def get_metrics(inputs, outputs, params):
"""Aggregate the metrics for rotator model.
Args:
inputs: Input dictionary of the rotator model.
outputs: Output dictionary returned by the rotator model.
params: Hyperparameters of the rotator model.
Returns:
names_to_values: metrics->values (dict).
names_to_updates: metrics->ops (dict).
"""
names_to_values = dict()
names_to_updates = dict()
tmp_values, tmp_updates = metrics.add_image_pred_metrics(
inputs, outputs, params.num_views, 3 * params.image_size**2)
names_to_values.update(tmp_values)
names_to_updates.update(tmp_updates)
tmp_values, tmp_updates = metrics.add_mask_pred_metrics(
inputs, outputs, params.num_views, params.image_size**2)
names_to_values.update(tmp_values)
names_to_updates.update(tmp_updates)
for name, value in names_to_values.items():
slim.summaries.add_scalar_summary(value,
name,
prefix='eval',
print_summary=True)
return names_to_values, names_to_updates
def get_metrics(inputs, outputs, params):
"""Aggregate the metrics for rotator model.
Args:
inputs: Input dictionary of the rotator model.
outputs: Output dictionary returned by the rotator model.
params: Hyperparameters of the rotator model.
Returns:
names_to_values: metrics->values (dict).
names_to_updates: metrics->ops (dict).
"""
names_to_values = dict()
names_to_updates = dict()
tmp_values, tmp_updates = metrics.add_image_pred_metrics(
inputs, outputs, params.num_views, 3*params.image_size**2)
names_to_values.update(tmp_values)
names_to_updates.update(tmp_updates)
tmp_values, tmp_updates = metrics.add_mask_pred_metrics(
inputs, outputs, params.num_views, params.image_size**2)
names_to_values.update(tmp_values)
names_to_updates.update(tmp_updates)
for name, value in names_to_values.iteritems():
slim.summaries.add_scalar_summary(
value, name, prefix='eval', print_summary=True)
return names_to_values, names_to_updates