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
117 lines
4.3 KiB
Python
117 lines
4.3 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 sparse_feature_hash
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# Module caffe2.python.layers.sparse_feature_hash
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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 schema
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from caffe2.python.layers.layers import (
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ModelLayer,
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IdList,
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IdScoreList,
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)
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import numpy as np
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class SparseFeatureHash(ModelLayer):
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def __init__(self, model, input_record, seed,
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name='sparse_feature_hash', **kwargs):
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super(SparseFeatureHash, self).__init__(model, name, input_record, **kwargs)
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self.seed = seed
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self.lengths_blob = schema.Scalar(
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np.int32,
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self.get_next_blob_reference("lengths"),
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)
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if schema.equal_schemas(input_record, IdList):
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self.modulo = self.extract_hash_size(input_record.items.metadata)
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metadata = schema.Metadata(
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categorical_limit=self.modulo,
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feature_specs=input_record.items.metadata.feature_specs,
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)
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hashed_indices = schema.Scalar(
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np.int64,
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self.get_next_blob_reference("hashed_idx")
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)
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hashed_indices.set_metadata(metadata)
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self.output_schema = schema.List(
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values=hashed_indices,
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lengths_blob=self.lengths_blob,
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)
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elif schema.equal_schemas(input_record, IdScoreList):
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self.values_blob = schema.Scalar(
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np.float32,
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self.get_next_blob_reference("values"),
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)
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self.modulo = self.extract_hash_size(input_record.keys.metadata)
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metadata = schema.Metadata(
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categorical_limit=self.modulo,
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feature_specs=input_record.keys.metadata.feature_specs,
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)
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hashed_indices = schema.Scalar(
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np.int64,
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self.get_next_blob_reference("hashed_idx")
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)
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hashed_indices.set_metadata(metadata)
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self.output_schema = schema.Map(
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keys=hashed_indices,
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values=self.values_blob,
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lengths_blob=self.lengths_blob,
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)
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else:
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assert False, "Input type must be one of (IdList, IdScoreList)"
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def extract_hash_size(self, metadata):
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if metadata.feature_specs and metadata.feature_specs.desired_hash_size:
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return metadata.feature_specs.desired_hash_size
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elif metadata.categorical_limit is not None:
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return metadata.categorical_limit
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else:
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assert False, "desired_hash_size or categorical_limit must be set"
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def add_ops(self, net):
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if schema.equal_schemas(self.output_schema, IdList):
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input_blobs = self.input_record.items.field_blobs()
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output_blobs = self.output_schema.items.field_blobs()
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net.Alias(
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self.input_record.lengths.field_blobs(),
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self.lengths_blob.field_blobs()
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)
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elif schema.equal_schemas(self.output_schema, IdScoreList):
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input_blobs = self.input_record.keys.field_blobs()
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output_blobs = self.output_schema.keys.field_blobs()
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net.Alias(
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self.input_record.values.field_blobs(),
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self.values_blob.field_blobs()
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)
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net.Alias(
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self.input_record.lengths.field_blobs(),
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self.lengths_blob.field_blobs()
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)
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else:
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raise NotImplementedError()
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net.IndexHash(input_blobs,
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output_blobs,
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seed=self.seed,
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modulo=self.modulo)
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