mirror of
https://github.com/zebrajr/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
86 lines
2.7 KiB
Python
86 lines
2.7 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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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 numpy as np
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from caffe2.python import core, schema
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from caffe2.python.layers.layers import ModelLayer
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class MapToRange(ModelLayer):
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"""
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This layer aims to build a mapping from raw keys to indices within [0, max_index).
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The mapping is continuously built during training. The mapping will be frozen during
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evaluation and prediction. Unseen keys will be assigned to index 0.
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"""
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def __init__(
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self, model,
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input_record,
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max_index,
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name='map_to_range',
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**kwargs
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):
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super(MapToRange, self).__init__(model, name, input_record, **kwargs)
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assert max_index > 0
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assert isinstance(input_record, schema.Scalar)
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self.max_index = max_index
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self.handler = self.create_param(
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param_name='handler',
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shape=None,
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initializer=('LongIndexCreate', {'max_elements': self.max_index}),
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optimizer=model.NoOptim
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)
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self.output_schema = schema.Struct(
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('indices', schema.Scalar(
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np.int64, self.get_next_blob_reference("indices")
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)),
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('handler', schema.Scalar(
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np.void, self.handler
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)),
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)
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def add_train_ops(self, net):
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if self.input_record.field_type().base != np.int64:
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keys = net.Cast(
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self.input_record(),
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net.NextScopedBlob("indices_before_mapping"),
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to=core.DataType.INT64
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)
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else:
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keys = self.input_record()
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# Load keys into indices
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indices = net.IndexGet([self.handler, keys],
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self.output_schema.indices())
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net.StopGradient(indices, indices)
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def add_eval_ops(self, net):
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net.IndexFreeze(self.handler, self.handler)
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self.add_train_ops(net)
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def add_ops(self, net):
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self.add_eval_ops(net)
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