def _visualize_outputs(c_img_recons, c_img_targets, smoothing_factor=8):
image_recons = complex_abs(c_img_recons)
image_targets = complex_abs(c_img_targets)
kspace_recons = make_k_grid(fft2(c_img_recons), smoothing_factor)
kspace_targets = make_k_grid(fft2(c_img_targets), smoothing_factor)
image_recons, image_targets, image_deltas = make_grid_triplet(
image_recons, image_targets)
return kspace_recons, kspace_targets, image_recons, image_targets, image_deltas
def _val_epoch(self, epoch):
self.model.eval()
torch.autograd.set_grad_enabled(False)
epoch_loss = list()
epoch_metrics = defaultdict(list)
# 1 based indexing for steps.
data_loader = enumerate(self.val_loader, start=1)
if not self.verbose:
data_loader = tqdm(data_loader, total=len(self.val_loader.dataset))
for step, data in data_loader:
inputs, targets, extra_params = self.input_val_transform(*data)
recons, step_loss, step_metrics = self._val_step(inputs, targets, extra_params)
epoch_loss.append(step_loss.detach())
if self.use_slice_metrics:
slice_metrics = self._get_slice_metrics(recons['img_recons'], targets['img_targets'])
step_metrics.update(slice_metrics)
[epoch_metrics[key].append(value.detach()) for key, value in step_metrics.items()]
if self.verbose:
self._log_step_outputs(epoch, step, step_loss, step_metrics, training=False)
# This numbering scheme seems to have issues for certain numbers.
# Please check cases when there is no remainder.
if self.display_interval and (step % self.display_interval == 0):
# Change image display function later.
img_recon_grid, img_target_grid, img_delta_grid = \
make_grid_triplet(recons['img_recons'], targets['img_targets'])
kspace_recon_grid = make_k_grid(recons['kspace_recons'], self.smoothing_factor)
kspace_target_grid = make_k_grid(targets['kspace_targets'], self.smoothing_factor)
self.writer.add_image(f'k-space_Recons/{step}', kspace_recon_grid, epoch, dataformats='HW')
self.writer.add_image(f'Image_Recons/{step}', img_recon_grid, epoch, dataformats='HW')
self.writer.add_image(f'Image_Deltas/{step}', img_delta_grid, epoch, dataformats='HW')
if 'semi_kspace_recons' in recons:
semi_kspace_recon_grid = make_k_grid(recons['semi_kspace_recons'])
self.writer.add_image(f'semi-k-space_Recons/{step}',
semi_kspace_recon_grid, epoch, dataformats='HW')
if epoch == 1: # Maybe add input images too later on.
self.writer.add_image(f'k-space_Targets/{step}', kspace_target_grid, epoch, dataformats='HW')
self.writer.add_image(f'Image_Targets/{step}', img_target_grid, epoch, dataformats='HW')
if 'semi_kspace_targets' in targets:
semi_kspace_target_grid = make_k_grid(targets['semi_kspace_targets'])
self.writer.add_image(f'semi-k-space_Targets/{step}',
semi_kspace_target_grid, epoch, dataformats='HW')
# Converted to scalar and dict with scalar values respectively.
return self._get_epoch_outputs(epoch, epoch_loss, epoch_metrics, training=False)
def _val_epoch(self, epoch):
self.model.eval()
torch.autograd.set_grad_enabled(False)
epoch_loss_lst = list()
epoch_metrics_lst = [list()
for _ in self.metrics] if self.metrics else None
for step, (inputs, targets, extra_params) in enumerate(self.val_loader,
start=1):
step_loss, image_recons, kspace_recons = self._val_step(
inputs, targets, extra_params)
epoch_loss_lst.append(step_loss.item())
# Step functions have internalized conditional statements deciding whether to execute or not.
step_metrics = self._get_step_metrics(image_recons, targets,
epoch_metrics_lst)
self._log_step_outputs(epoch,
step,
step_loss,
step_metrics,
training=False)
# Save images to TensorBoard. Send this to a separate function later on.
# Condition ensures that self.display_interval != 0 and that the step is right for display.
if self.display_interval and (step % self.display_interval == 0):
recons_grid, targets_grid, deltas_grid = make_grid_triplet(
image_recons, targets)
kspace_grid = make_k_grid(kspace_recons)
self.writer.add_image(f'k-space_Recons/{step}',
kspace_grid,
epoch,
dataformats='HW')
self.writer.add_image(f'Image_Recons/{step}',
recons_grid,
epoch,
dataformats='HW')
self.writer.add_image(f'Targets/{step}',
targets_grid,
epoch,
dataformats='HW')
self.writer.add_image(f'Deltas/{step}',
deltas_grid,
epoch,
dataformats='HW')
epoch_loss, epoch_metrics = self._get_epoch_outputs(epoch,
epoch_loss_lst,
epoch_metrics_lst,
training=False)
return epoch_loss, epoch_metrics
def _val_epoch(self, epoch):
self.generator.eval()
self.discriminator.eval()
torch.autograd.set_grad_enabled(False)
epoch_loss = list(
) # Appending values to list due to numerical underflow.
epoch_loss_components = defaultdict(list)
val_len = len(self.val_loader.dataset)
for step, (inputs, targets,
extra_params) in tqdm(enumerate(self.val_loader, start=1),
total=val_len):
recons, step_loss, step_loss_components = self._val_step(
inputs, targets, extra_params)
# Append to list to prevent errors from NaN and Inf values.
epoch_loss.append(step_loss)
for key, value in step_loss_components.items():
epoch_loss_components[key].append(value)
if self.verbose:
self._log_step_outputs(epoch,
step,
step_loss,
step_loss_components,
training=False)
# Save images to TensorBoard.
# Condition ensures that self.display_interval != 0 and that the step is right for display.
if self.display_interval and (step % self.display_interval == 0):
recon_grid, target_grid, delta_grid = make_grid_triplet(
recons, targets)
self.writer.add_image(f'Recons/{step}',
recon_grid,
global_step=epoch,
dataformats='HW')
self.writer.add_image(f'Targets/{step}',
target_grid,
global_step=epoch,
dataformats='HW')
self.writer.add_image(f'Deltas/{step}',
delta_grid,
global_step=epoch,
dataformats='HW')
return self._get_epoch_outputs(epoch,
epoch_loss,
epoch_loss_components,
training=False)
def _val_epoch(self, epoch):
self.model.eval()
torch.autograd.set_grad_enabled(False)
epoch_loss = list()
epoch_metrics = defaultdict(list)
# 1 based indexing for steps.
data_loader = enumerate(self.val_loader, start=1)
if not self.verbose:
data_loader = tqdm(data_loader, total=len(self.val_loader.dataset))
# 'targets' is a dictionary containing k-space targets, cmg_targets, and img_targets.
for step, data in data_loader:
inputs, targets, extra_params = self.input_val_transform(*data)
# 'recons' is a dictionary containing k-space, complex image, and real image reconstructions.
recons, step_loss, step_metrics = self._val_step(inputs, targets, extra_params)
epoch_loss.append(step_loss.detach())
if self.use_slice_metrics:
slice_metrics = self._get_slice_metrics(recons['img_recons'], targets['img_targets'])
step_metrics.update(slice_metrics)
[epoch_metrics[key].append(value.detach()) for key, value in step_metrics.items()]
if self.verbose:
self._log_step_outputs(epoch, step, step_loss, step_metrics, training=False)
# Save images to TensorBoard.
# Condition ensures that self.display_interval != 0 and that the step is right for display.
# This numbering scheme seems to have issues for certain numbers.
# Please check cases when there is no remainder.
if self.display_interval and (step % self.display_interval == 0):
img_recon_grid, img_target_grid, img_delta_grid = \
make_grid_triplet(recons['img_recons'], targets['img_targets'])
kspace_recon_grid = make_k_grid(recons['kspace_recons'], self.smoothing_factor)
kspace_target_grid = make_k_grid(targets['kspace_targets'], self.smoothing_factor)
self.writer.add_image(f'k-space_Recons/{step}', kspace_recon_grid, epoch, dataformats='HW')
self.writer.add_image(f'k-space_Targets/{step}', kspace_target_grid, epoch, dataformats='HW')
self.writer.add_image(f'Image_Recons/{step}', img_recon_grid, epoch, dataformats='HW')
self.writer.add_image(f'Image_Targets/{step}', img_target_grid, epoch, dataformats='HW')
self.writer.add_image(f'Image_Deltas/{step}', img_delta_grid, epoch, dataformats='HW')
epoch_loss, epoch_metrics = self._get_epoch_outputs(epoch, epoch_loss, epoch_metrics, training=False)
return epoch_loss, epoch_metrics