[Fix] fix_torchserver1.1 (#844)

* test_torchserver1.1

* test_torchserver1.2

* update

* update mmseg_handler.py

* update docs

* update torchserver

* tranfer torchserver to torchserve

* update docs

docs/useful_tools.md

@@ -304,7 +304,7 @@ In order to serve an `MMSegmentation` model with [`TorchServe`](https://pytorch.
 ### 1. Convert model from MMSegmentation to TorchServe
 
 ```shell
-python tools/mmseg2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
+python tools/torchserve/mmseg2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
 --output-folder ${MODEL_STORE} \
 --model-name ${MODEL_NAME}
 ```
@@ -359,3 +359,20 @@ plt.show()
 You should see something similar to:
 
 ![3dogs_mask](../resources/3dogs_mask.png)
+
+And you can use `test_torchserve.py` to compare result of torchserve and pytorch, and visualize them.
+
+```shell
+python tools/torchserve/test_torchserve.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME}
+[--inference-addr ${INFERENCE_ADDR}] [--result-image ${RESULT_IMAGE}] [--device ${DEVICE}]
+```
+
+Example:
+
+```shell
+python tools/torchserve/test_torchserve.py \
+demo/demo.png \
+configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py \
+checkpoint/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth \
+fcn
+```

setup.cfg

@@ -8,6 +8,6 @@ line_length = 79
 multi_line_output = 0
 known_standard_library = setuptools
 known_first_party = mmseg
-known_third_party = PIL,cityscapesscripts,cv2,detail,matplotlib,mmcv,numpy,onnxruntime,packaging,prettytable,pytest,pytorch_sphinx_theme,scipy,seaborn,torch,ts
+known_third_party = PIL,cityscapesscripts,cv2,detail,matplotlib,mmcv,numpy,onnxruntime,packaging,prettytable,pytest,pytorch_sphinx_theme,requests,scipy,seaborn,torch,ts
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY

tools/mmseg_handler.py → tools/torchserve/mmseg_handler.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import base64
-import io
 import os
 
 import cv2
 import mmcv
 import torch
+from mmcv.cnn.utils.sync_bn import revert_sync_batchnorm
 from ts.torch_handler.base_handler import BaseHandler
 
 from mmseg.apis import inference_segmentor, init_segmentor
@@ -27,6 +27,7 @@ class MMsegHandler(BaseHandler):
         self.config_file = os.path.join(model_dir, 'config.py')
 
         self.model = init_segmentor(self.config_file, checkpoint, self.device)
+        self.model = revert_sync_batchnorm(self.model)
         self.initialized = True
 
     def preprocess(self, data):
@@ -47,8 +48,10 @@ class MMsegHandler(BaseHandler):
 
     def postprocess(self, data):
         output = []
+
         for image_result in data:
-            buffer = io.BytesIO()
             _, buffer = cv2.imencode('.png', image_result[0].astype('uint8'))
-            output.append(buffer.tobytes())
+            bast64_data = base64.b64encode(buffer.tobytes())
+            bast64_str = str(bast64_data, 'utf-8')
+            output.append(bast64_str)
         return output

tools/torchserve/test_torchserve.py

+import base64
+from argparse import ArgumentParser
+from io import BytesIO
+
+import matplotlib.pyplot as plt
+import mmcv
+import requests
+
+from mmseg.apis import inference_segmentor, init_segmentor
+
+
+def parse_args():
+    parser = ArgumentParser(
+        description='Compare result of torchserve and pytorch,'
+        'and visualize them.')
+    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(
+        '--result-image',
+        type=str,
+        default=None,
+        help='save server output in result-image')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+
+    args = parser.parse_args()
+    return args
+
+
+def main(args):
+    url = 'http://' + args.inference_addr + '/predictions/' + args.model_name
+    with open(args.img, 'rb') as image:
+        tmp_res = requests.post(url, image)
+    base64_str = tmp_res.content
+    buffer = base64.b64decode(base64_str)
+    if args.result_image:
+        with open(args.result_image, 'wb') as out_image:
+            out_image.write(buffer)
+        plt.imshow(mmcv.imread(args.result_image, 'grayscale'))
+        plt.show()
+    else:
+        plt.imshow(plt.imread(BytesIO(buffer)))
+        plt.show()
+    model = init_segmentor(args.config, args.checkpoint, args.device)
+    image = mmcv.imread(args.img)
+    result = inference_segmentor(model, image)
+    plt.imshow(result[0])
+    plt.show()
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)