fix lint

open-mmlab · May 18, 2023 · 5a2b1bf · 5a2b1bf
1 parent 2c7d054
commit 5a2b1bf
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Showing 33 changed files with 231 additions and 179 deletions.
diff --git a/csrc/mmdeploy/triton/instance_state.cpp b/csrc/mmdeploy/triton/instance_state.cpp
@@ -247,7 +247,7 @@ TRITONSERVER_Error* ModelInstanceState::Execute(TRITONBACKEND_Request** requests
     }
   }
 
-  // merget inputs for example: [[a,a,a], [b,b,b], [c,c,c]] -> [[aaa], [(b,c), (b,c), (b,c)]]
+  // merge inputs for example: [[a,a,a], [b,b,b], [c,c,c]] -> [[aaa], [(b,c), (b,c), (b,c)]]
   if (!merge_inputs_.empty()) {
     int n_example = vec_inputs[0].size();
     ::mmdeploy::Value inputs;

diff --git a/demo/triton/image-classification/README.md b/demo/triton/image-classification/README.md
@@ -1,12 +1,13 @@
 # Image classification serving
 
-
 ## Starting a docker container
+
 ```
 docker run -it --rm --gpus all openmmlab/mmdeploy:triton-22.12
 ```
 
 ## Convert pytorch model to tensorrt model
+
 ```
 cd /root/workspace/mmdeploy
 python3 tools/deploy.py \
@@ -20,6 +21,7 @@ python3 tools/deploy.py \
 ```
 
 ## Convert tensorrt model to triton format
+
 ```
 cd /root/workspace/mmdeploy
 python3 demo/triton/to_triton_model.py \
@@ -28,11 +30,13 @@ python3 demo/triton/to_triton_model.py \
 ```
 
 ## Start triton server
+
 ```
 tritonserver --model-repository=/model-repository
 ```
 
 ## Run client code output container
+
 ```
 python3 demo/triton/image-classification/grpc_client.py \
     model \

diff --git a/demo/triton/image-classification/grpc_client.py b/demo/triton/image-classification/grpc_client.py
@@ -1,15 +1,15 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import argparse
+
 import cv2
-from tritonclient.grpc import InferenceServerClient, InferInput, InferRequestedOutput
+from tritonclient.grpc import (InferenceServerClient, InferInput,
+                               InferRequestedOutput)
 
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument('model_name', type=str,
-                        help='model name')
-    parser.add_argument('image', type=str,
-                        help='image path')
+    parser.add_argument('model_name', type=str, help='model name')
+    parser.add_argument('image', type=str, help='image path')
     return parser.parse_args()
 
 
@@ -22,14 +22,16 @@ def __init__(self, url, model_name, model_version):
         self._client = InferenceServerClient(self._url)
         model_config = self._client.get_model_config(self._model_name,
                                                      self._model_version)
-        model_metadata = self._client.get_model_metadata(self._model_name,
-                                                         self._model_version)
+        model_metadata = self._client.get_model_metadata(
+            self._model_name, self._model_version)
         print(f'[model config]:\n{model_config}')
         print(f'[model metadata]:\n{model_metadata}')
         self._inputs = {input.name: input for input in model_metadata.inputs}
         self._input_names = list(self._inputs)
         self._outputs = {
-            output.name: output for output in model_metadata.outputs}
+            output.name: output
+            for output in model_metadata.outputs
+        }
         self._output_names = list(self._outputs)
         self._outputs_req = [
             InferRequestedOutput(name) for name in self._outputs
@@ -43,8 +45,7 @@ def infer(self, image):
             results: dict, {name : numpy.array}
         """
 
-        inputs = [InferInput(self._input_names[0], image.shape,
-                             "UINT8")]
+        inputs = [InferInput(self._input_names[0], image.shape, 'UINT8')]
         inputs[0].set_data_from_numpy(image)
         results = self._client.infer(
             model_name=self._model_name,
@@ -65,11 +66,11 @@ def visualize(results):
         print(f'label {labels[i]} score {scores[i]}')
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     args = parse_args()
     model_name = args.model_name
-    model_version = "1"
-    url = "localhost:8001"
+    model_version = '1'
+    url = 'localhost:8001'
     client = GRPCTritonClient(url, model_name, model_version)
     img = cv2.imread(args.image)
     results = client.infer(img)

diff --git a/demo/triton/image-classification/serving/model/1/README.md b/demo/triton/image-classification/serving/model/1/README.md
@@ -1 +1 @@
-This directory holds the model files.
+This directory holds the model files.
diff --git a/demo/triton/instance-segmentation/README.md b/demo/triton/instance-segmentation/README.md
@@ -1,12 +1,13 @@
 # Instance segmentation serving
 
-
 ## Starting a docker container
+
 ```
 docker run -it --rm --gpus all openmmlab/mmdeploy:triton-22.12
 ```
 
 ## Convert pytorch model to tensorrt model
+
 ```
 cd /root/workspace/mmdeploy
 python3 tools/deploy.py \
@@ -20,6 +21,7 @@ python3 tools/deploy.py \
 ```
 
 ## Convert tensorrt model to triton format
+
 ```
 cd /root/workspace/mmdeploy
 python3 demo/triton/to_triton_model.py \
@@ -28,11 +30,13 @@ python3 demo/triton/to_triton_model.py \
 ```
 
 ## Start triton server
+
 ```
 tritonserver --model-repository=/model-repository
 ```
 
 ## Run client code output container
+
 ```
 python3 demo/triton/instance-segmentation/grpc_client.py \
     model \

diff --git a/demo/triton/instance-segmentation/grpc_client.py b/demo/triton/instance-segmentation/grpc_client.py
@@ -1,16 +1,16 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import argparse
-import cv2
-from tritonclient.grpc import InferenceServerClient, InferInput, InferRequestedOutput
 import math
 
+import cv2
+from tritonclient.grpc import (InferenceServerClient, InferInput,
+                               InferRequestedOutput)
+
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument('model_name', type=str,
-                        help='model name')
-    parser.add_argument('image', type=str,
-                        help='image path')
+    parser.add_argument('model_name', type=str, help='model name')
+    parser.add_argument('image', type=str, help='image path')
     return parser.parse_args()
 
 
@@ -23,14 +23,16 @@ def __init__(self, url, model_name, model_version):
         self._client = InferenceServerClient(self._url)
         model_config = self._client.get_model_config(self._model_name,
                                                      self._model_version)
-        model_metadata = self._client.get_model_metadata(self._model_name,
-                                                         self._model_version)
+        model_metadata = self._client.get_model_metadata(
+            self._model_name, self._model_version)
         print(f'[model config]:\n{model_config}')
         print(f'[model metadata]:\n{model_metadata}')
         self._inputs = {input.name: input for input in model_metadata.inputs}
         self._input_names = list(self._inputs)
         self._outputs = {
-            output.name: output for output in model_metadata.outputs}
+            output.name: output
+            for output in model_metadata.outputs
+        }
         self._output_names = list(self._outputs)
         self._outputs_req = [
             InferRequestedOutput(name) for name in self._outputs
@@ -44,8 +46,7 @@ def infer(self, image):
             results: dict, {name : numpy.array}
         """
 
-        inputs = [InferInput(self._input_names[0], image.shape,
-                             "UINT8")]
+        inputs = [InferInput(self._input_names[0], image.shape, 'UINT8')]
         inputs[0].set_data_from_numpy(image)
         results = self._client.infer(
             model_name=self._model_name,
@@ -83,11 +84,11 @@ def visualize(img, results):
     cv2.imwrite('instance-segmentation.jpg', img)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     args = parse_args()
     model_name = args.model_name
-    model_version = "1"
-    url = "localhost:8001"
+    model_version = '1'
+    url = 'localhost:8001'
     client = GRPCTritonClient(url, model_name, model_version)
     img = cv2.imread(args.image)
     results = client.infer(img)

diff --git a/demo/triton/instance-segmentation/serving/model/1/README.md b/demo/triton/instance-segmentation/serving/model/1/README.md
@@ -1 +1 @@
-This directory holds the model files.
+This directory holds the model files.
diff --git a/demo/triton/instance-segmentation/serving/model/config.pbtxt b/demo/triton/instance-segmentation/serving/model/config.pbtxt
@@ -27,4 +27,4 @@ output {
   name: "mask_offs"
   data_type: TYPE_INT32
   dims: [ -1, 3 ]
-}
+}
diff --git a/demo/triton/keypoint-detection/README.md b/demo/triton/keypoint-detection/README.md
@@ -1,11 +1,13 @@
 # Keypoint detection serving
 
 ## Starting a docker container
+
 ```
 docker run -it --rm --gpus all openmmlab/mmdeploy:triton-22.12
 ```
 
 ## Convert pytorch model to tensorrt model
+
 ```
 cd /root/workspace/mmdeploy
 python3 tools/deploy.py \
@@ -19,6 +21,7 @@ python3 tools/deploy.py \
 ```
 
 ## Convert tensorrt model to triton format
+
 ```
 cd /root/workspace/mmdeploy
 python3 demo/triton/to_triton_model.py \
@@ -27,13 +30,15 @@ python3 demo/triton/to_triton_model.py \
 ```
 
 ## Start triton server
+
 ```
 tritonserver --model-repository=/model-repository
 ```
 
 ## Run client code output container
+
 ```
 python3 demo/triton/keypoint-detection/grpc_client.py \
     model \
     /path/to/image
-```
+```
diff --git a/demo/triton/keypoint-detection/grpc_client.py b/demo/triton/keypoint-detection/grpc_client.py
@@ -1,17 +1,17 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import argparse
+import json
+
 import cv2
-from tritonclient.grpc import InferenceServerClient, InferInput, InferRequestedOutput
 import numpy as np
-import json
+from tritonclient.grpc import (InferenceServerClient, InferInput,
+                               InferRequestedOutput)
 
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument('model_name', type=str,
-                        help='model name')
-    parser.add_argument('image', type=str,
-                        help='image path')
+    parser.add_argument('model_name', type=str, help='model name')
+    parser.add_argument('image', type=str, help='image path')
     return parser.parse_args()
 
 
@@ -24,14 +24,16 @@ def __init__(self, url, model_name, model_version):
         self._client = InferenceServerClient(self._url)
         model_config = self._client.get_model_config(self._model_name,
                                                      self._model_version)
-        model_metadata = self._client.get_model_metadata(self._model_name,
-                                                         self._model_version)
+        model_metadata = self._client.get_model_metadata(
+            self._model_name, self._model_version)
         print(f'[model config]:\n{model_config}')
         print(f'[model metadata]:\n{model_metadata}')
         self._inputs = {input.name: input for input in model_metadata.inputs}
         self._input_names = list(self._inputs)
         self._outputs = {
-            output.name: output for output in model_metadata.outputs}
+            output.name: output
+            for output in model_metadata.outputs
+        }
         self._output_names = list(self._outputs)
         self._outputs_req = [
             InferRequestedOutput(name) for name in self._outputs
@@ -46,10 +48,10 @@ def infer(self, image, box):
             results: dict, {name : numpy.array}
         """
 
-        inputs = [InferInput(self._input_names[0], image.shape,
-                             "UINT8"),
-                  InferInput(self._input_names[1], box.shape,
-                             "BYTES")]
+        inputs = [
+            InferInput(self._input_names[0], image.shape, 'UINT8'),
+            InferInput(self._input_names[1], box.shape, 'BYTES')
+        ]
         inputs[0].set_data_from_numpy(image)
         inputs[1].set_data_from_numpy(box)
         results = self._client.infer(
@@ -72,20 +74,18 @@ def visualize(img, results):
     cv2.imwrite('keypoint-detection.jpg', img)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     args = parse_args()
     model_name = args.model_name
-    model_version = "1"
-    url = "localhost:8001"
+    model_version = '1'
+    url = 'localhost:8001'
     client = GRPCTritonClient(url, model_name, model_version)
     img = cv2.imread(args.image)
     bbox = {
         'type': 'PoseBbox',
-        'value': [
-            {
-                'bbox': [0.0, 0.0, img.shape[1], img.shape[0]]
-            }
-        ]
+        'value': [{
+            'bbox': [0.0, 0.0, img.shape[1], img.shape[0]]
+        }]
     }
     bbox = np.array([json.dumps(bbox).encode('utf-8')])
     results = client.infer(img, bbox)

diff --git a/demo/triton/keypoint-detection/serving/model/1/README.md b/demo/triton/keypoint-detection/serving/model/1/README.md
@@ -1 +1 @@
-This directory holds the model files.
+This directory holds the model files.
diff --git a/demo/triton/keypoint-detection/serving/model/config.pbtxt b/demo/triton/keypoint-detection/serving/model/config.pbtxt
@@ -26,4 +26,4 @@ parameters {
   value: {
     string_value: "0 1"
   }
-}
+}