def init_weights(self):
for m in self.cls_convs:
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
normal_init(m.conv, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.retina_cls, std=0.01, bias=bias_cls)
normal_init(self.retina_bbox_reg, std=0.01)
normal_init(self.retina_bbox_cls, std=0.01)
def init_weights(self):
"""Initialize weights of the head."""
for m in self.cls_convs:
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
normal_init(m.conv, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.atss_cls, std=0.01, bias=bias_cls)
normal_init(self.atss_reg, std=0.01)
normal_init(self.atss_centerness, std=0.01)
def init_weights(self):
for module_list in [
self.reg_cls_fcs, self.reg_offset_fcs, self.cls_fcs
]:
for m in module_list.modules():
if isinstance(m, nn.Linear):
xavier_init(m, distribution='uniform')
if self.reg_feat_up_ratio > 1:
kaiming_init(self.upsample_x, distribution='normal')
kaiming_init(self.upsample_y, distribution='normal')
normal_init(self.reg_conv_att_x, 0, 0.01)
normal_init(self.reg_conv_att_y, 0, 0.01)
normal_init(self.fc_reg_offset, 0, 0.001)
normal_init(self.fc_reg_cls, 0, 0.01)
normal_init(self.fc_cls, 0, 0.01)
def init_weights(self):
normal_init(self.conv_cls, std=0.01)
normal_init(self.conv_reg, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.conv_loc, std=0.01, bias=bias_cls)
normal_init(self.conv_shape, std=0.01)
self.feature_adaption.init_weights()
def init_weights(self):
"""Initialize weights of the head."""
for m in self.cls_convs:
if isinstance(m.conv, nn.Conv2d):
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
if isinstance(m.conv, nn.Conv2d):
normal_init(m.conv, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.conv_cls, std=0.01, bias=bias_cls)
normal_init(self.conv_reg, std=0.01)
def init_weights(self):
"""Initialize weights of the head."""
for m in self.cls_convs:
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
normal_init(m.conv, std=0.01)
# 这个操作非常关键，原因是anchor太多了，且没有faster rcnn里面的sample操作
# 故负样本远远大于正样本，也就是说分类分支，假设负样本：正样本数=1000:1
# 分类是sigmod输出，负数表示负样本label，bias_cls是一个负数
# 可以保证分类分支输出大部分是负数，这样算loss时候就会比较小，相当于强制输出的值偏向负类
bias_cls = bias_init_with_prob(0.01)
normal_init(self.retina_cls, std=0.01, bias=bias_cls)
normal_init(self.retina_reg, std=0.01)
def init_weights(self):
normal_init(self.conv_offset, std=0.1)
normal_init(self.conv_adaption, std=0.01)
def init_weights(self):
"""Initialize weights of the head."""
for m in self.cls_convs:
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
normal_init(m.conv, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.reppoints_cls_conv, std=0.01)
normal_init(self.reppoints_cls_out, std=0.01, bias=bias_cls)
normal_init(self.reppoints_pts_init_conv, std=0.01)
normal_init(self.reppoints_pts_init_out, std=0.01)
normal_init(self.reppoints_pts_refine_conv, std=0.01)
normal_init(self.reppoints_pts_refine_out, std=0.01)
def init_weights(self):
"""Initialize weights of the head."""
for m in self.convs_pred:
normal_init(m, std=0.01)
def init_weights(self):
"""Initialize weights of the head."""
normal_init(self.conv_cls, std=0.01)
normal_init(self.conv_reg, std=0.01)
def init_weights(self):
"""Initialize weights of the head."""
super().init_weights()
normal_init(self.conv_centerness, std=0.01)
def init_weights(self):
"""Initialize weights of the layer."""
for m in self.cls_convs:
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
normal_init(m.conv, std=0.01)
self.feature_adaption_cls.init_weights()
self.feature_adaption_reg.init_weights()
bias_cls = bias_init_with_prob(0.01)
normal_init(self.conv_loc, std=0.01, bias=bias_cls)
normal_init(self.conv_shape, std=0.01)
normal_init(self.retina_cls, std=0.01, bias=bias_cls)
normal_init(self.retina_reg, std=0.01)