add yolov3 save_inference_model example and refine link (#2369)

* add yolov3 save_inference_model example

* add save_inference_model in infer.py

* refine link

PaddleCV/yolov3/README.md

@@ -74,7 +74,7 @@ dataset/coco/
 
 **自定义数据集：**
 
-用户可使用自定义的数据集，我们推荐自定义数据集使用COCO数据集格式的标注，并可通过设置`--data_dir`或修改[reader.py](https://github.com/PaddlePaddle/models/blob/623698ef30cc2f7879e47621678292254d6af51e/PaddleCV/yolov3/reader.py#L39)指定数据集路径。使用COCO数据集格式标注时，目录结构可参考上述COCO数据集目录结构。
+用户可使用自定义的数据集，我们推荐自定义数据集使用COCO数据集格式的标注，并可通过设置`--data_dir`或修改[reader.py](./reader.py#L39)指定数据集路径。使用COCO数据集格式标注时，目录结构可参考上述COCO数据集目录结构。
 
 ### 模型训练
 
@@ -198,7 +198,7 @@ YOLOv3 预测可视化
 
 ### 服务部署
 
-进行YOLOv3的服务部署，用户可以在[eval.py](https://github.com/PaddlePaddle/models/blob/623698ef30cc2f7879e47621678292254d6af51e/PaddleCV/yolov3/eval.py#L58)中保存可部署的推断模型，该模型可以用Paddle预测库加载和部署，参考[Paddle预测库](http://paddlepaddle.org/documentation/docs/zh/1.4/advanced_usage/deploy/index_cn.html)
+进行YOLOv3的服务部署，用户可以在[eval.py](./eval.py#L54)或[infer.py](./infer.py#L47)中保存可部署的推断模型，该模型可以用Paddle预测库加载和部署，参考[Paddle预测库](http://paddlepaddle.org/documentation/docs/zh/1.4/advanced_usage/deploy/index_cn.html)
 
 ## 进阶使用
 
@@ -236,7 +236,7 @@ YOLOv3 的网络结构由基础特征提取网络、multi-scale特征融合层
 
 对YOLOv3进行fine-tune，用户可用`--pretrain`指定下载好的Paddle发布的YOLOv3[模型](https://paddlemodels.bj.bcebos.com/yolo/yolov3.tar.gz)，并把`--class_num`设置为用户数据集的类别数。
 
-在fine-tune时，若用户自定义数据集的类别数不等于COCO数据集的80类，则加载权重时不应加载`yolo_output`层的权重，可通过在[train.py](https://github.com/heavengate/models/blob/3fa6035550ebd4a425a2e354489967a829174155/PaddleCV/yolov3/train.py#L76)使用如下方式加载非`yolo_output`层的权重：
+在fine-tune时，若用户自定义数据集的类别数不等于COCO数据集的80类，则加载权重时不应加载`yolo_output`层的权重，可通过在[train.py](./train.py#L76)使用如下方式加载非`yolo_output`层的权重：
 
 ```python
 if cfg.pretrain:
@@ -295,7 +295,7 @@ if cfg.pretrain:
 **A:** YOLOv3中`learning_rate=0.001`的设置是针对总batch size为64的情况，若用户的batch size小于该值，建议调小学习率。
 
 **Q:** 我训练YOLOv3速度比较慢，要怎么提速？  
-**A:** YOLOv3的数据增强比较复杂，速度比较慢，可通过在[reader.py](https://github.com/PaddlePaddle/models/blob/66e135ccc4f35880d1cd625e9ec96c041835e37d/PaddleCV/yolov3/reader.py#L284)中增加数据读取的进程数来提速。若用户是进行fine-tune，也可将`--no_mixup_iter`设置大于`--max_iter`的值来禁用mixup提升速度。
+**A:** YOLOv3的数据增强比较复杂，速度比较慢，可通过在[reader.py](./reader.py#L284)中增加数据读取的进程数来提速。若用户是进行fine-tune，也可将`--no_mixup_iter`设置大于`--max_iter`的值来禁用mixup提升速度。
 
 ## 参考文献
 

PaddleCV/yolov3/README_en.md

@@ -74,7 +74,7 @@ The data catalog structure is as follows:
 
 **User defined dataset:**
 
-You can defined datasets by yourself, we recommend using annotations in COCO format, and you can set dataset directory by `--data_dir` or in [reader.py](https://github.com/PaddlePaddle/models/blob/623698ef30cc2f7879e47621678292254d6af51e/PaddleCV/yolov3/reader.py#L39). When using annotations in COCO format, you can reference the directory structure in COCO dataset above.
+You can defined datasets by yourself, we recommend using annotations in COCO format, and you can set dataset directory by `--data_dir` or in [reader.py](./reader.py#L39). When using annotations in COCO format, you can reference the directory structure in COCO dataset above.
 
 ### Training
 
@@ -199,7 +199,7 @@ Inference speed on single GPU:
 
 ### Inference deployment
 
-For YOLOv3 inference deployment, you can save YOLOv3 inference model in [eval.py](https://github.com/PaddlePaddle/models/blob/623698ef30cc2f7879e47621678292254d6af51e/PaddleCV/yolov3/eval.py#L58), inference model can be loaded and deployed by Paddle prediction library, see [Paddle Inference Lib](http://www.paddlepaddle.org/documentation/docs/en/1.4/advanced_usage/deploy/index_en.html).
+For YOLOv3 inference deployment, you can save YOLOv3 inference model in [eval.py](./eval.py#L54) or [infer.py](./infer.py#L47), inference model can be loaded and deployed by Paddle prediction library, see [Paddle Inference Lib](http://www.paddlepaddle.org/documentation/docs/en/1.4/advanced_usage/deploy/index_en.html).
 
 ## Advanced Usage
 
@@ -236,7 +236,7 @@ YOLOv3 networks are composed of base feature extraction network, multi-scale fea
 
 For YOLOv3 fine-tuning, you should set `--pretrain` as YOLOv3 [model](https://paddlemodels.bj.bcebos.com/yolo/yolov3.tar.gz) you download, set `--class_num` as category number in your dataset.
 
-In fine-tuning, weights of `yolo_output` layers should not be loaded when your `--class_num` is not equal to 80 as in COCO dataset, you can load pre-trained weights in [train.py](https://github.com/heavengate/models/blob/3fa6035550ebd4a425a2e354489967a829174155/PaddleCV/yolov3/train.py#L76) without `yolo_output` layers as:
+In fine-tuning, weights of `yolo_output` layers should not be loaded when your `--class_num` is not equal to 80 as in COCO dataset, you can load pre-trained weights in [train.py](./train.py#L76) without `yolo_output` layers as:
 
 ```python
 if cfg.pretrain:
@@ -295,7 +295,7 @@ if cfg.pretrain:
 **A:** `learning_rate=0.001` configuration is for training in 8 GPUs while total batch size is 64, if you train with smaller batch size, please decrease the learning rate.
 
 **Q:** YOLOv3 training in my machine is very slow, how can I speed it up?  
-**A:** Image augmentation is very complicated and time consuming in YOLOv3, you can set more workers for reader in [reader.py](https://github.com/PaddlePaddle/models/blob/66e135ccc4f35880d1cd625e9ec96c041835e37d/PaddleCV/yolov3/reader.py#L284) for speeding up. If you are fine-tuning, you can also set `--no_mixup_iter` greater than `--max_iter` to disable image mixup.
+**A:** Image augmentation is very complicated and time consuming in YOLOv3, you can set more workers for reader in [reader.py](./reader.py#L284) for speeding up. If you are fine-tuning, you can also set `--no_mixup_iter` greater than `--max_iter` to disable image mixup.
 
 ## Reference
 

PaddleCV/yolov3/eval.py

@@ -50,6 +50,13 @@ def eval():
             return os.path.exists(os.path.join(cfg.weights, var.name))
         fluid.io.load_vars(exe, cfg.weights, predicate=if_exist)
     # yapf: enable
+
+    # you can save inference model by following code
+    # fluid.io.save_inference_model("./output/yolov3", 
+    #                               feeded_var_names=['image', 'im_shape'],
+    #                               target_vars=outputs,
+    #                               executor=exe)
+
     input_size = cfg.input_size
     test_reader = reader.test(input_size, 1)
     label_names, label_ids = reader.get_label_infos()

PaddleCV/yolov3/infer.py

@@ -43,6 +43,13 @@ def infer():
             return os.path.exists(os.path.join(cfg.weights, var.name))
         fluid.io.load_vars(exe, cfg.weights, predicate=if_exist)
     # yapf: enable
+
+    # you can save inference model by following code
+    # fluid.io.save_inference_model("./output/yolov3", 
+    #                               feeded_var_names=['image', 'im_shape'],
+    #                               target_vars=outputs,
+    #                               executor=exe)
+
     feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())
     fetch_list = [outputs]
     image_names = []