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dfc4f2b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 | import torch
from torch import Tensor
import torch.nn.functional as F
from torch.nn import Sequential, Linear, BatchNorm1d
import torch_geometric
from torch_geometric.nn import (
Set2Set,
global_mean_pool,
global_add_pool,
global_max_pool,
GCNConv,
)
from torch_scatter import scatter_mean, scatter_add, scatter_max, scatter
# CGCNN
class GCN(torch.nn.Module):
def __init__(
self,
data,
dim1=64,
dim2=64,
pre_fc_count=1,
gc_count=3,
post_fc_count=1,
pool="global_mean_pool",
pool_order="early",
batch_norm="True",
batch_track_stats="True",
act="relu",
dropout_rate=0.0,
**kwargs
):
super(GCN, self).__init__()
if batch_track_stats == "False":
self.batch_track_stats = False
else:
self.batch_track_stats = True
self.batch_norm = batch_norm
self.pool = pool
self.act = act
self.pool_order = pool_order
self.dropout_rate = dropout_rate
##Determine gc dimension dimension
assert gc_count > 0, "Need at least 1 GC layer"
if pre_fc_count == 0:
gc_dim = data.num_features
else:
gc_dim = dim1
##Determine post_fc dimension
if pre_fc_count == 0:
post_fc_dim = data.num_features
else:
post_fc_dim = dim1
##Determine output dimension length
if data[0].y.ndim == 0:
output_dim = 1
else:
output_dim = len(data[0].y[0])
##Set up pre-GNN dense layers (NOTE: in v0.1 this is always set to 1 layer)
if pre_fc_count > 0:
self.pre_lin_list = torch.nn.ModuleList()
for i in range(pre_fc_count):
if i == 0:
lin = torch.nn.Linear(data.num_features, dim1)
self.pre_lin_list.append(lin)
else:
lin = torch.nn.Linear(dim1, dim1)
self.pre_lin_list.append(lin)
elif pre_fc_count == 0:
self.pre_lin_list = torch.nn.ModuleList()
##Set up GNN layers
self.conv_list = torch.nn.ModuleList()
self.bn_list = torch.nn.ModuleList()
for i in range(gc_count):
conv = GCNConv(
gc_dim, gc_dim, improved=True, add_self_loops=False
)
self.conv_list.append(conv)
##Track running stats set to false can prevent some instabilities; this causes other issues with different val/test performance from loader size?
if self.batch_norm == "True":
bn = BatchNorm1d(gc_dim, track_running_stats=self.batch_track_stats)
self.bn_list.append(bn)
##Set up post-GNN dense layers (NOTE: in v0.1 there was a minimum of 2 dense layers, and fc_count(now post_fc_count) added to this number. In the current version, the minimum is zero)
if post_fc_count > 0:
self.post_lin_list = torch.nn.ModuleList()
for i in range(post_fc_count):
if i == 0:
##Set2set pooling has doubled dimension
if self.pool_order == "early" and self.pool == "set2set":
lin = torch.nn.Linear(post_fc_dim * 2, dim2)
else:
lin = torch.nn.Linear(post_fc_dim, dim2)
self.post_lin_list.append(lin)
else:
lin = torch.nn.Linear(dim2, dim2)
self.post_lin_list.append(lin)
self.lin_out = torch.nn.Linear(dim2, output_dim)
elif post_fc_count == 0:
self.post_lin_list = torch.nn.ModuleList()
if self.pool_order == "early" and self.pool == "set2set":
self.lin_out = torch.nn.Linear(post_fc_dim*2, output_dim)
else:
self.lin_out = torch.nn.Linear(post_fc_dim, output_dim)
##Set up set2set pooling (if used)
if self.pool_order == "early" and self.pool == "set2set":
self.set2set = Set2Set(post_fc_dim, processing_steps=3)
elif self.pool_order == "late" and self.pool == "set2set":
self.set2set = Set2Set(output_dim, processing_steps=3, num_layers=1)
# workaround for doubled dimension by set2set; if late pooling not reccomended to use set2set
self.lin_out_2 = torch.nn.Linear(output_dim * 2, output_dim)
def forward(self, data):
##Pre-GNN dense layers
for i in range(0, len(self.pre_lin_list)):
if i == 0:
out = self.pre_lin_list[i](data.x)
out = getattr(F, self.act)(out)
else:
out = self.pre_lin_list[i](out)
out = getattr(F, self.act)(out)
##GNN layers
for i in range(0, len(self.conv_list)):
if len(self.pre_lin_list) == 0 and i == 0:
if self.batch_norm == "True":
out = self.conv_list[i](data.x, data.edge_index, data.edge_weight)
out = self.bn_list[i](out)
else:
out = self.conv_list[i](data.x, data.edge_index, data.edge_weight)
else:
if self.batch_norm == "True":
out = self.conv_list[i](out, data.edge_index, data.edge_weight)
out = self.bn_list[i](out)
else:
out = self.conv_list[i](out, data.edge_index, data.edge_weight)
out = getattr(F, self.act)(out)
out = F.dropout(out, p=self.dropout_rate, training=self.training)
##Post-GNN dense layers
if self.pool_order == "early":
if self.pool == "set2set":
out = self.set2set(out, data.batch)
else:
out = getattr(torch_geometric.nn, self.pool)(out, data.batch)
for i in range(0, len(self.post_lin_list)):
out = self.post_lin_list[i](out)
out = getattr(F, self.act)(out)
out = self.lin_out(out)
elif self.pool_order == "late":
for i in range(0, len(self.post_lin_list)):
out = self.post_lin_list[i](out)
out = getattr(F, self.act)(out)
out = self.lin_out(out)
if self.pool == "set2set":
out = self.set2set(out, data.batch)
out = self.lin_out_2(out)
else:
out = getattr(torch_geometric.nn, self.pool)(out, data.batch)
if out.shape[1] == 1:
return out.view(-1)
else:
return out |