def __init__(self, alpha=0.25, gamma=2.0, lambda_p=5.0, temperature=1. / 3, strides=None, iou_type='giou'):
self.alpha = alpha
self.gamma = gamma
self.lambda_p = lambda_p
self.temperature = temperature
if strides is None:
strides = [8, 16, 32, 64, 128]
self.strides = strides
self.box_coder = BoxCoder()
self.iou_loss_func = IOULoss(iou_type=iou_type, coord_type='ltrb')
def __init__(self, radius=1, strides=None, layer_limits=None):
self.radius = radius # decide matching method
self.box_coder = BoxCoder()
if strides is None:
strides = [8, 16, 32, 64, 128]
self.strides = torch.tensor(strides)
if layer_limits is None:
layer_limits = [64, 128, 256, 512]
expand_limits = np.array(layer_limits)[None].repeat(
2).tolist() # list, len=8
'''
FCOS通过规定每一层预测的尺度范围来避免一个sample匹配到多个target的情况
self.layer_limits=[ [-1,64], shape=[5,2]
[64,128],
[128,256],
[256,512],
[512,inf]]
'''
self.layer_limits = torch.tensor([-1.] + expand_limits + [INF]).view(
-1, 2)
def __init__(self,
alpha=0.25,
gamma=2.0,
lambda_p=5.0,
temperature=1. / 3,
strides=None,
iou_type='giou',
positive_weights=0.1,
negative_weights=1.0):
self.alpha = alpha
self.gamma = gamma
self.lambda_p = lambda_p # 平衡正负样本的损失权重
self.temperature = temperature # 突出具有高置信度的位置
# 正负样本损失的权值,论文中并未明确给出,待定
self.positive_weights = positive_weights
self.negative_weights = negative_weights
if strides is None:
strides = [8, 16, 32, 64, 128]
self.strides = strides
self.box_coder = BoxCoder()
self.iou_loss_func = IOULoss(iou_type=iou_type, coord_type='ltrb')