加载coco的netG模型报错

[LaolangOne]

RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for NetG: Missing key(s) in state_dict: "lstm.W", "lstm.U", "lstm.bias", "lstm.noise2h.weight", "lstm.noise2h.bias", "lstm.noise2c.weight", "lstm.noise2c.bias", "fc.weight", "fc.bias", "block0.gamma", "block0.lstm.W", "block0.lstm.U", "block0.lstm.bias", "block0.lstm.noise2h.weight", "block0.lstm.noise2h.bias",

[lcxsnow]

有同樣的問題,但缺的不同 Missing key(s) in state_dict: "block0.affine4.fc_gamma.linear1.weight", "block0.affine4.fc_gamma.linear1.bias", "block0.affine4.fc_gamma.linear2.weight", "block0.affine4.fc_gamma.linear2.bias", "block0.affine4.fc_beta.linear1.weight", "block0.affine4.fc_beta.linear1.bias", "block0.affine4.fc_beta.linear2.weight", "block0.affine4.fc_beta.linear2.bias", "block0.affine5.fc_gamma.linear1.weight", "block0.affine5.fc_gamma.linear1.bias", "block0.affine5.fc_gamma.linear2.weight", "block0.affine5.fc_gamma.linear2.bias", "block0.affine5.fc_beta.linear1.weight", "block0.affine5.fc_beta.linear1.bias", "block0.affine5.fc_beta.linear2.weight", "block0.affine5.fc_beta.linear2.bias", "block1.affine4.fc_gamma.linear1.weight", "block1.affine4.fc_gamma.linear1.bias", "block1.affine4.fc_gamma.linear2.weight", "block1.affine4.fc_gamma.linear2.bias", "block1.affine4.fc_beta.linear1.weight", "block1.affine4.fc_beta.linear1.bias", "block1.affine4.fc_beta.linear2.weight", "block1.affine4.fc_beta.linear2.bias", "block1.affine5.fc_gamma.linear1.weight", "block1.affine5.fc_gamma.linear1.bias", "block1.affine5.fc_gamma.linear2.weight", "block1.affine5.fc_gamma.linear2.bias", "block1.affine5.fc_beta.linear1.weight", "block1.affine5.fc_beta.linear1.bias", "block1.affine5.fc_beta.linear2.weight", "block1.affine5.fc_beta.linear2.bias", "block2.affine4.fc_gamma.linear1.weight", "block2.affine4.fc_gamma.linear1.bias", "block2.affine4.fc_gamma.linear2.weight", "block2.affine4.fc_gamma.linear2.bias", "block2.affine4.fc_beta.linear1.weight", "block2.affine4.fc_beta.linear1.bias", "block2.affine4.fc_beta.linear2.weight", "block2.affine4.fc_beta.linear2.bias", "block2.affine5.fc_gamma.linear1.weight", "block2.affine5.fc_gamma.linear1.bias", "block2.affine5.fc_gamma.linear2.weight", "block2.affine5.fc_gamma.linear2.bias", "block2.affine5.fc_beta.linear1.weight", "block2.affine5.fc_beta.linear1.bias", "block2.affine5.fc_beta.linear2.weight", "block2.affine5.fc_beta.linear2.bias", "block3.affine4.fc_gamma.linear1.weight", "block3.affine4.fc_gamma.linear1.bias", "block3.affine4.fc_gamma.linear2.weight", "block3.affine4.fc_gamma.linear2.bias", "block3.affine4.fc_beta.linear1.weight", "block3.affine4.fc_beta.linear1.bias", "block3.affine4.fc_beta.linear2.weight", "block3.affine4.fc_beta.linear2.bias", "block3.affine5.fc_gamma.linear1.weight", "block3.affine5.fc_gamma.linear1.bias", "block3.affine5.fc_gamma.linear2.weight", "block3.affine5.fc_gamma.linear2.bias", "block3.affine5.fc_beta.linear1.weight", "block3.affine5.fc_beta.linear1.bias", "block3.affine5.fc_beta.linear2.weight", "block3.affine5.fc_beta.linear2.bias", "block4.affine4.fc_gamma.linear1.weight", "block4.affine4.fc_gamma.linear1.bias", "block4.affine4.fc_gamma.linear2.weight", "block4.affine4.fc_gamma.linear2.bias", "block4.affine4.fc_beta.linear1.weight", "block4.affine4.fc_beta.linear1.bias", "block4.affine4.fc_beta.linear2.weight", "block4.affine4.fc_beta.linear2.bias", "block4.affine5.fc_gamma.linear1.weight", "block4.affine5.fc_gamma.linear1.bias", "block4.affine5.fc_gamma.linear2.weight", "block4.affine5.fc_gamma.linear2.bias", "block4.affine5.fc_beta.linear1.weight", "block4.affine5.fc_beta.linear1.bias", "block4.affine5.fc_beta.linear2.weight", "block4.affine5.fc_beta.linear2.bias", "block5.affine4.fc_gamma.linear1.weight", "block5.affine4.fc_gamma.linear1.bias", "block5.affine4.fc_gamma.linear2.weight", "block5.affine4.fc_gamma.linear2.bias", "block5.affine4.fc_beta.linear1.weight", "block5.affine4.fc_beta.linear1.bias", "block5.affine4.fc_beta.linear2.weight", "block5.affine4.fc_beta.linear2.bias", "block5.affine5.fc_gamma.linear1.weight", "block5.affine5.fc_gamma.linear1.bias", "block5.affine5.fc_gamma.linear2.weight", "block5.affine5.fc_gamma.linear2.bias", "block5.affine5.fc_beta.linear1.weight", "block5.affine5.fc_beta.linear1.bias", "block5.affine5.fc_beta.linear2.weight", "block5.affine5.fc_beta.linear2.bias", "block6.affine4.fc_gamma.linear1.weight", "block6.affine4.fc_gamma.linear1.bias", "block6.affine4.fc_gamma.linear2.weight", "block6.affine4.fc_gamma.linear2.bias", "block6.affine4.fc_beta.linear1.weight", "block6.affine4.fc_beta.linear1.bias", "block6.affine4.fc_beta.linear2.weight", "block6.affine4.fc_beta.linear2.bias", "block6.affine5.fc_gamma.linear1.weight", "block6.affine5.fc_gamma.linear1.bias", "block6.affine5.fc_gamma.linear2.weight", "block6.affine5.fc_gamma.linear2.bias", "block6.affine5.fc_beta.linear1.weight", "block6.affine5.fc_beta.linear1.bias", "block6.affine5.fc_beta.linear2.weight", "block6.affine5.fc_beta.linear2.bias"