mirror of
https://github.com/zebrajr/pytorch.git
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a8695178aa
Summary:
To achive this, I modified the blob name scheme defined in a layer.
Before it was scope/fc_w and scope/fc_w_auto_0 (if there is another fc
within the same scope).
Now I change it to scope/fc/w and scope/fc_auto_0/w.
That is, we rely on the uniqueness of the scoped layer name to define
names for blobs.
I also overwrote the create_param method in LayerModelHelper to let it
use the resolved name for blobs given the sharingparameter context.
There are some details such as making the initializer more structured
that I need to finalize.
Reviewed By: kennyhorror
Differential Revision: D5435132
fbshipit-source-id: a0525f5ea0977e255dd5ea765b38913f5951d455
103 lines
3.7 KiB
Python
103 lines
3.7 KiB
Python
## @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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