71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
# Copyright (c) 2020 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 argparse
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import base64
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import shutil
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import cv2
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import numpy as np
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from paddle.inference import Config
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from paddle.inference import create_predictor
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class Predictor(object):
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def __init__(self, args, inference_model_dir=None):
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# HALF precission predict only work when using tensorrt
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if args.use_fp16 is True:
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assert args.use_tensorrt is True
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self.args = args
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self.paddle_predictor, self.config = self.create_paddle_predictor(
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args, inference_model_dir)
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def predict(self, image):
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raise NotImplementedError
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def create_paddle_predictor(self, args, inference_model_dir=None):
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if inference_model_dir is None:
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inference_model_dir = args.inference_model_dir
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params_file = os.path.join(inference_model_dir, "inference.pdiparams")
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model_file = os.path.join(inference_model_dir, "inference.pdmodel")
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config = Config(model_file, 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.enable_mkldnn:
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# cache 10 different shapes for mkldnn to avoid memory leak
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config.set_mkldnn_cache_capacity(10)
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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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if args.enable_profile:
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config.enable_profile()
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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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if args.use_tensorrt:
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config.enable_tensorrt_engine(
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precision_mode=Config.Precision.Half
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if args.use_fp16 else Config.Precision.Float32,
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max_batch_size=args.batch_size,
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min_subgraph_size=30)
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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, config
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