def train(info: TrainInformation, split, fold):
"""주어진 split에 대한 학습과 테스트를 진행한다."""
bs = info.BS
init_lr = info.INIT_LR
lr_decay = info.LR_DECAY
momentum = info.MOMENTUM
weight_decay = info.WEIGHT_DECAY
optimizer_method = info.OPTIMIZER_METHOD
epoch = info.EPOCH
nchs = info.NCHS
filename = info.FILENAME
model_name = info.MODEL_NAME
exp_name = info.NAME
print("Using File {}".format(filename))
train_dataset = Dataset(split=split, fold=fold, phase="train", filename=filename, use_data_dropout=info.USE_DATA_DROPOUT)
#val_dataset = Dataset(split=split, fold=fold, phase="val", filename=filename)
test_dataset = Dataset(split=split, fold=fold, phase="test", filename=filename, use_data_dropout=False)
model = get_classifier_model(model_name, train_dataset.feature_size, nchs, info.ACTIVATION)
print(model)
# Optimizer 설정
optimizer = set_optimizer(
optimizer_method, model, init_lr, weight_decay, momentum=momentum
)
data_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=bs, shuffle=True, num_workers=0, drop_last=True
)
bce_loss = torch.nn.BCEWithLogitsLoss().cuda()
train_result = TrainResult()
train_result.set_sizes(
len(train_dataset.data), 0, len(test_dataset.data)
)
for ep in range(epoch):
global prev_plot
prev_plot = 0
train_step(
exp_name,
ep,
model,
train_dataset,
test_dataset,
optimizer,
init_lr,
lr_decay,
data_loader,
bce_loss,
train_result,
)
savedir = "/content/drive/My Drive/research/frontiers/checkpoints/%s" % exp_name
best_test_epoch = train_result.best_test_epoch #25
savepath = "%s/epoch_%04d_fold_%02d.pt" % (savedir, best_test_epoch, train_dataset.split)
#model.load_state_dict(torch.load(savepath))
model = torch.load(savepath)
model.eval()
test_preds = train_utils.get_preds(test_dataset.data[:, 1:], model)
test_AUC = train_utils.compute_AUC(test_dataset.data[:, :1], test_preds)
test_PRAUC = train_utils.compute_PRAUC(test_dataset.data[:, :1], test_preds)
train_utils.plot_AUC(test_dataset, test_preds, test_AUC, savepath=savepath.replace(".pt", "_AUC.tiff"))
contributing_variables = compute_contributing_variables(model, test_dataset)
with open(os.path.join(savedir, "contributing_variables_epoch_%04d_fold_%02d.txt" % (best_test_epoch, train_dataset.split)), "w") as f:
for (v, auc) in contributing_variables:
f.write("%s %f\n" % (v, auc))
info.split_index = split
info.result_dict = train_result
info.save_result()
return train_result
def train_step(
exp_name,
ep,
model,
train_dataset,
test_dataset,
optimizer,
init_lr,
lr_decay,
data_loader,
bce_loss,
train_result: TrainResult,
):
global prev_plot
model.train(True)
for _, (X, y) in enumerate(data_loader):
optimizer.zero_grad()
pred_out = model(X.cuda()).view(X.shape[0])
loss = bce_loss(pred_out, y.cuda())
loss.backward()
avg_loss = train_result.avg_loss * 0.98 + loss.detach().cpu().numpy() * 0.02
optimizer.step()
train_result.total_iter += len(y)
if train_result.total_iter % 10000 == 0:
print(
"Loss Iter %05d: %.4f\r" % (train_result.total_iter, avg_loss), end=""
)
train_result.loss_list.append(
(train_result.total_iter, "{:.4f}".format(avg_loss))
)
print("")
lr = init_lr * (lr_decay ** ep)
for param_group in optimizer.param_groups:
param_group["lr"] = lr
print("Learning rate = %f" % lr)
train_AUC, test_AUC, test_PRAUC, train_accuracy, test_accuracy, test_preds, test_TP, test_TN, test_FN, test_FP = print_metrics(model,
train_dataset,
test_dataset,
train_result)
savedir = "/content/drive/My Drive/research/frontiers/checkpoints/%s" % exp_name
os.makedirs(savedir, exist_ok=True)
split = train_dataset.split
savepath = "%s/epoch_%04d_fold_%02d.pt" % (savedir, ep, split)
torch.save(model, savepath)
if train_result.best_test_AUC < test_AUC:
train_result.best_test_AUC = test_AUC
train_result.best_test_epoch = ep
if ep - prev_plot > 10:
# 너무 자주 찍지 말고 한번 plot 찍고 epoch 10번 이상인 경우에만 찍는다.
prev_plot = ep
#train_utils.plot_AUC(test_dataset, test_preds, test_AUC)
#contributing_variables = compute_contributing_variables(model, test_dataset)
print(
"Epoch %03d: test_AUC: %.4f (best: %.4f epoch: %d), train_AUC: %.4f"
% (
ep,
test_AUC,
train_result.best_test_AUC,
train_result.best_test_epoch,
train_AUC,
)
)
print(
" test_accuracy {:.4f}, train_accuracy {:.4f}".format(
test_accuracy, train_accuracy,
)
)
print(
" test_TP {}, test_TN {}, test_FN {}, test_FP {},".format(
test_TP, test_TN, test_FN, test_FP
)
)