def call(self, y_true, y_pred):
"""Selects the largest bounding box of the predictions and the
ground truth and calculates the iou_loss between them. And adds the
MAE to it (to optimize the other coordinates).
Args:
y_true (tf.tensor): Ground truth
y_pred (tf.tensor): Predictions
Returns:
[tf.tensor]: IOU loss for largest bounding box
"""
x_min_pred = tf.reduce_min(y_pred[:, ::2], axis=1)
x_max_pred = tf.reduce_max(y_pred[:, ::2], axis=1)
y_min_pred = tf.reduce_min(y_pred[:, 1::2], axis=1)
y_max_pred = tf.reduce_max(y_pred[:, 1::2], axis=1)
box_pred = tf.stack([y_min_pred, x_min_pred, y_max_pred, x_max_pred],
axis=1)
# Bounding box for truth
x_min_true = tf.reduce_min(y_true[:, ::2], axis=1)
x_max_true = tf.reduce_max(y_true[:, ::2], axis=1)
y_min_true = tf.reduce_min(y_true[:, 1::2], axis=1)
y_max_true = tf.reduce_max(y_true[:, 1::2], axis=1)
box_true = tf.stack([y_min_true, x_min_true, y_max_true, x_max_true],
axis=1)
# Approximating corners of bounding box
iou = giou_loss(box_true, box_pred, mode='giou')
return iou
def test_with_integer(self):
boxes1 = tf.constant([[4, 3, 7, 5], [5, 6, 10, 7]], dtype=tf.int32)
boxes2 = tf.constant([[3, 4, 6, 8], [14, 14, 15, 15]], dtype=tf.int32)
expected_result = tf.constant(
[1.07500000298023224, 1.9333333373069763], dtype=tf.float32)
loss = giou_loss(boxes1, boxes2)
self.assertAllCloseAccordingToType(loss, expected_result)
def test_with_integer():
boxes1 = tf.constant([[4, 3, 7, 5], [5, 6, 10, 7]], dtype=tf.int32)
boxes2 = tf.constant([[3, 4, 6, 8], [14, 14, 15, 15]], dtype=tf.int32)
expected_result = tf.constant([1.07500000298023224, 1.9333333373069763],
dtype=tf.float32)
loss = giou_loss(boxes1, boxes2)
test_utils.assert_allclose_according_to_type(loss, expected_result)
def test_iou(self, dtype):
boxes1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]],
dtype=dtype)
boxes2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0]],
dtype=dtype)
expected_result = tf.constant([0.875, 1.], dtype=dtype)
loss = giou_loss(boxes1, boxes2, mode='iou')
self.assertAllCloseAccordingToType(loss, expected_result)
def test_iou(dtype):
boxes1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]],
dtype=dtype)
boxes2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0]],
dtype=dtype)
expected_result = tf.constant([0.875, 1.0], dtype=dtype)
loss = giou_loss(boxes1, boxes2, mode="iou")
test_utils.assert_allclose_according_to_type(loss, expected_result)
def test_giou_loss(self, dtype):
boxes1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]],
dtype=dtype)
boxes2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0]],
dtype=dtype)
expected_result = tf.constant(
[1.07500000298023224, 1.9333333373069763], dtype=dtype)
loss = giou_loss(boxes1, boxes2)
self.assertAllCloseAccordingToType(loss, expected_result)
def test_different_shapes(dtype):
boxes1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]],
dtype=dtype)
boxes2 = tf.constant([[3.0, 4.0, 6.0, 8.0]], dtype=dtype)
expand_boxes1 = tf.expand_dims(boxes1, -2)
expand_boxes2 = tf.expand_dims(boxes2, 0)
expected_result = tf.constant([1.07500000298023224, 1.366071], dtype=dtype)
loss = giou_loss(expand_boxes1, expand_boxes2)
test_utils.assert_allclose_according_to_type(loss, expected_result)
def test_giou_loss(dtype):
boxes1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]],
dtype=dtype)
boxes2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0]],
dtype=dtype)
expected_result = tf.constant([1.07500000298023224, 1.9333333373069763],
dtype=dtype)
loss = giou_loss(boxes1, boxes2)
test_utils.assert_allclose_according_to_type(loss, expected_result)
def test_different_shapes(self, dtype):
boxes1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]],
dtype=dtype)
boxes2 = tf.constant([[3.0, 4.0, 6.0, 8.0]], dtype=dtype)
tf.expand_dims(boxes1, -2)
tf.expand_dims(boxes2, 0)
expected_result = tf.constant([1.07500000298023224, 1.366071],
dtype=dtype)
loss = giou_loss(boxes1, boxes2)
self.assertAllCloseAccordingToType(loss, expected_result)
boxes1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]],
dtype=dtype)
boxes2 = tf.constant([[3.0, 4.0, 6.0, 8.0]], dtype=dtype)
tf.expand_dims(boxes1, 0)
tf.expand_dims(boxes2, -2)
expected_result = tf.constant([1.07500000298023224, 1.366071],
dtype=dtype)
loss = giou_loss(boxes1, boxes2)
self.assertAllCloseAccordingToType(loss, expected_result)
def call(self, y_true, y_pred):
"""Calculate sum of the two bounding boxes.
Args:
y_true (tf.tensor): Ground truth
y_pred (tf.tensor): Predictions
Returns:
[tf.tensor]: The loss calculated for the two bounding boxes.
"""
box1_true = tf.gather(y_true, self.box1_index, axis=1)
box1_pred = tf.gather(y_pred, self.box1_index, axis=1)
# Get box 2
box2_true = tf.gather(y_true, self.box2_index, axis=1)
box2_pred = tf.gather(y_pred, self.box2_index, axis=1)
# Bounding Boxes
box1_loss = giou_loss(box1_true, box1_pred, mode='giou')
box2_loss = giou_loss(box2_true, box2_pred, mode='giou')
box_loss = (box1_loss + box2_loss) / 2
return box_loss
def test_one_bbox(self, dtype):
boxes1 = tf.constant([4.0, 3.0, 7.0, 5.0], dtype=dtype)
boxes2 = tf.constant([3.0, 4.0, 6.0, 8.0], dtype=dtype)
expected_result = tf.constant(1.07500000298023224, dtype=dtype)
loss = giou_loss(boxes1, boxes2)
self.assertAllCloseAccordingToType(loss, expected_result)
def test_one_bbox(dtype):
boxes1 = tf.constant([4.0, 3.0, 7.0, 5.0], dtype=dtype)
boxes2 = tf.constant([3.0, 4.0, 6.0, 8.0], dtype=dtype)
expected_result = tf.constant(1.07500000298023224, dtype=dtype)
loss = giou_loss(boxes1, boxes2)
test_utils.assert_allclose_according_to_type(loss, expected_result)