def visualize(args, epoch, model, data_loader, writer):
def save_image(image, tag):
image -= image.min()
image /= image.max()
grid = torchvision.utils.make_grid(image, nrow=4, pad_value=1)
writer.add_image(tag, grid, epoch)
model.eval()
with torch.no_grad():
for iter, data in enumerate(data_loader):
input, target, mean, std, norm = data
input = input.to(args.device)
target = target.unsqueeze(1).to(args.device)
save_image(target, 'Target')
if epoch != 0:
output = model(input.clone())
# output = transforms.complex_abs(output) # complex to real
# output = transforms.root_sum_of_squares(output, dim=1).unsqueeze(1)
corrupted = model.module.subsampling(input)
corrupted = corrupted[..., 0] # complex to real
cor_all = transforms.root_sum_of_squares(corrupted,dim=1).unsqueeze(1)
save_image(output, 'Reconstruction')
save_image(corrupted[:, 0:1, :, :], 'Corrupted0')
save_image(corrupted[:, 1:2, :, :], 'Corrupted1')
save_image(cor_all, 'Corrupted')
save_image(torch.abs(target - output), 'Error')
break
def __call__(self, kspace, target, attrs, fname, slice):
"""
Args:
kspace (numpy.Array): k-space measurements
target (numpy.Array): Target image
attrs (dict): Acquisition related information stored in the HDF5 object
fname (pathlib.Path): Path to the input file
slice (int): Serial number of the slice
Returns:
(tuple): tuple containing:
image (torch.Tensor): Normalized zero-filled input image
mean (float): Mean of the zero-filled image
std (float): Standard deviation of the zero-filled image
fname (pathlib.Path): Path to the input file
slice (int): Serial number of the slice
"""
kspace = transforms.to_tensor(kspace)
image = transforms.ifft2(kspace)
image = transforms.complex_center_crop(
image, (self.resolution, self.resolution))
# Apply Root-Sum-of-Squares if multicoil data
if self.which_challenge == 'multicoil':
image = transforms.root_sum_of_squares(image)
image = transforms.complex_abs(image)
image, mean, std = transforms.normalize_instance(image, eps=1e-11)
image = image.clamp(-6, 6)
kspace = transforms.rfft2(image)
return kspace, mean, std, fname, slice
def resize(hparams, image, target):
smallest_width = min(hparams.resolution, image.shape[-2])
smallest_height = min(hparams.resolution, image.shape[-3])
if target is not None:
smallest_width = min(smallest_width, target.shape[-1])
smallest_height = min(smallest_height, target.shape[-2])
crop_size = (smallest_height, smallest_width)
image = transforms.complex_center_crop(image, crop_size)
# Absolute value
image_abs = transforms.complex_abs(image)
# Apply Root-Sum-of-Squares if multicoil data
if hparams.challenge == "multicoil":
image_abs = transforms.root_sum_of_squares(image_abs)
# Normalize input
image_abs, mean, std = transforms.normalize_instance(image_abs, eps=1e-11)
image_abs = image_abs.clamp(-6, 6)
# Normalize target
if target is not None:
target = transforms.to_tensor(target)
target = transforms.center_crop(target, crop_size)
target = transforms.normalize(target, mean, std, eps=1e-11)
target = target.clamp(-6, 6)
else:
target = torch.Tensor([0])
return image, image_abs, target, mean, std
def __call__(self, kspace, target, attrs, fname, slice):
"""
Args:
kspace (numpy.Array): k-space measurements
target (numpy.Array): Target image
attrs (dict): Acquisition related information stored in the HDF5 object
fname (pathlib.Path): Path to the input file
slice (int): Serial number of the slice
Returns:
(tuple): tuple containing:
image (torch.Tensor): Normalized zero-filled input image
mean (float): Mean of the zero-filled image
std (float): Standard deviation of the zero-filled image
fname (pathlib.Path): Path to the input file
slice (int): Serial number of the slice
"""
kspace = transforms.to_tensor(kspace)
if self.mask_func is not None:
seed = tuple(map(ord, fname))
masked_kspace, _ = transforms.apply_mask(kspace, self.mask_func, seed)
else:
masked_kspace = kspace
# Inverse Fourier Transform to get zero filled solution
image = transforms.ifft2(masked_kspace)
# Crop input image
image = transforms.complex_center_crop(image, (self.resolution, self.resolution))
# Absolute value
image = transforms.complex_abs(image)
# Apply Root-Sum-of-Squares if multicoil data
if self.which_challenge == 'multicoil':
image = transforms.root_sum_of_squares(image)
# Normalize input
image, mean, std = transforms.normalize_instance(image)
image = image.clamp(-6, 6)
return image, mean, std, fname, slice
def forward(self, masked_kspace, mask):
sens_maps = self.sens_net(masked_kspace, mask)
kspace_pred = masked_kspace.clone()
for cascade in self.cascades:
kspace_pred = cascade(kspace_pred, masked_kspace, mask, sens_maps)
return T.root_sum_of_squares(T.complex_abs(T.ifft2(kspace_pred)),
dim=1)
def __call__(self, kspace, target, challenge, fname, slice_index):
original_kspace = transforms.to_tensor(kspace)
if self.reduce:
original_kspace = reducedimension(original_kspace, self.resolution)
# Apply mask
seed = None if not self.use_seed else tuple(map(ord, fname))
masked_kspace, mask = transforms.apply_mask(original_kspace,
self.mask_func, seed)
# Inverse Fourier Transform to get zero filled solution
image = transforms.ifft2(masked_kspace)
# Crop input image
image = transforms.complex_center_crop(
image, (self.resolution, self.resolution))
# Absolute value
image = transforms.complex_abs(image)
# Apply Root-Sum-of-Squares if multicoil data
if challenge == 'multicoil':
image = transforms.root_sum_of_squares(image)
# Normalize input
image, mean, std = transforms.normalize_instance(image, eps=1e-11)
target = transforms.to_tensor(target)
# Normalize target
target = transforms.normalize(target, mean, std, eps=1e-11)
target = target.clamp(-6, 6)
if self.polar:
original_kspace = cartesianToPolar(original_kspace)
masked_kspace = cartesianToPolar(masked_kspace)
return original_kspace, masked_kspace, mask, target, fname, slice_index
def __call__(self, kspace, mask, target, attrs, fname, slice):
"""
Args:
kspace (numpy.array): Input k-space of shape (num_coils, rows, cols, 2) for multi-coil
data or (rows, cols, 2) for single coil data.
mask (numpy.array): Mask from the test dataset
target (numpy.array): Target image
attrs (dict): Acquisition related information stored in the HDF5 object.
fname (str): File name
slice (int): Serial number of the slice.
Returns:
(tuple): tuple containing:
image (torch.Tensor): Zero-filled input image.
target (torch.Tensor): Target image converted to a torch Tensor.
mean (float): Mean value used for normalization.
std (float): Standard deviation value used for normalization.
"""
kspace = transforms.to_tensor(kspace)
# Apply mask
if self.mask_func:
seed = None if not self.use_seed else tuple(map(ord, fname))
masked_kspace, mask = transforms.apply_mask(
kspace, self.mask_func, seed)
else:
masked_kspace = kspace
# Inverse Fourier Transform to get zero filled solution
image = transforms.ifft2(masked_kspace)
# Crop input image to given resolution if larger
smallest_width = min(self.resolution, image.shape[-2])
smallest_height = min(self.resolution, image.shape[-3])
if target is not None:
smallest_width = min(smallest_width, target.shape[-1])
smallest_height = min(smallest_height, target.shape[-2])
crop_size = (smallest_height, smallest_width)
image = transforms.complex_center_crop(image, crop_size)
# Absolute value
image = transforms.complex_abs(image)
# Apply Root-Sum-of-Squares if multicoil data
if self.which_challenge == 'multicoil':
image = transforms.root_sum_of_squares(image)
# Normalize input
image, mean, std = transforms.normalize_instance(image, eps=1e-11)
image = image.clamp(-6, 6)
# Normalize target
if target is not None:
target = transforms.to_tensor(target)
target = transforms.center_crop(target, crop_size)
target = transforms.normalize(target, mean, std, eps=1e-11)
target = target.clamp(-6, 6)
else:
target = torch.Tensor([0])
return image, target, mean, std, fname, slice
def __call__(self, kspace, target, attrs, fname, slice):
"""
Args:
kspace (numpy.array): Input k-space of shape (num_coils, rows, cols, 2) for multi-coil
data or (rows, cols, 2) for single coil data.
target (numpy.array): Target image
attrs (dict): Acquisition related information stored in the HDF5 object.
fname (str): File name
slice (int): Serial number of the slice.
Returns:
(tuple): tuple containing:
image (torch.Tensor): Zero-filled input image.
target (torch.Tensor): Target image converted to a torch Tensor.
mean (float): Mean value used for normalization.
std (float): Standard deviation value used for normalization.
norm (float): L2 norm of the entire volume.
"""
kspace = transforms.to_tensor(kspace)
# Apply mask
seed = None if not self.use_seed else tuple(map(ord, fname))
if self.use_mask:
mask = transforms.get_mask(kspace, self.mask_func, seed)
masked_kspace = mask * kspace
else:
masked_kspace = kspace
# Inverse Fourier Transform to get zero filled solution
image = transforms.ifft2(masked_kspace)
# Crop input image
image = transforms.complex_center_crop(
image, (self.resolution, self.resolution))
# Absolute value
image = transforms.complex_abs(image)
# Apply Root-Sum-of-Squares if multicoil data
if self.which_challenge == 'multicoil':
image = transforms.root_sum_of_squares(image)
# Normalize input
image, mean, std = transforms.normalize_instance(image, eps=1e-11)
if CLAMP:
image = image.clamp(-6, 6)
# Normalize target
target = transforms.to_tensor(target)
target_train = transforms.normalize(target, mean, std, eps=1e-11)
if CLAMP:
target_train = target_train.clamp(
-6,
6) # Return target (for viz) and target_clamped (for training)
return image, target_train, mean, std, attrs['norm'].astype(
np.float32), target
def __call__(self, kspace, target, attrs, fname, slice):
"""
Args:
kspace (numpy.Array): k-space measurements
target (numpy.Array): Target image
attrs (dict): Acquisition related information stored in the HDF5 object
fname (pathlib.Path): Path to the input file
slice (int): Serial number of the slice
Returns:
(tuple): tuple containing:
image (torch.Tensor): Normalized zero-filled input image
mean (float): Mean of the zero-filled image
std (float): Standard deviation of the zero-filled image
fname (pathlib.Path): Path to the input file
slice (int): Serial number of the slice
"""
kspace = transforms.to_tensor(kspace)
if self.mask_func is not None:
seed = tuple(map(ord, fname))
masked_kspace, _ = transforms.apply_mask(kspace, self.mask_func, seed)
else:
masked_kspace = kspace
# Inverse Fourier Transform to get zero filled solution
image = transforms.ifft2(masked_kspace)
# Crop input image
image = transforms.complex_center_crop(image, (self.resolution, self.resolution))
# Absolute value
image = transforms.complex_abs(image)
# Apply Root-Sum-of-Squares if multicoil data
if self.which_challenge == 'multicoil':
image = transforms.root_sum_of_squares(image)
# Normalize input
image, mean, std = transforms.normalize_instance(image)
image = image.clamp(-6, 6)
# difference between kspace actual and target dim
extra = int(masked_kspace.shape[1] - self.kspace_x)
# clip kspace at input dim
if extra > 0:
masked_kspace = masked_kspace[:, (extra//2):-(extra//2), :]
# zero pad if necessary
elif extra < 0:
empty_kspace = torch.zeros((masked_kspace.shape[0], self.kspace_x, masked_kspace.shape[2]))
empty_kspace[:, -(extra//2):(extra//2), :] = masked_kspace
masked_kspace = empty_kspace
#TODO return mask as well for exclusive updates
return masked_kspace, image, mean, std, fname, slice
def kspacetoimage(kspace, args):
# Inverse Fourier Transform to get zero filled solution
image = transforms.ifft2(kspace)
# Crop input image
image = transforms.complex_center_crop(image,
(args.resolution, args.resolution))
# Absolute value
image = transforms.complex_abs(image)
# Apply Root-Sum-of-Squares if multicoil data
if args.challenge == 'multicoil':
image = transforms.root_sum_of_squares(image)
# Normalize input
image, mean, std = transforms.normalize_instance(image, eps=1e-11)
image = image.clamp(-6, 6)
return image
def save_zero_filled(data_dir, out_dir, which_challenge, resolution):
reconstructions = {}
for file in data_dir.iterdir():
print("file:{}".format(file))
with h5py.File(file, "r") as hf:
masked_kspace = transforms.to_tensor(hf['kspace'][()])
# Inverse Fourier Transform to get zero filled solution
image = transforms.ifft2(masked_kspace)
# Crop input image
smallest_width = min(resolution, image.shape[-2])
smallest_height = min(resolution, image.shape[-3])
image = transforms.complex_center_crop(image, (smallest_height, smallest_width))
# Absolute value
image = transforms.complex_abs(image)
# Apply Root-Sum-of-Squares if multicoil data
if which_challenge == 'multicoil':
image = transforms.root_sum_of_squares(image, dim=1)
reconstructions[file.name] = image
save_reconstructions(reconstructions, out_dir)
def eval(args, model, data_loader):
model.eval()
reconstructions = defaultdict(list)
with torch.no_grad():
for (input, target, mean, std, norm, fnames, slices) in data_loader:
input = input.to(args.device)
# recons = model(input).to('cpu').squeeze(1)
corrupted = model.module.subsampling(input).to('cpu')
corrupted = corrupted[..., 0] # complex to real
cor_all = transforms.root_sum_of_squares(corrupted, dim=1)
for i in range(cor_all.shape[0]):
cor_all[i] = cor_all[i] * std[i] + mean[i]
reconstructions[fnames[i]].append(
(slices[i].numpy(), cor_all[i].numpy()))
# for i in range(corrupted.shape[0]):
# reconstructions[fnames[i]].append((slices[i].numpy(), corrupted[i].numpy()))
reconstructions = {
fname: np.stack([pred for _, pred in sorted(slice_preds)])
for fname, slice_preds in reconstructions.items()
}
return reconstructions
def __call__(self, kspace, target, attrs, fname, slice):
"""
Args:
kspace (numpy.array): Input k-space of shape (num_coils, rows, cols, 2) for multi-coil
data or (rows, cols, 2) for single coil data.
target (numpy.array): Target image
attrs (dict): Acquisition related information stored in the HDF5 object.
fname (str): File name
slice (int): Serial number of the slice.
Returns:
(tuple): tuple containing:
image (torch.Tensor): Zero-filled input image.
target (torch.Tensor): Target image converted to a torch Tensor.
mean (float): Mean value used for normalization.
std (float): Standard deviation value used for normalization.
norm (float): L2 norm of the entire volume.
"""
kspace = transforms.to_tensor(kspace)
# Apply mask
seed = None if not self.use_seed else tuple(map(ord, fname))
if self.use_mask:
mask = transforms.get_mask(kspace, self.mask_func, seed)
masked_kspace = mask * kspace
else:
masked_kspace = kspace
# Inverse Fourier Transform to get zero filled solution
image = transforms.ifft2(masked_kspace)
# Crop input image
image = transforms.complex_center_crop(
image, (self.resolution, self.resolution))
# Absolute value
image = transforms.complex_abs(image)
# Apply Root-Sum-of-Squares if multicoil data
if self.which_challenge == 'multicoil':
image = transforms.root_sum_of_squares(image)
# Normalize input
if self.normalize:
image, mean, std = transforms.normalize_instance(image, eps=1e-11)
if CLAMP:
image = image.clamp(-6, 6)
else:
mean = -1.0
std = -1.0
# Normalize target
if target is not None:
target = transforms.to_tensor(target)
target_train = target
if self.normalize:
target_train = transforms.normalize(target,
mean,
std,
eps=1e-11)
if CLAMP:
target_train = target_train.clamp(
-6, 6
) # Return target (for viz) and target_clamped (for training)
norm = attrs['norm'].astype(np.float32)
else:
target_train = []
target = []
norm = -1.0
image_updated = []
if os.path.exists(
'/home/manivasagam/code/fastMRIPrivate/models/unet_volumes/reconstructions_train/'
+ fname):
updated_fname = '/home/manivasagam/code/fastMRIPrivate/models/unet_volumes/reconstructions_train/' + fname
with h5py.File(updated_fname, 'r') as data:
image_updated = data['reconstruction'][slice]
image_updated = transforms.to_tensor(image_updated)
elif os.path.exists(
'/home/manivasagam/code/fastMRIPrivate/models/unet_volumes/reconstructions_val/'
+ fname):
updated_fname = '/home/manivasagam/code/fastMRIPrivate/models/unet_volumes/reconstructions_val/' + fname
with h5py.File(updated_fname, 'r') as data:
image_updated = data['reconstruction'][slice]
image_updated = transforms.to_tensor(image_updated)
elif os.path.exists(
'/home/manivasagam/code/fastMRIPrivate/models/unet_volumes/reconstructions_test/'
+ fname):
updated_fname = '/home/manivasagam/code/fastMRIPrivate/models/unet_volumes/reconstructions_test/' + fname
with h5py.File(updated_fname, 'r') as data:
image_updated = data['reconstruction'][slice]
image_updated = transforms.to_tensor(image_updated)
return image, target_train, mean, std, norm, target, image_updated
def closure():
optimizer.zero_grad()
out = net(net_input.type(dtype))
# training loss
if mask_var is not None:
loss = mse(out * mask_var, img_noisy_var * mask_var)
elif apply_f:
loss = mse(apply_f(out), img_noisy_var)
else:
loss = mse(out, img_noisy_var)
loss.backward()
mse_wrt_noisy[i] = loss.data.cpu().numpy()
# the actual loss
true_loss = mse(
Variable(out.data, requires_grad=False).type(dtype),
img_clean_var.type(dtype))
mse_wrt_truth[i] = true_loss.data.cpu().numpy()
if MRI_multicoil_reference is not None:
out_chs = net(net_input.type(dtype)).data.cpu().numpy()[0]
out_imgs = channels2imgs(out_chs)
out_img_np = transform.root_sum_of_squares(
torch.tensor(out_imgs), dim=0).numpy()
mse_wrt_truth[i] = np.linalg.norm(MRI_multicoil_reference -
out_img_np)
if output_gradients:
for ind, p in enumerate(
list(
filter(
lambda p: p.grad is not None and len(
p.data.shape) > 2, net.parameters()))):
out_grads[ind, i] = p.grad.data.norm(2).item()
#print(p.grad.data.norm(2).item())
#su += p.grad.data.norm(2).item()
#mse_wrt_noisy[i] = su
if i % 10 == 0:
out2 = net(Variable(net_input_saved).type(dtype))
loss2 = mse(out2, img_clean_var)
print(
'Iteration %05d Train loss %f Actual loss %f Actual loss orig %f'
% (i, loss.data, mse_wrt_truth[i], loss2.data),
'\r',
end='')
if show_images:
if i % 50 == 0:
print(i)
out_img_np = net(ni.type(dtype)).data.cpu().numpy()[0]
myimgshow(plt, out_img_np)
plt.show()
if plot_after is not None:
if i in plot_after:
out_imgs[plot_after.index(i), :] = net(
net_input_saved.type(dtype)).data.cpu().numpy()[0]
if output_weights:
out_weights[:, i] = np.array(
get_distances(init_weights, get_weights(net)))
return loss
def forward(self, input):
input = self.subsampling(input)
input = transforms.root_sum_of_squares(transforms.complex_abs(input),
dim=1).unsqueeze(1)
output = self.reconstruction_model(input)
return output
slice_kspace2 = T.to_tensor(
slice_kspace) # Convert from numpy array to pytorch tensor
slice_image = T.ifft2(
slice_kspace2) # Apply Inverse Fourier Transform to get the complex image
slice_image_abs = T.complex_abs(
slice_image) # Compute absolute value to get a real image
# In[10]:
show_slices(slice_image_abs, [0, 5, 10], cmap='gray')
# As we can see, each slice in a multi-coil MRI scan focusses on a different region of the image. These slices can be combined into the full image using the Root-Sum-of-Squares (RSS) transform.
# In[11]:
slice_image_rss = T.root_sum_of_squares(slice_image_abs, dim=0)
# In[12]:
plt.imshow(np.abs(slice_image_rss.numpy()), cmap='gray')
# So far, we have been looking at fully-sampled data. We can simulate under-sampled data by creating a mask and applying it to k-space.
# In[13]:
from common.subsample import MaskFunc
mask_func = MaskFunc(center_fractions=[0.04],
accelerations=[8]) # Create the mask function object
# In[14]:
def __call__(self, kspace, target, attrs, fname, slice):
"""
Args:
kspace (numpy.array): Input k-space of shape (num_coils, rows, cols, 2) for multi-coil
data or (rows, cols, 2) for single coil data.
target (numpy.array): Target image
attrs (dict): Acquisition related information stored in the HDF5 object.
fname (str): File name
slice (int): Serial number of the slice.
Returns:
(tuple): tuple containing:
image (torch.Tensor): Zero-filled input image.
target (torch.Tensor): Target image converted to a torch Tensor.
mean (float): Mean value used for normalization.
std (float): Standard deviation value used for normalization.
norm (float): L2 norm of the entire volume.
"""
kspace = transforms.to_tensor(kspace)
gt = transforms.ifft2(kspace)
gt = transforms.complex_center_crop(gt, (self.resolution, self.resolution))
kspace = transforms.fft2(gt)
# Apply mask
seed = None if not self.use_seed else tuple(map(ord, fname))
masked_kspace, mask = transforms.apply_mask(kspace, self.mask_func, seed)
# Inverse Fourier Transform to get zero filled solution
image = transforms.ifft2(masked_kspace)
masked_kspace = transforms.fft2_nshift(image)
# Crop input image
image = transforms.complex_center_crop(image, (self.resolution, self.resolution))
# Absolute value
image_mod = transforms.complex_abs(image).max()
image_r = image[:, :, 0]*6.0/image_mod
image_i = image[:, :, 1]*6.0/image_mod
# image_r = image[:, :, 0]
# image_i = image[:, :, 1]
# Apply Root-Sum-of-Squares if multicoil data
if self.which_challenge == 'multicoil':
image = transforms.root_sum_of_squares(image)
# Normalize input
image = np.stack((image_r, image_i), axis=-1)
image = image.transpose((2, 0, 1))
image = transforms.to_tensor(image)
target = transforms.ifft2(kspace)
target = transforms.complex_center_crop(target, (self.resolution, self.resolution))
# Normalize target
target_r = target[:, :, 0]*6.0/image_mod
target_i = target[:, :, 1]*6.0/image_mod
# target_r = target[:, :, 0]
# target_i = target[:, :, 1]
target = np.stack((target_r, target_i), axis=-1)
target = target.transpose((2, 0, 1))
target = transforms.to_tensor(target)
image_mod = np.stack((image_mod, image_mod), axis=0)
image_mod = transforms.to_tensor(image_mod)
norm = attrs['norm'].astype(np.float32)
norm = np.stack((norm, norm), axis=-1)
norm = transforms.to_tensor(norm)
mask = mask.expand(kspace.shape)
mask = mask.transpose(0, 2).transpose(1, 2)
mask = transforms.ifftshift(mask)
masked_kspace = masked_kspace.transpose(0, 2).transpose(1, 2)
return image, target
def test_root_sum_of_squares(shape, dim):
input = create_input(shape)
out_torch = transforms.root_sum_of_squares(input, dim).numpy()
out_numpy = np.sqrt(np.sum(input.numpy()**2, dim))
assert np.allclose(out_torch, out_numpy)
def __call__(self, kspace, target, attrs, fname, slice):
kspace_rect = transforms.to_tensor(kspace) ##rectangular kspace
image_rect = transforms.ifft2(kspace_rect) ##rectangular FS image
image_square = transforms.complex_center_crop(
image_rect,
(self.resolution, self.resolution)) ##cropped to FS square image
kspace_square = self.c3object.apply(
transforms.fft2(image_square)) * 10000 ##kspace of square iamge
image_square2 = ifft_c3(kspace_square) ##for training domain_transform
if self.augmentation:
kspace_square = self.augmentation.apply(kspace_square)
# image_square = ifft_c3(kspace_square)
# Apply mask
seed = None if not self.use_seed else tuple(map(ord, fname))
masked_kspace_square, mask = transforms.apply_mask(
kspace_square, self.mask_func, seed) ##ZF square kspace
# Inverse Fourier Transform to get zero filled solution
# image = transforms.ifft2(masked_kspace)
us_image_square = ifft_c3(
masked_kspace_square) ## US square complex image
# Crop input image
# image = transforms.complex_center_crop(image, (self.resolution, self.resolution))
# Absolute value
# image = transforms.complex_abs(image)
us_image_square_abs = transforms.complex_abs(
us_image_square) ## US square real image
us_image_square_rss = transforms.root_sum_of_squares(
us_image_square_abs, dim=0)
stacked_kspace_square = []
for i in (range(len(kspace_square[:, 0, 0, 0]))):
stacked_kspace_square.append(kspace_square[i, :, :, 0])
stacked_kspace_square.append(kspace_square[i, :, :, 1])
stacked_kspace_square = torch.stack(stacked_kspace_square)
stacked_masked_kspace_square = []
# masked_kspace_square = transforms.to_tensor(masked_kspace_square)
# for i in range(len(masked_kspace_square[:,0,0,0])):
# stacked_masked_kspace_square.stack(masked_kspace_square[i,:,:,0],masked_kspace_square[i,:,:,1])
for i in (range(len(masked_kspace_square[:, 0, 0, 0]))):
stacked_masked_kspace_square.append(masked_kspace_square[i, :, :,
0])
stacked_masked_kspace_square.append(masked_kspace_square[i, :, :,
1])
stacked_masked_kspace_square = torch.stack(
stacked_masked_kspace_square)
stacked_image_square = []
for i in (range(len(image_square[:, 0, 0, 0]))):
stacked_image_square.append(image_square2[i, :, :, 0])
stacked_image_square.append(image_square2[i, :, :, 1])
stacked_image_square = torch.stack(stacked_image_square)
return stacked_kspace_square,stacked_masked_kspace_square , stacked_image_square , \
us_image_square_rss , \
target *10000 \
#mean, std, attrs['norm'].astype(np.float32)
'''
