def __call__(self, subject_context: ctx.SubjectContext, task_context: ctx.TaskContext, context: ctx.Context) -> None:
probabilities = subject_context.subject_data['probabilities']
prediction = np.argmax(probabilities, axis=-1)
subject_context.subject_data['prediction'] = prediction
to_eval = {'prediction': prediction, 'probabilities': probabilities,
'target': subject_context.subject_data['labels'].squeeze(-1)}
results = {}
self.evaluate(to_eval, results)
subject_context.metrics.update(results)
def __call__(self, subject_context: ctx.SubjectContext, task_context: ctx.TaskContext, context: ctx.Context) -> None:
probabilities = subject_context.subject_data['probabilities']
prediction = np.argmax(probabilities, axis=-1)
pred = subject_context.subject_data['labels'][..., 1]
gt = subject_context.subject_data['labels'][..., 0]
target = pred != gt
to_eval = {'prediction': prediction, 'probabilities': probabilities,
'target': target}
results = {}
self.evaluate(to_eval, results)
subject_context.metrics.update(results)
subject_context.score = results['dice']
def __call__(self, subject_context: ctx.SubjectContext,
task_context: ctx.TaskContext, context: ctx.Context) -> None:
probabilities = subject_context.subject_data['probabilities']
_, prediction = probabilities.max(-1)
to_eval = {
'prediction': prediction,
'target': subject_context.subject_data['labels'].squeeze(),
'probabilities': probabilities
}
results = {}
self.evaluate(to_eval, results)
subject_context.metrics.update(results)
subject_context.score = results['dice']
def __call__(self, subject_context: ctx.SubjectContext,
task_context: ctx.TaskContext, context: ctx.Context) -> None:
if self.direct_extractor is None:
self.direct_extractor = factory.get_extractor(
task_context.data_config.direct_extractor)
self.direct_transform = factory.get_transform(
task_context.data_config.direct_transform)
extracted = task_context.data.dataset.direct_extract(
self.direct_extractor,
subject_context.subject_index,
transform=self.direct_transform)
for key, value in extracted.items():
subject_context.subject_data[key] = extracted[key]
def __call__(self, subject_context: ctx.SubjectContext,
task_context: ctx.TaskContext, context: ctx.Context) -> None:
probabilities = subject_context.subject_data['probabilities']
net_predictions = subject_context.subject_data['net_predictions']
target = net_predictions.squeeze(
-1) != subject_context.subject_data['labels'].squeeze(-1)
prediction = np.argmax(probabilities, axis=-1)
to_eval = {
'prediction': prediction,
'probabilities': probabilities,
'target': target
}
results = {}
self.evaluate(to_eval, results)
subject_context.metrics.update(results)
subject_context.score = results['dice']