@@ -161,7 +161,7 @@ Download the [Market-1501-v15.09.15.zip](https://pan.baidu.com/s/1ntIi2Op?_at_=1
#### 4.1 Model Evaluation
Prepare the `*.pdparams` model parameter file for evaluation. You can use the trained model or the model saved in [2.1.4 Model training] (#214-model training).
Prepare the `*.pdparams` model parameter file for evaluation. You can use the trained model or the model saved in [3.1.4 Model training](#314-model-training).
- Take the `latest.pdparams` saved during training as an example, execute the following command to evaluate.
...
...
@@ -191,8 +191,6 @@ Prepare the `*.pdparams` model parameter file for evaluation. You can use the tr
```log
...
...
ppcls INFO: unique_endpoints {''}
ppcls INFO: Found /root/.paddleclas/weights/resnet50-19c8e357_torch2paddle.pdparams
@@ -324,25 +320,25 @@ You can convert the model file saved during training into an inference model and
##### 4.2.3 Inference based on C++ prediction engine
PaddleClas provides an example of inference based on the C++ prediction engine, you can refer to [Server-side C++ prediction](../inference_deployment/cpp_deploy.md) to complete the corresponding inference deployment. If you are using the Windows platform, you can refer to the Visual Studio 2019 Community CMake Compilation Guide to complete the corresponding prediction library compilation and model prediction work.
PaddleClas provides an example of inference based on the C++ prediction engine, you can refer to [Server-side C++ prediction](../inference_deployment/cpp_deploy_en.md) to complete the corresponding inference deployment. If you are using the Windows platform, you can refer to the Visual Studio 2019 Community CMake Compilation Guide to complete the corresponding prediction library compilation and model prediction work.
#### 4.3 Service deployment
Paddle Serving provides high-performance, flexible and easy-to-use industrial-grade online inference services. Paddle Serving supports RESTful, gRPC, bRPC and other protocols, and provides inference solutions in a variety of heterogeneous hardware and operating system environments. For more introduction to Paddle Serving, please refer to the Paddle Serving code repository.
PaddleClas provides an example of model serving deployment based on Paddle Serving. You can refer to [Model serving deployment](../inference_deployment/paddle_serving_deploy.md) to complete the corresponding deployment.
PaddleClas provides an example of model serving deployment based on Paddle Serving. You can refer to [Model serving deployment](../inference_deployment/recognition_serving_deploy_en.md) to complete the corresponding deployment.
#### 4.4 Lite deployment
Paddle Lite is a high-performance, lightweight, flexible and easily extensible deep learning inference framework, positioned to support multiple hardware platforms including mobile, embedded and server. For more introduction to Paddle Lite, please refer to the Paddle Lite code repository.
PaddleClas provides an example of deploying models based on Paddle Lite. You can refer to [Deployment](../inference_deployment/paddle_lite_deploy.md) to complete the corresponding deployment.
PaddleClas provides an example of deploying models based on Paddle Lite. You can refer to [Deployment](../inference_deployment/paddle_lite_deploy_en.md) to complete the corresponding deployment.
#### 4.5 Paddle2ONNX Model Conversion and Prediction
Paddle2ONNX supports converting PaddlePaddle model format to ONNX model format. The deployment of Paddle models to various inference engines can be completed through ONNX, including TensorRT/OpenVINO/MNN/TNN/NCNN, and other inference engines or hardware that support the ONNX open source format. For more information about Paddle2ONNX, please refer to the Paddle2ONNX code repository.
PaddleClas provides an example of converting an inference model to an ONNX model and making inference prediction based on Paddle2ONNX. You can refer to [Paddle2ONNX model conversion and prediction](../../../deploy/paddle2onnx/readme.md) to complete the corresponding deployment work.
PaddleClas provides an example of converting an inference model to an ONNX model and making inference prediction based on Paddle2ONNX. You can refer to [Paddle2ONNX model conversion and prediction](../../../deploy/paddle2onnx/readme_en.md) to complete the corresponding deployment work.
@@ -181,7 +181,7 @@ Different from Python Serving, the C++ Serving client calls C++ OP to predict, s
# One-click compile and install Serving server, set SERVING_BIN
source ./build_server.sh python3.7
```
**Note: The path set by **[build_server.sh](./build_server.sh#L55-L62) may need to be modified according to the actual machine environment such as CUDA, python version, etc., and then compiled.
**Note: The path set by **[build_server.sh](../../../deploy/paddleserving/build_server.sh#L55-L62) may need to be modified according to the actual machine environment such as CUDA, python version, etc., and then compiled; If you encounter a non-network error during the execution of `build_server.sh`, you can manually copy the commands in the script to the terminal for execution.
- Modify the client file `ResNet50_client/serving_client_conf.prototxt` , change the field after `feed_type:` to 20, change the field after the first `shape:` to 1 and delete the rest of the `shape` fields.
```log
...
...
@@ -193,9 +193,9 @@ Different from Python Serving, the C++ Serving client calls C++ OP to predict, s
shape: 1
}
```
- Modify part of the code of [`test_cpp_serving_client`](./test_cpp_serving_client.py)
1. Modify the [`feed={"inputs": image}`](./test_cpp_serving_client.py#L28) part of the code, and change the path after `load_client_config` to `ResNet50_client/serving_client_conf.prototxt` .
2. Modify the [`feed={"inputs": image}`](./test_cpp_serving_client.py#L45) part of the code, and change `inputs` to be the same as the `feed_var` field in `ResNet50_client/serving_client_conf.prototxt` name` is the same. Since `name` in some model client files is `x` instead of `inputs` , you need to pay attention to this when using these models for C++ Serving deployment.
- Modify part of the code of [`test_cpp_serving_client`](../../../deploy/paddleserving/test_cpp_serving_client.py)
1. Modify the [`feed={"inputs": image}`](../../../deploy/paddleserving/test_cpp_serving_client.py#L28) part of the code, and change the path after `load_client_config` to `ResNet50_client/serving_client_conf.prototxt` .
2. Modify the [`feed={"inputs": image}`](../../../deploy/paddleserving/test_cpp_serving_client.py#L45) part of the code, and change `inputs` to be the same as the `feed_var` field in `ResNet50_client/serving_client_conf.prototxt` name` is the same. Since `name` in some model client files is `x` instead of `inputs` , you need to pay attention to this when using these models for C++ Serving deployment.
@@ -219,7 +219,7 @@ Different from Python Serving, the C++ Serving client calls C++ OP to predict, s
# One-click compile and install Serving server, set SERVING_BIN
source ./build_server.sh python3.7
```
**Note:** The path set by [build_server.sh](../build_server.sh#L55-L62) may need to be modified according to the actual machine environment such as CUDA, python version, etc., and then compiled.
**Note:** The path set by [build_server.sh](../build_server.sh#L55-L62) may need to be modified according to the actual machine environment such as CUDA, python version, etc., and then compiled; If you encounter a non-network error during the execution of `build_server.sh`, you can manually copy the commands in the script to the terminal for execution.
- The input and output format used by C++ Serving is different from that of Python, so you need to execute the following command to overwrite the files below [3.1] (#31-model conversion) by copying the 4 files to get the corresponding 4 prototxt files in the folder.
