def run_iteration(self,
data_generator,
do_backprop=True,
run_online_evaluation=False):
data_dict = next(data_generator)
data = data_dict['data']
target = data_dict['target']
data = maybe_to_torch(data)
target = maybe_to_torch(target)
if torch.cuda.is_available():
data = to_cuda(data)
target = to_cuda(target)
self.optimizer.zero_grad()
if self.fp16:
with autocast():
ret = self.network(data,
target,
return_hard_tp_fp_fn=run_online_evaluation)
if run_online_evaluation:
ces, tps, fps, fns, tp_hard, fp_hard, fn_hard = ret
self.run_online_evaluation(tp_hard, fp_hard, fn_hard)
else:
ces, tps, fps, fns = ret
del data, target
l = self.compute_loss(ces, tps, fps, fns)
if do_backprop:
self.amp_grad_scaler.scale(l).backward()
self.amp_grad_scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)
self.amp_grad_scaler.step(self.optimizer)
self.amp_grad_scaler.update()
else:
ret = self.network(data,
target,
return_hard_tp_fp_fn=run_online_evaluation)
if run_online_evaluation:
ces, tps, fps, fns, tp_hard, fp_hard, fn_hard = ret
self.run_online_evaluation(tp_hard, fp_hard, fn_hard)
else:
ces, tps, fps, fns = ret
del data, target
l = self.compute_loss(ces, tps, fps, fns)
if do_backprop:
l.backward()
torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)
self.optimizer.step()
return l.detach().cpu().numpy()
def run_iteration(self,
data_generator,
do_backprop=True,
run_online_evaluation=False):
"""
gradient clipping improves training stability
:param data_generator:
:param do_backprop:
:param run_online_evaluation:
:return:
"""
data_dict = next(data_generator)
data = data_dict['data']
target = data_dict['target']
data = maybe_to_torch(data)
target = maybe_to_torch(target)
if torch.cuda.is_available():
data = to_cuda(data)
target = to_cuda(target)
self.optimizer.zero_grad()
if self.fp16:
with autocast():
output = self.network(data)
del data
l = self.loss(output, target)
if do_backprop:
self.amp_grad_scaler.scale(l).backward()
self.amp_grad_scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)
self.amp_grad_scaler.step(self.optimizer)
self.amp_grad_scaler.update()
else:
output = self.network(data)
del data
l = self.loss(output, target)
if do_backprop:
l.backward()
torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)
self.optimizer.step()
if run_online_evaluation:
self.run_online_evaluation(output, target)
del target
return l.detach().cpu().numpy()
def _internal_maybe_mirror_and_pred_2D(
self,
x: Union[np.ndarray, torch.tensor],
mirror_axes: tuple,
do_mirroring: bool = True,
mult: np.ndarray or torch.tensor = None) -> torch.tensor:
# everything in here takes place on the GPU. If x and mult are not yet on GPU this will be taken care of here
# we now return a cuda tensor! Not numpy array!
assert len(x.shape) == 4, 'x must be (b, c, x, y)'
x = to_cuda(maybe_to_torch(x), gpu_id=self.get_device())
result_torch = torch.zeros([x.shape[0], self.num_classes] +
list(x.shape[2:]),
dtype=torch.float).cuda(self.get_device(),
non_blocking=True)
if mult is not None:
mult = to_cuda(maybe_to_torch(mult), gpu_id=self.get_device())
if do_mirroring:
mirror_idx = 4
num_results = 2**len(mirror_axes)
else:
mirror_idx = 1
num_results = 1
for m in range(mirror_idx):
if m == 0:
pred = self.inference_apply_nonlin(self(x))
result_torch += 1 / num_results * pred
if m == 1 and (1 in mirror_axes):
pred = self.inference_apply_nonlin(self(torch.flip(x, (3, ))))
result_torch += 1 / num_results * torch.flip(pred, (3, ))
if m == 2 and (0 in mirror_axes):
pred = self.inference_apply_nonlin(self(torch.flip(x, (2, ))))
result_torch += 1 / num_results * torch.flip(pred, (2, ))
if m == 3 and (0 in mirror_axes) and (1 in mirror_axes):
pred = self.inference_apply_nonlin(self(torch.flip(x, (3, 2))))
result_torch += 1 / num_results * torch.flip(pred, (3, 2))
if mult is not None:
result_torch[:, :] *= mult
return result_torch
def run_iteration(self,
data_generator,
do_backprop=True,
run_online_evaluation=False):
data_dict = next(data_generator)
data = data_dict['data']
target = data_dict['target']
data = maybe_to_torch(data)
target = maybe_to_torch(target)
if torch.cuda.is_available():
data = to_cuda(data)
target = to_cuda(target)
self.optimizer.zero_grad()
if self.fp16:
with autocast():
output = self.network(data)
del data
l = self.loss(output, target)
if do_backprop:
self.amp_grad_scaler.scale(l).backward()
self.amp_grad_scaler.step(self.optimizer)
self.amp_grad_scaler.update()
else:
output = self.network(data)
del data
l = self.loss(output, target)
if do_backprop:
l.backward()
self.optimizer.step()
if run_online_evaluation:
self.run_online_evaluation(output, target)
del target
return l.detach().cpu().numpy()
def run_iteration(self,
data_generator,
do_backprop=True,
run_online_evaluation=False):
raise NotImplementedError(
"this class has not been changed to work with pytorch amp yet!")
data_dict = next(data_generator)
data = data_dict['data']
target = data_dict['target']
data = maybe_to_torch(data)
target = maybe_to_torch(target)
if torch.cuda.is_available():
data = to_cuda(data, gpu_id=None)
target = to_cuda(target, gpu_id=None)
self.optimizer.zero_grad()
output = self.network(data)
del data
total_loss = None
for i in range(len(output)):
# Starting here it gets spicy!
axes = tuple(range(2, len(output[i].size())))
# network does not do softmax. We need to do softmax for dice
output_softmax = torch.sigmoid(output[i])
# get the tp, fp and fn terms we need
tp, fp, fn, _ = get_tp_fp_fn_tn(output_softmax,
target[i],
axes,
mask=None)
# for dice, compute nominator and denominator so that we have to accumulate only 2 instead of 3 variables
# do_bg=False in tuframeworkTrainer -> [:, 1:]
nominator = 2 * tp[:, 1:]
denominator = 2 * tp[:, 1:] + fp[:, 1:] + fn[:, 1:]
if self.batch_dice:
# for DDP we need to gather all nominator and denominator terms from all GPUS to do proper batch dice
nominator = awesome_allgather_function.apply(nominator)
denominator = awesome_allgather_function.apply(denominator)
nominator = nominator.sum(0)
denominator = denominator.sum(0)
else:
pass
ce_loss = self.ce_loss(output[i], target[i])
# we smooth by 1e-5 to penalize false positives if tp is 0
dice_loss = (-(nominator + 1e-5) / (denominator + 1e-5)).mean()
if total_loss is None:
total_loss = self.ds_loss_weights[i] * (ce_loss + dice_loss)
else:
total_loss += self.ds_loss_weights[i] * (ce_loss + dice_loss)
if run_online_evaluation:
with torch.no_grad():
output = output[0]
target = target[0]
out_sigmoid = torch.sigmoid(output)
out_sigmoid = (out_sigmoid > 0.5).float()
if self.threeD:
axes = (2, 3, 4)
else:
axes = (2, 3)
tp, fp, fn, _ = get_tp_fp_fn_tn(out_sigmoid, target, axes=axes)
tp_hard = awesome_allgather_function.apply(tp)
fp_hard = awesome_allgather_function.apply(fp)
fn_hard = awesome_allgather_function.apply(fn)
# print_if_rank0("after allgather", tp_hard.shape)
# print_if_rank0("after sum", tp_hard.shape)
self.run_online_evaluation(
tp_hard.detach().cpu().numpy().sum(0),
fp_hard.detach().cpu().numpy().sum(0),
fn_hard.detach().cpu().numpy().sum(0))
del target
if do_backprop:
if not self.fp16 or amp is None or not torch.cuda.is_available():
total_loss.backward()
else:
with amp.scale_loss(total_loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
_ = clip_grad_norm_(self.network.parameters(), 12)
self.optimizer.step()
return total_loss.detach().cpu().numpy()