[Runtime] Add Poros Backend Runtime demo for c++/python (#915)

* add onnx_ort_runtime demo

* rm in requirements

* support batch eval

* fixed MattingResults bug

* move assignment for DetectionResult

* integrated x2paddle

* add model convert readme

* update readme

* re-lint

* add processor api

* Add MattingResult Free

* change valid_cpu_backends order

* add ppocr benchmark

* mv bs from 64 to 32

* fixed quantize.md

* fixed quantize bugs

* Add Monitor for benchmark

* update mem monitor

* Set trt_max_batch_size default 1

* fixed ocr benchmark bug

* support yolov5 in serving

* Fixed yolov5 serving

* Fixed postprocess

* update yolov5 to 7.0

* add poros runtime demos

* update readme

* Support poros abi=1

* rm useless note

* deal with comments

Co-authored-by: Jason <jiangjiajun@baidu.com>

examples/runtime/python/infer_torchscript_poros.py

@@ -0,0 +1,62 @@
+# 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.
+
+from fastdeploy import ModelFormat
+
+import fastdeploy as fd
+import numpy as np
+
+
+def load_example_input_datas():
+    """prewarm datas"""
+    data_list = []
+    # max size
+    input_1 = np.ones((1, 3, 224, 224), dtype=np.float32)
+    max_inputs = [input_1]
+    data_list.append(tuple(max_inputs))
+
+    # min size
+    input_1 = np.ones((1, 3, 224, 224), dtype=np.float32)
+    min_inputs = [input_1]
+    data_list.append(tuple(min_inputs))
+
+    # opt size
+    input_1 = np.ones((1, 3, 224, 224), dtype=np.float32)
+    opt_inputs = [input_1]
+    data_list.append(tuple(opt_inputs))
+
+    return data_list
+
+
+if __name__ == '__main__':
+    # prewarm_datas
+    prewarm_datas = load_example_input_datas()
+    # download model
+    model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/std_resnet50_script.pt"
+    fd.download(model_url, path=".")
+
+    option = fd.RuntimeOption()
+    option.use_gpu(0)
+    option.use_poros_backend()
+    option.set_model_path(
+        "std_resnet50_script.pt", model_format=ModelFormat.TORCHSCRIPT)
+    option.is_dynamic = True
+    # compile
+    runtime = fd.Runtime(option)
+    runtime.compile(prewarm_datas)
+
+    # infer
+    input_data_0 = np.random.rand(1, 3, 224, 224).astype("float32")
+    result = runtime.forward(input_data_0)
+    print(result[0].shape)