vgg_model = VGGNet(freeze_max=False)
net = FCN8s(vgg_model)
checkpoint = torch.load('baseline.pth')
net.load_state_dict(checkpoint['model_state_dict'])
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device(
'cpu')
net.to(device)
# train dataloader
scale = Rescale(int(1.5 * 230))
crop = RandomCrop(224)
rotate = RandomRotate(20.0)
norm = Normalize()
tupled = ToTupleTensor()
tupled_with_roi_align = ToRoIAlignTensor()
composed_for_tracking = transforms.Compose(
[Rescale(224), norm, tupled_with_roi_align])
dataset = SISSDataset(num_slices=153,
num_scans=2,
root_dir=Path.cwd().parents[0],
transform=composed_for_tracking,
train=True)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=1,
fig.suptitle('Sample %d' % idx)
for slice_, scan in enumerate(['dwi', 'flair', 't1', 't2', 'label']):
ax = plt.subplot(1, 5, slice_ + 1)
show_single_img(sample[:, :, slice_], scan == 'label')
plt.tight_layout()
ax.set_title(scan)
ax.axis('off')
plt.show()
# train dataloader
scale = Rescale(int(1.5 * 230))
crop = RandomCrop(224)
rotate = RandomRotate(20.0)
norm = Normalize()
tupled = ToTupleTensor()
tupled_multiscaled_masks = ToMultiScaleMasks()
tupled_with_roialign = ToRoIAlignTensor()
composed = transforms.Compose(
[scale, rotate, crop, norm, tupled_multiscaled_masks])
# transforms coupling testing
composed_wo_tupling_norm = transforms.Compose([
Rescale(int(1.5 * 230)),
RandomRotate(50.0),
RandomCrop(224),
Normalize()
])