def train():
train_dataset = FurnitureDataset('train',
transform=preprocess_with_augmentation(
normalize_05, IMAGE_SIZE))
val_dataset = FurnitureDataset('val',
transform=preprocess(
normalize_05, IMAGE_SIZE))
training_data_loader = DataLoader(dataset=train_dataset,
num_workers=8,
batch_size=BATCH_SIZE,
shuffle=True)
validation_data_loader = DataLoader(dataset=val_dataset,
num_workers=1,
batch_size=BATCH_SIZE,
shuffle=False)
model = get_model()
criterion = nn.CrossEntropyLoss().cuda()
writer = SummaryWriter(log_dir='logs')
nb_learnable_params = sum(p.numel() for p in model.fresh_params())
print('nb learnable params: {}'.format(nb_learnable_params))
lx, px = utils.predict(model, validation_data_loader)
min_loss = criterion(Variable(px), Variable(lx)).data[0]
lr = 0
patience = 0
for epoch in range(30):
print('epoch {}'.format(epoch))
if epoch == 1:
lr = 0.00005
print('set lr={}'.format(lr))
if patience == 2:
patience = 0
model.load_state_dict(torch.load('inception4_lyc.pth'))
lr = lr / 5
print('set lr={}'.format(lr))
if epoch == 0:
lr = 0.001
print('set lr={}'.format(lr))
optimizer = torch.optim.Adam(model.fresh_params(), lr=lr)
else:
optimizer = torch.optim.Adam(model.parameters(),
lr=lr,
weight_decay=0.0001)
running_loss = RunningMean()
running_score = RunningMean()
step = 0
all_step = int(len(training_data_loader) / BATCH_SIZE)
model.train()
pbar = tqdm(training_data_loader, total=len(training_data_loader))
for inputs, labels in pbar:
step += 1
batch_size = inputs.size(0)
inputs = Variable(inputs)
labels = Variable(labels)
if use_gpu:
inputs = inputs.cuda()
labels = labels.cuda()
optimizer.zero_grad()
outputs = model(inputs)
_, preds = torch.max(outputs.data, dim=1)
loss = criterion(outputs, labels)
running_loss.update(loss.data[0], 1)
running_score.update(torch.sum(preds != labels.data), batch_size)
writer.add_scalar('train_loss', loss, epoch * all_step + step)
loss.backward()
optimizer.step()
pbar.set_description(
'running_loss.value{:.5f} running_score.value{:.3f}'.format(
running_loss.value, running_score.value))
print('epoch {}: running_loss.value{:.5f} running_score.value{:.3f}'.
format(epoch, running_loss.value, running_score.value))
lx, px = utils.predict(model, validation_data_loader)
log_loss = criterion(Variable(px), Variable(lx))
log_loss = log_loss.data[0]
_, preds = torch.max(px, dim=1)
accuracy = torch.mean((preds != lx).float())
print('val: log_loss{:.5f} accuracy{:.3f}'.format(log_loss, accuracy))
if log_loss < min_loss:
torch.save(model.state_dict(), 'inception4_lyc.pth')
print('val score improved from {:.5f} to {:.5f}. Saved!'.format(
min_loss, log_loss))
min_loss = log_loss
patience = 0
else:
patience += 1
writer.add_scalar('val_loss', log_loss, epoch + 1)
writer.add_scalar('val_acc', accuracy, epoch + 1)
writer.close()
def train(model_name, outputDir):
train_dataset = FurnitureDataset('train', transform=preprocess_with_augmentation)
val_dataset = FurnitureDataset('val', transform=preprocess)
training_data_loader = DataLoader(dataset=train_dataset, num_workers=12,
batch_size=BATCH_SIZE,
shuffle=True)
validation_data_loader = DataLoader(dataset=val_dataset, num_workers=1,
batch_size=BATCH_SIZE,
shuffle=False)
model = get_model(model_name)
nb_learnable_params = sum(p.numel() for p in model.fresh_params())
print('Number of learnable params: %s' % str(nb_learnable_params))
# Use model.fresh_params() to train only the newly initialized weights
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=1e-5)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=2)
if model_name.endswith("_focal"):
print ("Using Focal loss instead of normal cross-entropy")
criterion = FocalLoss(NB_CLASSES).to(device)
else:
criterion = nn.CrossEntropyLoss().to(device)
min_loss = float("inf")
max_acc = 0.0
patience = 0
for epoch in range(NUM_EPOCHS):
print('Epoch: %d' % epoch)
running_loss = RunningMean()
running_error = RunningMean()
running_accuracy = RunningMean()
model.train()
pbar = tqdm(training_data_loader, total=len(training_data_loader))
for inputs, labels in pbar:
batch_size = inputs.size(0)
inputs = Variable(inputs)
labels = Variable(labels)
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
_, preds = torch.max(outputs.data, dim=1)
loss = criterion(outputs, labels)
running_loss.update(loss.data[0], 1)
running_error.update(torch.sum(preds != labels.data), batch_size)
running_accuracy.update(torch.sum(preds == labels.data), batch_size)
loss.backward()
optimizer.step()
pbar.set_description('%.5f %.3f %.3f' % (running_loss.value, running_accuracy.value, running_error.value))
print('Epoch: %d | Running loss: %.5f | Running accuracy: %.3f | Running error: %.3f' % (epoch, running_loss.value, running_accuracy.value, running_error.value))
lx, px = utils.predict(model, validation_data_loader, device)
log_loss = criterion(Variable(px), Variable(lx))
log_loss = log_loss.data[0]
_, preds = torch.max(px, dim=1)
accuracy = torch.mean((preds == lx).float())
error = torch.mean((preds != lx).float())
print('Validation loss: %.5f | Accuracy: %.3f | Error: %.3f' % (log_loss, accuracy, error))
scheduler.step(log_loss)
# Save model after each epoch
torch.save(model.state_dict(), os.path.join(outputDir, 'weight_' + model_name + '.pth'))
betterModelFound = False
if log_loss < min_loss:
torch.save(model.state_dict(), os.path.join(outputDir, 'best_val_loss_weight_' + model_name + '.pth'))
print('Validation score improved from %.5f to %.5f. Model snapshot saved!' % (min_loss, log_loss))
min_loss = log_loss
patience = 0
betterModelFound = True
if accuracy > max_acc:
torch.save(model.state_dict(), os.path.join(outputDir, 'best_val_acc_weight_' + model_name + '.pth'))
print('Validation accuracy improved from %.5f to %.5f. Model snapshot saved!' % (max_acc, accuracy))
max_acc = accuracy
patience = 0
betterModelFound = True
if not betterModelFound:
patience += 1
def train():
train_dataset = ImageFolder('./data/train/', transform=preprocess_with_augmentation(normalize_torch, IMAGE_SIZE))
valid_dataset = ImageFolder('./data/train/', transform=preprocess(normalize_torch, IMAGE_SIZE))
training_data_loader, valid_data_loader = (split_train_val_loader(train_dataset, valid_dataset,
len(train_dataset), valid_size=VALID_SIZE, batch_size=BATCH_SIZE,
train_enlarge_factor=TRAIN_ENLARGE_FACTOR,
pin_memory=True, num_workers=1, random_seed=RANDOM_SEED
))
model = get_model(MODEL, NB_CLASSES)
criterion = nn.CrossEntropyLoss().cuda()
nb_learnable_params = sum(p.numel() for p in model.fresh_params())
print(f'[+] nb learnable params {nb_learnable_params}')
lx, px = utils.predict(model, valid_data_loader, prob=False)
min_loss = criterion(Variable(px), Variable(lx)).item()
_, preds = torch.max(px.data, dim=1)
accuracy = torch.mean((preds != lx).float())
print(f' original loss: {min_loss}, accuracy: {accuracy}')
lr = 0.001
patience = 0
earlystop = 0
optimizer = torch.optim.Adam(model.fresh_params(), lr=lr)
torch.save(model.state_dict(), MODEL_FILE_NAME)
for epoch in range(EPOCH):
if epoch == 1:
lr = 0.0005
print(f'[+] set lr={lr}')
if patience == PATIENCE_LIMIT:
patience = 0
model.load_state_dict(torch.load(MODEL_FILE_NAME))
lr = lr / 10
print(f'[+] set lr={lr}')
if earlystop > EARLY_STOP:
model.load_state_dict(torch.load(MODEL_FILE_NAME))
print('EARLY STOPPED')
continue
if epoch > 0:
optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=0.0001)
running_loss = RunningMean()
running_score = RunningMean()
model.train()
pbar = tqdm(training_data_loader, total=len(training_data_loader))
for inputs, labels in pbar:
batch_size = inputs.size(0)
inputs = Variable(inputs)
labels = Variable(labels)
if use_gpu:
inputs = inputs.cuda()
labels = labels.cuda()
optimizer.zero_grad()
outputs = model(inputs)
_, preds = torch.max(outputs.data, dim=1)
loss = criterion(outputs, labels)
running_loss.update(loss.item(), 1)
running_score.update(torch.sum(preds == labels.data).float(), batch_size)
loss.backward()
optimizer.step()
pbar.set_description(f'{epoch}: {running_loss.value:.5f} {running_score.value:.3f}')
model.eval()
lx, px = utils.predict(model, valid_data_loader)
log_loss = criterion(Variable(px), Variable(lx))
log_loss = log_loss.item()
_, preds = torch.max(px, dim=1)
accuracy = torch.mean((preds == lx).float())
print(f'[+] val loss: {log_loss:.5f} acc: {accuracy:.3f}')
if (log_loss < min_loss):
torch.save(model.state_dict(), MODEL_FILE_NAME)
print(f'[+] val loss improved from {min_loss:.5f} to {log_loss:.5f}, accuracy={accuracy}. Saved!')
min_loss = log_loss
patience = 0
else:
patience += 1
earlystop += 1
def train():
train_dataset = FurnitureDataset('train',
transform=preprocess_with_augmentation)
val_dataset = FurnitureDataset('val', transform=preprocess)
training_data_loader = DataLoader(dataset=train_dataset,
num_workers=8,
batch_size=BATCH_SIZE,
shuffle=True)
validation_data_loader = DataLoader(dataset=val_dataset,
num_workers=1,
batch_size=BATCH_SIZE,
shuffle=False)
model = get_model()
criterion = nn.CrossEntropyLoss().cuda()
nb_learnable_params = sum(p.numel() for p in model.fresh_params())
print(f'[+] nb learnable params {nb_learnable_params}')
min_loss = float("inf")
lr = 0
patience = 0
for epoch in range(20):
print(f'epoch {epoch}')
if epoch == 1:
lr = 0.00003
print(f'[+] set lr={lr}')
if patience == 2:
patience = 0
model.load_state_dict(torch.load('best_val_weight.pth'))
lr = lr / 10
print(f'[+] set lr={lr}')
if epoch == 0:
lr = 0.001
print(f'[+] set lr={lr}')
optimizer = torch.optim.Adam(model.fresh_params(), lr=lr)
else:
optimizer = torch.optim.Adam(model.parameters(),
lr=lr,
weight_decay=0.0001)
running_loss = RunningMean()
running_score = RunningMean()
model.train()
pbar = tqdm(training_data_loader, total=len(training_data_loader))
for inputs, labels in pbar:
batch_size = inputs.size(0)
inputs = Variable(inputs)
labels = Variable(labels)
if use_gpu:
inputs = inputs.cuda()
labels = labels.cuda()
optimizer.zero_grad()
outputs = model(inputs)
_, preds = torch.max(outputs.data, dim=1)
loss = criterion(outputs, labels)
running_loss.update(loss.data[0], 1)
running_score.update(torch.sum(preds != labels.data), batch_size)
loss.backward()
optimizer.step()
pbar.set_description(
f'{running_loss.value:.5f} {running_score.value:.3f}')
print(
f'[+] epoch {epoch} {running_loss.value:.5f} {running_score.value:.3f}'
)
lx, px = utils.predict(model, validation_data_loader)
log_loss = criterion(Variable(px), Variable(lx))
log_loss = log_loss.data[0]
_, preds = torch.max(px, dim=1)
accuracy = torch.mean((preds != lx).float())
print(f'[+] val {log_loss:.5f} {accuracy:.3f}')
if log_loss < min_loss:
torch.save(model.state_dict(), 'best_val_weight.pth')
print(
f'[+] val score improved from {min_loss:.5f} to {log_loss:.5f}. Saved!'
)
min_loss = log_loss
patience = 0
else:
patience += 1
def train(attention=False):
train_dataset = FurnitureDataset('train',
transform=preprocess_with_augmentation)
train_val_dataset = FurnitureDataset(
'validation', transform=preprocess_with_augmentation)
val_dataset = FurnitureDataset('validation', transform=preprocess)
training_data_loader = DataLoader(dataset=train_dataset,
num_workers=8,
batch_size=BATCH_SIZE,
shuffle=True)
train_val_data_loader = DataLoader(dataset=val_dataset,
num_workers=8,
batch_size=BATCH_SIZE,
shuffle=True)
validation_data_loader = DataLoader(dataset=val_dataset,
num_workers=0,
batch_size=BATCH_SIZE // 2,
shuffle=False)
if USE_FOCAL_LOSS:
criterion = nn.CrossEntropyLoss(reduce=False).cuda()
else:
criterion = nn.CrossEntropyLoss().cuda()
print("loading model...")
if not attention:
model = get_model()
save_name = ""
else:
save_name = "att_"
model = dense_attention201(num_classes=128)
if use_gpu:
model.cuda()
fresh_params = [p['params'] for p in model.fresh_params()]
nb_learnable_params = 0
for pp in fresh_params:
nb_learnable_params += sum(p.numel() for p in pp)
print('[+] nb learnable params {}'.format(nb_learnable_params))
print("done.")
min_loss = float("inf")
patience = 0
for epoch in range(STARTER, STARTER + 10):
print('epoch {}'.format(epoch))
if epoch == 1:
lr = 0.00002
model.load_state_dict(torch.load('best_val_weight_0.pth'))
print("[+] loading best_val_weight_0.pth")
if patience == 2:
patience = 0
model.load_state_dict(torch.load('best_val_weight.pth'))
lr = lr / 5
elif epoch + 1 % 3 == 0:
ckpt = save_name + 'best_val_weight_%s.pth' % (epoch - 1)
if not os.path.exists(ckpt):
ckpt = save_name + 'best_val_weight.pth'
print("loading {}".format(ckpt))
model.load_state_dict(torch.load(ckpt))
lr = lr / 2
if epoch == 0:
lr = 0.001
optimizer = torch.optim.Adam(model.fresh_params(), lr=lr)
else:
optimizer = torch.optim.Adam(model.parameters(),
lr=lr,
weight_decay=wd)
print('[+] set lr={}'.format(lr))
running_loss = RunningMean()
running_score = RunningMean()
model.train()
### FOR TRAINING VALIDATION SET
# if epoch - STARTER + 1 % 2 == 0 and epoch - STARTER > 4:
# loader =train_val_data_loader
# print("[+] trianing with validation set")
# else:
# loader = training_data_loader
### FOR TRAINING VALIDATION SET
loader = training_data_loader
pbar = tqdm(loader, total=len(loader))
for inputs, labels in pbar:
batch_size = inputs.size(0)
inputs = Variable(inputs)
target = Variable(labels)
if use_gpu:
inputs = inputs.cuda()
target = target.cuda()
optimizer.zero_grad()
outputs = model(inputs)
_, preds = torch.max(outputs.data, dim=1)
loss = criterion(outputs, target)
if USE_FOCAL_LOSS:
y_index = torch.LongTensor(np.arange(labels.shape[0])).cpu()
l_weight = F.softmax(outputs,
dim=1).cpu()[y_index,
torch.LongTensor(labels)]
l_weight = l_weight.detach()
loss = torch.mean(4 * l_weight.cuda() * loss)
running_loss.update(loss.data[0], 1)
running_score.update(
torch.sum(preds != target.data, dtype=torch.float32),
batch_size)
loss.backward()
optimizer.step()
pbar.set_description('{:.5f} {:.3f}'.format(
running_loss.value, running_score.value))
print('[+] epoch {} {:.5f} {:.3f}'.format(epoch, running_loss.value,
running_score.value))
torch.save(model.state_dict(),
save_name + 'best_val_weight_%s.pth' % epoch)
lx, px = utils.predict(model, validation_data_loader)
log_loss = criterion(Variable(px), Variable(lx))
log_loss = log_loss.data[0]
_, preds = torch.max(px, dim=1)
accuracy = torch.mean((preds != lx).float())
print('[+] val {:.5f} {:.3f}'.format(log_loss, accuracy))
if log_loss < min_loss:
torch.save(model.state_dict(), 'best_val_weight.pth')
print(
'[+] val score improved from {:.5f} to {:.5f}. Saved!'.format(
min_loss, log_loss))
min_loss = log_loss
patience = 0
else:
patience += 1
def train(args):
train_dataset = FurnitureDataset('train',
transform=preprocess_with_augmentation)
val_dataset = FurnitureDataset('val', transform=preprocess)
training_data_loader = DataLoader(dataset=train_dataset,
num_workers=8,
batch_size=BATCH_SIZE,
shuffle=True)
validation_data_loader = DataLoader(dataset=val_dataset,
num_workers=1,
batch_size=BATCH_SIZE,
shuffle=False)
model = get_model(args.name)
class_weight = np.load('./class_weight.npy')
#criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor(class_weight)).cuda()
criterion = nn.CrossEntropyLoss().cuda()
#criterion = FocalLoss(alpha=alpha, gamma=0).cuda()
nb_learnable_params = sum(p.numel() for p in model.fresh_params())
print(f'[+] nb learnable params {nb_learnable_params}')
min_loss = float("inf")
lr = 0
patience = 0
for epoch in range(30):
print(f'epoch {epoch}')
if epoch == 1:
lr = 0.00003
print(f'[+] set lr={lr}')
if patience == 2:
patience = 0
model.load_state_dict(
torch.load(
'models_trained/{}_{}_{}/best_val_weight_{}.pth'.format(
args.name, args.aug, args.alpha, args.name)))
lr = lr / 10
if lr < 3e-6:
lr = 3e-6
print(f'[+] set lr={lr}')
if epoch == 0:
lr = 0.001
print(f'[+] set lr={lr}')
optimizer = torch.optim.Adam(model.fresh_params(), lr=lr)
else:
optimizer = torch.optim.Adam(model.parameters(),
lr=lr,
weight_decay=0.0001)
running_loss = RunningMean()
running_score = RunningMean()
model.train()
pbar = tqdm(training_data_loader, total=len(training_data_loader))
for inputs, labels in pbar:
batch_size = inputs.size(0)
inputs = Variable(inputs)
labels = Variable(labels)
if use_gpu:
inputs = inputs.cuda()
labels = labels.cuda()
optimizer.zero_grad()
if args.aug:
inputs, targets_a, targets_b, lam = mixup_data(
inputs, labels, args.alpha, use_gpu)
outputs = model(inputs)
if args.aug:
loss_func = mixup_criterion(targets_a, targets_b, lam)
loss = loss_func(criterion, outputs)
else:
loss = criterion(outputs, labels)
_, preds = torch.max(outputs.data, dim=1)
running_loss.update(loss.data[0], 1)
if args.aug:
running_score.update(
batch_size - lam * preds.eq(targets_a.data).cpu().sum() -
(1 - lam) * preds.eq(targets_b.data).cpu().sum(),
batch_size)
else:
running_score.update(torch.sum(preds != labels.data),
batch_size)
loss.backward()
optimizer.step()
pbar.set_description(
f'{running_loss.value:.5f} {running_score.value:.3f}')
print(
f'[+] epoch {epoch} {running_loss.value:.5f} {running_score.value:.3f}'
)
lx, px = utils.predict(model, validation_data_loader)
log_loss = criterion(Variable(px), Variable(lx))
log_loss = log_loss.data[0]
_, preds = torch.max(px, dim=1)
accuracy = torch.mean((preds != lx).float())
print(f'[+] val {log_loss:.5f} {accuracy:.3f}')
if log_loss < min_loss:
torch.save(
model.state_dict(),
'models_trained/{}_{}_{}/best_val_weight_{}.pth'.format(
args.name, args.aug, args.alpha, args.name))
print(
f'[+] val score improved from {min_loss:.5f} to {log_loss:.5f}. Saved!'
)
min_loss = log_loss
patience = 0
else:
patience += 1