mirror of
https://github.com/saymrwulf/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
58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
## @package dot_product
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# Module caffe2.python.layers.dot_product
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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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class PairwiseDotProduct(ModelLayer):
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def __init__(self, model, input_record, output_dim,
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name='pairwise_dot_product', **kwargs):
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super(PairwiseDotProduct, self).__init__(model, name, input_record, **kwargs)
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assert isinstance(input_record, schema.Struct), (
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"Incorrect input type. Excpected Struct, but received: {0}".
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format(input_record))
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assert 'all_embeddings' in input_record, "all_embeddings is not given."
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all_embeddings = input_record['all_embeddings']
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assert isinstance(all_embeddings, schema.Scalar), (
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"Incorrect input type. Excpected Scalar, but received: {0}".
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format(all_embeddings))
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if 'indices_to_gather' in input_record:
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indices_to_gather = input_record['indices_to_gather']
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assert isinstance(indices_to_gather, schema.Scalar), (
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"Incorrect type of indices_to_gather. "
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"Expected Scalar, but received: {0}".format(indices_to_gather)
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)
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self.all_embeddings = all_embeddings
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self.indices_to_gather = indices_to_gather
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dtype = all_embeddings.field_types()[0].base
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self.output_schema = schema.Scalar(
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(dtype, (output_dim)),
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self.get_next_blob_reference('output')
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)
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def add_ops(self, net):
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Y = net.BatchMatMul(
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[self.all_embeddings(), self.all_embeddings()],
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[self.all_embeddings() + '_matmul'],
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trans_b=1,
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)
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if self.indices_to_gather:
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flattened = net.Flatten(
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Y, Y + '_flatten',
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)
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net.BatchGather(
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[flattened, self.indices_to_gather()],
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self.output_schema(),
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)
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else:
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net.Flatten(Y, self.output_schema())
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