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84 lines
3.0 KiB
84 lines
3.0 KiB
# Copyright (c) 2018 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 contextlib
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import core
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import executor
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import framework
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import io
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import parallel_executor
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import unique_name
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from trainer import check_and_get_place
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__all__ = ['Inferencer', ]
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class Inferencer(object):
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def __init__(self, infer_func, param_path, place=None, parallel=False):
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"""
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:param infer_func: a function that will return predict Variable
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:param param_path: the path where the inference model is saved by fluid.io.save_params
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:param place: place to do the inference
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:param parallel: use parallel_executor to run the inference, it will use multi CPU/GPU.
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"""
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self.param_path = param_path
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self.scope = core.Scope()
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self.parallel = parallel
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self.place = check_and_get_place(place)
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self.inference_program = framework.Program()
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with framework.program_guard(self.inference_program):
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with unique_name.guard():
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self.predict_var = infer_func()
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with self._prog_and_scope_guard():
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# load params from param_path into scope
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io.load_params(executor.Executor(self.place), param_path)
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if parallel:
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with self._prog_and_scope_guard():
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self.exe = parallel_executor.ParallelExecutor(
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use_cuda=isinstance(self.place, core.CUDAPlace),
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loss_name=self.predict_var.name)
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else:
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self.exe = executor.Executor(self.place)
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self.inference_program = self.inference_program.clone(for_test=True)
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def infer(self, inputs, return_numpy=True):
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"""
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:param inputs: a map of {"input_name": input_var} that will be feed into the inference program
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to get the predict value
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:return: the predict value of the inference model
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"""
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if not isinstance(inputs, dict):
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raise ValueError(
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"inputs should be a map of {'input_name': input_var}")
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with executor.scope_guard(self.scope):
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results = self.exe.run(self.inference_program,
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feed=inputs,
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fetch_list=[self.predict_var],
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return_numpy=return_numpy)
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return results
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@contextlib.contextmanager
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def _prog_and_scope_guard(self):
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with framework.program_guard(main_program=self.inference_program):
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with executor.scope_guard(self.scope):
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yield
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