wehub-resource-sync
108 lines
3.3 KiB
Python
108 lines
3.3 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 numpy as np
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from paddle import base
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from paddle.base.core import AnalysisConfig, create_paddle_predictor
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class PredictorTools:
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'''
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Paddle-Inference predictor
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'''
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def __init__(self, model_path, model_file, params_file, feeds_var):
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'''
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__init__
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'''
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self.model_path = model_path
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self.model_file = model_file
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self.params_file = params_file
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self.feeds_var = feeds_var
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def _load_model_and_set_config(self):
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'''
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load model from file and set analysis config
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'''
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if os.path.exists(os.path.join(self.model_path, self.params_file)):
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config = AnalysisConfig(
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os.path.join(self.model_path, self.model_file),
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os.path.join(self.model_path, self.params_file),
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)
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else:
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config = AnalysisConfig(os.path.join(self.model_path))
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if base.is_compiled_with_cuda():
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config.enable_use_gpu(100, 0)
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else:
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config.disable_gpu()
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config.switch_specify_input_names(True)
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config.switch_use_feed_fetch_ops(False)
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config.enable_memory_optim()
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config.disable_glog_info()
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# TODO: set it to True after PaddleInference fix the precision error
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# in CUDA11
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config.switch_ir_optim(False)
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config.enable_new_ir()
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config.enable_new_executor()
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if os.name == 'nt':
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config.delete_pass("conv2d_bn_fuse_pass")
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return config
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def _get_analysis_outputs(self, config):
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'''
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Return outputs of paddle inference
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Args:
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config (AnalysisConfig): predictor configs
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Returns:
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outs (numpy array): forward network prediction outputs
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'''
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predictor = create_paddle_predictor(config)
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names = predictor.get_input_names()
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for i, name in enumerate(names):
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# assert name in self.feeds_var, '{} not in feeded dict'.format(name)
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tensor = predictor.get_input_tensor(name)
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feed_data = self.feeds_var[i]
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tensor.copy_from_cpu(np.array(feed_data))
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if type(feed_data) == base.DenseTensor:
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tensor.set_lod(feed_data.lod())
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# ensure no diff in multiple repeat times
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repeat_time = 2
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for i in range(repeat_time):
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predictor.zero_copy_run()
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output_names = predictor.get_output_names()
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outs = [
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predictor.get_output_tensor(out_name).copy_to_cpu()
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for out_name in output_names
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]
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return outs
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def __call__(self):
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'''
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__call__
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'''
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config = self._load_model_and_set_config()
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outputs = self._get_analysis_outputs(config)
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return outputs
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