refine code and doc.

fluid/PaddleCV/yolov3/.gitignore

-*.pyc
-*.swp
 *.log
 *.json
 *.jpg
 *.png
 output/
-test/
 checkpoints/
 weights/
-!weights/*.py
 !weights/*.sh
 dataset/coco/
 log*

fluid/PaddleCV/yolov3/README.md

@@ -23,7 +23,7 @@ Running sample code in this directory requires PaddelPaddle Fluid v.1.4 and late
 YOLOv3 detection principle
 </p>
 
-YOLOv3 divides the input image in to S\*S grids and predict B bounding boxes in each grid, predictions of boxes include Location(x, y, w, h), Confidence Score and probabilities of C classes, therefore YOLOv3 output layer has S\*S\*B\*(5 + C) channels. YOLOv3 loss consist of three parts: location loss, IoU loss and classification loss.
+YOLOv3 divides the input image in to S\*S grids and predict B bounding boxes in each grid, predictions of boxes include Location(x, y, w, h), Confidence Score and probabilities of C classes, therefore YOLOv3 output layer has S\*S\*B\*(5 + C) channels. YOLOv3 loss consists of three parts: location loss, confidence loss and classification loss.
 The bone network of YOLOv3 is darknet53, the structure of YOLOv3 is as follow:
 <p align="center">
 <img src="image/YOLOv3_structure.jpg" height=400 width=400 hspace='10'/> <br />
@@ -95,7 +95,7 @@ To train the model, [cocoapi](https://github.com/cocodataset/cocoapi) is needed.
 **training strategy:**
 
 *  Use momentum optimizer with momentum=0.9.
-*  In first 1000 iteration, the learning rate increases linearly from 0.0 to 0.01. Then lr is decayed at 450000, 500000 iteration with multiplier 0.1, 0.1. The maximum iteration is 500000.
+*  In first 1000 iteration, the learning rate increases linearly from 0.0 to 0.01. Then lr is decayed at 450000, 500000 iteration with multiplier 0.1, 0.01. The maximum iteration is 500000.
 
 Training result is shown as below：
 <p align="center">

fluid/PaddleCV/yolov3/README_cn.md

@@ -23,7 +23,7 @@
 YOLOv3检测原理
 </p>
 
-YOLOv3将输入图像分成S\*S个格子，每个格子预测B个bounding box，每个bounding box预测内容包括: Location(x, y, w, h)、Confidence Score和C个类别的概率，因此YOLOv3输出层的channel数为S\*S\*B\*(5 + C)。YOLOv3的loss函数也有三部分组成：坐标误差，IOU误差和分类误差。
+YOLOv3将输入图像分成S\*S个格子，每个格子预测B个bounding box，每个bounding box预测内容包括: Location(x, y, w, h)、Confidence Score和C个类别的概率，因此YOLOv3输出层的channel数为S\*S\*B\*(5 + C)。YOLOv3的loss函数也有三部分组成：Location误差，Confidence误差和分类误差。
 
 YOLOv3的网络结构如下图所示:
 <p align="center">

fluid/PaddleCV/yolov3/box_utils.py

-# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

fluid/PaddleCV/yolov3/config.py

-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#  Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
 #Licensed under the Apache License, Version 2.0 (the "License");
 #you may not use this file except in compliance with the License.
 #You may obtain a copy of the License at

fluid/PaddleCV/yolov3/edict.py

-# Copyright (c) 2018 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.

fluid/PaddleCV/yolov3/eval.py

-#  Copyright (c) 2018 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.

fluid/PaddleCV/yolov3/image_utils.py

-# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

fluid/PaddleCV/yolov3/infer.py

+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
 import os
 import time
 import numpy as np

fluid/PaddleCV/yolov3/learning_rate.py

-#  Copyright (c) 2018 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.

fluid/PaddleCV/yolov3/models/yolov3.py

-    
-#  Copyright (c) 2018 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.
@@ -68,11 +67,40 @@ class YOLOv3(object):
         self.ignore_thresh = .7
         self.class_num = 80
 
-    def build_model(self):
-
-        self.img_height = cfg.input_size
-        self.img_width = cfg.input_size
+    def build_input(self):
+        self.image_shape = [3, cfg.input_size, cfg.input_size]
+        if self.use_pyreader and self.is_train:
+            self.py_reader = fluid.layers.py_reader(
+                capacity=64,
+                shapes = [[-1] + self.image_shape, [-1, cfg.max_box_num, 4], [-1, cfg.max_box_num], [-1, cfg.max_box_num]],
+                lod_levels=[0, 0, 0, 0],
+                dtypes=['float32'] * 2 + ['int32'] + ['float32'],
+                use_double_buffer=True)
+            self.image, self.gtbox, self.gtlabel, self.gtscore = fluid.layers.read_file(self.py_reader)
+        else:
+            self.image = fluid.layers.data(
+                    name='image', shape=self.image_shape, dtype='float32'
+                    )
+            self.gtbox = fluid.layers.data(
+                    name='gtbox', shape=[cfg.max_box_num, 4], dtype='float32'
+                    )
+            self.gtlabel = fluid.layers.data(
+                    name='gtlabel', shape=[cfg.max_box_num], dtype='int32'
+                    )
+            self.gtscore = fluid.layers.data(
+                    name='gtscore', shape=[cfg.max_box_num], dtype='float32'
+                    )
+            self.im_shape = fluid.layers.data(
+                    name="im_shape", shape=[2], dtype='int32')
+            self.im_id = fluid.layers.data(
+                    name="im_id", shape=[1], dtype='int32')
+    
+    def feeds(self):
+        if not self.is_train:
+            return [self.image, self.im_id, self.im_shape]
+        return [self.image, self.gtbox, self.gtlabel, self.gtscore]
 
+    def build_model(self):
         self.build_input()
 
         self.outputs = []
@@ -172,36 +200,3 @@ class YOLOv3(object):
                 background_label=-1,
                 name="multiclass_nms")
 
-    def build_input(self):
-        self.image_shape = [3, self.img_height, self.img_width]
-        if self.use_pyreader and self.is_train:
-            self.py_reader = fluid.layers.py_reader(
-                capacity=64,
-                shapes = [[-1] + self.image_shape, [-1, cfg.max_box_num, 4], [-1, cfg.max_box_num], [-1, cfg.max_box_num]],
-                lod_levels=[0, 0, 0, 0],
-                dtypes=['float32'] * 2 + ['int32'] + ['float32'],
-                use_double_buffer=True)
-            self.image, self.gtbox, self.gtlabel, self.gtscore = fluid.layers.read_file(self.py_reader)
-        else:
-            self.image = fluid.layers.data(
-                    name='image', shape=self.image_shape, dtype='float32'
-                    )
-            self.gtbox = fluid.layers.data(
-                    name='gtbox', shape=[cfg.max_box_num, 4], dtype='float32'
-                    )
-            self.gtlabel = fluid.layers.data(
-                    name='gtlabel', shape=[cfg.max_box_num], dtype='int32'
-                    )
-            self.gtscore = fluid.layers.data(
-                    name='gtscore', shape=[cfg.max_box_num], dtype='float32'
-                    )
-            self.im_shape = fluid.layers.data(
-                    name="im_shape", shape=[2], dtype='int32')
-            self.im_id = fluid.layers.data(
-                    name="im_id", shape=[1], dtype='int32')
-    
-    def feeds(self):
-        if not self.is_train:
-            return [self.image, self.im_id, self.im_shape]
-        return [self.image, self.gtbox, self.gtlabel, self.gtscore]
-

fluid/PaddleCV/yolov3/profile.py

-#  Copyright (c) 2018 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.

fluid/PaddleCV/yolov3/reader.py

-# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

fluid/PaddleCV/yolov3/train.py

-#  Copyright (c) 2018 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.

fluid/PaddleCV/yolov3/utility.py

-#  Copyright (c) 2018 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.