def __call__(self, batch_context: ctx.BatchContext,
task_context: ctx.TaskContext, context: ctx.Context) -> None:
if not isinstance(context, ctx.TorchTrainContext):
raise ValueError(
msg.get_type_error_msg(context, ctx.TorchTrainContext))
context.optimizer.zero_grad()
batch_context.input['images'] = batch_context.input['images'].float(
).to(context.device)
gt = batch_context.input['labels'].long().to(context.device)
net1_logits = self.test_model(batch_context.input['images'])
net_prediction = net1_logits.argmax(dim=1)
batch_context.input['labels'] = (net_prediction != gt).long()
logits = context.model(self.test_model.features)
batch_context.output['logits'] = logits
loss = self.criterion(logits, batch_context.input['labels'])
loss.backward()
context.optimizer.step()
batch_context.metrics['loss'] = loss.item()
def __call__(self, batch_context: ctx.BatchContext,
task_context: ctx.TaskContext, context: ctx.Context) -> None:
if not isinstance(context,
(ctx.TorchTrainContext, ctx.TorchTestContext)):
raise ValueError(
msg.get_type_error_msg(
context, (ctx.TorchTrainContext, ctx.TorchTestContext)))
batch_context.input['images'] = batch_context.input['images'].float(
).to(context.device)
batch_context.input['labels'] = batch_context.input['labels'].long(
).to(context.device)
pred = batch_context.input['labels'][:, 1]
inpt = torch.cat(
[batch_context.input['images'],
pred.unsqueeze(1).float()], dim=1)
logits = context.model(inpt)
batch_context.output['logits'] = logits
probabilities = F.softmax(logits, 1)
batch_context.output['probabilities'] = probabilities
# subject_eval needs clean (non-modified) labels
batch_context.output['labels'] = batch_context.input['labels']
def __call__(self, batch_context: ctx.BatchContext,
task_context: ctx.TaskContext, context: ctx.Context) -> None:
if not isinstance(context,
(ctx.TorchTrainContext, ctx.TorchTestContext)):
raise ValueError(
msg.get_type_error_msg(
context, (ctx.TorchTrainContext, ctx.TorchTestContext)))
batch_context.input['images'] = batch_context.input['images'].float(
).to(context.device)
batch_context.input['labels'] = batch_context.input['labels'].long(
).to(context.device)
pred = batch_context.input['labels'][:, 1]
inpt = torch.cat(
[batch_context.input['images'],
pred.unsqueeze(1).float()], dim=1)
logits = context.model(inpt)
batch_context.output['logits'] = logits
probabilities = F.softmax(logits, 1)
batch_context.output['probabilities'] = probabilities
# add the existing prediction to be reproduced
batch_context.output['orig_prediction'] = pred.unsqueeze(1)
def __call__(self, batch_context: ctx.BatchContext, task_context: ctx.TaskContext, context: ctx.Context) -> None:
if not isinstance(context, ctx.TorchTrainContext):
raise ValueError(msg.get_type_error_msg(context, ctx.TorchTrainContext))
context.optimizer.zero_grad()
batch_context.input['images'] = batch_context.input['images'].float().to(context.device)
batch_context.input['labels'] = batch_context.input['labels'].long().to(context.device)
prediciton = batch_context.input['labels'][:, 1, ...]
gt = batch_context.input['labels'][:, 0, ...]
labels = (prediciton != gt).long()
# update with correct label for the evaluation
batch_context.input['labels'] = labels
inpt = torch.cat([batch_context.input['images'], prediciton.unsqueeze(1).float()], dim=1)
logits = context.model(inpt)
batch_context.output['logits'] = logits
loss = self.criterion(logits, labels)
loss.backward()
context.optimizer.step()
batch_context.metrics['loss'] = loss.item()
def __call__(self, batch_context: ctx.BatchContext, task_context: ctx.TaskContext, context: ctx.Context) -> None:
if not isinstance(context, (ctx.TorchTrainContext, ctx.TorchTestContext)):
raise ValueError(msg.get_type_error_msg(context, (ctx.TorchTrainContext, ctx.TorchTestContext)))
batch_context.input['images'] = batch_context.input['images'].float().to(context.device)
ensemble_probabilities = []
logits = context.model(batch_context.input['images'])
probs = F.softmax(logits, 1)
ensemble_probabilities.append(probs)
for additional_model in self.additional_models:
logits = additional_model(batch_context.input['images'])
probs = F.softmax(logits, 1)
ensemble_probabilities.append(probs)
ensemble_probabilities = torch.stack(ensemble_probabilities)
batch_context.output['multi_probabilities'] = ensemble_probabilities
def __call__(self, batch_context: ctx.BatchContext, task_context: ctx.TaskContext, context: ctx.Context) -> None:
if not isinstance(context, ctx.TorchTestContext):
raise ValueError(msg.get_type_error_msg(context, ctx.TorchTestContext))
batch_context.input['images'] = batch_context.input['images'].float().to(context.device)
mean_logits, sigma = context.model(batch_context.input['images'])
batch_context.output['logits'] = mean_logits
if self.is_log_sigma:
sigma = sigma.exp()
else:
sigma = sigma.abs()
batch_context.output['sigma'] = sigma
probabilities = F.softmax(batch_context.output['logits'], 1)
batch_context.output['probabilities'] = probabilities
def __call__(self, batch_context: ctx.BatchContext,
task_context: ctx.TaskContext, context: ctx.Context) -> None:
if not isinstance(context,
(ctx.TorchTrainContext, ctx.TorchTestContext)):
raise ValueError(
msg.get_type_error_msg(
context, (ctx.TorchTrainContext, ctx.TorchTestContext)))
batch_context.input['images'] = batch_context.input['images'].float(
).to(context.device)
net1_logits = self.test_model(batch_context.input['images'])
net_prediction = net1_logits.argmax(dim=1, keepdim=True)
batch_context.output['net_predictions'] = net_prediction
logits = context.model(self.test_model.features)
probabilities = F.softmax(logits, 1)
batch_context.output['probabilities'] = probabilities
def __call__(self, batch_context: ctx.BatchContext,
task_context: ctx.TaskContext, context: ctx.Context) -> None:
probabilities = F.softmax(batch_context.output['logits'], 1)
probabilities = th.channel_to_end(probabilities).contiguous()
_, prediction = probabilities.max(-1)
batch_context.output['probabilities'] = probabilities
batch_context.output['prediction'] = prediction
to_eval = {
'prediction': prediction,
'probabilities': probabilities,
'target': batch_context.input['labels']
}
results = {}
self.evaluate(to_eval, results)
batch_context.metrics.update(results)
batch_context.score = results['dice']
def __call__(self, batch_context: ctx.BatchContext,
task_context: ctx.TaskContext, context: ctx.Context) -> None:
if not isinstance(context, ctx.TorchTrainContext):
raise ValueError(
msg.get_type_error_msg(context, ctx.TorchTrainContext))
context.optimizer.zero_grad()
batch_context.input['images'] = batch_context.input['images'].float(
).to(context.device)
batch_context.input['labels'] = batch_context.input['labels'].long(
).to(context.device)
mean_logits, sigma = context.model(batch_context.input['images'])
loss = self.criterion(mean_logits, sigma,
batch_context.input['labels'])
loss.backward()
context.optimizer.step()
batch_context.metrics['loss'] = loss.item()
def __call__(self, batch_context: ctx.BatchContext,
task_context: ctx.TaskContext, context: ctx.Context) -> None:
if not isinstance(context,
(ctx.TorchTrainContext, ctx.TorchTestContext)):
raise ValueError(
msg.get_type_error_msg(
context, (ctx.TorchTrainContext, ctx.TorchTestContext)))
batch_context.input['images'] = batch_context.input['images'].float(
).to(context.device)
if self.has_labels:
batch_context.input['labels'] = batch_context.input['labels'].long(
).to(context.device)
logits = context.model(batch_context.input['images'])
batch_context.output['logits'] = logits
if self.do_probs:
probabilities = F.softmax(logits, 1)
batch_context.output['probabilities'] = probabilities
def __call__(self, batch_context: ctx.BatchContext, task_context: ctx.TaskContext, context: ctx.Context) -> None:
if self.remove_multi_probs:
multi_probabilities = batch_context.output.pop('multi_probabilities')
else:
multi_probabilities = batch_context.output['multi_probabilities']
probabilities = multi_probabilities.mean(dim=0)
batch_context.output['probabilities'] = probabilities
entropy = th.entropy(probabilities, dim=1, keepdim=True)
batch_context.output['entropy'] = entropy
if self.do_mi:
expected_entropy = th.entropy(multi_probabilities, dim=2, keepdim=True).mean(dim=0)
mutual_info = entropy - expected_entropy
batch_context.output['mutual_info'] = mutual_info
if self.do_var:
# as done by the bayesian segnet -> not sure if best solution
variance = multi_probabilities.var(dim=0).mean(dim=1, keepdim=True)
batch_context.output['variance'] = variance
def __call__(self, batch_context: ctx.BatchContext, task_context: ctx.TaskContext, context: ctx.Context) -> None:
if not isinstance(context, (ctx.TorchTrainContext, ctx.TorchTestContext)):
raise ValueError(msg.get_type_error_msg(context, (ctx.TorchTrainContext, ctx.TorchTestContext)))
batch_context.input['images'] = batch_context.input['images'].float().to(context.device)
# weight scaling part, just for comparison
ws_logits = context.model(batch_context.input['images'])
ws_probabilities = F.softmax(ws_logits, 1)
batch_context.output['ws_probabilities'] = ws_probabilities
th.set_dropout_mode(context.model, is_train=True)
# mc part
mc_probabilities = []
for i in range(self.mc_steps):
logits = context.model(batch_context.input['images'])
probs = F.softmax(logits, 1)
mc_probabilities.append(probs)
mc_probabilities = torch.stack(mc_probabilities)
batch_context.output['multi_probabilities'] = mc_probabilities
# reset to eval for next batch
th.set_dropout_mode(context.model, is_train=False)
def __call__(self, batch_context: ctx.BatchContext,
task_context: ctx.TaskContext, context: ctx.Context) -> None:
batch_context.output['labels'] = batch_context.input[
'labels'].unsqueeze(1) # re-add previously removed dim
def __call__(self, batch_context: ctx.BatchContext,
task_context: ctx.TaskContext, context: ctx.Context) -> None:
probabilities = F.softmax(batch_context.output['logits'], 1)
batch_context.output['probabilities'] = probabilities
batch_context.output['labels'] = batch_context.input[
'labels'].unsqueeze(1) # re-add previously removed dim
