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DGCNN_embedding.py
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DGCNN_embedding.py
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from __future__ import print_function
import os
import sys
import numpy as np
import torch
import random
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tqdm import tqdm
import pdb
sys.path.append('%s/lib' % os.path.dirname(os.path.realpath(__file__)))
from gnn_lib import GNNLIB
from pytorch_util import weights_init, gnn_spmm
class DGCNN(nn.Module):
def __init__(self, output_dim, num_node_feats, num_edge_feats, latent_dim=[32, 32, 32, 1], k=30, conv1d_channels=[16, 32], conv1d_kws=[0, 5], conv1d_activation='ReLU'):
print('Initializing DGCNN')
super(DGCNN, self).__init__()
self.latent_dim = latent_dim
self.output_dim = output_dim
self.num_node_feats = num_node_feats
self.num_edge_feats = num_edge_feats
self.k = k
self.total_latent_dim = sum(latent_dim)
conv1d_kws[0] = self.total_latent_dim
self.conv_params = nn.ModuleList()
self.conv_params.append(nn.Linear(num_node_feats + num_edge_feats, latent_dim[0]))
for i in range(1, len(latent_dim)):
self.conv_params.append(nn.Linear(latent_dim[i-1], latent_dim[i]))
self.conv1d_params1 = nn.Conv1d(1, conv1d_channels[0], conv1d_kws[0], conv1d_kws[0])
self.maxpool1d = nn.MaxPool1d(2, 2)
self.conv1d_params2 = nn.Conv1d(conv1d_channels[0], conv1d_channels[1], conv1d_kws[1], 1)
dense_dim = int((k - 2) / 2 + 1)
self.dense_dim = (dense_dim - conv1d_kws[1] + 1) * conv1d_channels[1]
#if num_edge_feats > 0:
# self.w_e2l = nn.Linear(num_edge_feats, num_node_feats)
if output_dim > 0:
self.out_params = nn.Linear(self.dense_dim, output_dim)
self.conv1d_activation = eval('nn.{}()'.format(conv1d_activation))
weights_init(self)
def forward(self, graph_list, node_feat, edge_feat):
graph_sizes = [graph_list[i].num_nodes for i in range(len(graph_list))]
node_degs = [torch.Tensor(graph_list[i].degs) + 1 for i in range(len(graph_list))]
node_degs = torch.cat(node_degs).unsqueeze(1)
n2n_sp, e2n_sp, subg_sp = GNNLIB.PrepareSparseMatrices(graph_list)
if torch.cuda.is_available() and isinstance(node_feat, torch.cuda.FloatTensor):
n2n_sp = n2n_sp.cuda()
e2n_sp = e2n_sp.cuda()
subg_sp = subg_sp.cuda()
node_degs = node_degs.cuda()
node_feat = Variable(node_feat)
if edge_feat is not None:
edge_feat = Variable(edge_feat)
if torch.cuda.is_available() and isinstance(node_feat, torch.cuda.FloatTensor):
edge_feat = edge_feat.cuda()
n2n_sp = Variable(n2n_sp)
e2n_sp = Variable(e2n_sp)
subg_sp = Variable(subg_sp)
node_degs = Variable(node_degs)
h = self.sortpooling_embedding(node_feat, edge_feat, n2n_sp, e2n_sp, subg_sp, graph_sizes, node_degs)
return h
def sortpooling_embedding(self, node_feat, edge_feat, n2n_sp, e2n_sp, subg_sp, graph_sizes, node_degs):
''' if exists edge feature, concatenate to node feature vector '''
if edge_feat is not None:
#input_edge_linear = self.w_e2l(edge_feat)
input_edge_linear = edge_feat
e2npool_input = gnn_spmm(e2n_sp, input_edge_linear)
node_feat = torch.cat([node_feat, e2npool_input], 1)
''' graph convolution layers '''
lv = 0
cur_message_layer = node_feat
cat_message_layers = []
while lv < len(self.latent_dim):
n2npool = gnn_spmm(n2n_sp, cur_message_layer) + cur_message_layer # Y = (A + I) * X
node_linear = self.conv_params[lv](n2npool) # Y = Y * W
normalized_linear = node_linear.div(node_degs) # Y = D^-1 * Y
cur_message_layer = torch.tanh(normalized_linear)
cat_message_layers.append(cur_message_layer)
lv += 1
cur_message_layer = torch.cat(cat_message_layers, 1)
''' sortpooling layer '''
sort_channel = cur_message_layer[:, -1]
batch_sortpooling_graphs = torch.zeros(len(graph_sizes), self.k, self.total_latent_dim)
if torch.cuda.is_available() and isinstance(node_feat.data, torch.cuda.FloatTensor):
batch_sortpooling_graphs = batch_sortpooling_graphs.cuda()
batch_sortpooling_graphs = Variable(batch_sortpooling_graphs)
accum_count = 0
for i in range(subg_sp.size()[0]):
to_sort = sort_channel[accum_count: accum_count + graph_sizes[i]]
k = self.k if self.k <= graph_sizes[i] else graph_sizes[i]
_, topk_indices = to_sort.topk(k)
topk_indices += accum_count
sortpooling_graph = cur_message_layer.index_select(0, topk_indices)
if k < self.k:
to_pad = torch.zeros(self.k-k, self.total_latent_dim)
if torch.cuda.is_available() and isinstance(node_feat.data, torch.cuda.FloatTensor):
to_pad = to_pad.cuda()
to_pad = Variable(to_pad)
sortpooling_graph = torch.cat((sortpooling_graph, to_pad), 0)
batch_sortpooling_graphs[i] = sortpooling_graph
accum_count += graph_sizes[i]
''' traditional 1d convlution and dense layers '''
to_conv1d = batch_sortpooling_graphs.view((-1, 1, self.k * self.total_latent_dim))
conv1d_res = self.conv1d_params1(to_conv1d)
conv1d_res = self.conv1d_activation(conv1d_res)
conv1d_res = self.maxpool1d(conv1d_res)
conv1d_res = self.conv1d_params2(conv1d_res)
conv1d_res = self.conv1d_activation(conv1d_res)
to_dense = conv1d_res.view(len(graph_sizes), -1)
if self.output_dim > 0:
out_linear = self.out_params(to_dense)
reluact_fp = self.conv1d_activation(out_linear)
else:
reluact_fp = to_dense
return self.conv1d_activation(reluact_fp)