requires_grad=True), Variable(labels)
if gpu_id >= 0:
inputs, labels = inputs.cuda(), labels.cuda()
with torch.no_grad():
outputs = net.forward(inputs)
predictions = torch.max(outputs, 1)[1]
loss = criterion(outputs,
labels,
size_average=False,
batch_average=True)
running_loss_ts += loss.item()
total_miou += utils.get_iou(predictions, labels)
# Print stuff
if ii % num_img_ts == num_img_ts - 1:
miou = total_miou / (ii * testBatch + inputs.data.shape[0])
running_loss_ts = running_loss_ts / num_img_ts
print('Validation:')
print('[Epoch: %d, numImages: %5d]' %
(epoch, ii * testBatch + inputs.data.shape[0]))
writer.add_scalar('data/test_loss_epoch', running_loss_ts,
epoch)
writer.add_scalar('data/test_miour', miou, epoch)
print('Loss: %f' % running_loss_ts)
print('MIoU: %f\n' % miou)
# Forward-Backward of the mini-batch
inputs, labels = Variable(inputs,
requires_grad=True), Variable(labels)
global_step += inputs.data.shape[0]
if gpu_id >= 0:
inputs, labels = inputs.cuda(), labels.cuda()
outputs = net.forward(inputs)
pred = torch.max(outputs, 1)[1]
loss = criterion(outputs,
labels,
size_average=False,
batch_average=True)
running_loss += loss.item()
total_acc, total_miou = utils.get_iou(pred.cpu().numpy(),
labels.cpu().numpy())
running_acc += total_acc
running_miou += total_miou
# Print stuff
if ii % print_num == (print_num - 1) or ii == num_img_tr - 1:
running_loss = running_loss / (ii % print_num + 1)
running_acc = running_acc / (ii % print_num + 1)
running_miou = running_miou / (ii % print_num + 1)
stop_time = timeit.default_timer()
writer.add_scalar('data/training_loss', running_loss, epoch)
writer.add_scalar('data/training_acc', running_acc, epoch)
writer.add_scalar('data/training_miou', running_miou, epoch)
print('Epoch: %d, numImages: %5d ' %
(epoch, ii * p['trainBatch'] + inputs.data.shape[0]) +
inputs = inputs.to(device)
labels = labels.to(device)
with torch.no_grad():
outputs = net.forward(inputs)
# Apply a min confidence threshold
# outputs[outputs < conf_threshold] = 0
predictions = torch.max(outputs, 1)[1]
inputs = inputs.cpu()
labels = labels.cpu().type(torch.FloatTensor)
predictions = predictions.cpu().type(torch.FloatTensor)
if args.label_images_path:
_total_iou, per_class_iou, per_class_img_count = utils.get_iou(predictions, labels.squeeze(1), n_classes=args.num_of_classes)
total_iou += _total_iou
for i in range(len(per_class_iou)):
miou_per_class[i] += per_class_iou[i]
num_images_per_class[i] += per_class_img_count[i]
# Save the model output, 3 imgs in a row: Input, Prediction, Label
imgs_per_row = 3
predictions_colormap = utils.decode_seg_map_sequence(predictions.squeeze(1).numpy()).type(torch.FloatTensor)
labels_colormap = utils.decode_seg_map_sequence(labels.squeeze(1).numpy()).type(torch.FloatTensor)
sample = torch.cat((inputs, predictions_colormap, labels_colormap), 0)
img_grid = make_grid(sample, nrow=testBatchSize*imgs_per_row, padding=2)
save_image(img_grid, os.path.join(results_store_dir, sample_filename[0] + '-results.png'))
mask_out = predictions.squeeze(0).numpy() * 255
imageio.imwrite(os.path.join(results_store_dir, 'masks', sample_filename[0] + '.png'), mask_out.astype(np.uint8))
with torch.no_grad():
outputs = net.forward(inputs)
predictions = torch.max(outputs, 1)[1]
labels = labels.squeeze(1)
loss = criterion(outputs, labels)
# run validation dataset
if dataloader == validationloader:
# print('iter_num: ', ii + 1, '/', num_img_val)
running_loss_val += loss.item()
_total_iou, per_class_iou, num_images_per_class = utils.get_iou(predictions, labels, n_classes=num_of_classes)
total_iou += _total_iou
# Print stuff
if ii % num_img_val == num_img_val - 1:
miou = total_iou / (ii * p['trainBatchSize'] + inputs.shape[0])
running_loss_val = running_loss_val / num_img_val
print('Validation:')
print('[Epoch: %d, numImages: %5d]' % (epoch, ii * p['trainBatchSize'] + inputs.shape[0]))
writer.add_scalar('data/val_loss_epoch', running_loss_val, global_step)
writer.add_scalar('data/val_miour', miou, global_step)
print('Loss: %f' % running_loss_val)
print('MIoU: %f\n' % miou)
running_loss_val = 0
print(inputs.shape)
# Show 10 * 2 images results each epoch
def main(args):
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
if 'deeplab' in args.model_name:
if 'resnet101' in args.model_name:
net = Deeplabv3plus(nInputChannels=3, n_classes=args.num_classes, os=args.output_stride,
backbone_type='resnet101')
elif 'resnet50' in args.model_name:
net = Deeplabv3plus(nInputChannels=3, n_classes=args.num_classes, os=args.output_stride,
backbone_type='resnet50')
elif 'resnet34' in args.model_name:
net = Deeplabv3plus(nInputChannels=3, n_classes=args.num_classes, os=args.output_stride,
backbone_type='resnet34')
elif 'unet' in args.model_name:
net = Unet(in_ch=3, out_ch=1)
elif 'trfe' in args.model_name:
if args.model_name == 'trfe':
net = TRFENet(in_ch=3, out_ch=1)
elif args.model_name == 'trfe1':
net = TRFENet1(in_ch=3, out_ch=1)
elif args.model_name == 'trfe2':
net = TRFENet2(in_ch=3, out_ch=1)
elif 'mtnet' in args.model_name:
net = MTNet(in_ch=3, out_ch=1)
elif 'segnet' in args.model_name:
net = SegNet(input_channels=3, output_channels=1)
elif 'fcn' in args.model_name:
net = FCN8s(1)
else:
raise NotImplementedError
net.load_state_dict(torch.load(args.load_path))
net.cuda()
composed_transforms_ts = transforms.Compose([
trforms.FixedResize(size=(args.input_size, args.input_size)),
trforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
trforms.ToTensor()])
if args.test_dataset == 'TN3K':
test_data = tn3k.TN3K(mode='test', transform=composed_transforms_ts, return_size=True)
save_dir = args.save_dir + args.test_fold + '-' + args.test_dataset + os.sep + args.model_name + os.sep
testloader = DataLoader(test_data, batch_size=1, shuffle=False, num_workers=0)
num_iter_ts = len(testloader)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
net.cuda()
net.eval()
start_time = time.time()
with torch.no_grad():
total_iou = 0
for sample_batched in tqdm(testloader):
inputs, labels, label_name, size = sample_batched['image'], sample_batched['label'], sample_batched[
'label_name'], sample_batched['size']
inputs = Variable(inputs, requires_grad=False)
labels = Variable(labels)
labels = labels.cuda()
inputs = inputs.cuda()
if 'trfe' in args.model_name or 'mtnet' in args.model_name:
outputs, _ = net.forward(inputs)
else:
outputs = net.forward(inputs)
prob_pred = torch.sigmoid(outputs)
iou = utils.get_iou(prob_pred, labels)
total_iou += iou
shape = (size[0, 0], size[0, 1])
prob_pred = F.interpolate(prob_pred, size=shape, mode='bilinear', align_corners=True).cpu().data
save_data = prob_pred[0]
save_png = save_data[0].numpy()
save_png = np.round(save_png)
save_png = save_png * 255
save_png = save_png.astype(np.uint8)
save_path = save_dir + label_name[0]
if not os.path.exists(save_path[:save_path.rfind('/')]):
os.makedirs(save_path[:save_path.rfind('/')])
cv2.imwrite(save_dir + label_name[0], save_png)
print(args.model_name + ' iou:' + str(total_iou / len(testloader)))
duration = time.time() - start_time
print("-- %s contain %d images, cost time: %.4f s, speed: %.4f s." % (
args.test_dataset, num_iter_ts, duration, duration / num_iter_ts))
print("------------------------------------------------------------------")
def main(args):
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__)))
if args.resume_epoch != 0:
runs = sorted(glob.glob(os.path.join(save_dir_root, 'run', 'run_*')))
run_id = int(runs[-1].split('_')[-1]) if runs else 0
else:
runs = sorted(glob.glob(os.path.join(save_dir_root, 'run', 'run_*')))
run_id = int(runs[-1].split('_')[-1]) + 1 if runs else 0
if args.run_id >= 0:
run_id = args.run_id
save_dir = os.path.join(save_dir_root, 'run', 'run_' + str(run_id))
log_dir = os.path.join(
save_dir,
datetime.now().strftime('%b%d_%H-%M-%S') + '_' + socket.gethostname())
writer = SummaryWriter(log_dir=log_dir)
batch_size = args.batch_size
if 'deeplab' in args.model_name:
if 'resnet101' in args.model_name:
net = Deeplabv3plus(nInputChannels=3,
n_classes=args.num_classes,
os=args.output_stride,
backbone_type='resnet101')
elif 'resnet50' in args.model_name:
net = Deeplabv3plus(nInputChannels=3,
n_classes=args.num_classes,
os=args.output_stride,
backbone_type='resnet50')
elif 'resnet34' in args.model_name:
net = Deeplabv3plus(nInputChannels=3,
n_classes=args.num_classes,
os=args.output_stride,
backbone_type='resnet34')
else:
raise NotImplementedError
elif 'unet' in args.model_name:
net = Unet(in_ch=3, out_ch=1)
elif 'trfe' in args.model_name:
if args.model_name == 'trfe1':
net = TRFENet1(in_ch=3, out_ch=1)
elif args.model_name == 'trfe2':
net = TRFENet2(in_ch=3, out_ch=1)
elif args.model_name == 'trfe':
net = TRFENet(in_ch=3, out_ch=1)
batch_size = 4
elif 'mtnet' in args.model_name:
net = MTNet(in_ch=3, out_ch=1)
batch_size = 4
elif 'segnet' in args.model_name:
net = SegNet(input_channels=3, output_channels=1)
elif 'fcn' in args.model_name:
net = FCN8s(1)
else:
raise NotImplementedError
if args.resume_epoch == 0:
print('Training ' + args.model_name + ' from scratch...')
else:
load_path = os.path.join(
save_dir,
args.model_name + '_epoch-' + str(args.resume_epoch) + '.pth')
print('Initializing weights from: {}...'.format(load_path))
net.load_state_dict(torch.load(load_path))
if args.pretrain == 'THYROID':
net.load_state_dict(
torch.load('./pre_train/thyroid-pretrain.pth',
map_location=lambda storage, loc: storage))
print('loading pretrain model......')
torch.cuda.set_device(device=0)
net.cuda()
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum)
if args.criterion == 'Dice':
criterion = soft_dice
else:
raise NotImplementedError
composed_transforms_tr = transforms.Compose([
trforms.FixedResize(size=(args.input_size, args.input_size)),
trforms.RandomHorizontalFlip(),
trforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
trforms.ToTensor()
])
composed_transforms_ts = transforms.Compose([
trforms.FixedResize(size=(args.input_size, args.input_size)),
trforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
trforms.ToTensor()
])
if args.dataset == 'TN3K':
train_data = tn3k.TN3K(mode='train',
transform=composed_transforms_tr,
fold=args.fold)
val_data = tn3k.TN3K(mode='val',
transform=composed_transforms_ts,
fold=args.fold)
elif args.dataset == 'TG3K':
train_data = tg3k.TG3K(mode='train', transform=composed_transforms_tr)
val_data = tg3k.TG3K(mode='val', transform=composed_transforms_ts)
elif args.dataset == 'TATN':
train_data = tatn.TATN(mode='train',
transform=composed_transforms_tr,
fold=args.fold)
val_data = tatn.TATN(mode='val',
transform=composed_transforms_ts,
fold=args.fold)
trainloader = DataLoader(train_data,
batch_size=batch_size,
shuffle=True,
num_workers=0)
testloader = DataLoader(val_data,
batch_size=1,
shuffle=False,
num_workers=0)
num_iter_tr = len(trainloader)
num_iter_ts = len(testloader)
nitrs = args.resume_epoch * num_iter_tr
nsamples = args.resume_epoch * len(train_data)
print('nitrs: %d num_iter_tr: %d' % (nitrs, num_iter_tr))
print('nsamples: %d tot_num_samples: %d' % (nsamples, len(train_data)))
aveGrad = 0
global_step = 0
recent_losses = []
start_t = time.time()
best_f, cur_f = 0.0, 0.0
for epoch in range(args.resume_epoch, args.nepochs):
net.train()
epoch_losses = []
for ii, sample_batched in enumerate(trainloader):
if 'trfe' in args.model_name or args.model_name == 'mtnet':
nodules, glands = sample_batched
inputs_n, labels_n = nodules['image'].cuda(
), nodules['label'].cuda()
inputs_g, labels_g = glands['image'].cuda(
), glands['label'].cuda()
inputs = torch.cat(
[inputs_n[0].unsqueeze(0), inputs_g[0].unsqueeze(0)],
dim=0)
for i in range(1, inputs_n.size()[0]):
inputs = torch.cat([inputs, inputs_n[i].unsqueeze(0)],
dim=0)
inputs = torch.cat([inputs, inputs_g[i].unsqueeze(0)],
dim=0)
global_step += inputs.data.shape[0]
nodule, thyroid = net.forward(inputs)
loss = 0
for i in range(inputs.size()[0]):
if i % 2 == 0:
loss += criterion(nodule[i],
labels_n[int(i / 2)],
size_average=False,
batch_average=True)
else:
loss += 0.5 * criterion(thyroid[i],
labels_g[int((i - 1) / 2)],
size_average=False,
batch_average=True)
else:
inputs, labels = sample_batched['image'].cuda(
), sample_batched['label'].cuda()
global_step += inputs.data.shape[0]
outputs = net.forward(inputs)
loss = criterion(outputs,
labels,
size_average=False,
batch_average=True)
trainloss = loss.item()
epoch_losses.append(trainloss)
if len(recent_losses) < args.log_every:
recent_losses.append(trainloss)
else:
recent_losses[nitrs % len(recent_losses)] = trainloss
# Backward the averaged gradient
loss.backward()
aveGrad += 1
nitrs += 1
nsamples += args.batch_size
# Update the weights once in p['nAveGrad'] forward passes
if aveGrad % args.naver_grad == 0:
optimizer.step()
optimizer.zero_grad()
aveGrad = 0
if nitrs % args.log_every == 0:
meanloss = sum(recent_losses) / len(recent_losses)
print('epoch: %d ii: %d trainloss: %.2f timecost:%.2f secs' %
(epoch, ii, meanloss, time.time() - start_t))
writer.add_scalar('data/trainloss', meanloss, nsamples)
meanloss = sum(epoch_losses) / len(epoch_losses)
print('epoch: %d meanloss: %.2f' % (epoch, meanloss))
writer.add_scalar('data/epochloss', meanloss, nsamples)
if args.use_test == 1:
prec_lists = []
recall_lists = []
sum_testloss = 0.0
total_mae = 0.0
cnt = 0
count = 0
iou = 0
if args.use_eval == 1:
net.eval()
for ii, sample_batched in enumerate(testloader):
inputs, labels = sample_batched['image'].cuda(
), sample_batched['label'].cuda()
with torch.no_grad():
if 'trfe' in args.model_name or args.model_name == 'mtnet':
outputs, _ = net.forward(inputs)
else:
outputs = net.forward(inputs)
loss = criterion(outputs,
labels,
size_average=False,
batch_average=True)
sum_testloss += loss.item()
predictions = torch.sigmoid(outputs)
iou += utils.get_iou(predictions, labels)
count += 1
total_mae += utils.get_mae(predictions,
labels) * predictions.size(0)
prec_list, recall_list = utils.get_prec_recall(
predictions, labels)
prec_lists.extend(prec_list)
recall_lists.extend(recall_list)
cnt += predictions.size(0)
if ii % num_iter_ts == num_iter_ts - 1:
mmae = total_mae / cnt
mean_testloss = sum_testloss / num_iter_ts
mean_prec = sum(prec_lists) / len(prec_lists)
mean_recall = sum(recall_lists) / len(recall_lists)
fbeta = 1.3 * mean_prec * mean_recall / (0.3 * mean_prec +
mean_recall)
iou = iou / count
print('Validation:')
print(
'epoch: %d, numImages: %d testloss: %.2f mmae: %.4f fbeta: %.4f iou: %.4f'
% (epoch, cnt, mean_testloss, mmae, fbeta, iou))
writer.add_scalar('data/validloss', mean_testloss,
nsamples)
writer.add_scalar('data/validmae', mmae, nsamples)
writer.add_scalar('data/validfbeta', fbeta, nsamples)
writer.add_scalar('data/validiou', iou, epoch)
cur_f = iou
if cur_f > best_f:
save_path = os.path.join(
save_dir, args.model_name + '_best' + '.pth')
torch.save(net.state_dict(), save_path)
print("Save model at {}\n".format(save_path))
best_f = cur_f
if epoch % args.save_every == args.save_every - 1:
save_path = os.path.join(
save_dir, args.model_name + '_epoch-' + str(epoch) + '.pth')
torch.save(net.state_dict(), save_path)
print("Save model at {}\n".format(save_path))
