mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-25 09:31:38 +08:00
c8db4b442a
* 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>
63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
# 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)
|