mirror of
https://github.com/saymrwulf/pytorch.git
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fc3f88d8a4
Summary: Get higher order interaction of embeddings, similar to cross net but applied in the embedding level. Formula: e_(l+1,i) = element_wise_mul[e_(0,i), \sum_i(e_(l,i) * w_(l,i))] + e_(l,i) + b where l means the l-th layer of this higher order net, i means the i-th embedding in the list. Finally, concat all the embeddings in the last layer, or concat the sum of each embedding, and attach to the output blob of dot processor. Differential Revision: D6244001 fbshipit-source-id: 96292914158347b79fc1299694d65605999b55e8
88 lines
3.1 KiB
Python
88 lines
3.1 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 concat
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# Module caffe2.python.layers.concat
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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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from future.utils import viewitems
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import numpy as np
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class Concat(ModelLayer):
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def __init__(self, model, input_record, axis=1, add_axis=0,
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name='concat', **kwargs):
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super(Concat, self).__init__(model, name, input_record, **kwargs)
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self.axis = axis
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self.add_axis = add_axis
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assert not (axis == 0 and add_axis == 1), \
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"It's not allowed to add axis=0"
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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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shapes = []
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for field_name, field_type in viewitems(input_record.fields):
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assert isinstance(field_type, schema.Scalar),\
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"Incorrect input type for {}. Excpected Scalar, but got: {}".\
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format(field_name, field_type)
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# Assume that first dimension is batch, so actual axis in shape is
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# axis - 1
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shape = list(field_type.field_type().shape)
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if add_axis:
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shape.insert(axis - 1, 1)
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assert len(shape) >= axis,\
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"Concat expects that limited dimensions of the input tensor"
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shapes.append(shape)
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if axis == 0:
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self.output_schema = schema.from_blob_list(
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input_record[0],
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[self.get_next_blob_reference('output')]
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)
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return
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concat_dim = 0
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for shape in shapes:
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concat_dim += shape[axis - 1]
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shape[axis - 1] = 0
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assert shape == shapes[0],\
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"Shapes {0} and {1} are not compatible for Concat".\
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format(shape, shapes[0])
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output_dims = shapes[0]
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output_dims[axis - 1] = concat_dim
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self.output_schema = schema.Scalar(
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(np.float32, output_dims),
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self.get_next_blob_reference('output'))
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def add_ops(self, net):
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net.Concat(
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self.input_record.field_blobs(),
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[
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self.output_schema.field_blobs()[0],
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self.output_schema.field_blobs()[0] + "_concat_dims"
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],
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axis=self.axis,
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add_axis=self.add_axis,
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)
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