Thx for nice sharing!
My env:
Win10 + pytorch1.6
While I try to use
model = pycls.models.regnety(model_cate, pretrained=True)
Just got bad result, which equal when I set pretrained=False.
And, I also try to load by myself,
model.load_state_dict(torch.load(load_path),strict=True)
Got error below:
model.load_state_dict(torch.load(load_path),strict=True) File "F:\Software\Anaconda\envs\pth\lib\site-packages\torch\nn\modules\module.py", line 1045, in load_state_dict self.__class__.__name__, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for RegNet: Missing key(s) in state_dict: "stem.conv.weight", "stem.bn.weight", "stem.bn.bias", "stem.bn.running_mean", "stem.bn.running_var", "s1.b1.proj.weight", "s1.b1.bn.weight", "s1.b1.bn.bias", "s1.b1.bn.running_mean", "s1.b1.bn.running_var", "s1.b1.f.a.weight", "s1.b1.f.a_bn.weight", "s1.b1.f.a_bn.bias", "s1.b1.f.a_bn.running_mean", "s1.b1.f.a_bn.running_var", "s1.b1.f.b.weight", "s1.b1.f.b_bn.weight", "s1.b1.f.b_bn.bias", "s1.b1.f.b_bn.running_mean", "s1.b1.f.b_bn.running_var", "s1.b1.f.se.f_ex.0.weight", "s1.b1.f.se.f_ex.0.bias", "s1.b1.f.se.f_ex.2.weight", "s1.b1.f.se.f_ex.2.bias", "s1.b1.f.c.weight", "s1.b1.f.c_bn.weight", "s1.b1.f.c_bn.bias", "s1.b1.f.c_bn.running_mean", "s1.b1.f.c_bn.running_var", "s2.b1.proj.weight", "s2.b1.bn.weight", "s2.b1.bn.bias", "s2.b1.bn.running_mean", "s2.b1.bn.running_var", "s2.b1.f.a.weight", "s2.b1.f.a_bn.weight", "s2.b1.f.a_bn.bias", "s2.b1.f.a_bn.running_mean", "s2.b1.f.a_bn.running_var", "s2.b1.f.b.weight", "s2.b1.f.b_bn.weight", "s2.b1.f.b_bn.bias", "s2.b1.f.b_bn.running_mean", "s2.b1.f.b_bn.running_var", "s2.b1.f.se.f_ex.0.weight", "s2.b1.f.se.f_ex.0.bias", "s2.b1.f.se.f_ex.2.weight", "s2.b1.f.se.f_ex.2.bias", "s2.b1.f.c.weight", "s2.b1.f.c_bn.weight", "s2.b1.f.c_bn.bias", "s2.b1.f.c_bn.running_mean", "s2.b1.f.c_bn.running_var", "s3.b1.proj.weight", "s3.b1.bn.weight", "s3.b1.bn.bias", "s3.b1.bn.running_mean", "s3.b1.bn.running_var", "s3.b1.f.a.weight", "s3.b1.f.a_bn.weight", "s3.b1.f.a_bn.bias", "s3.b1.f.a_bn.running_mean", "s3.b1.f.a_bn.running_var", "s3.b1.f.b.weight", "s3.b1.f.b_bn.weight", "s3.b1.f.b_bn.bias", "s3.b1.f.b_bn.running_mean", "s3.b1.f.b_bn.running_var", "s3.b1.f.se.f_ex.0.weight", "s3.b1.f.se.f_ex.0.bias", "s3.b1.f.se.f_ex.2.weight", "s3.b1.f.se.f_ex.2.bias", "s3.b1.f.c.weight", "s3.b1.f.c_bn.weight", "s3.b1.f.c_bn.bias", "s3.b1.f.c_bn.running_mean", "s3.b1.f.c_bn.running_var", "s3.b2.f.a.weight", "s3.b2.f.a_bn.weight", "s3.b2.f.a_bn.bias", "s3.b2.f.a_bn.running_mean", "s3.b2.f.a_bn.running_var", "s3.b2.f.b.weight", "s3.b2.f.b_bn.weight", "s3.b2.f.b_bn.bias", "s3.b2.f.b_bn.running_mean", "s3.b2.f.b_bn.running_var", "s3.b2.f.se.f_ex.0.weight", "s3.b2.f.se.f_ex.0.bias", "s3.b2.f.se.f_ex.2.weight", "s3.b2.f.se.f_ex.2.bias", "s3.b2.f.c.weight", "s3.b2.f.c_bn.weight", "s3.b2.f.c_bn.bias", "s3.b2.f.c_bn.running_mean", "s3.b2.f.c_bn.running_var", "s3.b3.f.a.weight", "s3.b3.f.a_bn.weight", "s3.b3.f.a_bn.bias", "s3.b3.f.a_bn.running_mean", "s3.b3.f.a_bn.running_var", "s3.b3.f.b.weight", "s3.b3.f.b_bn.weight", "s3.b3.f.b_bn.bias", "s3.b3.f.b_bn.running_mean", "s3.b3.f.b_bn.running_var", "s3.b3.f.se.f_ex.0.weight", "s3.b3.f.se.f_ex.0.bias", "s3.b3.f.se.f_ex.2.weight", "s3.b3.f.se.f_ex.2.bias", "s3.b3.f.c.weight", "s3.b3.f.c_bn.weight", "s3.b3.f.c_bn.bias", "s3.b3.f.c_bn.running_mean", "s3.b3.f.c_bn.running_var", "s3.b4.f.a.weight", "s3.b4.f.a_bn.weight", "s3.b4.f.a_bn.bias", "s3.b4.f.a_bn.running_mean", "s3.b4.f.a_bn.running_var", "s3.b4.f.b.weight", "s3.b4.f.b_bn.weight", "s3.b4.f.b_bn.bias", "s3.b4.f.b_bn.running_mean", "s3.b4.f.b_bn.running_var", "s3.b4.f.se.f_ex.0.weight", "s3.b4.f.se.f_ex.0.bias", "s3.b4.f.se.f_ex.2.weight", "s3.b4.f.se.f_ex.2.bias", "s3.b4.f.c.weight", "s3.b4.f.c_bn.weight", "s3.b4.f.c_bn.bias", "s3.b4.f.c_bn.running_mean", "s3.b4.f.c_bn.running_var", "s4.b1.proj.weight", "s4.b1.bn.weight", "s4.b1.bn.bias", "s4.b1.bn.running_mean", "s4.b1.bn.running_var", "s4.b1.f.a.weight", "s4.b1.f.a_bn.weight", "s4.b1.f.a_bn.bias", "s4.b1.f.a_bn.running_mean", "s4.b1.f.a_bn.running_var", "s4.b1.f.b.weight", "s4.b1.f.b_bn.weight", "s4.b1.f.b_bn.bias", "s4.b1.f.b_bn.running_mean", "s4.b1.f.b_bn.running_var", "s4.b1.f.se.f_ex.0.weight", "s4.b1.f.se.f_ex.0.bias", "s4.b1.f.se.f_ex.2.weight", "s4.b1.f.se.f_ex.2.bias", "s4.b1.f.c.weight", "s4.b1.f.c_bn.weight", "s4.b1.f.c_bn.bias", "s4.b1.f.c_bn.running_mean", "s4.b1.f.c_bn.running_var", "s4.b2.f.a.weight", "s4.b2.f.a_bn.weight", "s4.b2.f.a_bn.bias", "s4.b2.f.a_bn.running_mean", "s4.b2.f.a_bn.running_var", "s4.b2.f.b.weight", "s4.b2.f.b_bn.weight", "s4.b2.f.b_bn.bias", "s4.b2.f.b_bn.running_mean", "s4.b2.f.b_bn.running_var", "s4.b2.f.se.f_ex.0.weight", "s4.b2.f.se.f_ex.0.bias", "s4.b2.f.se.f_ex.2.weight", "s4.b2.f.se.f_ex.2.bias", "s4.b2.f.c.weight", "s4.b2.f.c_bn.weight", "s4.b2.f.c_bn.bias", "s4.b2.f.c_bn.running_mean", "s4.b2.f.c_bn.running_var", "s4.b3.f.a.weight", "s4.b3.f.a_bn.weight", "s4.b3.f.a_bn.bias", "s4.b3.f.a_bn.running_mean", "s4.b3.f.a_bn.running_var", "s4.b3.f.b.weight", "s4.b3.f.b_bn.weight", "s4.b3.f.b_bn.bias", "s4.b3.f.b_bn.running_mean", "s4.b3.f.b_bn.running_var", "s4.b3.f.se.f_ex.0.weight", "s4.b3.f.se.f_ex.0.bias", "s4.b3.f.se.f_ex.2.weight", "s4.b3.f.se.f_ex.2.bias", "s4.b3.f.c.weight", "s4.b3.f.c_bn.weight", "s4.b3.f.c_bn.bias", "s4.b3.f.c_bn.running_mean", "s4.b3.f.c_bn.running_var", "s4.b4.f.a.weight", "s4.b4.f.a_bn.weight", "s4.b4.f.a_bn.bias", "s4.b4.f.a_bn.running_mean", "s4.b4.f.a_bn.running_var", "s4.b4.f.b.weight", "s4.b4.f.b_bn.weight", "s4.b4.f.b_bn.bias", "s4.b4.f.b_bn.running_mean", "s4.b4.f.b_bn.running_var", "s4.b4.f.se.f_ex.0.weight", "s4.b4.f.se.f_ex.0.bias", "s4.b4.f.se.f_ex.2.weight", "s4.b4.f.se.f_ex.2.bias", "s4.b4.f.c.weight", "s4.b4.f.c_bn.weight", "s4.b4.f.c_bn.bias", "s4.b4.f.c_bn.running_mean", "s4.b4.f.c_bn.running_var", "s4.b5.f.a.weight", "s4.b5.f.a_bn.weight", "s4.b5.f.a_bn.bias", "s4.b5.f.a_bn.running_mean", "s4.b5.f.a_bn.running_var", "s4.b5.f.b.weight", "s4.b5.f.b_bn.weight", "s4.b5.f.b_bn.bias", "s4.b5.f.b_bn.running_mean", "s4.b5.f.b_bn.running_var", "s4.b5.f.se.f_ex.0.weight", "s4.b5.f.se.f_ex.0.bias", "s4.b5.f.se.f_ex.2.weight", "s4.b5.f.se.f_ex.2.bias", "s4.b5.f.c.weight", "s4.b5.f.c_bn.weight", "s4.b5.f.c_bn.bias", "s4.b5.f.c_bn.running_mean", "s4.b5.f.c_bn.running_var", "s4.b6.f.a.weight", "s4.b6.f.a_bn.weight", "s4.b6.f.a_bn.bias", "s4.b6.f.a_bn.running_mean", "s4.b6.f.a_bn.running_var", "s4.b6.f.b.weight", "s4.b6.f.b_bn.weight", "s4.b6.f.b_bn.bias", "s4.b6.f.b_bn.running_mean", "s4.b6.f.b_bn.running_var", "s4.b6.f.se.f_ex.0.weight", "s4.b6.f.se.f_ex.0.bias", "s4.b6.f.se.f_ex.2.weight", "s4.b6.f.se.f_ex.2.bias", "s4.b6.f.c.weight", "s4.b6.f.c_bn.weight", "s4.b6.f.c_bn.bias", "s4.b6.f.c_bn.running_mean", "s4.b6.f.c_bn.running_var", "s4.b7.f.a.weight", "s4.b7.f.a_bn.weight", "s4.b7.f.a_bn.bias", "s4.b7.f.a_bn.running_mean", "s4.b7.f.a_bn.running_var", "s4.b7.f.b.weight", "s4.b7.f.b_bn.weight", "s4.b7.f.b_bn.bias", "s4.b7.f.b_bn.running_mean", "s4.b7.f.b_bn.running_var", "s4.b7.f.se.f_ex.0.weight", "s4.b7.f.se.f_ex.0.bias", "s4.b7.f.se.f_ex.2.weight", "s4.b7.f.se.f_ex.2.bias", "s4.b7.f.c.weight", "s4.b7.f.c_bn.weight", "s4.b7.f.c_bn.bias", "s4.b7.f.c_bn.running_mean", "s4.b7.f.c_bn.running_var", "head.fc.weight", "head.fc.bias". Unexpected key(s) in state_dict: "epoch", "model_state", "optimizer_state", "cfg".