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
104 lines
3.7 KiB
Python
104 lines
3.7 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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## @package reservoir_sampling
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# Module caffe2.python.layers.reservoir_sampling
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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 core, schema
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from caffe2.python.layers.layers import ModelLayer
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class ReservoirSampling(ModelLayer):
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"""
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Collect samples from input record w/ reservoir sampling. If you have complex
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data, use PackRecords to pack it before using this layer.
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This layer is not thread safe.
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"""
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def __init__(self, model, input_record, num_to_collect,
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name='reservoir_sampling', **kwargs):
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super(ReservoirSampling, self).__init__(
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model, name, input_record, **kwargs)
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assert num_to_collect > 0
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self.num_to_collect = num_to_collect
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self.reservoir = self.create_param(
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param_name='reservoir',
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shape=[0],
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initializer=('ConstantFill',),
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optimizer=model.NoOptim,
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)
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self.num_visited_blob = self.create_param(
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param_name='num_visited',
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shape=[],
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initializer=('ConstantFill', {
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'value': 0,
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'dtype': core.DataType.INT64,
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}),
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optimizer=model.NoOptim,
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)
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self.mutex = self.create_param(
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param_name='mutex',
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shape=None,
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initializer=('CreateMutex',),
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optimizer=model.NoOptim,
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)
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self.extra_input_blobs = []
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self.extra_output_blobs = []
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if 'object_id' in input_record:
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object_to_pos = self.create_param(
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param_name='object_to_pos',
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initializer=('CreateMap', {
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'key_dtype': core.DataType.INT64,
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'valued_dtype': core.DataType.INT32,
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}),
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optimizer=model.NoOptim,
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)
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pos_to_object = self.create_param(
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param_name='pos_to_object',
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shape=[0],
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initializer=('ConstantFill', {
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'value': 0,
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'dtype': core.DataType.INT64,
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}),
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optimizer=model.NoOptim,
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)
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self.extra_input_blobs.append(input_record.object_id())
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self.extra_input_blobs.extend([object_to_pos, pos_to_object])
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self.extra_output_blobs.extend([object_to_pos, pos_to_object])
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self.output_schema = schema.Struct(
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(
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'reservoir',
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schema.from_blob_list(input_record.data, [self.reservoir])
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),
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('num_visited', schema.Scalar(blob=self.num_visited_blob)),
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('mutex', schema.Scalar(blob=self.mutex)),
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)
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def add_ops(self, net):
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net.ReservoirSampling(
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[self.reservoir, self.num_visited_blob, self.input_record.data(),
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self.mutex] + self.extra_input_blobs,
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[self.reservoir, self.num_visited_blob] + self.extra_output_blobs,
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num_to_collect=self.num_to_collect,
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)
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