请问训练后输出的模型在哪里？

[qingzhu0214]

我把OWN/crnn中的pth文件复制到了output中，但是运行demo.py时报如下错误： Traceback (most recent call last): File "demo.py", line 66, in model.load_state_dict(torch.load(args.checkpoint)) File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 845, in load_state_dict self.class.name, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for CRNN: Missing key(s) in state_dict: "cnn.conv0.weight", "cnn.conv0.bias", "cnn.conv1.weight", "cnn.conv1.bias", "cnn.conv2.weight", "cnn.conv2.bias", "cnn.batchnorm2.weight", "cnn.batchnorm2.bias", "cnn.batchnorm2.running_mean", "cnn.batchnorm2.running_var", "cnn.conv3.weight", "cnn.conv3.bias", "cnn.conv4.weight", "cnn.conv4.bias", "cnn.batchnorm4.weight", "cnn.batchnorm4.bias", "cnn.batchnorm4.running_mean", "cnn.batchnorm4.running_var", "cnn.conv5.weight", "cnn.conv5.bias", "cnn.conv6.weight", "cnn.conv6.bias", "cnn.batchnorm6.weight", "cnn.batchnorm6.bias", "cnn.batchnorm6.running_mean", "cnn.batchnorm6.running_var", "rnn.0.rnn.weight_ih_l0", "rnn.0.rnn.weight_hh_l0", "rnn.0.rnn.bias_ih_l0", "rnn.0.rnn.bias_hh_l0", "rnn.0.rnn.weight_ih_l0_reverse", "rnn.0.rnn.weight_hh_l0_reverse", "rnn.0.rnn.bias_ih_l0_reverse", "rnn.0.rnn.bias_hh_l0_reverse", "rnn.0.embedding.weight", "rnn.0.embedding.bias", "rnn.1.rnn.weight_ih_l0", "rnn.1.rnn.weight_hh_l0", "rnn.1.rnn.bias_ih_l0", "rnn.1.rnn.bias_hh_l0", "rnn.1.rnn.weight_ih_l0_reverse", "rnn.1.rnn.weight_hh_l0_reverse", "rnn.1.rnn.bias_ih_l0_reverse", "rnn.1.rnn.bias_hh_l0_reverse", "rnn.1.embedding.weight", "rnn.1.embedding.bias". Unexpected key(s) in state_dict: "state_dict", "epoch", "best_acc".

[Sierkinhane]

'--checkpoint', type=str, default='output/checkpoints/mixed_second_finetune_acc_97P7.pth',

[Sierkinhane]

修改一下demo中的args

[lixiaofen2020]

train.py中保存模型的时候是这样的： torch.save( { "state_dict": model.state_dict(), "epoch": epoch + 1, "best_acc": best_acc, }, os.path.join(output_dict['chsdir'], "checkpoint{}acc{:.4f}.pth".format(epoch, acc)) )

demo.py加载训练好的模型的时候，改成这样就可以了： model.load_state_dict(torch.load(args.checkpoint)['state_dict'])