padding=1)
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.fovea_cls, std=0.01, bias=bias_cls)
After Change
padding=1)
def init_weights(self):
super().init_weights()
if self.with_deform:
self.feature_adaption.init_weights()
def forward_single(self, x):