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https://github.com/zebrajr/pytorch.git
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a597c7b167
Summary: - It's first prototype that includes simple unary test. - will probably need to iterate based on it to include more arches that we see promising offline results Differential Revision: D4208336 fbshipit-source-id: 5b2d2a5a0274a9dcad0fb169e43e78aa9d9a704d
38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
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 core, 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 DotProduct(ModelLayer):
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def __init__(self, model, input_record, name='dot_product', **kwargs):
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super(DotProduct, 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 len(input_record.get_children()) == 2, (
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"DotProduct accept 2 inputs")
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assert len(set(input_record.field_types())) == 1, (
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"Inputs should be of the same field type")
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for field_name, field_type in input_record.fields.items():
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assert isinstance(field_type, schema.Scalar),\
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"Incorrect input type. Excpected Scalar, but received: {0}".\
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format(field_type)
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self.output_schema = schema.Scalar(
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(input_record.field_types()[0].base, ()),
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core.ScopedBlobReference(model.net.NextName(self.name + '_output')))
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def add_ops(self, net):
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net.DotProduct(
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self.input_record.field_blobs(),
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self.output_schema(),
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)
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