model.to(device)
optimizer = torch.optim.Adam(model.parameters(),
lr=learn_rate,
weight_decay=p.weight_decay)
writer = SummaryWriter(
comment='model:exp2_3hops_lr:{}_shuffle:{}_seed:{}'.format(
learn_rate, p.shuffle_dataset, p.random_seed))
max_roc_auc = 0
# ---- Training ----
print('Training...')
for epoch in range(1, epochs + 1):
trainset.transform = Compose(
(Center(), RandomRotate(90, epoch % 3), AddPositionalData(), TwoHop()))
validset.transform = Compose(
(Center(), RandomRotate(90, epoch % 3), AddPositionalData(), TwoHop()))
train_loader = DataLoader(trainset,
shuffle=p.shuffle_dataset,
batch_size=p.batch_size)
val_loader = DataLoader(validset,
shuffle=False,
batch_size=p.test_batch_size)
learn_rate = optimizer.param_groups[0][
'lr'] # for when it may be modified during run
model.train()
pred = torch.Tensor()
tr_weights = torch.Tensor()
loss = []
# ---- Training ----
print('Training...')
for epoch in range(1, epochs+1):
# rotate the structures between epochs
if 'pos' in p.dataset: # Is there positional data in the features?
degrees = 0
if epoch > 200:
if epoch < 700:
degrees = 90*((epoch-200)/500)
else:
degrees = 90
rotation_axis = axes[epoch % 3] # only for structural data.
trainset.transform = Compose((RemovePositionalData(),
RandomRotate(degrees, axis=rotation_axis),
AddPositionalData(),
RemoveXYZ()))
validset.transform = RemoveXYZ()
# Using shape index data:
trainset.transform = Compose((Center(), FaceAttributes(),
NodeCurvature(), FaceToEdge(),
TwoHop()), AddShapeIndex())
validset.transform = Compose((Center(), FaceAttributes(),
NodeCurvature(), FaceToEdge(),
TwoHop()), AddShapeIndex())
train_loader = DataListLoader(trainset, shuffle=p.shuffle_dataset, batch_size=p.batch_size)
val_loader = DataListLoader(validset, shuffle=False, batch_size=p.test_batch_size)
learn_rate = optimizer.param_groups[0]['lr'] # for when it may be modified during run
# rotator()