[Tool] Add a tool to test TorchServe. (#468)

* Add `title` option in `show_result_pyplot`.

* Add test_torchserver.py

* Add docs about test torchserve

* Update docs and result output.

* Update chinese docs.

docs/tools/model_serving.md

@@ -10,7 +10,19 @@ python tools/deployment/mmcls2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
 --model-name ${MODEL_NAME}
 ```
 
-**Note**: ${MODEL_STORE} needs to be an absolute path to a folder.
+```{note}
+${MODEL_STORE} needs to be an absolute path to a folder.
+```
+
+Example:
+
+```shell
+python tools/deployment/mmcls2torchserve.py \
+  configs/resnet/resnet18_b32x8_imagenet.py \
+  checkpoints/resnet18_8xb32_in1k_20210831-fbbb1da6.pth \
+  --output-folder ./checkpoints \
+  --model-name resnet18_in1k
+```
 
 ## 2. Build `mmcls-serve` docker image
 
@@ -31,25 +43,45 @@ docker run --rm \
 --cpus 8 \
 --gpus device=0 \
 -p8080:8080 -p8081:8081 -p8082:8082 \
---mount type=bind,source=$MODEL_STORE,target=/home/model-server/model-store \
+--mount type=bind,source=`realpath ./checkpoints`,target=/home/model-server/model-store \
 mmcls-serve:latest
 ```
 
+```{note}
+`realpath ./checkpoints` points to the absolute path of "./checkpoints", and you can replace it with the absolute path where you store torchserve models.
+```
+
 [Read the docs](https://github.com/pytorch/serve/blob/master/docs/rest_api.md) about the Inference (8080), Management (8081) and Metrics (8082) APis
 
 ## 4. Test deployment
 
 ```shell
-curl -O https://raw.githubusercontent.com/pytorch/serve/master/docs/images/3dogs.jpg
-curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T 3dogs.jpg
+curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T demo/demo.JPEG
 ```
 
 You should obtain a response similar to:
 
 ```json
 {
-  "pred_label": 245,
-  "pred_score": 0.5536593794822693,
-  "pred_class": "French bulldog"
+  "pred_label": 58,
+  "pred_score": 0.38102269172668457,
+  "pred_class": "water snake"
 }
 ```
+
+And you can use `test_torchserver.py` to compare result of TorchServe and PyTorch, and visualize them.
+
+```shell
+python tools/deployment/test_torchserver.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME}
+[--inference-addr ${INFERENCE_ADDR}] [--device ${DEVICE}]
+```
+
+Example:
+
+```shell
+python tools/deployment/test_torchserver.py \
+  demo/demo.JPEG \
+  configs/resnet/resnet18_b32x8_imagenet.py \
+  checkpoints/resnet18_8xb32_in1k_20210831-fbbb1da6.pth \
+  resnet18_in1k
+```

docs_zh-CN/tools/model_serving.md

@@ -10,7 +10,19 @@ python tools/deployment/mmcls2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
 --model-name ${MODEL_NAME}
 ```
 
-**注意**: ${MODEL_STORE} 需要是一个文件夹的绝对路径。
+```{note}
+${MODEL_STORE} 需要是一个文件夹的绝对路径。
+```
+
+示例：
+
+```shell
+python tools/deployment/mmcls2torchserve.py \
+  configs/resnet/resnet18_b32x8_imagenet.py \
+  checkpoints/resnet18_8xb32_in1k_20210831-fbbb1da6.pth \
+  --output-folder ./checkpoints \
+  --model-name resnet18_in1k
+```
 
 ## 2. 构建 `mmcls-serve` docker 镜像
 
@@ -31,25 +43,45 @@ docker run --rm \
 --cpus 8 \
 --gpus device=0 \
 -p8080:8080 -p8081:8081 -p8082:8082 \
---mount type=bind,source=$MODEL_STORE,target=/home/model-server/model-store \
+--mount type=bind,source=`realpath ./checkpoints`,target=/home/model-server/model-store \
 mmcls-serve:latest
 ```
 
+```{note}
+`realpath ./checkpoints` 是 "./checkpoints" 的绝对路径，你可以将其替换为你保存 TorchServe 模型的目录的绝对路径。
+```
+
 参考 [该文档](https://github.com/pytorch/serve/blob/master/docs/rest_api.md) 了解关于推理 (8080)，管理 (8081) 和指标 (8082) 等 API 的信息。
 
 ## 4. 测试部署
 
 ```shell
-curl -O https://raw.githubusercontent.com/pytorch/serve/master/docs/images/3dogs.jpg
-curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T 3dogs.jpg
+curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T demo/demo.JPEG
 ```
 
 您应该获得类似于以下内容的响应：
 
 ```json
 {
-  "pred_label": 245,
-  "pred_score": 0.5536593794822693,
-  "pred_class": "French bulldog"
+  "pred_label": 58,
+  "pred_score": 0.38102269172668457,
+  "pred_class": "water snake"
 }
 ```
+
+另外，你也可以使用 `test_torchserver.py` 来比较 TorchServe 和 PyTorch 的结果，并进行可视化。
+
+```shell
+python tools/deployment/test_torchserver.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME}
+[--inference-addr ${INFERENCE_ADDR}] [--device ${DEVICE}]
+```
+
+示例：
+
+```shell
+python tools/deployment/test_torchserver.py \
+  demo/demo.JPEG \
+  configs/resnet/resnet18_b32x8_imagenet.py \
+  checkpoints/resnet18_8xb32_in1k_20210831-fbbb1da6.pth \
+  resnet18_in1k
+```

mmcls/apis/inference.py

@@ -89,7 +89,12 @@ def inference_model(model, img):
     return result
 
 
-def show_result_pyplot(model, img, result, fig_size=(15, 10), wait_time=0):
+def show_result_pyplot(model,
+                       img,
+                       result,
+                       fig_size=(15, 10),
+                       title='result',
+                       wait_time=0):
     """Visualize the classification results on the image.
 
     Args:
@@ -98,10 +103,17 @@ def show_result_pyplot(model, img, result, fig_size=(15, 10), wait_time=0):
         result (list): The classification result.
         fig_size (tuple): Figure size of the pyplot figure.
             Defaults to (15, 10).
+        title (str): Title of the pyplot figure.
+            Defaults to 'result'.
         wait_time (int): How many seconds to display the image.
             Defaults to 0.
     """
     if hasattr(model, 'module'):
         model = model.module
     model.show_result(
-        img, result, show=True, fig_size=fig_size, wait_time=wait_time)
+        img,
+        result,
+        show=True,
+        fig_size=fig_size,
+        win_name=title,
+        wait_time=wait_time)

requirements/optional.txt

@@ -1 +1,2 @@
 albumentations>=0.3.2 --no-binary imgaug,albumentations
+requests

setup.cfg

@@ -14,7 +14,7 @@ line_length = 79
 multi_line_output = 0
 known_standard_library = pkg_resources,setuptools
 known_first_party = mmcls
-known_third_party = PIL,m2r,matplotlib,mmcv,mmdet,numpy,onnxruntime,packaging,pytest,pytorch_sphinx_theme,recommonmark,rich,seaborn,sphinx,torch,torchvision,ts
+known_third_party = PIL,m2r,matplotlib,mmcv,mmdet,numpy,onnxruntime,packaging,pytest,pytorch_sphinx_theme,recommonmark,requests,rich,seaborn,sphinx,torch,torchvision,ts
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY
 

tools/deployment/test_torchserver.py

@@ -0,0 +1,44 @@
+from argparse import ArgumentParser
+
+import numpy as np
+import requests
+
+from mmcls.apis import inference_model, init_model, show_result_pyplot
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('img', help='Image file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument('model_name', help='The model name in the server')
+    parser.add_argument(
+        '--inference-addr',
+        default='127.0.0.1:8080',
+        help='Address and port of the inference server')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    args = parser.parse_args()
+    return args
+
+
+def main(args):
+    # Inference single image by native apis.
+    model = init_model(args.config, args.checkpoint, device=args.device)
+    model_result = inference_model(model, args.img)
+    show_result_pyplot(model, args.img, model_result, title='pytorch_result')
+
+    # Inference single image by torchserve engine.
+    url = 'http://' + args.inference_addr + '/predictions/' + args.model_name
+    with open(args.img, 'rb') as image:
+        response = requests.post(url, image)
+    server_result = response.json()
+    show_result_pyplot(model, args.img, server_result, title='server_result')
+
+    assert np.allclose(model_result['pred_score'], server_result['pred_score'])
+    print('Test complete, the results of PyTorch and TorchServe are the same.')
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)