def call(self,
features: Mapping[str, tf.Tensor],
boxes: tf.Tensor,
training: bool = None):
"""Generates ROIs.
Args:
features: A dictionary with key as pyramid level and value as features.
The features are in shape of
[batch_size, height_l, width_l, num_filters].
boxes: A 3-D `tf.Tensor` of shape [batch_size, num_boxes, 4]. Each row
represents a box with [y1, x1, y2, x2] in un-normalized coordinates.
from grid point.
training: A `bool` of whether it is in training mode.
Returns:
A 5-D `tf.Tensor` representing feature crop of shape
[batch_size, num_boxes, crop_size, crop_size, num_filters].
"""
roi_features = spatial_transform_ops.multilevel_crop_and_resize(
features,
boxes,
output_size=self._config_dict['crop_size'],
sample_offset=self._config_dict['sample_offset'])
return roi_features
def test_multilevel_crop_and_resize_large_input(self):
"""Test 512 boxes on TPU."""
input_size = 1408
min_level = 2
max_level = 6
batch_size = 2
num_boxes = 512
num_filters = 256
output_size = 7
strategy = tf.distribute.experimental.TPUStrategy()
with strategy.scope():
features = {}
for level in range(min_level, max_level + 1):
feat_size = int(input_size / 2**level)
features[level] = tf.constant(np.reshape(
np.arange(batch_size * feat_size * feat_size * num_filters,
dtype=np.float32),
[batch_size, feat_size, feat_size, num_filters]),
dtype=tf.bfloat16)
boxes = np.array([
[[0, 0, 256, 256]] * num_boxes,
],
dtype=np.float32)
boxes = np.tile(boxes, [batch_size, 1, 1])
tf_boxes = tf.constant(boxes, dtype=tf.float32)
tf_roi_features = spatial_transform_ops.multilevel_crop_and_resize(
features, tf_boxes)
roi_features = tf_roi_features.numpy()
self.assertEqual(
roi_features.shape,
(batch_size, num_boxes, output_size, output_size, num_filters))
def test_multilevel_crop_and_resize_two_boxes(self):
"""Test two boxes."""
input_size = 4
min_level = 0
max_level = 0
batch_size = 1
output_size = 2
num_filters = 1
features = {}
for level in range(min_level, max_level + 1):
feat_size = int(input_size / 2**level)
features[level] = tf.range(batch_size * feat_size * feat_size *
num_filters,
dtype=tf.float32)
features[level] = tf.reshape(
features[level],
[batch_size, feat_size, feat_size, num_filters])
boxes = tf.constant([
[[0, 0, 2, 2], [0, 0, 2, 3]],
], dtype=tf.float32)
tf_roi_features = spatial_transform_ops.multilevel_crop_and_resize(
features, boxes, output_size)
roi_features = tf_roi_features.numpy()
self.assertAllClose(
roi_features,
np.array([[[2.5, 3.5], [6.5, 7.5]], [[2.75, 4.25], [6.75, 8.25]]
]).reshape([batch_size, 2, output_size, output_size, 1]))
def test_multilevel_crop_and_resize_feature_level_assignment(self):
"""Test feature level assignment."""
input_size = 640
min_level = 2
max_level = 5
batch_size = 1
output_size = 2
num_filters = 1
features = {}
for level in range(min_level, max_level + 1):
feat_size = int(input_size / 2**level)
features[level] = float(level) * tf.ones(
[batch_size, feat_size, feat_size, num_filters],
dtype=tf.float32)
boxes = tf.constant(
[
[
[0, 0, 111, 111], # Level 2.
[0, 0, 113, 113], # Level 3.
[0, 0, 223, 223], # Level 3.
[0, 0, 225, 225], # Level 4.
[0, 0, 449, 449]
], # Level 5.
],
dtype=tf.float32)
tf_roi_features = spatial_transform_ops.multilevel_crop_and_resize(
features, boxes, output_size)
roi_features = tf_roi_features.numpy()
self.assertAllClose(roi_features[0, 0], 2 * np.ones((2, 2, 1)))
self.assertAllClose(roi_features[0, 1], 3 * np.ones((2, 2, 1)))
self.assertAllClose(roi_features[0, 2], 3 * np.ones((2, 2, 1)))
self.assertAllClose(roi_features[0, 3], 4 * np.ones((2, 2, 1)))
self.assertAllClose(roi_features[0, 4], 5 * np.ones((2, 2, 1)))
def test_multilevel_crop_and_resize_square(self):
"""Example test case.
Input =
[
[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11],
[12, 13, 14, 15],
]
output_size = 2x2
box =
[
[[0, 0, 2, 2]]
]
Gathered data =
[
[0, 1, 1, 2],
[4, 5, 5, 6],
[4, 5, 5, 6],
[8, 9, 9, 10],
]
Interpolation kernel =
[
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
]
Output =
[
[2.5, 3.5],
[6.5, 7.5]
]
"""
input_size = 4
min_level = 0
max_level = 0
batch_size = 1
output_size = 2
num_filters = 1
features = {}
for level in range(min_level, max_level + 1):
feat_size = int(input_size / 2**level)
features[str(level)] = tf.range(
batch_size * feat_size * feat_size * num_filters, dtype=tf.float32)
features[str(level)] = tf.reshape(
features[str(level)], [batch_size, feat_size, feat_size, num_filters])
boxes = tf.constant([
[[0, 0, 2, 2]],
], dtype=tf.float32)
tf_roi_features = spatial_transform_ops.multilevel_crop_and_resize(
features, boxes, output_size)
roi_features = tf_roi_features.numpy()
self.assertAllClose(
roi_features,
np.array([[2.5, 3.5],
[6.5,
7.5]]).reshape([batch_size, 1, output_size, output_size, 1]))