def _log(self, state, prefix, predictions, targets):
score, num, denom = cohen_kappa_score(predictions, targets, weights='quadratic')
if self.class_names is None:
class_names = [str(i) for i in range(num.shape[1])]
else:
class_names = self.class_names
num_classes = len(class_names)
num_fig = plot_matrix(num / np.sum(num),
figsize=(6 + num_classes // 3, 6 + num_classes // 3),
class_names=class_names,
noshow=True)
denom_fig = plot_matrix(denom / np.sum(denom),
figsize=(6 + num_classes // 3, 6 + num_classes // 3),
class_names=class_names,
noshow=True)
num_fig = render_figure_to_tensor(num_fig)
denom_fig = render_figure_to_tensor(denom_fig)
logger = get_tensorboard_logger(state)
logger.add_image(f'{prefix}/epoch/num', num_fig, global_step=state.step)
logger.add_image(f'{prefix}/epoch/denom', denom_fig, global_step=state.step)
state.metrics.epoch_values[state.loader_name][prefix] = score
def on_loader_end(self, state):
if self.class_names is None:
class_names = [str(i) for i in range(self.num_classes)]
else:
class_names = self.class_names
num_classes = len(class_names)
cm = self.confusion_matrix.value()
fig = plot_confusion_matrix(
cm,
figsize=(6 + num_classes // 3, 6 + num_classes // 3),
class_names=class_names,
normalize=True,
noshow=True,
)
fig = render_figure_to_tensor(fig)
logger = get_tensorboard_logger(state)
logger.add_image(f"{self.prefix}/epoch", fig, global_step=state.step)
def on_loader_end(self, state):
targets = self.targets
outputs = self.outputs
if self.class_names is None:
class_names = [str(i) for i in range(targets.shape[1])]
else:
class_names = self.class_names
num_classes = len(class_names)
cm = confusion_matrix(outputs, targets, labels=range(num_classes))
fig = plot_confusion_matrix(cm,
class_names=class_names,
normalize=True,
noshow=True)
fig = render_figure_to_tensor(fig)
logger = _get_tensorboard_logger(state)
logger.add_image(f'{self.prefix}/epoch', fig, global_step=state.step)