mirror of
https://github.com/saymrwulf/pytorch.git
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8286ce1e3a
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902
101 lines
3.4 KiB
Python
101 lines
3.4 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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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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##############################################################################
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## @package seq2seq_model_helper
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# Module caffe2.python.models.seq2seq.seq2seq_model_helper
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import scope
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from caffe2.python.model_helper import ModelHelper
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class Seq2SeqModelHelper(ModelHelper):
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def __init__(self, init_params=True, **kwargs):
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arg_scope = {
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'use_cudnn': kwargs.pop('use_cudnn', True),
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'cudnn_exhaustive_search': kwargs.pop('cudnn_exhaustive_search', False),
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'order': 'NHWC',
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}
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if kwargs.get('ws_nbytes_limit', None):
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arg_scope['ws_nbytes_limit'] = kwargs.pop('ws_nbytes_limit')
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super(Seq2SeqModelHelper, self).__init__(
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init_params=init_params,
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arg_scope=arg_scope,
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**kwargs
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)
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self.non_trainable_params = []
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def AddParam(self, name, init=None, init_value=None, trainable=True):
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"""Adds a parameter to the model's net and it's initializer if needed
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Args:
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init: a tuple (<initialization_op_name>, <initialization_op_kwargs>)
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init_value: int, float or str. Can be used instead of `init` as a
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simple constant initializer
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trainable: bool, whether to compute gradient for this param or not
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"""
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if init_value is not None:
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assert init is None
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assert type(init_value) in [int, float, str]
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init = ('ConstantFill', dict(
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shape=[1],
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value=init_value,
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))
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if self.init_params:
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param = self.param_init_net.__getattr__(init[0])(
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[],
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name,
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**init[1]
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)
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else:
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param = self.net.AddExternalInput(name)
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if trainable:
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self.params.append(param)
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else:
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self.non_trainable_params.append(param)
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return param
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def GetNonTrainableParams(self, namescope=None):
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'''
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Returns the params in current namescope
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'''
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if namescope is None:
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namescope = scope.CurrentNameScope()
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else:
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if not namescope.endswith(scope._NAMESCOPE_SEPARATOR):
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namescope += scope._NAMESCOPE_SEPARATOR
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if namescope == '':
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return self.non_trainable_params[:]
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else:
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return [
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p for p in self.non_trainable_params
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if p.GetNameScope() == namescope
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]
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def GetAllParams(self, namescope=None):
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return (
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self.GetParams(namescope) +
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self.GetComputedParams(namescope) +
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self.GetNonTrainableParams(namescope)
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)
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