mirror of
https://github.com/zebrajr/pytorch.git
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75bece6ede
Summary: Closes https://github.com/caffe2/caffe2/pull/1316 Differential Revision: D6026557 Pulled By: Yangqing fbshipit-source-id: 95c634872ac02be721257169e38c8fead04cd66b
82 lines
2.9 KiB
Python
82 lines
2.9 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 position_weighted
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# Module caffe2.python.layers.position_weighted
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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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import logging
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import numpy as np
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from caffe2.python import schema
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from caffe2.python.layers.layers import (
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get_categorical_limit,
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ModelLayer,
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)
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from caffe2.python.layers.tags import Tags
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logging.basicConfig()
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logger = logging.getLogger(__name__)
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class PositionWeighted(ModelLayer):
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def __init__(self, model, input_record, weight_optim=None,
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name="position_weights"):
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super(PositionWeighted, self).__init__(model, name, input_record)
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assert isinstance(input_record, schema.List), "Incorrect input type"
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length_metadata = input_record.lengths.metadata
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max_length = (length_metadata.categorical_limit if length_metadata is
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not None else None)
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if max_length is not None:
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self.shape = max_length
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else:
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self.shape = get_categorical_limit(input_record)
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logger.warning(
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'{}: categorical_limit of lengths is not available, using '
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'categorical_limit of the keys: {}'.format(
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str(input_record.lengths()), self.shape))
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self.pos_w = self.create_param(param_name='pos_w',
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shape=[self.shape, ],
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initializer=('ConstantFill', {'value': 1.0}),
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optimizer=weight_optim)
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self.output_schema = schema.Struct(
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('position_weights',
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schema.Scalar((np.float32, self.shape),
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self.get_next_blob_reference("pos_w_gather")))
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)
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self.tags.update({Tags.HANDLE_AS_SPARSE_LAYER})
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self.tags.update({Tags.GRADIENT_FROM_PS})
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def get_memory_usage(self):
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return self.shape
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def add_ops(self, net):
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inc_seq = net.LengthsRangeFill(
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[self.input_record.lengths()],
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self.input_record.lengths() + '_pos_w_seq'
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)
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net.Gather(
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[self.pos_w, inc_seq],
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self.output_schema.position_weights.field_blobs())
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