def main(config):
# Create test dataloader
test_ds_cfg = config['test_data_loader']['args']
test_dataset = MNISTDataset(test_ds_cfg['root'],
test_ds_cfg['csv_file'],
False,
transforms=transforms)
test_loader = DataLoader(test_dataset,
test_ds_cfg['batch_size'],
test_ds_cfg['shuffle'],
num_workers=test_ds_cfg['num_workers'])
# Create classification model
model = CNNModel()
# Create criterion (loss function)
criterion = getattr(loss_md, config['loss'])
# Create metrics for evaluation
metrics = [getattr(loss_md, x) for x in config['metrics']]
# Create optimizer
optimizer = getattr(otpm, config['optimizer']['name'])(
model.parameters(), **config['optimizer']['args'])
# Create learning rate scheduler
lr_scheduler = getattr(otpm.lr_scheduler, config['lr_scheduler']['name'])\
(optimizer, **config['lr_scheduler']['args'])
# Create train procedure: classification trainer
csf_trainer = ClassificationTrainer(config, model, criterion, metrics,
optimizer, lr_scheduler)
csf_trainer.test(test_loader)
####################### Dataloading ################################################
train_dataset = Fashion_MNIST_Data("./data/train.csv")
trainloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
test_dataset = Fashion_MNIST_Data("./data/test.csv")
testloader = DataLoader(test_dataset, batch_size=batch_size,shuffle=False, num_workers=4)
classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot')
####################################################################################
####################### Network ####################################################
net = CNNModel()
net.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(),lr=learning_rate)
####################################################################################
####################### Training ###################################################
for epoch in range(num_epochs):
running_loss = 0.0
for i, (images, labels) in enumerate(trainloader):
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
outputs = net(images)
loss = criterion(outputs, torch.max(labels,1)[1])
loss.backward()
optimizer.step()
running_loss += loss.item()
loss_name = loss.__class__.__name__
print(f"Loss: {loss_name}\n")
for noise_value in noise_values:
# RUN Experiments
name = f'CNN_{loss_name}_{tp_noise}_{noise_value}'
print(f"Training {name} with noise of type {tp_noise} and probability {noise_value}...")
# data preparation
dataset = FashionMnistHandler(data_dir, False)
dataset.load()
train_loader, val_loader, test_loader = dataset.get_noisy_loaders(p_noise=noise_value,
type_noise=tp_noise,
val_size=1 / 6,
train_batch_size=batch_size,
val_batch_size=128,
test_batch_size=128)
# model, optimizer, summary
model = CNNModel()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
summ = Summary(name, type_noise=tp_noise, noise_rate=noise_value)
solver = Solver(name, PROJECT_DIR, batch_model_dir, batch_summaries_dir, model,
optimizer, loss, summ, train_loader, val_loader, test_loader)
solver.pretrain()
solver.train(loss)
print(f"Completed training...")