File size: 6,616 Bytes
717576e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 |
import os.path as osp
import torch
import torch.nn.functional as F
from torch.nn import ModuleList, Embedding
from torch.nn import Sequential, ReLU, Linear
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch_geometric.utils import degree
from torch_geometric.datasets import ZINC
from torch_geometric.data import DataLoader
from torch_geometric.nn import PNAConv, BatchNorm, global_add_pool
import sys
import time
import numpy as np
train_pred = []
train_act = []
test_pred = []
test_act = []
fold = int(sys.argv[1])
st = time.process_time()
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'ZINC')
train_dataset = ZINC(path, subset=True, split='train')
val_dataset = ZINC(path, subset=True, split='val')
test_dataset = ZINC(path, subset=True, split='test')
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=128)
test_loader = DataLoader(test_dataset, batch_size=128)
# Compute in-degree histogram over training data.
deg = torch.zeros(5, dtype=torch.long)
for data in train_dataset:
d = degree(data.edge_index[1], num_nodes=data.num_nodes, dtype=torch.long)
deg += torch.bincount(d, minlength=deg.numel())
class UAF(torch.nn.Module):
def __init__(self):
super().__init__()
self.A = torch.nn.Parameter(torch.tensor(1.1, requires_grad=True))
self.B = torch.nn.Parameter(torch.tensor(-0.01, requires_grad=True))
self.C = torch.nn.Parameter(torch.tensor(0.00001, requires_grad=True))
self.D = torch.nn.Parameter(torch.tensor(-0.9, requires_grad=True))
self.E = torch.nn.Parameter(torch.tensor(0.00001, requires_grad=True))
self.Softplus = torch.nn.Softplus()
def forward(self, input):
return self.Softplus((self.A*(input+self.B)) + (self.C * torch.square(input))) - self.Softplus((self.D*(input-self.B))) + self.E
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.node_emb = Embedding(21, 75)
self.edge_emb = Embedding(4, 50)
aggregators = ['mean', 'min', 'max', 'std']
scalers = ['identity', 'amplification', 'attenuation']
self.convs = ModuleList()
self.batch_norms = ModuleList()
for _ in range(4):
conv = PNAConv(in_channels=75, out_channels=75,
aggregators=aggregators, scalers=scalers, deg=deg,
edge_dim=50, towers=5, pre_layers=1, post_layers=1,
divide_input=False)
self.convs.append(conv)
self.batch_norms.append(BatchNorm(75))
self.func = UAF()
self.mlp = Sequential(Linear(75, 50), self.func, Linear(50, 25), self.func,
Linear(25, 1))
def forward(self, x, edge_index, edge_attr, batch):
x = self.node_emb(x.squeeze())
edge_attr = self.edge_emb(edge_attr)
for conv, batch_norm in zip(self.convs, self.batch_norms):
x = self.func(batch_norm(conv(x, edge_index, edge_attr)))
x = global_add_pool(x, batch)
return self.mlp(x)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)
#optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
optimizer = torch.optim.Adam([
dict(params=model.convs.parameters()),
dict(params=model.batch_norms.parameters()),
dict(params=model.mlp.parameters())
], lr=0.001)
scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=20,
min_lr=0.00001)
optimizer2 = torch.optim.Adam([
dict(params=model.func.parameters())
], lr=0.001)
scheduler2 = torch.optim.lr_scheduler.StepLR(optimizer2, step_size=300, gamma=1e-10)
def train(epoch):
model.train()
train_pred_temp = []
train_act_temp = []
first = True
total_loss = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
optimizer2.zero_grad()
out = model(data.x, data.edge_index, data.edge_attr, data.batch)
loss = (out.squeeze() - data.y).abs().mean()
pred = out.squeeze()
if (first):
train_pred_temp = pred.cpu().detach().numpy()
train_act_temp = data.y.cpu().detach().numpy()
first = False
else:
train_pred_temp = np.append(train_pred_temp, pred.cpu().detach().numpy())
train_act_temp = np.append(train_act_temp, data.y.cpu().detach().numpy())
loss.backward()
total_loss += loss.item() * data.num_graphs
optimizer.step()
optimizer2.step()
train_pred.append(train_pred_temp)
train_act.append(train_act_temp)
return total_loss / len(train_loader.dataset)
@torch.no_grad()
def test(loader):
model.eval()
test_pred_temp = []
test_act_temp = []
first = True
total_error = 0
for data in loader:
data = data.to(device)
out = model(data.x, data.edge_index, data.edge_attr, data.batch)
total_error += (out.squeeze() - data.y).abs().sum().item()
pred = out.squeeze()
if (first):
test_pred_temp = pred.cpu().detach().numpy()
test_act_temp = data.y.cpu().detach().numpy()
first = False
else:
test_pred_temp = np.append(test_pred_temp, pred.cpu().detach().numpy())
test_act_temp = np.append(test_act_temp, data.y.cpu().detach().numpy())
test_pred.append(test_pred_temp)
test_act.append(test_act_temp)
return total_error / len(loader.dataset)
@torch.no_grad()
def test_val(loader):
model.eval()
total_error = 0
for data in loader:
data = data.to(device)
out = model(data.x, data.edge_index, data.edge_attr, data.batch)
total_error += (out.squeeze() - data.y).abs().sum().item()
return total_error / len(loader.dataset)
for epoch in range(1, 601):
loss = train(epoch)
val_mae = test_val(val_loader)
test_mae = test(test_loader)
if (epoch == 302):
scheduler.optimizer.param_groups[0]['lr'] = 0.001
scheduler.step(val_mae)
scheduler2.step()
print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}, Val: {val_mae:.4f}, '
f'Test: {test_mae:.4f}')
elapsed_time = time.process_time() - st
np.save("time_" + str(fold), np.array([elapsed_time]))
np.save("train_pred_" + str(fold), train_pred)
np.save("train_act_" + str(fold), train_act)
np.save("test_pred_" + str(fold), test_pred)
np.save("test_act_" + str(fold), test_act)
|