def _do_iteration(self,
data: Dict[str, torch.Tensor],
loss_fns: Dict[str, Callable]) -> Tuple[torch.Tensor, Dict]:
# Target is not needed in the model input
target = data['target'].align_to('batch', 'complex', 'height', 'width').to(self.device) # type: ignore
# The first input_image in the iteration is the input_image with the mask applied and no first hidden state.
input_image = data.pop('masked_image').to(self.device) # type: ignore
hidden_state = None
output_image = None
loss_dicts = []
for rim_step in range(self.cfg.model.steps):
reconstruction_iter, hidden_state = self.model(
**dict_to_device(data, self.device),
input_image=input_image,
hidden_state=hidden_state,
)
# TODO: Unclear why this refining is needed.
output_image = reconstruction_iter[-1].refine_names('batch', 'complex', 'height', 'width')
loss_dict = {k: torch.tensor([0.], dtype=target.dtype).to(self.device) for k in loss_fns.keys()}
loss = torch.tensor([0.], device=output_image.device)
for output_image_iter in reconstruction_iter:
for k, v in loss_dict.items():
loss_dict[k] = v + loss_fns[k](
output_image_iter.rename(None), target.rename(None), reduction='mean'
)
# for output_image_iter in reconstruction_iter:
# loss_dict = {
# k: v + loss_fns[k](output_image_iter.rename(None), target.rename(None), reduction='mean')
# for k, v in loss_dict.items()}
loss_dict = {k: v / len(reconstruction_iter) for k, v in loss_dict.items()}
loss = sum(loss_dict.values())
if self.model.training:
if self.mixed_precision:
with amp.scale_loss(loss, self.__optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward() # type: ignore
# Detach hidden state from computation graph, to ensure loss is only computed per RIM block.
hidden_state = hidden_state.detach()
input_image = output_image.detach()
loss_dicts.append(detach_dict(loss_dict)) # Need to detach dict as this is only used for logging.
# Add the loss dicts together over RIM steps, divide by the number of steps.
loss_dict = reduce_list_of_dicts(loss_dicts, mode='sum', divisor=self.cfg.model.steps)
return output_image, loss_dict
def _do_iteration(
self,
data: Dict[str, torch.Tensor],
loss_fns: Optional[Dict[str, Callable]] = None,
regularizer_fns: Optional[Dict[str, Callable]] = None,
) -> namedtuple:
# loss_fns can be done, e.g. during validation
if loss_fns is None:
loss_fns = {}
if regularizer_fns is None:
regularizer_fns = {}
# The first input_image in the iteration is the input_image with the mask applied and no first hidden state.
input_image = None
hidden_state = None
output_image = None
loss_dicts = []
regularizer_dicts = []
data = dict_to_device(data, self.device)
# TODO(jt): keys=['sampling_mask', 'sensitivity_map', 'target', 'masked_kspace', 'scaling_factor']
sensitivity_map = data["sensitivity_map"]
if "noise_model" in self.models:
raise NotImplementedError()
# Some things can be done with the sensitivity map here, e.g. apply a u-net
if "sensitivity_model" in self.models:
# Move channels to first axis
sensitivity_map = sensitivity_map.align_to(*self.complex_names(
add_coil=True))
sensitivity_map = (self.compute_model_per_coil(
"sensitivity_model",
sensitivity_map).refine_names(*sensitivity_map.names).align_to(
*self.complex_names_complex_last(add_coil=True)))
# Output has channel first, it is ("batch, "coil", "complex", ...)
# The sensitivity map needs to be normalized such that
# So \sum_{i \in \text{coils}} S_i S_i^* = 1
sensitivity_map_norm = torch.sqrt(
((sensitivity_map**2).sum("complex")).sum("coil"))
data["sensitivity_map"] = T.safe_divide(sensitivity_map,
sensitivity_map_norm)
if self.cfg.model.scale_loglikelihood:
scaling_factor = (1.0 * self.cfg.model.scale_loglikelihood /
(data["scaling_factor"]**2))
scaling_factor = scaling_factor.reshape(-1, 1).refine_names(
"batch", "complex")
self.logger.debug(f"Scaling factor is: {scaling_factor}")
else:
# Needs fixing.
scaling_factor = (torch.tensor([1.0]).to(
sensitivity_map.device).refine_names("complex"))
for _ in range(self.cfg.model.steps):
with autocast(enabled=self.mixed_precision):
reconstruction_iter, hidden_state = self.model(
**data,
input_image=input_image,
hidden_state=hidden_state,
loglikelihood_scaling=scaling_factor,
)
# TODO: Unclear why this refining is needed.
output_image = reconstruction_iter[-1].refine_names(
*self.complex_names())
loss_dict = {
k: torch.tensor([0.0],
dtype=data["target"].dtype).to(self.device)
for k in loss_fns.keys()
}
regularizer_dict = {
k: torch.tensor([0.0],
dtype=data["target"].dtype).to(self.device)
for k in regularizer_fns.keys()
}
# TODO: This seems too similar not to be able to do this, perhaps a partial can help here
for output_image_iter in reconstruction_iter:
for k, v in loss_dict.items():
loss_dict[k] = v + loss_fns[k](
output_image_iter,
**data,
reduction="mean",
)
for k, v in regularizer_dict.items():
regularizer_dict[k] = (v + regularizer_fns[k](
output_image_iter,
**data,
).rename(None))
loss_dict = {
k: v / len(reconstruction_iter)
for k, v in loss_dict.items()
}
regularizer_dict = {
k: v / len(reconstruction_iter)
for k, v in regularizer_dict.items()
}
loss = sum(loss_dict.values()) + sum(regularizer_dict.values())
if self.model.training:
self._scaler.scale(loss).backward()
# Detach hidden state from computation graph, to ensure loss is only computed per RIM block.
hidden_state = hidden_state.detach()
input_image = output_image.detach()
loss_dicts.append(detach_dict(loss_dict))
regularizer_dicts.append(
detach_dict(regularizer_dict)
) # Need to detach dict as this is only used for logging.
# Add the loss dicts together over RIM steps, divide by the number of steps.
loss_dict = reduce_list_of_dicts(loss_dicts,
mode="sum",
divisor=self.cfg.model.steps)
regularizer_dict = reduce_list_of_dicts(regularizer_dicts,
mode="sum",
divisor=self.cfg.model.steps)
output = namedtuple(
"do_iteration",
["output_image", "sensitivity_map", "data_dict"],
)
return output(
output_image=output_image,
sensitivity_map=data["sensitivity_map"],
data_dict={
**loss_dict,
**regularizer_dict
},
)
def evaluate(
self,
data_loader: DataLoader,
loss_fns: Optional[Dict[str, Callable]],
regularizer_fns: Optional[Dict[str, Callable]] = None,
crop: Optional[str] = None,
is_validation_process=True,
):
self.models_to_device()
self.models_validation_mode()
torch.cuda.empty_cache()
# Variables required for evaluation.
# TODO(jt): Consider if this needs to be in the main engine.py or here. Might be possible we have different
# types needed, perhaps even a FastMRI engine or something similar depending on the metrics.
volume_metrics = self.build_metrics(self.cfg.validation.metrics)
# filenames can be in the volume_indices attribute of the dataset
if hasattr(data_loader.dataset, "volume_indices"):
all_filenames = list(data_loader.dataset.volume_indices.keys())
num_for_this_process = len(
list(data_loader.batch_sampler.sampler.volume_indices.keys()))
self.logger.info(
f"Reconstructing a total of {len(all_filenames)} volumes. "
f"This process has {num_for_this_process} volumes (world size: {communication.get_world_size()})."
)
else:
num_for_this_process = None
filenames_seen = 0
reconstruction_output = defaultdict(list)
targets_output = defaultdict(list)
val_losses = []
val_volume_metrics = defaultdict(dict)
last_filename = None
# Container to for the slices which can be visualized in TensorBoard.
visualize_slices = []
visualize_target = []
visualizations = {}
extra_visualization_keys = (self.cfg.logging.log_as_image
if self.cfg.logging.log_as_image else [])
# Loop over dataset. This requires the use of direct.data.sampler.DistributedSequentialSampler as this sampler
# splits the data over the different processes, and outputs the slices linearly. The implicit assumption here is
# that the slices are outputted from the Dataset *sequentially* for each volume one by one.
time_start = time.time()
for iter_idx, data in enumerate(data_loader):
data = AddNames()(data)
filenames = data.pop("filename")
if len(set(filenames)) != 1:
raise ValueError(
f"Expected a batch during validation to only contain filenames of one case. "
f"Got {set(filenames)}.")
slice_nos = data.pop("slice_no")
scaling_factors = data["scaling_factor"]
resolution = self.compute_resolution(
key=self.cfg.validation.crop,
reconstruction_size=data.get("reconstruction_size", None),
)
# Compute output and loss.
iteration_output = self._do_iteration(
data, loss_fns, regularizer_fns=regularizer_fns)
output = iteration_output.output_image
loss_dict = iteration_output.data_dict
# sensitivity_map = iteration_output.sensitivity_map
loss_dict = detach_dict(loss_dict)
output = output.detach()
val_losses.append(loss_dict)
# Output is complex-valued, and has to be cropped. This holds for both output and target.
output_abs = self.process_output(
output.refine_names(*self.complex_names()),
scaling_factors,
resolution=resolution,
)
if is_validation_process:
target_abs = self.process_output(
data["target"].detach().refine_names(*self.real_names()),
scaling_factors,
resolution=resolution,
)
for key in extra_visualization_keys:
curr_data = data[key].detach()
# Here we need to discover which keys are actually normalized or not
# this requires a solution to issue #23: https://github.com/directgroup/direct/issues/23
del output # Explicitly call delete to clear memory.
# TODO: Is a hack.
# Aggregate volumes to be able to compute the metrics on complete volumes.
for idx, filename in enumerate(filenames):
if last_filename is None:
last_filename = (
filename # First iteration last_filename is not set.
)
# If the new filename is not the previous one, then we can reconstruct the volume as the sampling
# is linear.
# For the last case we need to check if we are at the last batch *and* at the last element in the batch.
is_last_element_of_last_batch = iter_idx + 1 == len(
data_loader) and idx + 1 == len(data["target"])
if filename != last_filename or is_last_element_of_last_batch:
filenames_seen += 1
# Now we can ditch the reconstruction dict by reconstructing the volume,
# will take too much memory otherwise.
# TODO: Stack does not support named tensors.
volume = torch.stack([
_[1].rename(None)
for _ in reconstruction_output[last_filename]
])
if is_validation_process:
target = torch.stack([
_[1].rename(None)
for _ in targets_output[last_filename]
])
curr_metrics = {
metric_name: metric_fn(target, volume)
for metric_name, metric_fn in
volume_metrics.items()
}
val_volume_metrics[last_filename] = curr_metrics
# Log the center slice of the volume
if (len(visualize_slices) <
self.cfg.logging.tensorboard.num_images):
visualize_slices.append(volume[volume.shape[0] //
2])
visualize_target.append(target[target.shape[0] //
2])
# Delete outputs from memory, and recreate dictionary. This is not needed when not in validation
# as we are actually interested in the output
del targets_output
targets_output = defaultdict(list)
del reconstruction_output
reconstruction_output = defaultdict(list)
if all_filenames:
log_prefix = f"{filenames_seen} of {num_for_this_process} volumes reconstructed:"
else:
log_prefix = f"{iter_idx + 1} of {len(data_loader)} slices reconstructed:"
self.logger.info(
f"{log_prefix} {last_filename}"
f" (shape = {list(volume.shape)}) in {time.time() - time_start:.3f}s."
)
# restart timer
time_start = time.time()
last_filename = filename
curr_slice = output_abs[idx].detach()
slice_no = int(slice_nos[idx].numpy())
# TODO: CPU?
reconstruction_output[filename].append(
(slice_no, curr_slice.cpu()))
if is_validation_process:
targets_output[filename].append(
(slice_no, target_abs[idx].cpu()))
# Average loss dict
loss_dict = reduce_list_of_dicts(val_losses)
reduce_tensor_dict(loss_dict)
communication.synchronize()
torch.cuda.empty_cache()
# TODO: Does not work yet with normal gather.
all_gathered_metrics = merge_list_of_dicts(
communication.all_gather(val_volume_metrics))
if not is_validation_process:
return loss_dict, reconstruction_output
# TODO: Apply named tuples where applicable
# TODO: Several functions have multiple output values, in many cases
# TODO: it would be more convenient to convert this to namedtuples.
return loss_dict, all_gathered_metrics, visualize_slices, visualize_target
def _do_iteration(
self, data: Dict[str, torch.Tensor],
loss_fns: Optional[Dict[str,
Callable]]) -> Tuple[torch.Tensor, Dict]:
# loss_fns can be done, e.g. during validation
if loss_fns is None:
loss_fns = {}
# TODO(jt): Target is not needed in the model input, but in the loss computation. Keep it here for now.
target = data["target"].align_to(*self.complex_names).to(
self.device) # type: ignore
# The first input_image in the iteration is the input_image with the mask applied and no first hidden state.
input_image = data.pop("masked_image").to(self.device) # type: ignore
hidden_state = None
output_image = None
loss_dicts = []
# TODO: Target might not need to be copied.
data = dict_to_device(data, self.device)
# TODO(jt): keys=['sampling_mask', 'sensitivity_map', 'target', 'masked_kspace', 'scaling_factor']
sensitivity_map = data["sensitivity_map"]
# Some things can be done with the sensitivity map here, e.g. apply a u-net
if "sensitivity_model" in self.models:
sensitivity_map = self.compute_model_per_coil(
self.models["sensitivity_model"], sensitivity_map)
# The sensitivity map needs to be normalized such that
# So \sum_{i \in \text{coils}} S_i S_i^* = 1
sensitivity_map_norm = modulus(sensitivity_map).sum("coil")
data["sensitivity_map"] = safe_divide(sensitivity_map,
sensitivity_map_norm)
for rim_step in range(self.cfg.model.steps):
with autocast(enabled=self.mixed_precision):
reconstruction_iter, hidden_state = self.model(
**data,
input_image=input_image,
hidden_state=hidden_state,
)
# TODO: Unclear why this refining is needed.
output_image = reconstruction_iter[-1].refine_names(
*self.complex_names)
loss_dict = {
k: torch.tensor([0.0], dtype=target.dtype).to(self.device)
for k in loss_fns.keys()
}
for output_image_iter in reconstruction_iter:
for k, v in loss_dict.items():
loss_dict[k] = v + loss_fns[k](
output_image_iter,
target,
reduction="mean",
)
loss_dict = {
k: v / len(reconstruction_iter)
for k, v in loss_dict.items()
}
loss = sum(loss_dict.values())
if self.model.training:
self._scaler.scale(loss).backward()
# Detach hidden state from computation graph, to ensure loss is only computed per RIM block.
hidden_state = hidden_state.detach()
input_image = output_image.detach()
loss_dicts.append(
detach_dict(loss_dict)
) # Need to detach dict as this is only used for logging.
# Add the loss dicts together over RIM steps, divide by the number of steps.
loss_dict = reduce_list_of_dicts(loss_dicts,
mode="sum",
divisor=self.cfg.model.steps)
return output_image, loss_dict