test=develop, reconstruct layer helper to fit imperative usage (#15938)
* test=develop, reconstruct layer helper to fit imperative usage * test=develop, fix import error on py35 * test=develop, fix rnn gradient error * test=develop, delete test use code * test=develop, remove helper from imperative usage * test=develop, fix test_base_layer using new helper * test=develop, reconstruct layerhelper for imperative mode * test=develop, reconstruct layerhelper for imperative mode * test=develop, fix bug * test=develop, fix test failed bug * test=develop, fix test failed bug * test=develop, fix test failed bug * test=develop, fix bug * test=develop, polish codealign_pyramid
Jiabin Yang
7 years ago
committed by
GitHub
parent
14b4337663
commit
654825cfe3
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# Copyright (c) 2019 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 copy
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import six
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from ..framework import Parameter, _in_imperative_mode
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from ..param_attr import ParamAttr
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from .. import core
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from six.moves import zip
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from ..layer_helper_base import LayerHelperBase
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class LayerObjectHelper(LayerHelperBase):
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def __init__(self, name):
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super(LayerObjectHelper, self).__init__(name, layer_type=name)
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def append_op(self,
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type=None,
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inputs=None,
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outputs=None,
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attrs=None,
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stop_gradient=None):
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"""append an operator for this layer object.
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Args:
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type: operator type
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inputs: input variable of the operator
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dtype: data type of this parameter
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is_bias: if this is a bias parameter
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default_initializer: set the default initializer for this parameter
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Returns created parameter Variable.
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"""
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return self.main_program.current_block().append_op(
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type=type,
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inputs=inputs,
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outputs=outputs,
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attrs=attrs,
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stop_gradient=stop_gradient)
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def _multiple_input(self, inputs_in):
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inputs = inputs_in
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ret = []
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if isinstance(inputs, (list, tuple)):
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for inp in inputs:
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ret.append(self.to_variable(inp))
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else:
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ret.append(self.to_variable(inputs))
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return ret
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# TODO: make it public when we need it
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def _input(self, inputs_in):
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inputs = self._multiple_input(inputs_in)
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if len(inputs) != 1:
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raise "{0} layer only takes one input".format(self.layer_type)
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return inputs[0]
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def _multiple_param_attr(self, length, param_attr_in=None):
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param_attr = param_attr_in
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if isinstance(param_attr, ParamAttr):
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param_attr = [param_attr]
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if len(param_attr) != 1 and len(param_attr) != length:
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raise ValueError("parameter number mismatch")
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elif len(param_attr) == 1 and length != 1:
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tmp = [None] * length
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for i in six.moves.range(length):
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tmp[i] = copy.deepcopy(param_attr[0])
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param_attr = tmp
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return param_attr
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def iter_inputs_and_params(self, inputs_in, param_attr_in=None):
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"""Access all inputs and params one by one
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Args:
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inputs_in: inputs to be iter
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param_attr_in: param_attr to be iter
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Returns input, param_attr
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"""
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inputs = inputs_in if (inputs_in is not None) else []
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inputs = self._multiple_input(inputs)
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param_attrs = self._multiple_param_attr(len(inputs), param_attr_in)
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for ipt, param_attr in zip(inputs, param_attrs):
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yield ipt, param_attr
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def input_dtype(self, inputs_in):
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"""Get input data type
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Args:
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inputs_in: inputs wanted know the data type
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Returns dtype of the input
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"""
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inputs = self._multiple_input(inputs_in)
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dtype = None
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for each in inputs:
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if dtype is None:
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dtype = each.dtype
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elif dtype != each.dtype:
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raise ValueError("Data Type mismatch: %d to %d" %
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(dtype, each.dtype))
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return dtype
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def get_parameter(self, name):
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"""Get parameter specifically
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Args:
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name: parameter's name
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Returns target parameter
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"""
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param = self.main_program.global_block().var(name)
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if not isinstance(param, Parameter):
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raise ValueError("no Parameter name %s found" % name)
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return param
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def append_bias_op(self,
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input_var,
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dim_start=1,
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dim_end=None,
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bias_attr=None):
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"""Append bias operator and return its output. If the user does not set bias_attr, append_bias_op will return input_var
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Args:
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input_var: the input variable. The len(input_var.shape) is
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larger or equal than 2.
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dim_start:
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dim_end: the shape of the bias will be
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bias_attr: the bias_attr of it
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Return the Variable of after append bias op
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"""
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size = list(input_var.shape[dim_start:dim_end])
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bias_attr = bias_attr
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if not bias_attr:
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return input_var
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b = self.create_parameter(
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attr=bias_attr, shape=size, dtype=input_var.dtype, is_bias=True)
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tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
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self.append_op(
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type='elementwise_add',
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inputs={'X': [input_var],
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'Y': [b]},
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outputs={'Out': [tmp]},
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attrs={'axis': dim_start})
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return tmp
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# TODO: this should not be called anymore after all activation func move to Layers
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def append_activation(self,
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input_var,
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act=None,
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use_cudnn=None,
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use_mkl_dnn=None):
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"""Append activation
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Args:
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input_var: the input variable. The len(input_var.shape) is
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larger or equal than 2.
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act: activation type
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use_mkl_dnn: if use mkldnn
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use_cudnn: if use cudnn
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Return the Variable of after append activation
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"""
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act = act
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if act is None:
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return input_var
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if isinstance(act, six.string_types):
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act = {'type': act}
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else:
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raise TypeError(str(act) + " should be unicode or str")
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if (use_cudnn is not None) and use_cudnn:
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act['use_cudnn'] = use_cudnn
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if (use_mkl_dnn is not None) and use_mkl_dnn:
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act['use_mkldnn'] = use_mkl_dnn
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act_type = act.pop('type')
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tmp = input_var
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# NOTE(dzhwinter): some activation support inplace compution.
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# NOTE(minqiyang): currently, we don't support inplace in imperative mode
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if not _in_imperative_mode() and core.IsInplace(act_type):
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tmp = input_var
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else:
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tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
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self.append_op(
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type=act_type,
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inputs={"X": [input_var]},
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outputs={"Out": [tmp]},
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attrs=act)
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return tmp
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def is_instance(self, param, cls):
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"""Check if the input parameter is instance of input class
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Args:
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param: parameter to be check
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cls: class of the parameter
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Return result of the check (True or False)
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"""
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param = param
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if not isinstance(param, cls):
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raise TypeError("The input {0} parameter of method {1} must be {2}",
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param, self.layer_type, cls.__name__)
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