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116 lines
3.7 KiB
Python
116 lines
3.7 KiB
Python
# Copyright (c) 2021 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 os
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import numpy as np
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import time
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import argparse
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from paddle.inference import Config
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from paddle.inference import create_predictor
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def parse_args():
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def str2bool(v):
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return v.lower() in ("true", "t", "1")
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# general params
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parser = argparse.ArgumentParser()
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parser.add_argument("--use_gpu", type=str2bool, default=False)
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parser.add_argument("--gpu_mem", type=int, default=1000)
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# params for predict
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parser.add_argument("--model_file", type=str)
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parser.add_argument("--params_file", type=str)
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parser.add_argument("-b", "--batch_size", type=int, default=1)
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parser.add_argument("--ir_optim", type=str2bool, default=True)
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parser.add_argument("--use_mkldnn", type=str2bool, default=True)
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parser.add_argument("--cpu_num_threads", type=int, default=10)
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parser.add_argument("--model", type=str)
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return parser.parse_args()
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def create_paddle_predictor(args):
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config = Config(args.model_file, args.params_file)
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if args.use_gpu:
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config.enable_use_gpu(args.gpu_mem, 0)
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else:
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config.disable_gpu()
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if args.use_mkldnn:
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config.enable_mkldnn()
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config.set_cpu_math_library_num_threads(args.cpu_num_threads)
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config.set_mkldnn_cache_capacity(100)
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config.disable_glog_info()
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config.switch_ir_optim(args.ir_optim) # default true
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config.enable_memory_optim()
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# use zero copy
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config.switch_use_feed_fetch_ops(False)
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predictor = create_predictor(config)
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return predictor
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class Predictor(object):
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def __init__(self, args):
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self.args = args
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self.paddle_predictor = create_paddle_predictor(args)
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input_names = self.paddle_predictor.get_input_names()
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self.input_tensor = self.paddle_predictor.get_input_handle(input_names[
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0])
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output_names = self.paddle_predictor.get_output_names()
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self.output_tensor = self.paddle_predictor.get_output_handle(
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output_names[0])
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def predict(self, batch_input):
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self.input_tensor.copy_from_cpu(batch_input)
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self.paddle_predictor.run()
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batch_output = self.output_tensor.copy_to_cpu()
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return batch_output
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def benchmark_predict(self):
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test_num = 500
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test_time = 0.0
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for i in range(0, test_num + 10):
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inputs = np.random.rand(args.batch_size, 3, 112,
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112).astype(np.float32)
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start_time = time.time()
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batch_output = self.predict(inputs).flatten()
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if i >= 10:
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test_time += time.time() - start_time
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# time.sleep(0.01) # sleep for T4 GPU
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print("{0}\tbatch size: {1}\ttime(ms): {2}".format(
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args.model, args.batch_size, 1000 * test_time / test_num))
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if __name__ == "__main__":
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args = parse_args()
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assert os.path.exists(
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args.model_file), "The path of 'model_file' does not exist: {}".format(
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args.model_file)
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assert os.path.exists(
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args.params_file
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), "The path of 'params_file' does not exist: {}".format(args.params_file)
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predictor = Predictor(args)
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assert args.model is not None
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predictor.benchmark_predict()
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