def train():
pretrain, code_id, word_id = source_prepare()
print('%d different words, %d different codes' % (len(word_id), len(code_id)), flush=True)
criterion = nn.CrossEntropyLoss()
Acc = 0.
for fold in range(5):
Net = TransformerNet(Pretrain_type, pretrain, Max_seq_len, Embedding_size, Inner_hid_size, len(code_id), D_k,
D_v, dropout_ratio=Dropout, num_layers=Num_layers, num_head=Num_head, Freeze=Freeze_emb).cuda()
optimizer = optim.Adam(Net.parameters(), lr=Learning_rate, eps=1e-08, weight_decay=Weight_decay)
# train_file = DATA_path + 'AskAPatient/AskAPatient.fold-' + str(fold) + '.train.txt'
# val_file = DATA_path + 'AskAPatient/AskAPatient.fold-' + str(fold) + '.validation.txt'
train_file = DATA_path + 'trainsplit_' + str(fold) + '.csv'
val_file = DATA_path + 'val_' + str(fold) + '.csv'
train_data = tokenizer(word_id, code_id, train_file, pretrain_type=Pretrain_type)
val_data = tokenizer(word_id, code_id, val_file, pretrain_type=Pretrain_type)
print('Fold %d: %d training data, %d validation data' % (fold, len(train_data.data), len(val_data.data)))
print('max length: %d %d' % (train_data.max_length, val_data.max_length), flush=True)
for e in range(Epoch):
train_data.reset_epoch()
Net.train()
while not train_data.epoch_finish:
optimizer.zero_grad()
seq, label, seq_length, mask, seq_pos, standard_emb = train_data.get_batch(Batch_size)
results = Net(seq, seq_pos, standard_emb)
loss = criterion(results, label)
loss.backward()
optimizer.step()
if (e + 1) % Val_every == 0:
Net.eval()
train_data.reset_epoch()
train_correct = 0
i = 0
while not train_data.epoch_finish:
seq, label, seq_length, mask, seq_pos, standard_emb = train_data.get_batch(Batch_size)
results = Net(seq, seq_pos, standard_emb)
_, idx = results.max(1)
train_correct += len((idx == label).nonzero())
i += Batch_size
# assert i == len(train_data.data)
train_accuracy = float(train_correct) / float(i)
val_data.reset_epoch()
val_correct = 0
i = 0
while not val_data.epoch_finish:
seq, label, seq_length, mask, seq_pos, standard_emb = val_data.get_batch(Batch_size)
results = Net(seq, seq_pos, standard_emb)
_, idx = results.max(1)
val_correct += len((idx == label).nonzero())
i += Batch_size
# assert i == len(val_data.data)
val_accuracy = float(val_correct) / float(len(val_data.data))
print('[fold %d epoch %d] training loss: %.4f, % d correct, %.4f accuracy;'
' validation: %d correct, %.4f accuracy' %
(fold, e, loss.item(), train_correct, train_accuracy, val_correct, val_accuracy), flush=True)
torch.save(Net.state_dict(), SAVE_DIR + 'data1_false_biobert_' + str(fold) + '_' + str(e))
if (e + 1) % LR_decay_epoch == 0:
adjust_learning_rate(optimizer, LR_decay)
print('learning rate decay!', flush=True)
Acc += val_accuracy
del train_data, val_data
gc.collect()
print('finial validation accuracy: %.4f' % (Acc / 5))
def train(args):
device = torch.device("cpu")
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# load dataset
transform = transforms.Compose([
transforms.Resize(args.image_size),
transforms.CenterCrop(args.image_size),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))
])
train_dataset = datasets.ImageFolder(args.dataset, transform)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size)
# load style image
style_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))
])
style = utils.load_image(args.style_image, size=args.style_size)
style = style_transform(style)
style = style.repeat(args.batch_size, 1, 1, 1).to(device)
# load network
transformer = TransformerNet().to(device)
vgg = Vgg16(requires_grad=False).to(device)
# define optimizer and loss function
optimizer = Adam(transformer.parameters(), args.lr)
mse_loss = torch.nn.MSELoss()
features_style = vgg(utils.normalize_batch(style))
gram_style = [utils.gram_matrix(y) for y in features_style]
for e in range(args.epochs):
transformer.train()
count = 0
for batch_id, (x, _) in enumerate(train_loader):
count += len(x)
optimizer.zero_grad()
image_original = x.to(device)
image_transformed = transformer(x)
image_original = utils.normalize_batch(image_original)
image_transformed = utils.normalize_batch(image_transformed)
# extract features for compute content loss
features_original= vgg(image_original)
features_transformed = vgg(image_transformed)
content_loss = args.content_weight * mse_loss(features_transformed.relu3_3, features_original.relu3_3)
# extract features for compute style loss
style_loss = 0.
for ft_y, gm_s in zip(features_transformed, gram_style):
gm_y = utils.gram_matrix(ft_y)
style_loss += mse_loss(gm_y, gm_s[:len(x), :, :])
style_loss *= args.style_weight
total_loss = content_loss + style_loss
total_loss.backward()
optimizer.step()
if (batch_id + 1) % 200 == 0:
print("Epoch {}:[{}/{}]".format(e + 1, count, len(train_dataset)))
# save model
transformer.eval().cpu()
save_model_filename = "epoch_" + str(args.epochs) + "_" + str(args.content_weight) + "_" + str(args.style_weight) + ".model"
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
torch.save(transformer.state_dict(), save_model_path)
print("\nDone, trained model saved at", save_model_path)
def train(content_img_name=None, style_img_name=None, features=None):
transformer = TransformerNet()
# features = Vgg16()
lr = 0.001
weight_content = 1e5
weight_style = 1e10
optimizer = torch.optim.Adam(transformer.parameters(), lr)
mse_loss = torch.nn.MSELoss()
style = load_image(style_img_name)
style = style_transform(style)
style = style.unsqueeze(0)
style_v = Variable(style)
style_v = normalize_batch(style_v)
features_style = features(style_v)
gram_style = [gram_matrix(y) for y in features_style]
transformer.train()
x = load_image(content_img_name)
x = content_transform(x)
x = x.unsqueeze(0)
x = Variable(x)
if use_cuda:
transformer.cuda()
features.cuda()
x = x.cuda()
gram_style = [gram.cuda() for gram in gram_style]
# training
count = 0
log_name = './logs/log_exp_{}.txt'.format(exp_num)
log = []
while count < iteration_total:
optimizer.zero_grad()
y = transformer(x)
y = normalize_batch(y)
x = normalize_batch(x)
features_y = features(y)
features_x = features(x)
loss_content = mse_loss(features_y[1], features_x[1])
loss_style = 0.
for ft_y, gm_s in zip(features_y, gram_style):
gm_y = gram_matrix(ft_y)
loss_style = loss_style + mse_loss(gm_y, gm_s)
total_loss = weight_content * loss_content + weight_style * loss_style
total_loss.backward()
optimizer.step()
# log show
count += 1
msg = '{}\titeration: {}\tcontent: {:.6f}\tstyle: {:.6f}\ttotal: {:.6f}\n'.format(
time.ctime(), count, loss_content.item(), loss_style.item(),
total_loss.item())
log.append(msg)
if count % 50 == 0:
print(''.join(log))
with open(log_name, 'a') as f:
f.writelines(''.join(log))
log.clear()
# save model
transformer.eval()
if use_cuda:
transformer.cpu()
save_model_name = './models/model_exp_{}.pt'.format(exp_num)
torch.save(transformer.state_dict(), save_model_name)
def train(args):
device = torch.device("cuda" if args.cuda else "cpu")
if args.backbone == "vgg":
content_layer = ['relu_4']
style_layer = ['relu_2', 'relu_4', 'relu_7', 'relu_10']
elif args.backbone == "resnet":
content_layer = ["conv_3"]
style_layer = ["conv_1", "conv_2", "conv_3", "conv_4"]
total_layer = list(dict.fromkeys(content_layer + style_layer))
np.random.seed(args.seed)
torch.manual_seed(args.seed)
transform = transforms.Compose([
transforms.Resize(args.image_size),
transforms.CenterCrop(args.image_size),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))
])
train_dataset = datasets.ImageFolder(args.dataset, transform)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size)
transformer = TransformerNet().to(device)
optimizer = Adam(transformer.parameters(), args.lr)
mse_loss = torch.nn.MSELoss()
if args.backbone == "vgg":
loss_model = vgg16().eval().to(device)
elif args.backbone == "resnet":
loss_model = resnet().eval().to(device)
style_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))])
style = utils.load_image(args.style_image, size=args.style_size)
style = style_transform(style)
style = style.repeat(args.batch_size, 1, 1, 1).to(device)
feature_style = loss_model(utils.normalize_batch(style), style_layer)
gram_style = {
key: utils.gram_matrix(val)
for key, val in feature_style.items()
}
for e in range(args.epochs):
transformer.train()
agg_content_loss = 0.
agg_style_loss = 0.
count = 0
for batch_id, (x, _) in enumerate(train_loader):
n_batch = len(x)
count += n_batch
optimizer.zero_grad()
x = x.to(device)
y = transformer(x)
y = utils.normalize_batch(y)
x = utils.normalize_batch(x)
feature_y = loss_model(y, total_layer)
feature_x = loss_model(x, content_layer)
content_loss = 0.
for layer in content_layer:
content_loss += args.content_weight * mse_loss(
feature_y[layer], feature_x[layer])
style_loss = 0.
for name in style_layer:
gm_y = utils.gram_matrix(feature_y[name])
style_loss += mse_loss(gm_y, gram_style[name][:n_batch, :, :])
style_loss *= args.style_weight
total_loss = content_loss + style_loss
total_loss.backward()
optimizer.step()
agg_content_loss += content_loss.item()
agg_style_loss += style_loss.item()
if (batch_id + 1) % args.log_interval == 0:
mesg = "{}\tEpoch {}:\t[{}/{}]\tcontent: {:.6f}\tstyle: {:.6f}\ttotal: {:.6f}".format(
time.ctime(), e + 1, count, len(train_dataset),
agg_content_loss / (batch_id + 1),
agg_style_loss / (batch_id + 1),
(agg_content_loss + agg_style_loss) / (batch_id + 1))
print(mesg)
if args.checkpoint_model_dir is not None and (
batch_id + 1) % args.checkpoint_interval == 0:
transformer.eval().cpu()
ckpt_model_filename = "ckpt_epoch_" + str(
e) + "_batch_id_" + str(batch_id + 1) + ".pth"
ckpt_model_path = os.path.join(args.checkpoint_model_dir,
ckpt_model_filename)
torch.save(transformer.state_dict(), ckpt_model_path)
transformer.to(device).train()
# save model
transformer.eval().cpu()
save_model_filename = "epoch_" + str(args.epochs) + "_" + str(
time.ctime()).replace(' ', '_') + "_" + str(
args.content_weight) + "_" + str(args.style_weight) + ".model"
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
torch.save(transformer.state_dict(), save_model_path)
print("\nDone, trained model saved at", save_model_path)