def process_slices_for_visualization(self, visualize_slices, visualize_target):
# Log slices.
# Compute the difference as well, and normalize for visualization
difference_slices = [a - b for a, b in zip(visualize_slices, visualize_target)]
# Normalize slices
difference_slices = [(d / np.abs(d)) * 0.5 + 0.5 for d in difference_slices]
visualize_slices = [normalize_image(image) for image in visualize_slices]
# Visualize slices, and crop to the largest volume
visualize_slices = make_grid(
crop_to_largest(visualize_slices + difference_slices, pad_value=0),
nrow=self.cfg.logging.tensorboard.num_images,
scale_each=True,
)
return visualize_slices
def training_loop(
self,
data_loader: DataLoader,
start_iter: int,
validation_data_loaders: Optional[List[DataLoader]] = None,
experiment_directory: Optional[pathlib.Path] = None,
):
self.logger.info(f"Local rank: {communication.get_local_rank()}.")
self.models_training_mode()
loss_fns = self.build_loss()
metric_fns = self.build_metrics(self.cfg.training.metrics)
storage = get_event_storage()
total_iter = self.cfg.training.num_iterations # noqa
for data, iter_idx in zip(data_loader, range(start_iter, total_iter)):
data = AddNames()(data)
if iter_idx == start_iter:
self.ndim = self.compute_dimensionality_from_sample(data)
self.logger.info(f"Data dimensionality: {self.ndim}.")
if iter_idx == 0:
self.log_first_training_example(data)
try:
output, loss_dict = self._do_iteration(data, loss_fns)
except ProcessKilledException as e:
# If the process is killed, the output if saved at state iter_idx, which is the current state,
# so the computation can restart from the last iteration.
self.logger.exception(f"Exiting with exception: {e}.")
self.checkpointer.save(
iter_idx) # Save checkpoint at kill. # noqa
self.write_to_logs(
) # TODO: This causes the issue that current metrics are not written,
# and you end up with an empty line.
sys.exit(-1)
# Gradient accumulation
if (iter_idx +
1) % self.cfg.training.gradient_steps == 0: # type: ignore
if self.cfg.training.gradient_steps > 1: # type: ignore
for parameter in self.model.parameters():
if parameter.grad is not None:
# In-place division
parameter.grad.div_(self.cfg.training.
gradient_steps) # type: ignore
if self.cfg.training.gradient_clipping > 0.0: # type: ignore
self._scaler.unscale_(self.__optimizer)
torch.nn.utils.clip_grad_norm_(
self.model.parameters(),
self.cfg.training.gradient_clipping)
# Gradient norm
if self.cfg.training.gradient_debug: # type: ignore
warnings.warn(
f"Gradient debug set. This will affect training performance. Only use for debugging."
f"This message will only be displayed once.")
parameters = list(
filter(lambda p: p.grad is not None,
self.model.parameters()))
gradient_norm = sum([
parameter.grad.data**2 for parameter in parameters
]).sqrt() # typing: ignore
storage.add_scalar("train/gradient_norm", gradient_norm)
# Same as self.__optimizer.step() for mixed precision.
self._scaler.step(self.__optimizer)
# Updates the scale for next iteration.
self._scaler.update()
# Incorrect inference by mypy and pyflake
self.__lr_scheduler.step() # type: ignore # noqa
storage.add_scalar("lr",
self.__optimizer.param_groups[0]["lr"],
smoothing_hint=False)
self.__optimizer.zero_grad() # type: ignore
# Reduce the loss over all devices
loss_dict_reduced = communication.reduce_tensor_dict(loss_dict)
loss_reduced = sum(loss_dict_reduced.values())
metrics_dict = evaluate_dict(
metric_fns,
transforms.modulus_if_complex(output.detach()).rename(None),
data["target"].rename(None).detach().to(self.device),
reduction="mean",
)
metrics_dict_reduced = (
communication.reduce_tensor_dict(metrics_dict)
if metrics_dict else {})
storage.add_scalars(loss=loss_reduced,
**loss_dict_reduced,
**metrics_dict_reduced)
if iter_idx > 5 and (
iter_idx % self.cfg.training.checkpointer.checkpoint_steps
== 0 or (iter_idx + 1) == total_iter):
self.logger.info(f"Checkpointing at iteration {iter_idx}.")
self.checkpointer.save(iter_idx)
if (validation_data_loaders is not None and iter_idx > 5
and (iter_idx % self.cfg.training.validation_steps == 0 or
(iter_idx + 1) == total_iter)):
for (
curr_dataset_name,
curr_validation_data_loader,
) in validation_data_loaders:
self.logger.info(
f"Evaluating: {curr_dataset_name}..."
) # TODO(jt): Fix with better names and stuff.
(
curr_val_loss_dict,
curr_val_metric_dict_per_case,
visualize_slices,
visualize_target,
) = self.evaluate(
curr_validation_data_loader,
loss_fns,
is_validation_process=True,
)
if experiment_directory:
# Make dictionary serializable for logging
serializable_val_metric_dict = {
k0: {k1: float(v1)
for k1, v1 in v0.items()}
for k0, v0 in
curr_val_metric_dict_per_case.items()
}
write_json(
experiment_directory /
f"metrics_val_{curr_dataset_name}_{iter_idx}.json",
serializable_val_metric_dict,
)
# Metric dict still needs to be reduced as it gives values *per* data
curr_val_metric_dict = reduce_list_of_dicts(list(
curr_val_metric_dict_per_case.values()),
mode="average")
key_prefix = ("val/" if not curr_dataset_name else
f"val/{curr_dataset_name}/")
val_loss_reduced = sum(curr_val_loss_dict.values())
storage.add_scalars(
**{key_prefix + "loss": val_loss_reduced},
**{
**prefix_dict_keys(curr_val_metric_dict, key_prefix),
**prefix_dict_keys(curr_val_loss_dict, key_prefix),
},
smoothing_hint=False,
)
visualize_slices = self.process_slices_for_visualization(
visualize_slices, visualize_target)
storage.add_image(f"{key_prefix}prediction",
visualize_slices)
if iter_idx // self.cfg.training.validation_steps - 1 == 0:
visualize_target = make_grid(
crop_to_largest(visualize_target, pad_value=0),
nrow=self.cfg.tensorboard.num_images,
scale_each=True,
)
storage.add_image(f"{key_prefix}target",
visualize_target)
self.logger.info(
f"Done evaluation of {curr_dataset_name} at iteration {iter_idx}."
)
self.model.train()
# Log every 20 iterations, or at a validation step or at the end of training.
if iter_idx > 5 and (iter_idx % 20 == 0 or iter_idx %
self.cfg.training.validation_steps == 0 or
(iter_idx + 1) == total_iter):
self.write_to_logs()
storage.step()
def validation_loop(
self,
validation_datasets,
loss_fns,
experiment_directory,
iter_idx,
num_workers: int = 6,
):
if not validation_datasets:
return
storage = get_event_storage()
data_loaders = self.build_validation_loaders(
validation_data=validation_datasets,
num_workers=num_workers,
)
for curr_dataset_name, curr_data_loader in data_loaders:
self.logger.info(f"Evaluating: {curr_dataset_name}...")
(
curr_loss_dict,
curr_metrics_per_case,
visualize_slices,
visualize_target,
) = self.evaluate(
curr_data_loader,
loss_fns,
is_validation_process=True,
)
if experiment_directory:
json_output_fn = (
experiment_directory
/ f"metrics_val_{curr_dataset_name}_{iter_idx}.json"
)
json_output_fn.parent.mkdir(
exist_ok=True, parents=True
) # A / in the filename can create a folder
if communication.is_main_process():
write_json(
json_output_fn,
curr_metrics_per_case,
)
self.logger.info(f"Wrote per image logs to: {json_output_fn}.")
# Metric dict still needs to be reduced as it gives values *per* data
curr_metric_dict = reduce_list_of_dicts(
list(curr_metrics_per_case.values()), mode="average"
)
key_prefix = (
"val/" if not curr_dataset_name else f"val/{curr_dataset_name}/"
)
loss_reduced = sum(curr_loss_dict.values())
storage.add_scalars(
**{key_prefix + "loss": loss_reduced},
**{
**prefix_dict_keys(curr_metric_dict, key_prefix),
**prefix_dict_keys(curr_loss_dict, key_prefix),
},
smoothing_hint=False,
)
visualize_slices = self.process_slices_for_visualization(
visualize_slices, visualize_target
)
storage.add_image(f"{key_prefix}prediction", visualize_slices)
if iter_idx // self.cfg.training.validation_steps - 1 == 0:
visualize_target = make_grid(
crop_to_largest(visualize_target, pad_value=0),
nrow=self.cfg.logging.tensorboard.num_images,
scale_each=True,
)
storage.add_image(f"{key_prefix}target", visualize_target)
self.logger.info(
f"Done evaluation of {curr_dataset_name} at iteration {iter_idx}."
)
self.model.train()