mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-08 07:39:33 +01:00
ae5673741b
Summary: as desc. Differential Revision: D6240061 fbshipit-source-id: 814a541a3e7f09ebbe2df63fd9202312e9f4c8d4
103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
##############################################################################
|
|
|
|
## @package sparse_feature_hash
|
|
# Module caffe2.python.layers.sparse_feature_hash
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
from caffe2.python import schema
|
|
from caffe2.python.layers.layers import (
|
|
ModelLayer,
|
|
IdList,
|
|
IdScoreList,
|
|
)
|
|
|
|
import numpy as np
|
|
|
|
|
|
class SparseFeatureHash(ModelLayer):
|
|
|
|
def __init__(self, model, input_record, seed=0, modulo=None,
|
|
use_hashing=True, name='sparse_feature_hash', **kwargs):
|
|
super(SparseFeatureHash, self).__init__(model, name, input_record, **kwargs)
|
|
|
|
self.seed = seed
|
|
self.use_hashing = use_hashing
|
|
if schema.equal_schemas(input_record, IdList):
|
|
self.modulo = modulo or self.extract_hash_size(input_record.items.metadata)
|
|
metadata = schema.Metadata(
|
|
categorical_limit=self.modulo,
|
|
feature_specs=input_record.items.metadata.feature_specs,
|
|
)
|
|
hashed_indices = schema.Scalar(
|
|
np.int64,
|
|
self.get_next_blob_reference("hashed_idx")
|
|
)
|
|
hashed_indices.set_metadata(metadata)
|
|
self.output_schema = schema.List(
|
|
values=hashed_indices,
|
|
lengths_blob=input_record.lengths,
|
|
)
|
|
elif schema.equal_schemas(input_record, IdScoreList):
|
|
self.modulo = modulo or self.extract_hash_size(input_record.keys.metadata)
|
|
metadata = schema.Metadata(
|
|
categorical_limit=self.modulo,
|
|
feature_specs=input_record.keys.metadata.feature_specs,
|
|
)
|
|
hashed_indices = schema.Scalar(
|
|
np.int64,
|
|
self.get_next_blob_reference("hashed_idx")
|
|
)
|
|
hashed_indices.set_metadata(metadata)
|
|
self.output_schema = schema.Map(
|
|
keys=hashed_indices,
|
|
values=input_record.values,
|
|
lengths_blob=input_record.lengths,
|
|
)
|
|
else:
|
|
assert False, "Input type must be one of (IdList, IdScoreList)"
|
|
|
|
assert self.modulo >= 1, 'Unexpected modulo: {}'.format(self.modulo)
|
|
|
|
def extract_hash_size(self, metadata):
|
|
if metadata.feature_specs and metadata.feature_specs.desired_hash_size:
|
|
return metadata.feature_specs.desired_hash_size
|
|
elif metadata.categorical_limit is not None:
|
|
return metadata.categorical_limit
|
|
else:
|
|
assert False, "desired_hash_size or categorical_limit must be set"
|
|
|
|
def add_ops(self, net):
|
|
if schema.equal_schemas(self.output_schema, IdList):
|
|
input_blob = self.input_record.items()
|
|
output_blob = self.output_schema.items()
|
|
elif schema.equal_schemas(self.output_schema, IdScoreList):
|
|
input_blob = self.input_record.keys()
|
|
output_blob = self.output_schema.keys()
|
|
else:
|
|
raise NotImplementedError()
|
|
|
|
if self.use_hashing:
|
|
net.IndexHash(
|
|
input_blob, output_blob, seed=self.seed, modulo=self.modulo
|
|
)
|
|
else:
|
|
net.Mod(
|
|
input_blob, output_blob, divisor=self.modulo
|
|
)
|