mirror of
https://github.com/saymrwulf/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
95 lines
3.6 KiB
Python
95 lines
3.6 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 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 (
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('all_embeddings' in input_record) ^
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('x_embeddings' in input_record and 'y_embeddings' in input_record)
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), (
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"either (all_embeddings) xor (x_embeddings and y_embeddings) " +
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"should be given."
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)
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x_embeddings = (
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input_record['all_embeddings'] if 'all_embeddings' in input_record
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else input_record['x_embeddings']
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)
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y_embeddings = (input_record['all_embeddings']
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if 'all_embeddings' in input_record
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else input_record['y_embeddings'])
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assert isinstance(x_embeddings, schema.Scalar), (
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"Incorrect input type for x. Expected Scalar, " +
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"but received: {0}".format(x_embeddings))
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if 'y_embeddings' in input_record:
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assert isinstance(y_embeddings, schema.Scalar), (
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"Incorrect input type for y. Expected Scalar, " +
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"but received: {0}".format(y_embeddings)
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)
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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.indices_to_gather = indices_to_gather
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else:
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self.indices_to_gather = None
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self.x_embeddings = x_embeddings
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self.y_embeddings = y_embeddings
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dtype = x_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.x_embeddings(), self.y_embeddings()],
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[self.x_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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