Merge pull request #1166 from zhangjun/download-fix

fix serving_bin download, add low-precision example

doc/LOW_PRECISION_DEPLOYMENT.md

@@ -17,7 +17,7 @@ python -m paddle_serving_client.convert --dirname ResNet50_quant
 ```
 Start RPC service, specify the GPU id and precision mode
 ```
-python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_gpu --use_trt --precision int8 
+python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_trt --precision int8 
 ```
 Request the serving service with Client
 ```
@@ -27,7 +27,7 @@ from paddle_serving_app.reader import RGB2BGR, Transpose, Div, Normalize
 
 client = Client()
 client.load_client_config(
-    "resnet_v2_50_imagenet_client/serving_client_conf.prototxt")
+    "serving_client/serving_client_conf.prototxt")
 client.connect(["127.0.0.1:9393"])
 
 seq = Sequential([
@@ -37,11 +37,11 @@ seq = Sequential([
 
 image_file = "daisy.jpg"
 img = seq(image_file)
-fetch_map = client.predict(feed={"image": img}, fetch=["score"])
-print(fetch_map["score"].reshape(-1))
+fetch_map = client.predict(feed={"image": img}, fetch=["save_infer_model/scale_0.tmp_0"])
+print(fetch_map["save_infer_model/scale_0.tmp_0"].reshape(-1))
 ```
 
 ## Reference
 * [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)
 * [Deploy the quantized model Using Paddle Inference on Intel CPU](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_x86_cpu_int8.html)
-* [Deploy the quantized model Using Paddle Inference on Nvidia GPU](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_trt.html)
\ No newline at end of file
+* [Deploy the quantized model Using Paddle Inference on Nvidia GPU](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_trt.html)

doc/LOW_PRECISION_DEPLOYMENT_CN.md

@@ -16,7 +16,7 @@ python -m paddle_serving_client.convert --dirname ResNet50_quant
 ```
 启动rpc服务, 设定所选GPU id、部署模型精度
 ```
-python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_gpu --use_trt --precision int8 
+python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_trt --precision int8 
 ```
 使用client进行请求
 ```
@@ -43,4 +43,4 @@ print(fetch_map["score"].reshape(-1))
 ## 参考文档
 * [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)
 * PaddleInference Intel CPU部署量化模型[文档](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_x86_cpu_int8.html)
-* PaddleInference NV GPU部署量化模型[文档](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_trt.html)
\ No newline at end of file
+* PaddleInference NV GPU部署量化模型[文档](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_trt.html)

python/examples/low_precision/resnet50/README.md

+# resnet50 int8 example
+(English|[简体中文](./README_CN.md))
+
+## Obtain the quantized model through PaddleSlim tool
+Train the low-precision models please refer to [PaddleSlim](https://paddleslim.readthedocs.io/zh_CN/latest/tutorials/quant/overview.html).
+
+## Deploy the quantized model from PaddleSlim using Paddle Serving with Nvidia TensorRT int8 mode
+
+Firstly, download the [Resnet50 int8 model](https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50_quant.tar.gz) and convert to Paddle Serving's saved model。
+```
+wget https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50_quant.tar.gz
+tar zxvf ResNet50_quant.tar.gz
+
+python -m paddle_serving_client.convert --dirname ResNet50_quant
+```
+Start RPC service, specify the GPU id and precision mode
+```
+python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_trt --precision int8 
+```
+Request the serving service with Client
+```
+python resnet50_client.py
+```
+
+## Reference
+* [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)
+* [Deploy the quantized model Using Paddle Inference on Intel CPU](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_x86_cpu_int8.html)
+* [Deploy the quantized model Using Paddle Inference on Nvidia GPU](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_trt.html)

python/examples/low_precision/resnet50/README_CN.md

+# resnet50 int8示例
+(简体中文|[English](./README.md))
+
+## 通过PaddleSlim量化生成低精度模型
+详细见[PaddleSlim量化](https://paddleslim.readthedocs.io/zh_CN/latest/tutorials/quant/overview.html)
+
+## 使用TensorRT int8加载PaddleSlim Int8量化模型进行部署
+首先下载Resnet50 [PaddleSlim量化模型](https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50_quant.tar.gz),并转换为Paddle Serving支持的部署模型格式。
+```
+wget https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50_quant.tar.gz
+tar zxvf ResNet50_quant.tar.gz
+
+python -m paddle_serving_client.convert --dirname ResNet50_quant
+```
+启动rpc服务, 设定所选GPU id、部署模型精度
+```
+python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_trt --precision int8 
+```
+使用client进行请求
+```
+python resnet50_client.py
+```
+
+## 参考文档
+* [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)
+* PaddleInference Intel CPU部署量化模型[文档](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_x86_cpu_int8.html)
+* PaddleInference NV GPU部署量化模型[文档](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_trt.html)

python/examples/low_precision/resnet50/resnet50_client.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, File2Image, Resize, CenterCrop
+from paddle_serving_app.reader import RGB2BGR, Transpose, Div, Normalize
+
+client = Client()
+client.load_client_config(
+    "serving_client/serving_client_conf.prototxt")
+client.connect(["127.0.0.1:9303"])
+
+seq = Sequential([
+    File2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
+    Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
+])
+
+image_file = "daisy.jpg"
+img = seq(image_file)
+fetch_map = client.predict(feed={"image": img}, fetch=["save_infer_model/scale_0.tmp_0"])
+print(fetch_map["save_infer_model/scale_0.tmp_0"].reshape(-1))

python/paddle_serving_server/server.py

@@ -386,8 +386,6 @@ class Server(object):
             return
 
         if not os.path.exists(self.server_path):
-            os.system("touch {}/{}.is_download".format(self.module_path,
-                                                       folder_name))
             print('Frist time run, downloading PaddleServing components ...')
 
             r = os.system('wget ' + bin_url + ' --no-check-certificate')
@@ -403,9 +401,10 @@ class Server(object):
                     tar = tarfile.open(tar_name)
                     tar.extractall()
                     tar.close()
+                    open(download_flag, "a").close()
                 except:
-                    if os.path.exists(exe_path):
-                        os.remove(exe_path)
+                    if os.path.exists(self.server_path):
+                        os.remove(self.server_path)
                     raise SystemExit(
                         'Decompressing failed, please check your permission of {} or disk space left.'
                         .format(self.module_path))