def main():
parser = argparse.ArgumentParser(description='ViT')
parser.add_argument('--data_dir', default='data/sph_dogs_vs_cats')
parser.add_argument('--dataset', default='dvsc')
parser.add_argument('--exp_id', default='sdvsc-adam')
parser.add_argument('--mode', default='normal')
parser.add_argument('--batch', default=128)
parser.add_argument('--epochs', default=10)
parser.add_argument('--cuda', default=True)
parser.add_argument('--optim', default='SGD')
args = parser.parse_args()
os.system('mkdir -p weights')
dataset = {'smnist': SMNIST, 'dvsc': DVSC}
if args.dataset == 'smnist':
image_size = 60
patch_size = 10
num_classes = 10
samp = 6
elif args.dataset == 'dvsc':
image_size = 384
patch_size = 32
num_classes = 2
samp = 12
if args.mode == 'normal':
model = ViT(image_size=image_size,
patch_size=patch_size,
num_classes=num_classes,
dim=512,
depth=4,
heads=8,
mlp_dim=512,
dropout=0.1,
emb_dropout=0.1)
else:
model = ViT_sphere(
image_size=image_size,
patch_size=patch_size,
num_classes=num_classes,
dim=512,
depth=4,
heads=8,
mlp_dim=512,
base_order=1,
mode=args.mode, # face, vertex and regular
samp=samp,
dropout=0.1,
emb_dropout=0.1)
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print("Trainable parameters", params)
cuda = args.cuda
epochs = args.epochs
batch = args.batch
path = 'weights/'
train_data = dataset[args.dataset](args.data_dir, 'train', image_size,
image_size, None)
valid_data = dataset[args.dataset](args.data_dir, 'valid', image_size,
image_size, None)
train_loader = DataLoader(dataset=train_data,
batch_size=batch,
shuffle=True)
valid_loader = DataLoader(dataset=valid_data,
batch_size=batch,
shuffle=True)
if cuda:
model = model.cuda()
model.train()
if args.optim == 'SGD':
optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.9)
else:
optimizer = optim.Adam(model.parameters(), lr=1e-3) #, momentum=0.9)
cla_loss = torch.nn.CrossEntropyLoss()
valid_loss = 1000
valid_acc = 0
print("Training Start")
T_L = []
V_L = []
V_a = []
for i in range(epochs):
print("Epoch", i + 1)
model.train()
L = []
for i, data in enumerate(tqdm(train_loader)):
img, target = data
if cuda:
img = img.cuda()
target = target.cuda()
preds = model(img)
output = cla_loss(preds, target)
L.append(output.cpu().item())
output.backward()
optimizer.step()
optimizer.zero_grad()
T_L.append(np.mean(L))
print("train loss:", np.mean(L))
sum_acc = 0
total = len(valid_data)
model.eval()
for i, data in enumerate(tqdm(valid_loader)):
img, target = data
if cuda:
img = img.cuda()
target = target.cuda()
preds = model(img)
L.append(cla_loss(preds, target).item())
probabilities = torch.nn.functional.softmax(preds, dim=1)
preds = torch.argmax(probabilities, dim=1)
acc = torch.sum(
torch.where(preds == target,
torch.tensor(1, device=preds.device),
torch.tensor(0, device=preds.device)))
sum_acc += acc
v_l = np.mean(L)
v_a = sum_acc.item() / total * 100
if v_a > valid_acc:
valid_acc = v_a
torch.save(
{
'epoch': epochs,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, path + args.exp_id + 'model_acc.pth')
if v_l < valid_loss:
valid_loss = v_l
torch.save(
{
'epoch': epochs,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, path + args.exp_id + 'model_loss.pth')
V_L.append(v_l)
V_a.append(v_a)
print("val loss:", v_l)
print("val acc:", v_a)
print(T_L)
plt.plot(T_L, label='Total_loss', color='blue')
plt.plot(V_L, label='Valid_loss', color='red')
plt.legend(loc="upper left")
plt.xlabel("num of epochs")
plt.ylabel("loss")
plt.savefig(path + args.exp_id + 'Learning_Curves.png')
plt.clf()
plt.plot(V_a, label='Valid_acc', color='cyan')
plt.legend(loc="upper left")
plt.xlabel("num of epochs")
plt.ylabel("accuracy")
plt.savefig(path + args.exp_id + 'Val_acc.png')
torch.save(
{
'epoch': epochs,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, path + args.exp_id + 'model_last.pth')
def main():
parser = argparse.ArgumentParser(description='ViT')
parser.add_argument('--data_dir', default='data/sph_dogs_vs_cats')
parser.add_argument('--dataset', default='dvsc')
parser.add_argument('--resume', default='dvsc-sgd-regularmodel_last.pth')
parser.add_argument('--set', default = 'test')
parser.add_argument('--mode', default='regular')
parser.add_argument('--batch', default=8)
parser.add_argument('--cuda', default=True)
args = parser.parse_args()
os.system('mkdir -p weights')
dataset = {'smnist': SMNIST, 'dvsc': DVSC}
if args.dataset == 'smnist':
image_size = 60
patch_size = 10
num_classes = 10
samp = 6
elif args.dataset == 'dvsc':
image_size = 384
patch_size = 32
num_classes = 2
samp = 12
if args.mode == 'normal':
model = ViT(
image_size = image_size,
patch_size = patch_size,
num_classes = num_classes,
dim = 512,
depth = 4,
heads = 8,
mlp_dim = 512,
dropout = 0.1,
emb_dropout = 0.1
)
else :
model = ViT_sphere(
image_size = image_size,
patch_size = patch_size,
num_classes = num_classes,
dim = 512,
depth = 4,
heads = 8,
mlp_dim = 512,
base_order = 1,
mode = args.mode, # face, vertex and regular
samp = samp,
dropout = 0.1,
emb_dropout = 0.1
)
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print("Trainable parameters", params)
path = 'weights/'
model = load_model(model, os.path.join(path, args.resume))
cuda = args.cuda
batch = args.batch
test_data = dataset[args.dataset](args.data_dir, args.set, image_size, image_size, None)
test_loader = DataLoader(dataset=test_data, batch_size=batch, shuffle=False)
if cuda:
model = model.cuda()
model.eval()
P=np.array([])
T=np.array([])
#df = pd.read_csv("dvsc.csv")
for i, data in enumerate(tqdm(test_loader)):
img, target = data
if cuda:
img = img.cuda()
target = target.cuda()
preds = model(img)
probabilities = torch.nn.functional.softmax(preds, dim=1)
preds = torch.argmax(probabilities, dim =1)
P = np.concatenate([P,preds.cpu().numpy()])
T = np.concatenate([T,target.cpu().numpy()])
confusion = confusion_matrix(P, T)
#df['pred_class'] = P
#df.to_csv('dvsc_p_regular.csv')
print('Confusion Matrix\n')
print(confusion)
print('\nClassification Report\n')
print(classification_report(P, T, target_names=test_data.category))
'best_model+' + str(iter) + '.pt')
best_acc = total_acc
print("===> Best model saved in epoch:", i, ", iter:",
iter, ", acc:", total_acc)
# writer
# writer.add_scalar('test_epoch_loss', test_loss.data, j)
writer.add_scalar('val_accuracy', total_acc, iter)
# writer.add_scalar('test_epoch_precision', test_precision, j)
# writer.add_scalar('test_epoch_recall', test_recall, j)
# writer.add_scalar('test_epoch_f1score', test_f1_score, j)
# writer.add_hparams({"test_TP": TP_test, "test_TN": TN_test, "test_FP": FP_test, "test_FN": FN_test})
train_iter = iter
##### TEST #####
v.eval()
all_preds = []
all_labels = []
total_acc = 0
with torch.no_grad():
for iter, batch in enumerate(test_loader):
imgs, labels = Variable(batch[0]).cuda(), Variable(batch[1]).cuda()
preds = v(imgs)
loss = criterion(preds, labels)
##### For Confusion Marix #####
for i in range(len(labels)):
all_preds.append(torch.argmax(preds[i]).cpu())
all_labels.append(labels[i].cpu())