def train(batch_size=64, window_size=3, epochs=100):
train_dataset = SupervisedDataset(mode='train',
window_size=window_size,
log_reg=True)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
val_dataset = SupervisedDataset(mode='val',
window_size=window_size,
log_reg=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
base_lr_rate = 1e-3
weight_decay = 1e-6 #0.000016
#model = LSTM(input_size=78, hidden_size=128, num_classes=170, n_layers=2).to(device=torch.device('cuda:0'))
model = LogisticRegression(
num_keypoints=78, num_features=2,
num_classes=170).to(device=torch.device('cuda:0'))
'''
for name, param in model.named_parameters():
if 'bias' in name:
nn.init.constant_(param, 0.0)
elif 'weight' in name:
nn.init.xavier_uniform_(param)
'''
criterion = nn.BCEWithLogitsLoss(
) # Use this for Logistic Regression training
#criterion = nn.BCELoss() # Use this for LSTM training (with Softmax)
optimizer = optim.Adam(
model.parameters(),
lr=base_lr_rate) #, weight_decay=weight_decay, amsgrad=True)
best_epoch_train_accuracy = 0.0
best_epoch_val_accuracy = 0.0
for current_epoch in range(epochs):
current_train_iter = 0
current_val_iter = 0
running_train_loss = 0.0
current_average_train_loss = 0.0
running_val_loss = 0.0
current_average_val_loss = 0.0
num_train_data = 0
num_val_data = 0
running_train_correct_preds = 0
running_train_correct_classwise_preds = [0] * 170
running_val_correct_preds = 0
running_val_correct_classwise_preds = [0] * 170
for phase in ['train', 'val']:
# Train loop
if phase == 'train':
train_epoch_since = time.time()
model.train()
for train_batch_window, train_batch_label in train_loader:
current_train_iter += 1
outs = model(train_batch_window)
#scheduler = poly_lr_scheduler(optimizer = optimizer, init_lr = base_lr_rate, iter = current_iter, lr_decay_iter = 1,
# max_iter = max_iter, power = power) # max_iter = len(train_loader)
optimizer.zero_grad()
loss = criterion(outs, train_batch_label)
gt_confidence, gt_index = torch.max(train_batch_label,
dim=1)
#loss = criterion(outs, gt_index)
running_train_loss += loss.item()
current_average_train_loss = running_train_loss / current_train_iter
loss.backward(retain_graph=False)
optimizer.step()
pred_confidence, pred_index = torch.max(outs, dim=1)
#gt_confidence, gt_index = torch.max(train_batch_label, dim=1)
batch_correct_preds = torch.eq(
pred_index, gt_index).long().sum().item()
batch_accuracy = (batch_correct_preds /
train_batch_window.shape[0]) * 100
num_train_data += train_batch_window.shape[0]
running_train_correct_preds += batch_correct_preds
if current_train_iter % 10 == 0:
#print(outs)
print(
f"\nITER#{current_train_iter} ({current_epoch+1}) BATCH TRAIN ACCURACY: {batch_accuracy:.4f}, RUNNING TRAIN LOSS: {loss.item():.8f}"
)
print(
f"Predicted / GT index:\n{pred_index}\n{gt_index}\n"
)
last_epoch_average_train_loss = current_average_train_loss
epoch_accuracy = (running_train_correct_preds /
num_train_data) * 100
print(
f"\n\nEPOCH#{current_epoch+1} EPOCH TRAIN ACCURACY (BEST): {epoch_accuracy:.4f} ({best_epoch_train_accuracy:.4f}), AVERAGE TRAIN LOSS: {last_epoch_average_train_loss:.8f}\n\n"
)
if epoch_accuracy > best_epoch_train_accuracy:
best_epoch_train_accuracy = epoch_accuracy
torch.save(
model.state_dict(),
f"./model_params/logistic_regression/train/train_logreg_epoch{current_epoch+1}_acc{best_epoch_train_accuracy:.4f}.pth"
)
print("\n\nSAVED BASED ON TRAIN ACCURACY!\n\n")
train_time_elapsed = time.time() - train_epoch_since
# Validation loop
elif phase == 'val':
val_epoch_since = time.time()
model.eval()
with torch.no_grad():
for val_batch_window, val_batch_label in val_loader:
current_val_iter += 1
outs = model(val_batch_window)
gt_confidence, gt_index = torch.max(val_batch_label,
dim=1)
val_loss = criterion(outs, val_batch_label)
#val_loss = criterion(outs, gt_index)
running_val_loss += val_loss.item()
current_average_val_loss = running_val_loss / current_val_iter
pred_confidence, pred_index = torch.max(outs, dim=1)
#gt_confidence, gt_index = torch.max(val_batch_label, dim=1)
batch_correct_preds = torch.eq(
pred_index, gt_index).long().sum().item()
batch_accuracy = (batch_correct_preds /
val_batch_window.shape[0]) * 100
num_val_data += val_batch_window.shape[0]
running_val_correct_preds += batch_correct_preds
if current_val_iter % 10 == 0:
print(
f"\nITER#{current_val_iter} ({current_epoch+1}) BATCH VALIDATION ACCURACY: {batch_accuracy:.4f}, RUNNING VALIDATION LOSS: {val_loss.item():.8f}"
)
print(
f"Predicted / GT index:\n{pred_index}\n{gt_index}\n"
)
last_epoch_average_val_loss = current_average_val_loss
epoch_accuracy = (running_val_correct_preds /
num_val_data) * 100
print(
f"\n\nEPOCH#{current_epoch+1} EPOCH VALIDATION ACCURACY (BEST): {epoch_accuracy:.4f} ({best_epoch_val_accuracy:.4f}), AVERAGE VALIDATION LOSS: {last_epoch_average_val_loss:.8f}\n\n"
)
if epoch_accuracy > best_epoch_val_accuracy:
best_epoch_val_accuracy = epoch_accuracy
torch.save(
model.state_dict(),
f"./model_params/logistic_regression/val/val_logreg_epoch{current_epoch+1}_acc{best_epoch_val_accuracy:.4f}.pth"
)
print("\n\nSAVED BASED ON VAL ACCURACY!\n\n")
val_time_elapsed = time.time() - val_epoch_since
writer.add_scalar('Stat. Parity/%s' % split, tot_stat_par.mean(), epoch)
writer.add_scalar('Equalized Odds/%s' % split, tot_eq_odds.mean(), epoch)
writer.add_histogram('L-inf Differences/%s' % split, l_inf_diffs, epoch)
return tot_mix_loss
print('saving model to', models_dir)
writer = SummaryWriter(log_dir)
for epoch in range(args.num_epochs):
run(autoencoder, classifier, optimizer, train_loader, 'train', epoch)
autoencoder.eval()
classifier.eval()
valid_mix_loss = run(
autoencoder, classifier, optimizer, val_loader, 'valid', epoch
)
scheduler.step(valid_mix_loss.mean())
torch.save(
autoencoder.state_dict(),
path.join(models_dir, f'autoencoder_{epoch}.pt')
)
torch.save(
classifier.state_dict(),
path.join(models_dir, f'classifier_{epoch}.pt')
)
writer.close()
def main():
args = parser.parse_args()
# seed everything to ensure reproducible results from different runs
if args.seed is not None:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
###########################################################################
# Model
###########################################################################
global best_acc1
# create model
if args.arch == 'LogisticRegression':
model = LogisticRegression(input_size=13, n_classes=args.classes)
elif args.arch == 'NeuralNet':
model = NeuralNet(input_size=13, hidden_size=[32, 16], n_classes=args.classes) #hidden_size=[64, 32]
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
torch.cuda.set_device(args.gpu)
torch.backends.cudnn.benchmark = True
model = model.cuda(args.gpu)
# print(model)
if args.train_file:
print(30 * '=')
print(summary(model, input_size=(1, 13),
batch_size=args.batch_size, device='cpu'))
print(30 * '=')
###########################################################################
# save directory
###########################################################################
save_dir = os.path.join(os.getcwd(), args.save_dir)
save_dir += ('/arch[{}]_optim[{}]_lr[{}]_lrsch[{}]_batch[{}]_'
'WeightedSampling[{}]').format(args.arch,
args.optim,
args.lr,
args.lr_scheduler,
args.batch_size,
args.weighted_sampling)
if args.suffix:
save_dir += '_{}'.format(args.suffix)
save_dir = save_dir[:]
if not os.path.exists(save_dir):
os.makedirs(save_dir)
###########################################################################
# Criterion and optimizer
###########################################################################
# Initialise criterion and optimizer
if args.gpu is not None:
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
else:
criterion = nn.CrossEntropyLoss()
# define optimizer
print("=> using '{}' optimizer".format(args.optim))
if args.optim == 'sgd':
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=True)
else: # default is adam
optimizer = torch.optim.Adam(model.parameters(), args.lr,
betas=(0.9, 0.999), eps=1e-08,
weight_decay=args.weight_decay,
amsgrad=False)
###########################################################################
# Resume training and load a checkpoint
###########################################################################
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
if args.gpu is None:
checkpoint = torch.load(args.resume)
else:
# Map model to be loaded to specified single gpu.
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.resume, map_location=loc)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
if args.gpu is not None:
# best_acc1 may be from a checkpoint from a different GPU
best_acc1 = best_acc1.to(args.gpu)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
###########################################################################
# Data Augmentation
###########################################################################
# TODO
###########################################################################
# Learning rate scheduler
###########################################################################
print("=> using '{}' initial learning rate (lr)".format(args.lr))
# define learning rate scheduler
scheduler = args.lr_scheduler
if args.lr_scheduler == 'reduce':
print("=> using '{}' lr_scheduler".format(args.lr_scheduler))
# Reduce learning rate when a metric has stopped improving.
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='min',
factor=0.5,
patience=10)
elif args.lr_scheduler == 'cyclic':
print("=> using '{}' lr_scheduler".format(args.lr_scheduler))
scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer,
base_lr=0.00005,
max_lr=0.005)
elif args.lr_scheduler == 'cosine':
print("=> using '{}' lr_scheduler".format(args.lr_scheduler))
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
T_max=100,
eta_min=0,
last_epoch=-1)
###########################################################################
# load train data
###########################################################################
if args.train_file:
train_dataset = HeartDiseaseDataset(csv=args.train_file, label_names=LABELS)
if args.weighted_sampling:
train_sampler = torch.utils.data.WeightedRandomSampler(train_dataset.sample_weights,
len(train_dataset),
replacement=True)
else:
train_sampler = None
###########################################################################
# update criterion
print('class_sample_count ', train_dataset.class_sample_count)
print('class_probability ', train_dataset.class_probability)
print('class_weights ', train_dataset.class_weights)
print('sample_weights ', train_dataset.sample_weights)
if args.weighted_loss:
if args.gpu is not None:
criterion = nn.CrossEntropyLoss(weight=train_dataset.class_weights).cuda(args.gpu)
else:
criterion = nn.CrossEntropyLoss(weight=train_dataset.class_weights)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
###########################################################################
# load validation data
###########################################################################
if args.valid_file:
valid_dataset = HeartDiseaseDataset(csv=args.valid_file, label_names=LABELS)
val_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
if args.evaluate:
# retrieve correct save path from saved model
save_dir = os.path.split(args.resume)[0]
validate(val_loader, model, criterion, save_dir, args)
return
###########################################################################
# Train the model
###########################################################################
for epoch in range(args.start_epoch, args.epochs):
# adjust_learning_rate(optimizer, epoch, args)
print_learning_rate(optimizer, epoch)
# train for one epoch
train(train_loader, model, criterion, optimizer,
scheduler, epoch, args)
# evaluate on validation set
acc1 = validate(val_loader, model, criterion, save_dir, args)
# update learning rate based on lr_scheduler
if args.lr_scheduler == 'reduce':
scheduler.step(acc1)
elif args.lr_scheduler == 'cosine':
scheduler.step()
# remember best acc@1 and save checkpoint
is_best = acc1 >= best_acc1
best_acc1 = max(acc1, best_acc1)
print("Saving model [{}]...".format(save_dir))
save_checkpoint({'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
'criterion': criterion, },
is_best,
save_dir=save_dir)
print(30 * '=')
