def main():
model_dir = './checkpoints/seg2/segnet_gen1/model_at_epoch_013.dat'
save_dir = './test/0610/segnet_gen1/test'
test_txt_path = './data/seg/valid.txt'
# model = unet(in_channel=1, n_classes=1)
model = SegNet(input_nbr = 1, label_nbr = 1)
model = load_model(model, model_dir)
model = model.cuda()
model.eval()
test_dataset = GuideWireDataset(test_txt_path)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=NUM_WORKERS)
prefetcher = data_prefetcher(test_loader)
input, target, distance = prefetcher.next()
dice = []
IoU = []
precision = []
recall = []
i = -1
while input is not None:
i += 1
with torch.no_grad():
output = model(input)
dice.append(dice_coeff(output, target).item())
IoU.append(iou_coeff(output, target).item())
precision.append(Precision(output, target).item())
recall.append(Recall(output, target).item())
output = torch.sigmoid(output).squeeze().data.cpu().numpy()
output[output < 0.5] = 0
output[output >= 0.5] = 1
# output = torch.argmax(output, dim=1).squeeze().data.cpu().numpy()
# output = output.squeeze().data.cpu().numpy()
# output = np.argmax(output, axis=0)
cv2.imwrite(os.path.join(save_dir, str(i) + '_output.jpg'), output * 255)
print(str(i) + ' finish!')
input, target, distance = prefetcher.next()
print('dice: ', np.mean(dice), np.max(dice), np.min(dice), np.std(dice))
print('iou: ', np.mean(IoU), np.max(IoU), np.min(IoU), np.std(IoU))
print('precision: ', np.mean(precision), np.max(precision), np.min(precision), np.std(precision))
print('recall: ', np.mean(recall), np.max(recall), np.min(recall), np.std(recall))
def train(epoch, dataloader, model, criterion, optimizer, image_set = 'train'):
loss_meter = 0
acc_meter = 0
for i, (input, target) in enumerate(dataloader):
if image_set == 'train':
input = input.requires_grad_(True).float().cuda()
else:
input = input.float().cuda()
target = target.float().cuda()
# Get the model output
output =
# Introducing the loss here. Compute the loss value
loss =
loss_meter += loss.item()
# Compute acc here
acc = compute_acc(output, target)
acc_meter += acc.item()
if image_set == 'train':
# In the next three lines:
# Zero the existing gadients
# Do a backward pass
# Update the weights
if i % 3 == 0:
print(image_set, ' loss at epoch ', str(epoch), ' iteration ', str(i), ' is: ', loss_meter / (i+1),
' and acc is: ', acc_meter / (i+1))
if __name__ == "__main__":
train_dataset = VOC('./VOCdevkit/', 'train')
val_dataset = VOC('./VOCdevkit/', 'val')
train_dataloader = data.DataLoader(
train_dataset,
batch_size = 6,
shuffle = True,
num_workers = 4)
val_dataloader = data.DataLoader(
val_dataset,
batch_size = 1,
shuffle = False,
num_workers = 1)
model = SegNet()
# criterion = nn.MSELoss()
# criterion = nn.BCELoss()
criterion = nn.BCEWithLogitsLoss()
# Comment if not using a GPU
model = model.cuda()
criterion = criterion.cuda()
# Inititialize the optimizer.
lr = 0.1
optimizer = torch.optim.Adam(model.parameters(), lr)
n_epochs = 10
for i in range(n_epochs):
train(i, train_dataloader, model, criterion, optimizer, 'train')
if i % 2 == 0:
train(i, val_dataloader, model, criterion, optimizer, 'val')
# iou = compute_iou(output, target)
# iou_meter += iou
if image_set == 'train':
optimizer.zero_grad() # Why did we do this?
loss.backward()
optimizer.step()
print('loss at epoch ', str(epoch), ' iteration ', str(i), ' is: ',
loss.data.cpu().numpy())
if __name__ == "__main__":
train_dataset = VOC('./', 'train')
test_dataset = VOC('./', 'val')
train_dataloader = data.DataLoader(train_dataset,
batch_size=8,
shuffle=False,
num_workers=4)
model = SegNet()
criterion = nn.MSELoss()
# Comment if not using a GPU
model = model.cuda()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(model.parameters(), 0.01, momentum=0.9)
n_epochs = 10
for i in range(n_epochs):
train(i, train_dataloader, model, criterion, optimizer, 'train')