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
212 lines
8.7 KiB
Python
212 lines
8.7 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 sparse_to_dense
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# Module caffe2.python.layers.sparse_to_dense
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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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)
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import numpy as np
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class FeatureSparseToDense(ModelLayer):
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_known_types = ['FLOAT', 'ID_LIST']
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def __init__(self, model, input_record, input_specs,
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name='feature_sparse_to_dense', **kwargs):
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"""
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`input_specs` follows the format of FeatureSpec from schema. To be more
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precise it's a namedtuple that should have:
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'feature_type', 'feature_names', 'feature_ids'
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"""
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super(FeatureSparseToDense, self).__init__(model, name,
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input_record, **kwargs)
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self.input_specs = input_specs
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outputs = []
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for field, feature_specs in self.input_specs:
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assert len(feature_specs.feature_names) ==\
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len(feature_specs.feature_ids)
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if feature_specs.feature_type == 'FLOAT':
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outputs.append((
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field,
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schema.Scalar(
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(np.float32, (len(feature_specs.feature_ids), )),
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self.get_next_blob_reference(field + '_output')
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)
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))
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elif feature_specs.feature_type == 'ID_LIST':
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outputs.append((
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field,
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schema.Struct(
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('ranges',
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schema.Scalar(
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(
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np.int32,
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(len(feature_specs.feature_ids), 2)
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),
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self.get_next_blob_reference(
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field + '_ranges')
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),
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),
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('values',
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schema.Scalar(np.int64,
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self.get_next_blob_reference(
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field + '_values')
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),
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)
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)
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))
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elif feature_specs.feature_type == 'ID_SCORE_LIST':
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outputs.append((
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field,
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schema.Struct(
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('ranges',
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schema.Scalar(
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(
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np.int32,
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(len(feature_specs.feature_ids), 2)
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),
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self.get_next_blob_reference(
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field + '_ranges')
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),
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),
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('ids',
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schema.Scalar(np.int64,
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self.get_next_blob_reference(
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field + '_ids')
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),
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),
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('scores',
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schema.Scalar(np.float32,
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self.get_next_blob_reference(
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field + '_scores')
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),
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)
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)
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))
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else:
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raise TypeError(
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"Unsupported input type: {0}".
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format(feature_specs.feature_type))
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# TODO(amalevich): This schema is producing ranges. And thus if there is
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# something using it it should support ranges as well. It might be
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# confusing, if we don't add better support for ranges/have it as a
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# first layer
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self.output_schema = schema.Struct(
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*outputs
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)
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# TODO(amalevich): Consider moving this data to schema, instead
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# Structs doens't support attaching metadata to them and clonning
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# will break things badly, but this is the most elegant way to pass
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# this info around. Should we change it or it'll be too much work and
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# not worse it?
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for field, feature_specs in input_specs:
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schema.attach_metadata_to_scalars(
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self.output_schema[field],
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schema.Metadata(
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feature_specs=feature_specs)
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)
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self.zero = model.global_constants['ZERO']
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self.zero_range = model.global_constants['ZERO_RANGE']
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# Add operators to all types that need to be densified
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def add_ops(self, net):
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record = self.input_record
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for field, feature_specs in self.input_specs:
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if feature_specs.feature_type == 'FLOAT':
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net.SparseToDenseMask(
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[
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record[field].keys(),
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record[field].values(),
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self.zero,
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record[field].lengths(),
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],
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[
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self.output_schema[field](),
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],
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mask=feature_specs.feature_ids,
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)
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elif feature_specs.feature_type == 'ID_LIST':
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id_list_ranges = net.LengthsToRanges(
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record[field].values.lengths(),
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net.NextScopedBlob('id_list_ranges')
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)
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net.SparseToDenseMask(
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[
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record[field].keys(), id_list_ranges, self.zero_range,
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record[field].lengths()
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],
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self.output_schema[field].ranges(),
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mask=feature_specs.feature_ids,
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)
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# Alias helps to enforce the fact that all SparseToDense calls
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# produce new blobs.
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# Reusing blob names might result in some weird consequences
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# during the delivery time, when content of the blobs is
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# generated based on the inputSpecs.
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net.Alias(record[field].values.items(),
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self.output_schema[field].values())
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elif feature_specs.feature_type == 'ID_SCORE_LIST':
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# TODO: merge this to the case above?
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id_list_ranges = net.LengthsToRanges(
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record[field].values.lengths(),
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net.NextScopedBlob('id_score_list_ranges')
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)
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net.SparseToDenseMask(
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[
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record[field].keys(), id_list_ranges, self.zero_range,
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record[field].lengths()
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],
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self.output_schema[field].ranges(),
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mask=feature_specs.feature_ids,
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)
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# Alias helps to enforce the fact that all SparseToDense calls
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# produce new blobs.
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# Reusing blob names might result in some weird consequences
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# during the delivery time, when content of the blobs is
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# generated based on the inputSpecs.
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net.Alias(record[field].values.keys(),
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self.output_schema[field].ids())
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net.Alias(record[field].values.values(),
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self.output_schema[field].scores())
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def get_metadata(self):
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metadata = []
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for field, feature_specs in self.input_specs:
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metadata.append(
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(
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{
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'type': feature_specs.feature_type,
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'names': feature_specs.feature_names,
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'ids': feature_specs.feature_ids,
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},
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self.output_schema[field].field_blobs(),
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self.output_schema[field].field_types()
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)
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)
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if feature_specs.feature_type == 'FLOAT':
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metadata[-1][0]['cardinality'] = 1
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return metadata
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