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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>
90 lines
2.6 KiB
Python
90 lines
2.6 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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"""
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test resnet50
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"""
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import time
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import poros
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import torch
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from torchvision import models
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torch.set_grad_enabled(False)
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def load_example_input_datas():
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"""fake data"""
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data_list = []
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input_1 = torch.randn(1, 3, 224, 224, dtype=torch.float32).cuda()
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data_list.append(input_1)
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return data_list
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if __name__ == '__main__':
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input_datas = load_example_input_datas()
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original_model = models.resnet50(pretrained=True).cuda().eval()
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option = poros.PorosOptions()
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# option.max_workspace_size = 1 << 30
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# option.is_dynamic = False
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# option.debug = True
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# option.unconst_ops_thres = 0
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try:
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poros_model = poros.compile(torch.jit.script(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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exit(0)
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for input in input_datas:
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ori_res = original_model(input)
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poros_res = poros_model(input)
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res_diff = torch.abs(ori_res - poros_res)
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print("max_diff", torch.max(res_diff))
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print(poros_res.shape)
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# warm up
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for i in range (100):
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for input in input_datas:
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ori_res = original_model(input)
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poros_res = poros_model(input)
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count = 1000
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# POROS benchmark
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torch.cuda.synchronize()
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st = time.time()
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for i in range (count):
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# step4: 预测。
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for input in input_datas:
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poros_res = poros_model(input)
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torch.cuda.synchronize()
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poros_elapsed_time = time.time() - st
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print("poros infer time:{:.5f}ms/infer".format(poros_elapsed_time))
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# original benchmark
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torch.cuda.synchronize()
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st = time.time()
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for i in range (count):
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# step4: 预测。
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for input in input_datas:
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ori_res = original_model(input)
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torch.cuda.synchronize()
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original_elapsed_time = time.time() - st
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print("original infer time/:{:.5f}ms/infer".format(original_elapsed_time))
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print("speedup: +{:.2f}%".format((original_elapsed_time / poros_elapsed_time - 1 ) * 100)) |