def sample_from_instances(inputs, outputs, num_samples):
"""Samples equally from instances."""
input_tensors = {}
output_tensors = {}
input_tensors[
standard_fields.InputDataFields.object_instance_id_image] = tf.reshape(
inputs[standard_fields.InputDataFields.object_instance_id_image],
[-1])
input_tensors[standard_fields.InputDataFields.
object_instance_id_image] = isu.map_labels_to_0_to_n(
input_tensors[standard_fields.InputDataFields.
object_instance_id_image])
seed_indices = isu.randomly_select_n_points_per_segment(
labels=input_tensors[
standard_fields.InputDataFields.object_instance_id_image],
num_points=num_samples,
include_ignore_label=False)
seed_indices = tf.reshape(seed_indices, [-1])
for field in standard_fields.get_input_image_fields():
if field in inputs:
input_tensors[field] = tf.gather(inputs[field], seed_indices)
for field in standard_fields.get_output_image_fields():
if field in outputs:
output_tensors[field] = tf.gather(outputs[field], seed_indices)
return input_tensors, output_tensors
def get_batch_size_1_output_images(outputs, b):
"""Returns output dictionary containing tensors with batch size of 1.
Note that this function only applies its example selection to the image
tensors.
Args:
outputs: A dictionary of tf.Tensors with the network output.
b: Example index in the batch.
Returns:
outputs_1: A dictionary of tf.Tensors with batch size of one.
"""
b_1_outputs = {}
for field in standard_fields.get_output_image_fields():
if field in outputs:
b_1_outputs[field] = outputs[field][b:b + 1, Ellipsis]
return b_1_outputs