mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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[Serving] add ppdet serving example (#641)
* serving support ppdet * Update README.md update ppadet/README
This commit is contained in:
heliqi
@@ -13,7 +13,7 @@ tar -xvf ResNet50_vd_infer.tgz
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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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# 将配置文件放入预处理目录
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mv ResNet50_vd_infer/inference_cls.yaml models/preprocess/1/
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mv ResNet50_vd_infer/inference_cls.yaml models/preprocess/1/inference_cls.yaml
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# 将模型放入 models/runtime/1目录下, 并重命名为model.pdmodel和model.pdiparams
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mv ResNet50_vd_infer/inference.pdmodel models/runtime/1/model.pdmodel
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@@ -47,3 +47,4 @@
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- [Python部署](python)
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- [C++部署](cpp)
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- [服务化部署](serving)
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@@ -0,0 +1,110 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import numpy as np
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import time
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import fastdeploy as fd
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# triton_python_backend_utils is available in every Triton Python model. You
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# need to use this module to create inference requests and responses. It also
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# contains some utility functions for extracting information from model_config
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# and converting Triton input/output types to numpy types.
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import triton_python_backend_utils as pb_utils
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class TritonPythonModel:
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"""Your Python model must use the same class name. Every Python model
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that is created must have "TritonPythonModel" as the class name.
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"""
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def initialize(self, args):
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"""`initialize` is called only once when the model is being loaded.
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Implementing `initialize` function is optional. This function allows
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the model to intialize any state associated with this model.
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Parameters
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----------
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args : dict
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Both keys and values are strings. The dictionary keys and values are:
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* model_config: A JSON string containing the model configuration
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* model_instance_kind: A string containing model instance kind
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* model_instance_device_id: A string containing model instance device ID
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* model_repository: Model repository path
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* model_version: Model version
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* model_name: Model name
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"""
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# You must parse model_config. JSON string is not parsed here
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self.model_config = json.loads(args['model_config'])
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print("model_config:", self.model_config)
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self.input_names = []
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for input_config in self.model_config["input"]:
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self.input_names.append(input_config["name"])
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print("postprocess input names:", self.input_names)
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self.output_names = []
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self.output_dtype = []
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for output_config in self.model_config["output"]:
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self.output_names.append(output_config["name"])
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dtype = pb_utils.triton_string_to_numpy(output_config["data_type"])
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self.output_dtype.append(dtype)
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print("postprocess output names:", self.output_names)
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self.postprocess_ = fd.vision.detection.PaddleDetPostprocessor()
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def execute(self, requests):
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"""`execute` must be implemented in every Python model. `execute`
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function receives a list of pb_utils.InferenceRequest as the only
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argument. This function is called when an inference is requested
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for this model. Depending on the batching configuration (e.g. Dynamic
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Batching) used, `requests` may contain multiple requests. Every
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Python model, must create one pb_utils.InferenceResponse for every
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pb_utils.InferenceRequest in `requests`. If there is an error, you can
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set the error argument when creating a pb_utils.InferenceResponse.
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Parameters
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----------
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requests : list
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A list of pb_utils.InferenceRequest
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Returns
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-------
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list
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A list of pb_utils.InferenceResponse. The length of this list must
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be the same as `requests`
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"""
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responses = []
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for request in requests:
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infer_outputs = []
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for name in self.input_names:
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infer_output = pb_utils.get_input_tensor_by_name(request, name)
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if infer_output:
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infer_output = infer_output.as_numpy()
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infer_outputs.append(infer_output)
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results = self.postprocess_.run(infer_outputs)
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r_str = fd.vision.utils.fd_result_to_json(results)
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r_np = np.array(r_str, dtype=np.object)
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out_tensor = pb_utils.Tensor(self.output_names[0], r_np)
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inference_response = pb_utils.InferenceResponse(
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output_tensors=[out_tensor, ])
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responses.append(inference_response)
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return responses
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def finalize(self):
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"""`finalize` is called only once when the model is being unloaded.
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Implementing `finalize` function is optional. This function allows
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the model to perform any necessary clean ups before exit.
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"""
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print('Cleaning up...')
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@@ -0,0 +1,30 @@
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name: "postprocess"
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backend: "python"
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input [
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{
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name: "post_input1"
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data_type: TYPE_FP32
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dims: [ -1, 6 ]
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},
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{
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name: "post_input2"
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data_type: TYPE_INT32
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dims: [ -1 ]
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}
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]
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output [
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{
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name: "post_output"
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data_type: TYPE_STRING
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dims: [ -1 ]
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}
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]
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instance_group [
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{
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count: 1
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kind: KIND_CPU
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}
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]
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@@ -0,0 +1,34 @@
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backend: "python"
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input [
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{
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name: "post_input1"
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data_type: TYPE_FP32
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dims: [ -1, 6 ]
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},
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{
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name: "post_input2"
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data_type: TYPE_INT32
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dims: [ -1 ]
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},
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{
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name: "post_input3"
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data_type: TYPE_INT32
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dims: [ -1, -1, -1 ]
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}
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]
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output [
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{
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name: "post_output"
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data_type: TYPE_STRING
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dims: [ -1 ]
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}
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]
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instance_group [
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{
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count: 1
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kind: KIND_CPU
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}
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]
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@@ -0,0 +1,3 @@
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# PaddleDetection Pipeline
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The pipeline directory does not have model files, but a version number directory needs to be maintained.
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@@ -0,0 +1,80 @@
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platform: "ensemble"
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input [
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{
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name: "INPUT"
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data_type: TYPE_UINT8
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dims: [ -1, -1, -1, 3 ]
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}
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]
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output [
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{
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name: "DET_RESULT"
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data_type: TYPE_STRING
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dims: [ -1 ]
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}
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]
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ensemble_scheduling {
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step [
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{
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model_name: "preprocess"
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model_version: 1
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input_map {
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key: "preprocess_input"
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value: "INPUT"
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}
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output_map {
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key: "preprocess_output1"
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value: "RUNTIME_INPUT1"
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}
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output_map {
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key: "preprocess_output2"
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value: "RUNTIME_INPUT2"
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}
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output_map {
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key: "preprocess_output3"
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value: "RUNTIME_INPUT3"
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}
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},
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{
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model_name: "runtime"
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model_version: 1
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input_map {
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key: "image"
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value: "RUNTIME_INPUT1"
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}
|
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input_map {
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key: "scale_factor"
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value: "RUNTIME_INPUT2"
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}
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input_map {
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key: "im_shape"
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value: "RUNTIME_INPUT3"
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}
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output_map {
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key: "concat_12.tmp_0"
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value: "RUNTIME_OUTPUT1"
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}
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output_map {
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key: "concat_8.tmp_0"
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value: "RUNTIME_OUTPUT2"
|
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}
|
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},
|
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{
|
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model_name: "postprocess"
|
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model_version: 1
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input_map {
|
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key: "post_input1"
|
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value: "RUNTIME_OUTPUT1"
|
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}
|
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input_map {
|
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key: "post_input2"
|
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value: "RUNTIME_OUTPUT2"
|
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}
|
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output_map {
|
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key: "post_output"
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value: "DET_RESULT"
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}
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}
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]
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}
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@@ -0,0 +1,88 @@
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platform: "ensemble"
|
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|
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input [
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{
|
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name: "INPUT"
|
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data_type: TYPE_UINT8
|
||||
dims: [ -1, -1, -1, 3 ]
|
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}
|
||||
]
|
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output [
|
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{
|
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name: "DET_RESULT"
|
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data_type: TYPE_STRING
|
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dims: [ -1 ]
|
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}
|
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]
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ensemble_scheduling {
|
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step [
|
||||
{
|
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model_name: "preprocess"
|
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model_version: 1
|
||||
input_map {
|
||||
key: "preprocess_input"
|
||||
value: "INPUT"
|
||||
}
|
||||
output_map {
|
||||
key: "preprocess_output1"
|
||||
value: "RUNTIME_INPUT1"
|
||||
}
|
||||
output_map {
|
||||
key: "preprocess_output2"
|
||||
value: "RUNTIME_INPUT2"
|
||||
}
|
||||
output_map {
|
||||
key: "preprocess_output3"
|
||||
value: "RUNTIME_INPUT3"
|
||||
}
|
||||
},
|
||||
{
|
||||
model_name: "runtime"
|
||||
model_version: 1
|
||||
input_map {
|
||||
key: "image"
|
||||
value: "RUNTIME_INPUT1"
|
||||
}
|
||||
input_map {
|
||||
key: "scale_factor"
|
||||
value: "RUNTIME_INPUT2"
|
||||
}
|
||||
input_map {
|
||||
key: "im_shape"
|
||||
value: "RUNTIME_INPUT3"
|
||||
}
|
||||
output_map {
|
||||
key: "concat_9.tmp_0"
|
||||
value: "RUNTIME_OUTPUT1"
|
||||
}
|
||||
output_map {
|
||||
key: "concat_5.tmp_0"
|
||||
value: "RUNTIME_OUTPUT2"
|
||||
},
|
||||
output_map {
|
||||
key: "tmp_109"
|
||||
value: "RUNTIME_OUTPUT3"
|
||||
}
|
||||
},
|
||||
{
|
||||
model_name: "postprocess"
|
||||
model_version: 1
|
||||
input_map {
|
||||
key: "post_input1"
|
||||
value: "RUNTIME_OUTPUT1"
|
||||
}
|
||||
input_map {
|
||||
key: "post_input2"
|
||||
value: "RUNTIME_OUTPUT2"
|
||||
}
|
||||
input_map {
|
||||
key: "post_input3"
|
||||
value: "RUNTIME_OUTPUT3"
|
||||
}
|
||||
output_map {
|
||||
key: "post_output"
|
||||
value: "DET_RESULT"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,80 @@
|
||||
platform: "ensemble"
|
||||
|
||||
input [
|
||||
{
|
||||
name: "INPUT"
|
||||
data_type: TYPE_UINT8
|
||||
dims: [ -1, -1, -1, 3 ]
|
||||
}
|
||||
]
|
||||
output [
|
||||
{
|
||||
name: "DET_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_output1"
|
||||
value: "RUNTIME_INPUT1"
|
||||
}
|
||||
output_map {
|
||||
key: "preprocess_output2"
|
||||
value: "RUNTIME_INPUT2"
|
||||
}
|
||||
output_map {
|
||||
key: "preprocess_output3"
|
||||
value: "RUNTIME_INPUT3"
|
||||
}
|
||||
},
|
||||
{
|
||||
model_name: "runtime"
|
||||
model_version: 1
|
||||
input_map {
|
||||
key: "image"
|
||||
value: "RUNTIME_INPUT1"
|
||||
}
|
||||
input_map {
|
||||
key: "scale_factor"
|
||||
value: "RUNTIME_INPUT2"
|
||||
}
|
||||
input_map {
|
||||
key: "im_shape"
|
||||
value: "RUNTIME_INPUT3"
|
||||
}
|
||||
output_map {
|
||||
key: "matrix_nms_0.tmp_0"
|
||||
value: "RUNTIME_OUTPUT1"
|
||||
}
|
||||
output_map {
|
||||
key: "matrix_nms_0.tmp_2"
|
||||
value: "RUNTIME_OUTPUT2"
|
||||
}
|
||||
},
|
||||
{
|
||||
model_name: "postprocess"
|
||||
model_version: 1
|
||||
input_map {
|
||||
key: "post_input1"
|
||||
value: "RUNTIME_OUTPUT1"
|
||||
}
|
||||
input_map {
|
||||
key: "post_input2"
|
||||
value: "RUNTIME_OUTPUT2"
|
||||
}
|
||||
output_map {
|
||||
key: "post_output"
|
||||
value: "DET_RESULT"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,72 @@
|
||||
platform: "ensemble"
|
||||
|
||||
input [
|
||||
{
|
||||
name: "INPUT"
|
||||
data_type: TYPE_UINT8
|
||||
dims: [ -1, -1, -1, 3 ]
|
||||
}
|
||||
]
|
||||
output [
|
||||
{
|
||||
name: "DET_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_output1"
|
||||
value: "RUNTIME_INPUT1"
|
||||
}
|
||||
output_map {
|
||||
key: "preprocess_output2"
|
||||
value: "RUNTIME_INPUT2"
|
||||
}
|
||||
},
|
||||
{
|
||||
model_name: "runtime"
|
||||
model_version: 1
|
||||
input_map {
|
||||
key: "image"
|
||||
value: "RUNTIME_INPUT1"
|
||||
}
|
||||
input_map {
|
||||
key: "scale_factor"
|
||||
value: "RUNTIME_INPUT2"
|
||||
}
|
||||
output_map {
|
||||
key: "multiclass_nms3_0.tmp_0"
|
||||
value: "RUNTIME_OUTPUT1"
|
||||
}
|
||||
output_map {
|
||||
key: "multiclass_nms3_0.tmp_2"
|
||||
value: "RUNTIME_OUTPUT2"
|
||||
}
|
||||
},
|
||||
{
|
||||
model_name: "postprocess"
|
||||
model_version: 1
|
||||
input_map {
|
||||
key: "post_input1"
|
||||
value: "RUNTIME_OUTPUT1"
|
||||
}
|
||||
input_map {
|
||||
key: "post_input2"
|
||||
value: "RUNTIME_OUTPUT2"
|
||||
}
|
||||
output_map {
|
||||
key: "post_output"
|
||||
value: "DET_RESULT"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,114 @@
|
||||
# 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 PaddleClasPreprocess class
|
||||
yaml_path = os.path.abspath(os.path.dirname(
|
||||
__file__)) + "/infer_cfg.yml"
|
||||
self.preprocess_ = fd.vision.detection.PaddleDetPreprocessor(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:
|
||||
data = pb_utils.get_input_tensor_by_name(request,
|
||||
self.input_names[0])
|
||||
data = data.as_numpy()
|
||||
outputs = self.preprocess_.run(data)
|
||||
|
||||
output_tensors = []
|
||||
for idx, name in enumerate(self.output_names):
|
||||
dlpack_tensor = outputs[idx].to_dlpack()
|
||||
output_tensor = pb_utils.Tensor.from_dlpack(name,
|
||||
dlpack_tensor)
|
||||
output_tensors.append(output_tensor)
|
||||
|
||||
inference_response = pb_utils.InferenceResponse(
|
||||
output_tensors=output_tensors)
|
||||
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...')
|
||||
@@ -0,0 +1,35 @@
|
||||
name: "preprocess"
|
||||
backend: "python"
|
||||
|
||||
input [
|
||||
{
|
||||
name: "preprocess_input"
|
||||
data_type: TYPE_UINT8
|
||||
dims: [ -1, -1, -1, 3 ]
|
||||
}
|
||||
]
|
||||
|
||||
output [
|
||||
{
|
||||
name: "preprocess_output1"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 3, -1, -1 ]
|
||||
},
|
||||
{
|
||||
name: "preprocess_output2"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 2 ]
|
||||
},
|
||||
{
|
||||
name: "preprocess_output3"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 2 ]
|
||||
}
|
||||
]
|
||||
|
||||
instance_group [
|
||||
{
|
||||
count: 1
|
||||
kind: KIND_CPU
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,5 @@
|
||||
# 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.
|
||||
@@ -0,0 +1,58 @@
|
||||
backend: "fastdeploy"
|
||||
|
||||
# Input configuration of the model
|
||||
input [
|
||||
{
|
||||
# input name
|
||||
name: "image"
|
||||
# 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， The batch dimension is omitted and the actual shape is [batch, c, h, w]
|
||||
dims: [ -1, 3, -1, -1 ]
|
||||
},
|
||||
{
|
||||
name: "scale_factor"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 2 ]
|
||||
},
|
||||
{
|
||||
name: "im_shape"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 2 ]
|
||||
}
|
||||
]
|
||||
|
||||
# The output of the model is configured in the same format as the input
|
||||
output [
|
||||
{
|
||||
name: "concat_12.tmp_0"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 6 ]
|
||||
},
|
||||
{
|
||||
name: "concat_8.tmp_0"
|
||||
data_type: TYPE_INT32
|
||||
dims: [ -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 Paddle engine
|
||||
name: "paddle",
|
||||
}
|
||||
]
|
||||
}}
|
||||
@@ -0,0 +1,63 @@
|
||||
backend: "fastdeploy"
|
||||
|
||||
# Input configuration of the model
|
||||
input [
|
||||
{
|
||||
# input name
|
||||
name: "image"
|
||||
# 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， The batch dimension is omitted and the actual shape is [batch, c, h, w]
|
||||
dims: [ -1, 3, -1, -1 ]
|
||||
},
|
||||
{
|
||||
name: "scale_factor"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 2 ]
|
||||
},
|
||||
{
|
||||
name: "im_shape"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 2 ]
|
||||
}
|
||||
]
|
||||
|
||||
# The output of the model is configured in the same format as the input
|
||||
output [
|
||||
{
|
||||
name: "concat_9.tmp_0"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 6 ]
|
||||
},
|
||||
{
|
||||
name: "concat_5.tmp_0"
|
||||
data_type: TYPE_INT32
|
||||
dims: [ -1 ]
|
||||
},
|
||||
{
|
||||
name: "tmp_109"
|
||||
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 Paddle engine
|
||||
name: "paddle",
|
||||
}
|
||||
]
|
||||
}}
|
||||
@@ -0,0 +1,58 @@
|
||||
backend: "fastdeploy"
|
||||
|
||||
# Input configuration of the model
|
||||
input [
|
||||
{
|
||||
# input name
|
||||
name: "image"
|
||||
# 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， The batch dimension is omitted and the actual shape is [batch, c, h, w]
|
||||
dims: [ -1, 3, -1, -1 ]
|
||||
},
|
||||
{
|
||||
name: "scale_factor"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 2 ]
|
||||
},
|
||||
{
|
||||
name: "im_shape"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 2 ]
|
||||
}
|
||||
]
|
||||
|
||||
# The output of the model is configured in the same format as the input
|
||||
output [
|
||||
{
|
||||
name: "matrix_nms_0.tmp_0"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 6 ]
|
||||
},
|
||||
{
|
||||
name: "matrix_nms_0.tmp_2"
|
||||
data_type: TYPE_INT32
|
||||
dims: [ -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 Paddle engine
|
||||
name: "paddle",
|
||||
}
|
||||
]
|
||||
}}
|
||||
@@ -0,0 +1,55 @@
|
||||
# optional, If name is specified it must match the name of the model repository directory containing the model.
|
||||
name: "ppyoloe_runtime"
|
||||
backend: "fastdeploy"
|
||||
|
||||
# Input configuration of the model
|
||||
input [
|
||||
{
|
||||
# input name
|
||||
name: "image"
|
||||
# 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， The batch dimension is omitted and the actual shape is [batch, c, h, w]
|
||||
dims: [ -1, 3, -1, -1 ]
|
||||
},
|
||||
{
|
||||
name: "scale_factor"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 2 ]
|
||||
}
|
||||
]
|
||||
|
||||
# The output of the model is configured in the same format as the input
|
||||
output [
|
||||
{
|
||||
name: "multiclass_nms3_0.tmp_0"
|
||||
data_type: TYPE_FP32
|
||||
dims: [ -1, 6 ]
|
||||
},
|
||||
{
|
||||
name: "multiclass_nms3_0.tmp_2"
|
||||
data_type: TYPE_INT32
|
||||
dims: [ -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 Paddle engine
|
||||
name: "paddle",
|
||||
}
|
||||
]
|
||||
}}
|
||||
@@ -0,0 +1,109 @@
|
||||
import logging
|
||||
import numpy as np
|
||||
import time
|
||||
from typing import Optional
|
||||
import cv2
|
||||
import json
|
||||
|
||||
from tritonclient import utils as client_utils
|
||||
from tritonclient.grpc import InferenceServerClient, InferInput, InferRequestedOutput, service_pb2_grpc, service_pb2
|
||||
|
||||
LOGGER = logging.getLogger("run_inference_on_triton")
|
||||
|
||||
|
||||
class SyncGRPCTritonRunner:
|
||||
DEFAULT_MAX_RESP_WAIT_S = 120
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
server_url: str,
|
||||
model_name: str,
|
||||
model_version: str,
|
||||
*,
|
||||
verbose=False,
|
||||
resp_wait_s: Optional[float]=None, ):
|
||||
self._server_url = server_url
|
||||
self._model_name = model_name
|
||||
self._model_version = model_version
|
||||
self._verbose = verbose
|
||||
self._response_wait_t = self.DEFAULT_MAX_RESP_WAIT_S if resp_wait_s is None else resp_wait_s
|
||||
|
||||
self._client = InferenceServerClient(
|
||||
self._server_url, verbose=self._verbose)
|
||||
error = self._verify_triton_state(self._client)
|
||||
if error:
|
||||
raise RuntimeError(
|
||||
f"Could not communicate to Triton Server: {error}")
|
||||
|
||||
LOGGER.debug(
|
||||
f"Triton server {self._server_url} and model {self._model_name}:{self._model_version} "
|
||||
f"are up and ready!")
|
||||
|
||||
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)
|
||||
LOGGER.info(f"Model config {model_config}")
|
||||
LOGGER.info(f"Model metadata {model_metadata}")
|
||||
|
||||
for tm in model_metadata.inputs:
|
||||
print("tm:", tm)
|
||||
self._inputs = {tm.name: tm for tm in model_metadata.inputs}
|
||||
self._input_names = list(self._inputs)
|
||||
self._outputs = {tm.name: tm for tm in model_metadata.outputs}
|
||||
self._output_names = list(self._outputs)
|
||||
self._outputs_req = [
|
||||
InferRequestedOutput(name) for name in self._outputs
|
||||
]
|
||||
|
||||
def Run(self, inputs):
|
||||
"""
|
||||
Args:
|
||||
inputs: list, Each value corresponds to an input name of self._input_names
|
||||
Returns:
|
||||
results: dict, {name : numpy.array}
|
||||
"""
|
||||
infer_inputs = []
|
||||
for idx, data in enumerate(inputs):
|
||||
infer_input = InferInput(self._input_names[idx], data.shape,
|
||||
"UINT8")
|
||||
infer_input.set_data_from_numpy(data)
|
||||
infer_inputs.append(infer_input)
|
||||
|
||||
results = self._client.infer(
|
||||
model_name=self._model_name,
|
||||
model_version=self._model_version,
|
||||
inputs=infer_inputs,
|
||||
outputs=self._outputs_req,
|
||||
client_timeout=self._response_wait_t, )
|
||||
results = {name: results.as_numpy(name) for name in self._output_names}
|
||||
return results
|
||||
|
||||
def _verify_triton_state(self, triton_client):
|
||||
if not triton_client.is_server_live():
|
||||
return f"Triton server {self._server_url} is not live"
|
||||
elif not triton_client.is_server_ready():
|
||||
return f"Triton server {self._server_url} is not ready"
|
||||
elif not triton_client.is_model_ready(self._model_name,
|
||||
self._model_version):
|
||||
return f"Model {self._model_name}:{self._model_version} is not ready"
|
||||
return None
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
model_name = "ppdet"
|
||||
model_version = "1"
|
||||
url = "localhost:8001"
|
||||
runner = SyncGRPCTritonRunner(url, model_name, model_version)
|
||||
im = cv2.imread("000000014439.jpg")
|
||||
im = np.array([im, ])
|
||||
# batch input
|
||||
# im = np.array([im, im, im])
|
||||
for i in range(1):
|
||||
result = runner.Run([im, ])
|
||||
for name, values in result.items():
|
||||
print("output_name:", name)
|
||||
# values is batch
|
||||
for value in values:
|
||||
value = json.loads(value)
|
||||
print(value['boxes'])
|
||||
Reference in New Issue
Block a user