def main():
# Device
SEED = 1
cuda = torch.cuda.is_available()
print("Cuda is available ?", cuda)
torch.manual_seed(SEED)
if cuda:
torch.cuda.manual_seed(SEED)
device = torch.device("cuda" if cuda else "cpu")
# Create Train and Test Loader
trainloader = Loader.getDataLoader(dataset_name, trainSet_dict,
trainLoad_dict)
testloader = Loader.getDataLoader(dataset_name, testSet_dict,
testLoad_dict)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
# Loss Function
criterion = nn.NLLLoss()
# Optimizer
model = Net().to(device)
optimizer = optim.SGD(model.parameters(),
lr=LR,
momentum=MOMENTUM,
weight_decay=WEIGHT_DECAY)
# Start training
for epoch in range(EPOCHS):
train_loss, train_acc = TrainModel.train(model, device, trainloader,
criterion, optimizer, epoch)
train_losses.append(train_loss)
train_accuracy.append(train_acc)
test_loss, test_acc = TestModel.test(model, device, testloader,
criterion)
test_losses.append(test_loss)
test_accuracy.append(test_acc)
# Plot and Save Graph
getPlottedGraph(EPOCHS,
train_losses,
train_accuracy,
test_losses,
test_accuracy,
name="cifar_10_plot",
PATH=IMAGE_PATH)
# Save Models
torch.save(model.state_dict(), MODEL_PATH + "model7.pth")
def main():
# Device
SEED = 1
cuda = torch.cuda.is_available()
print("Cuda is available ?", cuda)
torch.manual_seed(SEED)
if cuda:
torch.cuda.manual_seed(SEED)
device = torch.device("cuda" if cuda else "cpu")
# Create Train and Test Loader
trainloader = Loader.getDataLoader(dataset_name, trainSet_dict, trainLoad_dict)
testloader = Loader.getDataLoader(dataset_name, testSet_dict, testLoad_dict)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# Loss Function
criterion = nn.CrossEntropyLoss()
# Optimizer
model = ResNet18().to(device)
optimizer = optim.SGD(model.parameters(), lr=LR, momentum=MOMENTUM, weight_decay=WEIGHT_DECAY)
scheduler = StepLR(optimizer, step_size=13, gamma=0.1)
# Start training
for epoch in range(EPOCHS):
train_loss, train_acc = TrainModel.train(model, device, trainloader, criterion, optimizer, epoch)
# scheduler.step()
train_losses.append(train_loss)
train_accuracy.append(train_acc)
test_loss, test_acc = TestModel.test(model, device, testloader, criterion)
test_losses.append(test_loss)
test_accuracy.append(test_acc)
# Plot and Save Graph
getPlottedGraph(EPOCHS, train_losses, train_accuracy, test_losses, test_accuracy,name="cifar_10_plot_using_resnet18_v3", PATH=IMAGE_PATH)
# Save Models
torch.save(model.state_dict(), MODEL_PATH+"model8_v3.pth")
#misclassified images
ms.show_save_misclassified_images(model, device, testloader, classes, list(img_mean), list(img_std), "fig_cifar10_v1", IMAGE_PATH, 25)
def main():
# Create Train Loader and Test Loader
trainloader = Loader.getDataLoader(dataset_name, trainSet_dict, trainLoad_dict)
testloader = Loader.getDataLoader(dataset_name, testSet_dict, testLoad_dict)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# Start training
for epoch in range(EPOCHS):
#Train
train_loss, train_acc = TrainModel.train(model, device, trainloader, criterion, optimizer, epoch)
scheduler.step()
train_losses.append(train_loss)
train_accuracy.append(train_acc)
#Test
test_loss, test_acc = TestModel.test(model, device, testloader, criterion)
test_losses.append(test_loss)
test_accuracy.append(test_acc)
#Save model
state = {'epoch': epoch + 1, 'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()}
torch.save(state, filename)
# Plot and Save Graph
getPlottedGraph(EPOCHS, train_losses, train_accuracy, test_losses, test_accuracy, name="S9_plot_final",
PATH=MODEL_PATH)
# Show and Save correct classified images
show_save_correctly_classified_images(model, testloader, device, IMAGE_PATH, name="correct_classified_imgs",
max_correctly_classified_images_imgs=25, labels_list=classes)
# Show and Save misclassified images
show_save_misclassified_images(model, testloader, device, IMAGE_PATH, name="misclassified_imgs",
max_misclassified_imgs=25, labels_list=classes)
# Visualize Activation Map
misclassified_imgs, correctly_classified_images = classify_images(model, testloader, device, 5)
layers_list = ["layer1", "layer2", "layer3", "layer4"]
display_gradcam = VisualizeCam(model, classes, layers_list)
correct_pred_imgs = []
for i in range(len(correctly_classified_images)):
correct_pred_imgs.append(torch.as_tensor(correctly_classified_images[i]["img"]))
display_gradcam(torch.stack(correct_pred_imgs), layers_list, PATH="./" + str("visualization"), metric="correct")
