not None:
+        print('load test model:', ckpt_dir)
+        try:
+            fluid.load(test_prog, os.path.join(ckpt_dir, 'model'), exe)
+        except:
+            fluid.io.load_params(exe, ckpt_dir, main_program=test_prog)
 
     save_dir = vis_dir
     makedirs(save_dir)
@@ -169,18 +174,13 @@ def visualize(cfg,
                 print("VisualDL visualization epoch", epoch)
 
                 pred_mask_np = np.array(pred_mask.convert("RGB"))
-                log_writer.add_image(
-                    "Predict/{}".format(img_name),
-                    pred_mask_np,
-                    epoch)
+                log_writer.add_image("Predict/{}".format(img_name),
+                                     pred_mask_np, epoch)
                 # Original image
                 # BGR->RGB
-                img = cv2.imread(
-                    os.path.join(cfg.DATASET.DATA_DIR, img_name))[..., ::-1]
-                log_writer.add_image(
-                    "Images/{}".format(img_name),
-                    img,
-                    epoch)
+                img = cv2.imread(os.path.join(cfg.DATASET.DATA_DIR,
+                                              img_name))[..., ::-1]
+                log_writer.add_image("Images/{}".format(img_name), img, epoch)
                 # add ground truth (label) images
                 grt = grts[i]
                 if grt is not None:
@@ -189,10 +189,8 @@ def visualize(cfg,
                     grt_pil.putpalette(color_map)
                     grt_pil = grt_pil.resize((org_shape[1], org_shape[0]))
                     grt = np.array(grt_pil.convert("RGB"))
-                    log_writer.add_image(
-                        "Label/{}".format(img_name),
-                        grt,
-                        epoch)
+                    log_writer.add_image("Label/{}".format(img_name), grt,
+                                         epoch)
 
         # If in local_test mode, only visualize 5 images just for testing
         # procedure

pretrained_model/download_model.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,
@@ -42,7 +43,7 @@ model_urls = {
     "hrnet_w30_bn_imagenet":
     "https://paddleseg.bj.bcebos.com/models/hrnet_w30_imagenet.tar",
     "hrnet_w32_bn_imagenet":
-    "https://paddleseg.bj.bcebos.com/models/hrnet_w32_imagenet.tar" ,
+    "https://paddleseg.bj.bcebos.com/models/hrnet_w32_imagenet.tar",
     "hrnet_w40_bn_imagenet":
     "https://paddleseg.bj.bcebos.com/models/hrnet_w40_imagenet.tar",
     "hrnet_w44_bn_imagenet":

slim/distillation/model_builder.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

slim/distillation/train_distill.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -44,6 +44,7 @@ from model_builder import parse_shape_from_file
 from eval import evaluate
 from vis import visualize
 from utils import dist_utils
+from utils.load_model_utils import load_pretrained_weights
 
 import solver
 from paddleslim.dist.single_distiller import merge, l2_loss
@@ -116,38 +117,7 @@ def parse_args():
     return parser.parse_args()
 
 
-def save_vars(executor, dirname, program=None, vars=None):
-    """
-    Temporary resolution for Win save variables compatability.
-    Will fix in PaddlePaddle v1.5.2
-    """
-
-    save_program = fluid.Program()
-    save_block = save_program.global_block()
-
-    for each_var in vars:
-        # NOTE: don't save the variable which type is RAW
-        if each_var.type == fluid.core.VarDesc.VarType.RAW:
-            continue
-        new_var = save_block.create_var(
-            name=each_var.name,
-            shape=each_var.shape,
-            dtype=each_var.dtype,
-            type=each_var.type,
-            lod_level=each_var.lod_level,
-            persistable=True)
-        file_path = os.path.join(dirname, new_var.name)
-        file_path = os.path.normpath(file_path)
-        save_block.append_op(
-            type='save',
-            inputs={'X': [new_var]},
-            outputs={},
-            attrs={'file_path': file_path})
-
-    executor.run(save_program)
-
-
-def save_checkpoint(exe, program, ckpt_name):
+def save_checkpoint(program, ckpt_name):
     """
     Save checkpoint for evaluation or resume training
     """
@@ -156,29 +126,22 @@ def save_checkpoint(exe, program, ckpt_name):
     if not os.path.isdir(ckpt_dir):
         os.makedirs(ckpt_dir)
 
-    save_vars(
-        exe,
-        ckpt_dir,
-        program,
-        vars=list(filter(fluid.io.is_persistable, program.list_vars())))
+    fluid.save(program, os.path.join(ckpt_dir, 'model'))
 
     return ckpt_dir
 
 
 def load_checkpoint(exe, program):
     """
-    Load checkpoiont from pretrained model directory for resume training
+    Load checkpoiont for resuming training
     """
-
-    print('Resume model training from:', cfg.TRAIN.RESUME_MODEL_DIR)
-    if not os.path.exists(cfg.TRAIN.RESUME_MODEL_DIR):
-        raise ValueError("TRAIN.PRETRAIN_MODEL {} not exist!".format(
-            cfg.TRAIN.RESUME_MODEL_DIR))
-
-    fluid.io.load_persistables(
-        exe, cfg.TRAIN.RESUME_MODEL_DIR, main_program=program)
-
     model_path = cfg.TRAIN.RESUME_MODEL_DIR
+    print('Resume model training from:', model_path)
+    if not os.path.exists(model_path):
+        raise ValueError(
+            "TRAIN.PRETRAIN_MODEL {} not exist!".format(model_path))
+    fluid.load(program, os.path.join(model_path, 'model'), exe)
+
     # Check is path ended by path spearator
     if model_path[-1] == os.sep:
         model_path = model_path[0:-1]
@@ -193,7 +156,6 @@ def load_checkpoint(exe, program):
     else:
         raise ValueError("Resume model path is not valid!")
     print("Model checkpoint loaded successfully!")
-
     return begin_epoch
 
 
@@ -289,7 +251,11 @@ def train(cfg):
     ckpt_dir = cfg.SLIM.KNOWLEDGE_DISTILL_TEACHER_MODEL_DIR
     assert ckpt_dir is not None
     print('load teacher model:', ckpt_dir)
-    fluid.io.load_params(exe, ckpt_dir, main_program=teacher_program)
+    if os.path.exists(ckpt_dir):
+        try:
+            fluid.load(teacher_program, os.path.join(ckpt_dir, 'model'), exe)
+        except:
+            fluid.io.load_params(exe, ckpt_dir, main_program=teacher_program)
 
     # cfg = load_config(FLAGS.config)
     cfg.update_from_file(args.cfg_file)
@@ -355,42 +321,8 @@ def train(cfg):
         begin_epoch = load_checkpoint(exe, fluid.default_main_program())
     # Load pretrained model
     elif os.path.exists(cfg.TRAIN.PRETRAINED_MODEL_DIR):
-        print_info('Pretrained model dir: ', cfg.TRAIN.PRETRAINED_MODEL_DIR)
-        load_vars = []
-        load_fail_vars = []
-
-        def var_shape_matched(var, shape):
-            """
-            Check whehter persitable variable shape is match with current network
-            """
-            var_exist = os.path.exists(
-                os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
-            if var_exist:
-                var_shape = parse_shape_from_file(
-                    os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
-                return var_shape == shape
-            return False
-
-        for x in fluid.default_main_program().list_vars():
-            if isinstance(x, fluid.framework.Parameter):
-                shape = tuple(fluid.global_scope().find_var(
-                    x.name).get_tensor().shape())
-                if var_shape_matched(x, shape):
-                    load_vars.append(x)
-                else:
-                    load_fail_vars.append(x)
-
-        fluid.io.load_vars(
-            exe, dirname=cfg.TRAIN.PRETRAINED_MODEL_DIR, vars=load_vars)
-        for var in load_vars:
-            print_info("Parameter[{}] loaded sucessfully!".format(var.name))
-        for var in load_fail_vars:
-            print_info(
-                "Parameter[{}] don't exist or shape does not match current network, skip"
-                " to load it.".format(var.name))
-        print_info("{}/{} pretrained parameters loaded successfully!".format(
-            len(load_vars),
-            len(load_vars) + len(load_fail_vars)))
+        load_pretrained_weights(exe, fluid.default_main_program(),
+                                cfg.TRAIN.PRETRAINED_MODEL_DIR)
     else:
         print_info(
             'Pretrained model dir {} not exists, training from scratch...'.
@@ -475,12 +407,9 @@ def train(cfg):
                                                   step)
                             log_writer.add_scalar('Train/mean_acc', mean_acc,
                                                   step)
-                            log_writer.add_scalar('Train/loss', avg_loss,
-                                                  step)
-                            log_writer.add_scalar('Train/lr', lr[0],
-                                                  step)
-                            log_writer.add_scalar('Train/step/sec', speed,
-                                                  step)
+                            log_writer.add_scalar('Train/loss', avg_loss, step)
+                            log_writer.add_scalar('Train/lr', lr[0], step)
+                            log_writer.add_scalar('Train/step/sec', speed, step)
                         sys.stdout.flush()
                         avg_loss = 0.0
                         cm.zero_matrix()
@@ -503,16 +432,13 @@ def train(cfg):
                         speed = args.log_steps / timer.elapsed_time()
                         print((
                             "epoch={} step={} lr={:.5f} loss={:.4f} teacher loss={:.4f} distill loss={:.4f} step/sec={:.3f} | ETA {}"
-                        ).format(epoch, step, lr[0], avg_loss,
-                                 avg_t_loss, avg_d_loss, speed,
+                        ).format(epoch, step, lr[0], avg_loss, avg_t_loss,
+                                 avg_d_loss, speed,
                                  calculate_eta(all_step - step, speed)))
                         if args.use_vdl:
-                            log_writer.add_scalar('Train/loss', avg_loss,
-                                                  step)
-                            log_writer.add_scalar('Train/lr', lr[0],
-                                                  step)
-                            log_writer.add_scalar('Train/speed', speed,
-                                                  step)
+                            log_writer.add_scalar('Train/loss', avg_loss, step)
+                            log_writer.add_scalar('Train/lr', lr[0], step)
+                            log_writer.add_scalar('Train/speed', speed, step)
                         sys.stdout.flush()
                         avg_loss = 0.0
                         avg_t_loss = 0.0
@@ -527,7 +453,7 @@ def train(cfg):
 
         if (epoch % cfg.TRAIN.SNAPSHOT_EPOCH == 0
                 or epoch == cfg.SOLVER.NUM_EPOCHS) and cfg.TRAINER_ID == 0:
-            ckpt_dir = save_checkpoint(exe, fluid.default_main_program(), epoch)
+            ckpt_dir = save_checkpoint(fluid.default_main_program(), epoch)
 
             if args.do_eval:
                 print("Evaluation start")
@@ -537,10 +463,8 @@ def train(cfg):
                     use_gpu=args.use_gpu,
                     use_mpio=args.use_mpio)
                 if args.use_vdl:
-                    log_writer.add_scalar('Evaluate/mean_iou', mean_iou,
-                                          step)
-                    log_writer.add_scalar('Evaluate/mean_acc', mean_acc,
-                                          step)
+                    log_writer.add_scalar('Evaluate/mean_iou', mean_iou, step)
+                    log_writer.add_scalar('Evaluate/mean_acc', mean_acc, step)
 
                 if mean_iou > best_mIoU:
                     best_mIoU = mean_iou
@@ -560,11 +484,11 @@ def train(cfg):
                     ckpt_dir=ckpt_dir,
                     log_writer=log_writer)
         if cfg.TRAINER_ID == 0:
-            ckpt_dir = save_checkpoint(exe, fluid.default_main_program(), epoch)
+            ckpt_dir = save_checkpoint(fluid.default_main_program(), epoch)
 
     # save final model
     if cfg.TRAINER_ID == 0:
-        save_checkpoint(exe, fluid.default_main_program(), 'final')
+        save_checkpoint(fluid.default_main_program(), 'final')
 
 
 def main(args):

slim/nas/deeplab.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -27,6 +27,7 @@ from models.libs.model_libs import separate_conv
 from models.backbone.mobilenet_v2 import MobileNetV2 as mobilenet_backbone
 from models.backbone.xception import Xception as xception_backbone
 
+
 def encoder(input):
     # 编码器配置，采用ASPP架构，pooling + 1x1_conv + 三个不同尺度的空洞卷积并行, concat后1x1conv
     # ASPP_WITH_SEP_CONV：默认为真，使用depthwise可分离卷积，否则使用普通卷积
@@ -47,8 +48,7 @@ def encoder(input):
     with scope('encoder'):
         channel = 256
         with scope("image_pool"):
-            image_avg = fluid.layers.reduce_mean(
-                input, [2, 3], keep_dim=True)
+            image_avg = fluid.layers.reduce_mean(input, [2, 3], keep_dim=True)
             image_avg = bn_relu(
                 conv(
                     image_avg,
@@ -191,7 +191,10 @@ def nas_backbone(input, arch):
     end_points = 8
     decode_point = 3
     data, decode_shortcuts = arch(
-        input, end_points=end_points, return_block=decode_point, output_stride=16)
+        input,
+        end_points=end_points,
+        return_block=decode_point,
+        output_stride=16)
     decode_shortcut = decode_shortcuts[decode_point]
     return data, decode_shortcut
 

slim/nas/eval_nas.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -123,7 +123,10 @@ def evaluate(cfg, ckpt_dir=None, use_gpu=False, use_mpio=False, **kwargs):
 
     if ckpt_dir is not None:
         print('load test model:', ckpt_dir)
-        fluid.io.load_params(exe, ckpt_dir, main_program=test_prog)
+        try:
+            fluid.load(test_prog, os.path.join(ckpt_dir, 'model'), exe)
+        except:
+            fluid.io.load_params(exe, ckpt_dir, main_program=test_prog)
 
     # Use streaming confusion matrix to calculate mean_iou
     np.set_printoptions(

slim/nas/mobilenetv2_search_space.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,
@@ -31,7 +32,7 @@ __all__ = ["MobileNetV2SpaceSeg"]
 class MobileNetV2SpaceSeg(SearchSpaceBase):
     def __init__(self, input_size, output_size, block_num, block_mask=None):
         super(MobileNetV2SpaceSeg, self).__init__(input_size, output_size,
-                                               block_num, block_mask)
+                                                  block_num, block_mask)
         # self.head_num means the first convolution channel
         self.head_num = np.array([3, 4, 8, 12, 16, 24, 32])  #7
         # self.filter_num1 ~ self.filter_num6 means following convlution channel
@@ -48,7 +49,7 @@ class MobileNetV2SpaceSeg(SearchSpaceBase):
         self.k_size = np.array([3, 5])  #2
         # self.multiply means expansion_factor of each _inverted_residual_unit
         self.multiply = np.array([1, 2, 3, 4, 6])  #5
-        # self.repeat means repeat_num _inverted_residual_unit in each _invresi_blocks 
+        # self.repeat means repeat_num _inverted_residual_unit in each _invresi_blocks
         self.repeat = np.array([1, 2, 3, 4, 5, 6])  #6
 
     def init_tokens(self):
@@ -72,7 +73,7 @@ class MobileNetV2SpaceSeg(SearchSpaceBase):
 
     def range_table(self):
         """
-        Get range table of current search space, constrains the range of tokens. 
+        Get range table of current search space, constrains the range of tokens.
         """
         # head_num + 6 * [multiple(expansion_factor), filter_num, repeat, kernel_size]
         # yapf: disable
@@ -95,8 +96,8 @@ class MobileNetV2SpaceSeg(SearchSpaceBase):
             tokens = self.init_tokens()
 
         self.bottleneck_params_list = []
-        self.bottleneck_params_list.append(
-            (1, self.head_num[tokens[0]], 1, 1, 3))
+        self.bottleneck_params_list.append((1, self.head_num[tokens[0]], 1, 1,
+                                            3))
         self.bottleneck_params_list.append(
             (self.multiply[tokens[1]], self.filter_num1[tokens[2]],
              self.repeat[tokens[3]], 2, self.k_size[tokens[4]]))
@@ -150,7 +151,7 @@ class MobileNetV2SpaceSeg(SearchSpaceBase):
                         return (True if count == points else False)
 
             #conv1
-            # all padding is 'SAME' in the conv2d, can compute the actual padding automatic. 
+            # all padding is 'SAME' in the conv2d, can compute the actual padding automatic.
             input = conv_bn_layer(
                 input,
                 num_filters=int(32 * self.scale),

slim/nas/model_builder.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

slim/nas/train_nas.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -47,6 +47,7 @@ from model_builder import parse_shape_from_file
 from eval_nas import evaluate
 from vis import visualize
 from utils import dist_utils
+from utils.load_model_utils import load_pretrained_weights
 
 from mobilenetv2_search_space import MobileNetV2SpaceSeg
 from paddleslim.nas.search_space.search_space_factory import SearchSpaceFactory
@@ -116,38 +117,7 @@ def parse_args():
     return parser.parse_args()
 
 
-def save_vars(executor, dirname, program=None, vars=None):
-    """
-    Temporary resolution for Win save variables compatability.
-    Will fix in PaddlePaddle v1.5.2
-    """
-
-    save_program = fluid.Program()
-    save_block = save_program.global_block()
-
-    for each_var in vars:
-        # NOTE: don't save the variable which type is RAW
-        if each_var.type == fluid.core.VarDesc.VarType.RAW:
-            continue
-        new_var = save_block.create_var(
-            name=each_var.name,
-            shape=each_var.shape,
-            dtype=each_var.dtype,
-            type=each_var.type,
-            lod_level=each_var.lod_level,
-            persistable=True)
-        file_path = os.path.join(dirname, new_var.name)
-        file_path = os.path.normpath(file_path)
-        save_block.append_op(
-            type='save',
-            inputs={'X': [new_var]},
-            outputs={},
-            attrs={'file_path': file_path})
-
-    executor.run(save_program)
-
-
-def save_checkpoint(exe, program, ckpt_name):
+def save_checkpoint(program, ckpt_name):
     """
     Save checkpoint for evaluation or resume training
     """
@@ -156,29 +126,22 @@ def save_checkpoint(exe, program, ckpt_name):
     if not os.path.isdir(ckpt_dir):
         os.makedirs(ckpt_dir)
 
-    save_vars(
-        exe,
-        ckpt_dir,
-        program,
-        vars=list(filter(fluid.io.is_persistable, program.list_vars())))
+    fluid.save(program, os.path.join(ckpt_dir, 'model'))
 
     return ckpt_dir
 
 
 def load_checkpoint(exe, program):
     """
-    Load checkpoiont from pretrained model directory for resume training
+    Load checkpoiont for resuming training
     """
-
-    print('Resume model training from:', cfg.TRAIN.RESUME_MODEL_DIR)
-    if not os.path.exists(cfg.TRAIN.RESUME_MODEL_DIR):
-        raise ValueError("TRAIN.PRETRAIN_MODEL {} not exist!".format(
-            cfg.TRAIN.RESUME_MODEL_DIR))
-
-    fluid.io.load_persistables(
-        exe, cfg.TRAIN.RESUME_MODEL_DIR, main_program=program)
-
     model_path = cfg.TRAIN.RESUME_MODEL_DIR
+    print('Resume model training from:', model_path)
+    if not os.path.exists(model_path):
+        raise ValueError(
+            "TRAIN.PRETRAIN_MODEL {} not exist!".format(model_path))
+    fluid.load(program, os.path.join(model_path, 'model'), exe)
+
     # Check is path ended by path spearator
     if model_path[-1] == os.sep:
         model_path = model_path[0:-1]
@@ -193,7 +156,6 @@ def load_checkpoint(exe, program):
     else:
         raise ValueError("Resume model path is not valid!")
     print("Model checkpoint loaded successfully!")
-
     return begin_epoch
 
 
@@ -245,8 +207,6 @@ def train(cfg):
                 yield item[0], item[1], item[2]
 
     # Get device environment
-    # places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places()
-    # place = places[0]
     gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
     place = fluid.CUDAPlace(gpu_id) if args.use_gpu else fluid.CPUPlace()
     places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places()
@@ -326,43 +286,8 @@ def train(cfg):
             begin_epoch = load_checkpoint(exe, train_prog)
         # Load pretrained model
         elif os.path.exists(cfg.TRAIN.PRETRAINED_MODEL_DIR):
-            print_info('Pretrained model dir: ', cfg.TRAIN.PRETRAINED_MODEL_DIR)
-            load_vars = []
-            load_fail_vars = []
-
-            def var_shape_matched(var, shape):
-                """
-                Check whehter persitable variable shape is match with current network
-                """
-                var_exist = os.path.exists(
-                    os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
-                if var_exist:
-                    var_shape = parse_shape_from_file(
-                        os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
-                    return var_shape == shape
-                return False
-
-            for x in train_prog.list_vars():
-                if isinstance(x, fluid.framework.Parameter):
-                    shape = tuple(fluid.global_scope().find_var(
-                        x.name).get_tensor().shape())
-                    if var_shape_matched(x, shape):
-                        load_vars.append(x)
-                    else:
-                        load_fail_vars.append(x)
-
-            fluid.io.load_vars(
-                exe, dirname=cfg.TRAIN.PRETRAINED_MODEL_DIR, vars=load_vars)
-            for var in load_vars:
-                print_info("Parameter[{}] loaded sucessfully!".format(var.name))
-            for var in load_fail_vars:
-                print_info(
-                    "Parameter[{}] don't exist or shape does not match current network, skip"
-                    " to load it.".format(var.name))
-            print_info(
-                "{}/{} pretrained parameters loaded successfully!".format(
-                    len(load_vars),
-                    len(load_vars) + len(load_fail_vars)))
+            load_pretrained_weights(exe, train_prog,
+                                    cfg.TRAIN.PRETRAINED_MODEL_DIR)
         else:
             print_info(
                 'Pretrained model dir {} not exists, training from scratch...'.
@@ -419,8 +344,7 @@ def train(cfg):
                 except Exception as e:
                     print(e)
             if epoch > cfg.SLIM.NAS_START_EVAL_EPOCH:
-                ckpt_dir = save_checkpoint(exe, train_prog,
-                                           '{}_tmp'.format(port))
+                ckpt_dir = save_checkpoint(train_prog, '{}_tmp'.format(port))
                 _, mean_iou, _, mean_acc = evaluate(
                     cfg=cfg,
                     arch=arch,

slim/prune/eval_prune.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

slim/prune/train_prune.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -46,6 +46,7 @@ from models.model_builder import parse_shape_from_file
 from eval_prune import evaluate
 from vis import visualize
 from utils import dist_utils
+from utils.load_model_utils import load_pretrained_weights
 
 from paddleslim.prune import Pruner, save_model
 from paddleslim.analysis import flops
@@ -285,42 +286,7 @@ def train(cfg):
         begin_epoch = load_checkpoint(exe, train_prog)
     # Load pretrained model
     elif os.path.exists(cfg.TRAIN.PRETRAINED_MODEL_DIR):
-        print_info('Pretrained model dir: ', cfg.TRAIN.PRETRAINED_MODEL_DIR)
-        load_vars = []
-        load_fail_vars = []
-
-        def var_shape_matched(var, shape):
-            """
-            Check whehter persitable variable shape is match with current network
-            """
-            var_exist = os.path.exists(
-                os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
-            if var_exist:
-                var_shape = parse_shape_from_file(
-                    os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
-                return var_shape == shape
-            return False
-
-        for x in train_prog.list_vars():
-            if isinstance(x, fluid.framework.Parameter):
-                shape = tuple(fluid.global_scope().find_var(
-                    x.name).get_tensor().shape())
-                if var_shape_matched(x, shape):
-                    load_vars.append(x)
-                else:
-                    load_fail_vars.append(x)
-
-        fluid.io.load_vars(
-            exe, dirname=cfg.TRAIN.PRETRAINED_MODEL_DIR, vars=load_vars)
-        for var in load_vars:
-            print_info("Parameter[{}] loaded sucessfully!".format(var.name))
-        for var in load_fail_vars:
-            print_info(
-                "Parameter[{}] don't exist or shape does not match current network, skip"
-                " to load it.".format(var.name))
-        print_info("{}/{} pretrained parameters loaded successfully!".format(
-            len(load_vars),
-            len(load_vars) + len(load_fail_vars)))
+        load_pretrained_weights(exe, train_prog, cfg.TRAIN.PRETRAINED_MODEL_DIR)
     else:
         print_info(
             'Pretrained model dir {} not exists, training from scratch...'.
@@ -409,12 +375,9 @@ def train(cfg):
                                                   step)
                             log_writer.add_scalar('Train/mean_acc', mean_acc,
                                                   step)
-                            log_writer.add_scalar('Train/loss', avg_loss,
-                                                  step)
-                            log_writer.add_scalar('Train/lr', lr[0],
-                                                  step)
-                            log_writer.add_scalar('Train/step/sec', speed,
-                                                  step)
+                            log_writer.add_scalar('Train/loss', avg_loss, step)
+                            log_writer.add_scalar('Train/lr', lr[0], step)
+                            log_writer.add_scalar('Train/step/sec', speed, step)
                         sys.stdout.flush()
                         avg_loss = 0.0
                         cm.zero_matrix()
@@ -436,12 +399,9 @@ def train(cfg):
                         ).format(epoch, step, lr[0], avg_loss, speed,
                                  calculate_eta(all_step - step, speed)))
                         if args.use_vdl:
-                            log_writer.add_scalar('Train/loss', avg_loss,
-                                                  step)
-                            log_writer.add_scalar('Train/lr', lr[0],
-                                                  step)
-                            log_writer.add_scalar('Train/speed', speed,
-                                                  step)
+                            log_writer.add_scalar('Train/loss', avg_loss, step)
+                            log_writer.add_scalar('Train/lr', lr[0], step)
+                            log_writer.add_scalar('Train/speed', speed, step)
                         sys.stdout.flush()
                         avg_loss = 0.0
                         timer.restart()
@@ -464,10 +424,8 @@ def train(cfg):
                     use_gpu=args.use_gpu,
                     use_mpio=args.use_mpio)
                 if args.use_vdl:
-                    log_writer.add_scalar('Evaluate/mean_iou', mean_iou,
-                                          step)
-                    log_writer.add_scalar('Evaluate/mean_acc', mean_acc,
-                                          step)
+                    log_writer.add_scalar('Evaluate/mean_iou', mean_iou, step)
+                    log_writer.add_scalar('Evaluate/mean_acc', mean_acc, step)
 
             # Use VisualDL to visualize results
             if args.use_vdl and cfg.DATASET.VIS_FILE_LIST is not None:

slim/quantization/eval_quant.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

slim/quantization/export_model.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

slim/quantization/train_quant.py

 # coding: utf8
-# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -40,7 +40,8 @@ from models.model_builder import parse_shape_from_file
 from eval_quant import evaluate
 from vis import visualize
 from utils import dist_utils
-from train import save_vars, save_checkpoint, load_checkpoint, update_best_model, print_info
+from utils.load_model_utils import load_pretrained_weights
+from train import update_best_model, print_info
 
 from paddleslim.quant import quant_aware
 
@@ -103,6 +104,55 @@ def parse_args():
     return parser.parse_args()
 
 
+def save_checkpoint(exe, program, ckpt_name):
+    """
+    Save checkpoint for evaluation or resume training
+    """
+    ckpt_dir = os.path.join(cfg.TRAIN.MODEL_SAVE_DIR, str(ckpt_name))
+    print("Save model checkpoint to {}".format(ckpt_dir))
+    if not os.path.isdir(ckpt_dir):
+        os.makedirs(ckpt_dir)
+
+    fluid.io.save_vars(
+        exe,
+        ckpt_dir,
+        program,
+        vars=list(filter(fluid.io.is_persistable, program.list_vars())))
+    return ckpt_dir
+
+
+def load_checkpoint(exe, program):
+    """
+    Load checkpoiont from pretrained model directory for resume training
+    """
+
+    print('Resume model training from:', cfg.TRAIN.RESUME_MODEL_DIR)
+    if not os.path.exists(cfg.TRAIN.RESUME_MODEL_DIR):
+        raise ValueError("TRAIN.PRETRAIN_MODEL {} not exist!".format(
+            cfg.TRAIN.RESUME_MODEL_DIR))
+
+    fluid.io.load_persistables(
+        exe, cfg.TRAIN.RESUME_MODEL_DIR, main_program=program)
+
+    model_path = cfg.TRAIN.RESUME_MODEL_DIR
+    # Check is path ended by path spearator
+    if model_path[-1] == os.sep:
+        model_path = model_path[0:-1]
+    epoch_name = os.path.basename(model_path)
+    # If resume model is final model
+    if epoch_name == 'final':
+        begin_epoch = cfg.SOLVER.NUM_EPOCHS
+    # If resume model path is end of digit, restore epoch status
+    elif epoch_name.isdigit():
+        epoch = int(epoch_name)
+        begin_epoch = epoch + 1
+    else:
+        raise ValueError("Resume model path is not valid!")
+    print("Model checkpoint loaded successfully!")
+
+    return begin_epoch
+
+
 def train_quant(cfg):
     startup_prog = fluid.Program()
     train_prog = fluid.Program()
@@ -182,42 +232,7 @@ def train_quant(cfg):
         begin_epoch = load_checkpoint(exe, train_prog)
     # Load pretrained model
     elif os.path.exists(cfg.TRAIN.PRETRAINED_MODEL_DIR):
-        print_info('Pretrained model dir: ', cfg.TRAIN.PRETRAINED_MODEL_DIR)
-        load_vars = []
-        load_fail_vars = []
-
-        def var_shape_matched(var, shape):
-            """
-            Check whehter persitable variable shape is match with current network
-            """
-            var_exist = os.path.exists(
-                os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
-            if var_exist:
-                var_shape = parse_shape_from_file(
-                    os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
-                return var_shape == shape
-            return False
-
-        for x in train_prog.list_vars():
-            if isinstance(x, fluid.framework.Parameter):
-                shape = tuple(fluid.global_scope().find_var(
-                    x.name).get_tensor().shape())
-                if var_shape_matched(x, shape):
-                    load_vars.append(x)
-                else:
-                    load_fail_vars.append(x)
-
-        fluid.io.load_vars(
-            exe, dirname=cfg.TRAIN.PRETRAINED_MODEL_DIR, vars=load_vars)
-        for var in load_vars:
-            print_info("Parameter[{}] loaded sucessfully!".format(var.name))
-        for var in load_fail_vars:
-            print_info(
-                "Parameter[{}] don't exist or shape does not match current network, skip"
-                " to load it.".format(var.name))
-        print_info("{}/{} pretrained parameters loaded successfully!".format(
-            len(load_vars),
-            len(load_vars) + len(load_fail_vars)))
+        load_pretrained_weights(exe, train_prog, cfg.TRAIN.PRETRAINED_MODEL_DIR)
     else:
         print_info(
             'Pretrained model dir {} not exists, training from scratch...'.

test/local_test_cityscapes.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

test/local_test_pet.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,

test/test_utils.py

-# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding: utf8
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
 #
-# Licensed under the Apache License, Version 2.0 (the "License"
+# 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
+#    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,