def __init__(self, stages, channels, anchors, strides, classes, alloc_size=(128, 128),
nms_thresh=0.45, nms_topk=400, post_nms=100, pos_iou_thresh=1.0,
ignore_iou_thresh=0.7, num_sync_bn_devices=-1, **kwargs):
super(YOLOV3, self).__init__(**kwargs)
self._classes = classes
self.nms_thresh = nms_thresh
self.nms_topk = nms_topk
self.post_nms = post_nms
self._pos_iou_thresh = pos_iou_thresh
self._ignore_iou_thresh = ignore_iou_thresh
if pos_iou_thresh >= 1:
self._target_generator = YOLOV3TargetMerger(len(classes), ignore_iou_thresh)
else:
raise NotImplementedError(
"pos_iou_thresh({}) < 1.0 is not implemented!".format(pos_iou_thresh))
self._loss = YOLOV3Loss()
with self.name_scope():
self.stages = nn.HybridSequential()
self.transitions = nn.HybridSequential()
self.yolo_blocks = nn.HybridSequential()
self.yolo_outputs = nn.HybridSequential()
# note that anchors and strides should be used in reverse order
for i, stage, channel, anchor, stride in zip(
range(len(stages)), stages, channels, anchors[::-1], strides[::-1]):
self.stages.add(stage)
block = YOLODetectionBlockV3(channel, num_sync_bn_devices)
self.yolo_blocks.add(block)
output = YOLOOutputV3(i, len(classes), anchor, stride, alloc_size=alloc_size)
self.yolo_outputs.add(output)
if i > 0:
self.transitions.add(_conv2d(channel, 1, 0, 1, num_sync_bn_devices))
def reset_class(self, classes):
"""Reset class categories and class predictors.
Parameters
----------
classes : iterable of str
The new categories. ['apple', 'orange'] for example.
"""
self._clear_cached_op()
self._classes = classes
if self._pos_iou_thresh >= 1:
self._target_generator = YOLOV3TargetMerger(len(classes), self._ignore_iou_thresh)
for outputs in self.yolo_outputs:
outputs.reset_class(classes)
def __init__(self,
batch_axis=0,
weight=None,
box_loss_type='mse',
**kwargs):
super(YOLOv3Loss, self).__init__(weight, batch_axis, **kwargs)
self._sigmoid_ce = mloss.SigmoidBinaryCrossEntropyLoss(
from_sigmoid=False)
self.target = YOLOV3TargetMerger(20, ignore_iou_thresh=0.5)
self._loss_type = box_loss_type
if box_loss_type == 'mse':
self._l1_loss = mloss.L1Loss()
# self._l2_loss = mloss.L2Loss()
else:
self._iou_loss = IoULoss(x1y1x2y2=True, loss_type=box_loss_type)