def extract_images_and_targets(read_data):
"""Extract images and targets from the input dict."""
image = read_data[fields.InputDataFields.image]
key = ''
if fields.InputDataFields.source_id in read_data:
key = read_data[fields.InputDataFields.source_id]
location_gt = read_data[fields.InputDataFields.groundtruth_boxes]
classes_gt = tf.cast(
read_data[fields.InputDataFields.groundtruth_classes], tf.int32)
classes_gt -= label_id_offset
if merge_multiple_label_boxes:
location_gt, classes_gt, _ = util_ops.merge_boxes_with_multiple_labels(
location_gt, classes_gt, num_classes)
else:
classes_gt = util_ops.padded_one_hot_encoding(indices=classes_gt,
depth=num_classes,
left_pad=0)
masks_gt = read_data.get(
fields.InputDataFields.groundtruth_instance_masks)
keypoints_gt = read_data.get(
fields.InputDataFields.groundtruth_keypoints)
if (merge_multiple_label_boxes
and (masks_gt is not None or keypoints_gt is not None)):
raise NotImplementedError('Multi-label support is only for boxes.')
return image, key, location_gt, classes_gt, masks_gt, keypoints_gt
def extract_images_and_targets(read_data):
image = read_data[fields.InputDataFields.image]
location_gt = read_data[fields.InputDataFields.groundtruth_rboxes]
classes_gt = tf.cast(read_data[fields.InputDataFields.groundtruth_classes], tf.int32)
classes_gt -= label_id_offset
classes_gt = util_ops.padded_one_hot_encoding(indices=classes_gt,
depth=num_classes, left_pad=0)
masks_gt = read_data.get(fields.InputDataFields.groundtruth_instance_masks)
return image, location_gt, classes_gt, masks_gt
def test_correct_padded_one_hot_tensor_with_empty_indices(self):
depth = 6
pad = 2
indices = tf.constant([])
one_hot_tensor = ops.padded_one_hot_encoding(indices,
depth=depth,
left_pad=pad)
expected_tensor = np.zeros((0, depth + pad))
with self.test_session() as sess:
out_one_hot_tensor = sess.run(one_hot_tensor)
self.assertAllClose(out_one_hot_tensor,
expected_tensor,
rtol=1e-10,
atol=1e-10)
def test_correct_one_hot_tensor_with_pad_one(self):
indices = tf.constant([1, 2, 3, 5])
one_hot_tensor = ops.padded_one_hot_encoding(indices,
depth=6,
left_pad=1)
expected_tensor = np.array(
[[0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1]], np.float32)
with self.test_session() as sess:
out_one_hot_tensor = sess.run(one_hot_tensor)
self.assertAllClose(out_one_hot_tensor,
expected_tensor,
rtol=1e-10,
atol=1e-10)
def test_raise_value_error_on_float_depth(self):
indices = tf.constant(1.0, shape=(2, 3))
with self.assertRaises(ValueError):
ops.padded_one_hot_encoding(indices, depth=0.1, left_pad=2)
def test_raise_value_error_on_negative_pad(self):
indices = tf.constant(1.0, shape=(2, 3))
with self.assertRaises(ValueError):
ops.padded_one_hot_encoding(indices, depth=6, left_pad=-1)
def test_return_none_on_zero_depth(self):
indices = tf.constant([1, 2, 3, 4, 5])
one_hot_tensor = ops.padded_one_hot_encoding(indices,
depth=0,
left_pad=2)
self.assertEqual(one_hot_tensor, None)
