Merge pull request #13442 from reyoung/feature/remove_trainer_api
Move trainer to contribfixCMakeFileInDoc2
Yu Yang
7 years ago
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GitHub
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606dfb13d5
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# 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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from __future__ import print_function
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import contextlib
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from .. import core
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from .. import executor
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from .. import framework
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from .. import io
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from .. import parallel_executor
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from .. 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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"""
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Inferencer High Level API.
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Args:
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infer_func (Python func): Infer function that will return predict Variable
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param_path (str): The path where the inference model is saved by fluid.io.save_params
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place (Place): place to do the inference
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parallel (bool): use parallel_executor to run the inference, it will use multi CPU/GPU.
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Examples:
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.. code-block:: python
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def inference_program():
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x = fluid.layers.data(name='x', shape=[13], dtype='float32')
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y_predict = fluid.layers.fc(input=x, size=1, act=None)
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return y_predict
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place = fluid.CPUPlace()
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inferencer = fluid.Inferencer(
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infer_func=inference_program, param_path="/tmp/model", place=place)
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"""
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def __init__(self, infer_func, param_path, place=None, parallel=False):
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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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Do Inference for Inputs
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Args:
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inputs (map): a map of {"input_name": input_var} that will be feed into the inference program
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return_numpy (bool): transform return value into numpy or not
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Returns:
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Tensor or Numpy: the predict value of the inference model for the inputs
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Examples:
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.. code-block:: python
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tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
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results = inferencer.infer({'x': tensor_x})
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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 self._prog_and_scope_guard():
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results = self.exe.run(feed=inputs,
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fetch_list=[self.predict_var.name],
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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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