PaddleSeg supports triton serving

examples/vision/segmentation/paddleseg/serving/models/paddleseg/1/README.md

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+# PaddleSeg Pipeline
+
+The pipeline directory does not have model files, but a version number directory needs to be maintained.

examples/vision/segmentation/paddleseg/serving/models/paddleseg/config.pbtxt

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+platform: "ensemble"
+
+input [
+  {
+    name: "INPUT"
+    data_type: TYPE_UINT8
+    dims: [-1, -1, -1, 3 ]
+  }
+]
+
+output [
+  {
+    name: "SEG_RESULT"
+    data_type: TYPE_STRING
+    dims: [ -1 ]
+  }
+]
+
+ensemble_scheduling {
+  step [
+    {
+      model_name: "preprocess"
+      model_version: 1
+      input_map {
+        key: "preprocess_input"
+        value: "INPUT"
+      }
+      output_map {
+        key: "preprocess_output_1"
+        value: "RUNTIME_INPUT_1"
+      }
+      output_map {
+        key: "preprocess_output_2"
+        value: "POSTPROCESS_INPUT_2"
+      }
+    },
+    {
+      model_name: "runtime"
+      model_version: 1
+      input_map {
+        key: "x"
+        value: "RUNTIME_INPUT_1"
+      }
+      output_map {
+        key: "argmax_0.tmp_0"
+        value: "RUNTIME_OUTPUT"
+      }
+    },
+    {
+      model_name: "postprocess"
+      model_version: 1
+      input_map {
+        key: "post_input_1"
+        value: "RUNTIME_OUTPUT"
+      }
+      input_map {
+        key: "post_input_2"
+        value: "POSTPROCESS_INPUT_2"
+      }
+      output_map {
+        key: "post_output"
+        value: "SEG_RESULT"
+      }
+    }
+  ]
+}
+

examples/vision/segmentation/paddleseg/serving/models/postprocess/1/model.py

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+# Copyright (c) 2022 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.
+
+import json
+import numpy as np
+import time
+import os
+import fastdeploy as fd
+
+# triton_python_backend_utils is available in every Triton Python model. You
+# need to use this module to create inference requests and responses. It also
+# contains some utility functions for extracting information from model_config
+# and converting Triton input/output types to numpy types.
+import triton_python_backend_utils as pb_utils
+
+
+class TritonPythonModel:
+    """Your Python model must use the same class name. Every Python model
+    that is created must have "TritonPythonModel" as the class name.
+    """
+
+    def initialize(self, args):
+        """`initialize` is called only once when the model is being loaded.
+        Implementing `initialize` function is optional. This function allows
+        the model to intialize any state associated with this model.
+        Parameters
+        ----------
+        args : dict
+          Both keys and values are strings. The dictionary keys and values are:
+          * model_config: A JSON string containing the model configuration
+          * model_instance_kind: A string containing model instance kind
+          * model_instance_device_id: A string containing model instance device ID
+          * model_repository: Model repository path
+          * model_version: Model version
+          * model_name: Model name
+        """
+        # You must parse model_config. JSON string is not parsed here
+        self.model_config = json.loads(args['model_config'])
+        print("model_config:", self.model_config)
+
+        self.input_names = []
+        for input_config in self.model_config["input"]:
+            self.input_names.append(input_config["name"])
+        print("postprocess input names:", self.input_names)
+
+        self.output_names = []
+        self.output_dtype = []
+        for output_config in self.model_config["output"]:
+            self.output_names.append(output_config["name"])
+            dtype = pb_utils.triton_string_to_numpy(output_config["data_type"])
+            self.output_dtype.append(dtype)
+        print("postprocess output names:", self.output_names)
+
+        yaml_path = os.path.abspath(os.path.dirname(__file__)) + "/deploy.yaml"
+        self.postprocess_ = fd.vision.segmentation.PaddleSegPostprocessor(
+            yaml_path)
+
+    def execute(self, requests):
+        """`execute` must be implemented in every Python model. `execute`
+        function receives a list of pb_utils.InferenceRequest as the only
+        argument. This function is called when an inference is requested
+        for this model. Depending on the batching configuration (e.g. Dynamic
+        Batching) used, `requests` may contain multiple requests. Every
+        Python model, must create one pb_utils.InferenceResponse for every
+        pb_utils.InferenceRequest in `requests`. If there is an error, you can
+        set the error argument when creating a pb_utils.InferenceResponse.
+        Parameters
+        ----------
+        requests : list
+          A list of pb_utils.InferenceRequest
+        Returns
+        -------
+        list
+          A list of pb_utils.InferenceResponse. The length of this list must
+          be the same as `requests`
+        """
+        responses = []
+        for request in requests:
+            infer_outputs = pb_utils.get_input_tensor_by_name(
+                request, self.input_names[0])
+            im_info = pb_utils.get_input_tensor_by_name(request,
+                                                        self.input_names[1])
+            infer_outputs = infer_outputs.as_numpy()
+            im_info = im_info.as_numpy()
+            for i in range(im_info.shape[0]):
+                im_info[i] = json.loads(im_info[i].decode('utf-8').replace(
+                    "'", '"'))
+
+            results = self.postprocess_.run([infer_outputs], im_info[0])
+            r_str = fd.vision.utils.fd_result_to_json(results)
+
+            r_np = np.array(r_str, dtype=np.object_)
+            out_tensor = pb_utils.Tensor(self.output_names[0], r_np)
+            inference_response = pb_utils.InferenceResponse(
+                output_tensors=[out_tensor, ])
+            responses.append(inference_response)
+        return responses
+
+    def finalize(self):
+        """`finalize` is called only once when the model is being unloaded.
+        Implementing `finalize` function is optional. This function allows
+        the model to perform any necessary clean ups before exit.
+        """
+        print('Cleaning up...')

examples/vision/segmentation/paddleseg/serving/models/postprocess/config.pbtxt

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+name: "postprocess"
+backend: "python"
+
+input [
+  {
+    name: "post_input_1"
+    data_type: TYPE_INT32
+    dims: [-1, -1, -1]
+  },
+  {
+    name: "post_input_2"
+    data_type: TYPE_STRING
+    dims: [ -1 ]
+  }
+]
+
+output [
+  {
+    name: "post_output"
+    data_type: TYPE_STRING
+    dims: [ -1 ]
+  }
+]
+
+instance_group [
+  {
+      count: 1
+      kind: KIND_CPU
+  }
+]

examples/vision/segmentation/paddleseg/serving/models/preprocess/1/deploy.yaml

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+Deploy:
+  input_shape:
+  - -1
+  - 3
+  - -1
+  - -1
+  model: model.pdmodel
+  output_dtype: int32
+  output_op: argmax
+  params: model.pdiparams
+  transforms:
+  - type: Normalize

examples/vision/segmentation/paddleseg/serving/models/preprocess/1/model.py

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+# Copyright (c) 2022 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.
+
+import json
+import numpy as np
+import os
+
+import fastdeploy as fd
+
+# triton_python_backend_utils is available in every Triton Python model. You
+# need to use this module to create inference requests and responses. It also
+# contains some utility functions for extracting information from model_config
+# and converting Triton input/output types to numpy types.
+import triton_python_backend_utils as pb_utils
+
+
+class TritonPythonModel:
+    """Your Python model must use the same class name. Every Python model
+    that is created must have "TritonPythonModel" as the class name.
+    """
+
+    def initialize(self, args):
+        """`initialize` is called only once when the model is being loaded.
+        Implementing `initialize` function is optional. This function allows
+        the model to intialize any state associated with this model.
+        Parameters
+        ----------
+        args : dict
+          Both keys and values are strings. The dictionary keys and values are:
+          * model_config: A JSON string containing the model configuration
+          * model_instance_kind: A string containing model instance kind
+          * model_instance_device_id: A string containing model instance device ID
+          * model_repository: Model repository path
+          * model_version: Model version
+          * model_name: Model name
+        """
+        # You must parse model_config. JSON string is not parsed here
+        self.model_config = json.loads(args['model_config'])
+        print("model_config:", self.model_config)
+
+        self.input_names = []
+        for input_config in self.model_config["input"]:
+            self.input_names.append(input_config["name"])
+        print("preprocess input names:", self.input_names)
+
+        self.output_names = []
+        self.output_dtype = []
+        for output_config in self.model_config["output"]:
+            self.output_names.append(output_config["name"])
+            # dtype = pb_utils.triton_string_to_numpy(output_config["data_type"])
+            # self.output_dtype.append(dtype)
+            self.output_dtype.append(output_config["data_type"])
+        print("preprocess output names:", self.output_names)
+
+        # init PaddleSegPreprocess class
+        yaml_path = os.path.abspath(os.path.dirname(__file__)) + "/deploy.yaml"
+        self.preprocess_ = fd.vision.segmentation.PaddleSegPreprocessor(
+            yaml_path)
+        #if args['model_instance_kind'] == 'GPU':
+        #    device_id = int(args['model_instance_device_id'])
+        #    self.preprocess_.use_gpu(device_id)
+
+    def execute(self, requests):
+        """`execute` must be implemented in every Python model. `execute`
+        function receives a list of pb_utils.InferenceRequest as the only
+        argument. This function is called when an inference is requested
+        for this model. Depending on the batching configuration (e.g. Dynamic
+        Batching) used, `requests` may contain multiple requests. Every
+        Python model, must create one pb_utils.InferenceResponse for every
+        pb_utils.InferenceRequest in `requests`. If there is an error, you can
+        set the error argument when creating a pb_utils.InferenceResponse.
+        Parameters
+        ----------
+        requests : list
+          A list of pb_utils.InferenceRequest
+        Returns
+        -------
+        list
+          A list of pb_utils.InferenceResponse. The length of this list must
+          be the same as `requests`
+        """
+        responses = []
+        for request in requests:
+            data = pb_utils.get_input_tensor_by_name(request,
+                                                     self.input_names[0])
+            data = data.as_numpy()
+            outputs, im_info = self.preprocess_.run(data)
+
+            # PaddleSeg preprocess has two outputs
+            dlpack_tensor = outputs[0].to_dlpack()
+            output_tensor_0 = pb_utils.Tensor.from_dlpack(self.output_names[0],
+                                                          dlpack_tensor)
+            output_tensor_1 = pb_utils.Tensor(
+                self.output_names[1], np.array(
+                    [im_info], dtype=np.object_))
+            inference_response = pb_utils.InferenceResponse(
+                output_tensors=[output_tensor_0, output_tensor_1])
+            responses.append(inference_response)
+        return responses
+
+    def finalize(self):
+        """`finalize` is called only once when the model is being unloaded.
+        Implementing `finalize` function is optional. This function allows
+        the model to perform any necessary clean ups before exit.
+        """
+        print('Cleaning up...')

examples/vision/segmentation/paddleseg/serving/models/preprocess/config.pbtxt

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+name: "preprocess"
+backend: "python"
+
+input [
+  {
+    name: "preprocess_input"
+    data_type: TYPE_UINT8
+    dims: [-1, -1, -1, 3 ]
+  }
+]
+
+output [
+  {
+    name: "preprocess_output_1"
+    data_type: TYPE_FP32
+    dims: [-1, 3, -1, -1 ]
+  },
+  {
+    name: "preprocess_output_2"
+    data_type: TYPE_STRING
+    dims: [ -1]
+  }
+]
+
+instance_group [
+  {
+    # The number of instances is 1
+    count: 1
+    # Use CPU, GPU inference option is:KIND_GPU
+    kind: KIND_CPU
+    # The instance is deployed on the 0th GPU card
+    # gpus: [0]
+  }
+]

examples/vision/segmentation/paddleseg/serving/models/runtime/1/README.md

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+# Runtime Directory
+
+This directory holds the model files.
+Paddle models must be model.pdmodel and model.pdiparams files.
+ONNX models must be model.onnx files.

examples/vision/segmentation/paddleseg/serving/models/runtime/config.pbtxt

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+# optional, If name is specified it must match the name of the model repository directory containing the model.
+name: "runtime"
+backend: "fastdeploy"
+
+# Input configuration of the model
+input [
+  {
+    # input name
+    name: "x"
+    # input type such as TYPE_FP32、TYPE_UINT8、TYPE_INT8、TYPE_INT16、TYPE_INT32、TYPE_INT64、TYPE_FP16、TYPE_STRING
+    data_type: TYPE_FP32
+    # input shape
+    dims: [-1, 3, -1, -1 ]
+  }
+]
+
+# The output of the model is configured in the same format as the input
+output [
+  {
+    name: "argmax_0.tmp_0"
+    data_type: TYPE_INT32
+    dims: [ -1, -1, -1 ]
+  }
+]
+
+# Number of instances of the model
+instance_group [
+  {
+    # The number of instances is 1
+    count: 1
+    # Use GPU, CPU inference option is:KIND_CPU
+    kind: KIND_GPU
+    # The instance is deployed on the 0th GPU card
+    gpus: [0]
+  }
+]
+
+optimization {
+  execution_accelerators {
+  gpu_execution_accelerator : [ {
+    # use TRT engine
+    name: "paddle",
+    #name: "tensorrt",
+    # use fp16 on TRT engine
+    parameters { key: "precision" value: "trt_fp32" }
+  },
+  {
+    name: "min_shape"
+    parameters { key: "x" value: "1 3 256 256" }
+  },
+  {
+    name: "opt_shape"
+    parameters { key: "x" value: "1 3 1024 1024" }
+  },
+  {
+    name: "max_shape"
+    parameters { key: "x" value: "16 3 2048 2048" }
+  }
+  ]
+}}