def eval_models(models_paths: list, path_to_data: str):
if len(models_paths) == 0:
return 0.0
# getting test loader
ds = FashionMnistHandler(path_to_data, False)
ds.download()
ds.load()
# noise parameters are not relevant since test loader shouldn't have noise
_, _, test_loader = ds.get_noisy_loaders(0, '1', 0.2, 128, 128, 128)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
test_acc = []
for model_file in models_paths:
# creating model
checkpoint = torch.load(model_file, map_location=device)
model_name = model_file.split("/")[-1]
# loading from checkpoint
model = CNNModel()
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
loss_fn = torch.nn.CrossEntropyLoss()
# evaluating
_, acc = Solver.eval(model,
device,
loss_fn=loss_fn,
data_loader=test_loader)
test_acc.append(acc)
print(f"Model {model_name} has {acc:.4f} acc in test dataset")
return test_acc
num_epochs = 100
####################################################################################
####################### 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()
