def get_losses(self):
"""Generates a dictionary of losses to apply to each path.
Done in the detection generator because all parameters are the same
across both loss and detection generator.
Returns:
Dict[str, tf.Tensor] of losses
"""
loss = yolo_loss.YoloLoss(keys=self._keys,
classes=self._classes,
anchors=self._anchors,
path_strides=self._path_scale,
truth_thresholds=self._truth_thresh,
ignore_thresholds=self._ignore_thresh,
loss_types=self._loss_type,
iou_normalizers=self._iou_normalizer,
cls_normalizers=self._cls_normalizer,
object_normalizers=self._object_normalizer,
objectness_smooths=self._objectness_smooth,
box_types=self._box_type,
max_deltas=self._max_delta,
scale_xys=self._scale_xy,
use_scaled_loss=self._use_scaled_loss,
update_on_repeat=self._update_on_repeat,
label_smoothing=self._label_smoothing)
return loss
def test_loss_init(self, scaled):
"""Test creation of YOLO family models."""
def inpdict(input_shape, dtype=tf.float32):
inputs = {}
for key in input_shape:
inputs[key] = tf.ones(input_shape[key], dtype=dtype)
return inputs
tf.keras.backend.set_image_data_format('channels_last')
input_shape = {
'3': [1, 52, 52, 255],
'4': [1, 26, 26, 255],
'5': [1, 13, 13, 255]
}
classes = 80
anchors = {
'3': [[12.0, 19.0], [31.0, 46.0], [96.0, 54.0]],
'4': [[46.0, 114.0], [133.0, 127.0], [79.0, 225.0]],
'5': [[301.0, 150.0], [172.0, 286.0], [348.0, 340.0]]
}
keys = ['3', '4', '5']
path_strides = {key: 2**int(key) for key in keys}
loss = yolo_loss.YoloLoss(
keys,
classes,
anchors,
path_strides=path_strides,
truth_thresholds={key: 1.0
for key in keys},
ignore_thresholds={key: 0.7
for key in keys},
loss_types={key: 'ciou'
for key in keys},
iou_normalizers={key: 0.05
for key in keys},
cls_normalizers={key: 0.5
for key in keys},
obj_normalizers={key: 1.0
for key in keys},
objectness_smooths={key: 1.0
for key in keys},
box_types={key: 'scaled'
for key in keys},
scale_xys={key: 2.0
for key in keys},
max_deltas={key: 30.0
for key in keys},
label_smoothing=0.0,
use_scaled_loss=scaled,
update_on_repeat=True)
count = inpdict({
'3': [1, 52, 52, 3, 1],
'4': [1, 26, 26, 3, 1],
'5': [1, 13, 13, 3, 1]
})
ind = inpdict({
'3': [1, 300, 3],
'4': [1, 300, 3],
'5': [1, 300, 3]
}, tf.int32)
truths = inpdict({
'3': [1, 300, 6],
'4': [1, 300, 6],
'5': [1, 300, 6]
})
boxes = tf.ones([1, 300, 4], dtype=tf.float32)
classes = tf.ones([1, 300], dtype=tf.float32)
gt = {
'true_conf': count,
'inds': ind,
'upds': truths,
'bbox': boxes,
'classes': classes
}
_, _, _ = loss(gt, inpdict(input_shape))