def test_sample_based_on_scores_and_distances(self):
embedding = tf.reshape(self.get_test_embedding(), [-1, 3])
scores = tf.reshape(self.get_test_semantic_scores(), [-1])
samples, indices = instance_sampling_utils.sample_based_on_scores_and_distances(
embedding, scores, 6, 1.0)
expected_samples = tf.gather(embedding, indices)
sample_scores = tf.gather(scores, indices)
self.assertAllClose(expected_samples.numpy(), samples.numpy())
self.assertAllEqual(indices.shape, np.array([6]))
self.assertAllEqual(sample_scores.shape, np.array([6]))
def _sample_furthest_voxels(scores, instance_embeddings,
num_furthest_voxel_samples,
sampler_score_vs_distance_coef):
"""Samples voxels based on distance in embedding space and scores."""
num_furthest_voxel_samples = tf.minimum(num_furthest_voxel_samples,
tf.shape(scores)[0])
_, seed_indices = instance_sampling_utils.sample_based_on_scores_and_distances(
inputs=instance_embeddings,
scores=tf.reduce_max(scores, axis=1),
num_samples=num_furthest_voxel_samples,
scores_coef=sampler_score_vs_distance_coef)
scores = tf.gather(scores, seed_indices)
instance_embeddings = tf.gather(instance_embeddings, seed_indices)
return scores, instance_embeddings
def _sample_furthest_voxels(outputs, num_furthest_voxel_samples,
sampler_score_vs_distance_coef):
"""Samples voxels based on distance and scores."""
num_furthest_voxel_samples = tf.minimum(
num_furthest_voxel_samples,
tf.shape(
outputs[standard_fields.DetectionResultFields.objects_center])[0])
_, seed_indices = instance_sampling_utils.sample_based_on_scores_and_distances(
inputs=outputs[standard_fields.DetectionResultFields.objects_center],
scores=tf.reduce_max(
outputs[standard_fields.DetectionResultFields.objects_score],
axis=1),
num_samples=num_furthest_voxel_samples,
scores_coef=sampler_score_vs_distance_coef)
for tensor_name in standard_fields.get_output_object_fields():
if tensor_name in outputs and outputs[tensor_name] is not None:
outputs[tensor_name] = tf.gather(outputs[tensor_name],
seed_indices)