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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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"""This package implements common layers to help building pooling operators.
"""
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
from __future__ import unicode_literals
import paddle.fluid as F
import paddle.fluid.layers as L
import pgl
__all__ = ['Set2Set']
[docs]class Set2Set(object):
"""Implementation of set2set pooling operator.
This is an implementation of the paper ORDER MATTERS: SEQUENCE TO SEQUENCE
FOR SETS (https://arxiv.org/pdf/1511.06391.pdf).
"""
def __init__(self, input_dim, n_iters, n_layers):
"""
Args:
input_dim: hidden size of input data.
n_iters: number of set2set iterations.
n_layers: number of lstm layers.
"""
self.input_dim = input_dim
self.output_dim = 2 * input_dim
self.n_iters = n_iters
# this's set2set n_layers, lstm n_layers = 1
self.n_layers = n_layers
[docs] def forward(self, feat):
"""
Args:
feat: input feature with shape [batch, n_edges, dim].
Return:
output_feat: output feature of set2set pooling with shape [batch, 2*dim].
"""
seqlen = 1
h = L.fill_constant_batch_size_like(
feat, [1, self.n_layers, self.input_dim], "float32", 0)
h = L.transpose(h, [1, 0, 2])
c = h
# [seqlen, batch, dim]
q_star = L.fill_constant_batch_size_like(
feat, [1, seqlen, self.output_dim], "float32", 0)
q_star = L.transpose(q_star, [1, 0, 2])
for _ in range(self.n_iters):
# q [seqlen, batch, dim]
# h [layer, batch, dim]
q, h, c = L.lstm(
q_star,
h,
c,
seqlen,
self.input_dim,
self.n_layers,
is_bidirec=False)
# e [batch, seqlen, n_edges]
e = L.matmul(L.transpose(q, [1, 0, 2]), feat, transpose_y=True)
# alpha [batch, seqlen, n_edges]
alpha = L.softmax(e)
# readout [batch, seqlen, dim]
readout = L.matmul(alpha, feat)
readout = L.transpose(readout, [1, 0, 2])
# q_star [seqlen, batch, dim + dim]
q_star = L.concat([q, readout], -1)
return L.squeeze(q_star, [0])