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kiddyjinjin
d38aa4560c
* add poros to fastdeploy * update readme * update readme & add license for all files * update benchmark * update copyright for some files Co-authored-by: tianjinjin <tianjinjin@baidu.com>
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
# Copyright (c) 2022 Baidu, Inc. 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 os
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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import torch
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import poros
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import numpy as np
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def load_example_model():
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"""load model示例，正常load model即可"""
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import torchvision.models as models
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std_resnet = models.resnet50(pretrained=True)
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std_resnet.cuda()
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std_resnet.eval()
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return std_resnet
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def load_example_input_datas():
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"""加载预热数据"""
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data_list = []
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#max size
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input_1 = np.ones((3, 3, 96, 320), np.float32)
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input_tensor = torch.from_numpy(input_1).cuda()
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data_list.append(input_tensor)
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#min size
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input_2 = np.ones((1, 3, 96, 320), np.float32)
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input_tensor2 = torch.from_numpy(input_2).cuda()
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data_list.append(input_tensor2)
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#opt size
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input_3 = np.ones((1, 3, 96, 320), np.float32)
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input_tensor3 = torch.from_numpy(input_3).cuda()
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data_list.append(input_tensor3)
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return data_list
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if __name__ == '__main__':
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print("this is an example for poros")
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# step1: 按照正常的torch模块的步骤，load模型和参数，此处以resnet50为例
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# load_example_model 过程中load的原始pytorch模型（python代码），必须是完成了poros预处理的python代码
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# poros预处理相关wiki: 【待补充】
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original_model = load_example_model()
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# step2: 准备预热数据。
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# 请准备 1-3 份内容不一样的预热数据(example中是准备了3份一样的预热数据，只是示例，实际中尽量不要这样做)
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# 每一份预热数据用tuple封装，除非该模型只有一个输入，且这个输入类型是torch.Tensor.
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# 多份预热数据用list连接。
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# ！！！注意: 预热数据是必须的。
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input_datas = load_example_input_datas()
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# step3: 调用poros，编译原始的model，得到PorosModel
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# 当 option.is_dynamic 为true时，设置的预热数据的个数必须为3的倍数。
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# 当 option.is_dynamic 为false是，设置的预热数据至少为1份。
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option = poros.PorosOptions()
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option.is_dynamic = True
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#option.debug = True
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try:
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poros_model = poros.compile(original_model, input_datas, option)
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except Exception as e:
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print("compile poros_model failed. error msg: {}".format(e))
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#poros_model = original_model
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exit(0)
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# 序列化&反序列化
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# poros.save(poros_model, "poros_model.pt")
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# poros_model = poros.load("poros_model.pt", option)
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# 准备测试用的batch数据
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input = np.ones((3, 3, 96, 320), np.float32)
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batch_tensor = torch.from_numpy(input).cuda()
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# step4: 预测。
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#result = poros_model(input_datas[0])
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result = poros_model(batch_tensor)
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#result = original_model(batch_tensor)
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print(result.size())
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print(result)
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