第八章的demo运行不了

[Agiroy4712]

首先是训练的代码有问题，保存不了模型。 设置后，有保存模型了，在demo.py测试时加载模型却报错

[Agiroy4712]

loading pretrained model from /home/data/liaohaiqing/CRNN/chapter-8/expr/CRNN_0_10.pth
Traceback (most recent call last):
File "demo.py", line 29, in
model.load_state_dict(torch.load(model_path))
File "/home/data/liaohaiqing/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
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".
Unexpected key(s) in state_dict: "module.cnn.conv0.weight", "module.cnn.conv0.bias", "module.cnn.conv1.weight", "module.cnn.conv1.bias", "module.cnn.conv2.weight", "module.cnn.conv2.bias", "module.cnn.batchnorm2.weight", "module.cnn.batchnorm2.bias", "module.cnn.batchnorm2.running_mean", "module.cnn.batchnorm2.running_var", "module.cnn.batchnorm2.num_batches_tracked", "module.cnn.conv3.weight", "module.cnn.conv3.bias", "module.cnn.conv4.weight", "module.cnn.conv4.bias", "module.cnn.batchnorm4.weight", "module.cnn.batchnorm4.bias", "module.cnn.batchnorm4.running_mean", "module.cnn.batchnorm4.running_var", "module.cnn.batchnorm4.num_batches_tracked", "module.cnn.conv5.weight", "module.cnn.conv5.bias", "module.cnn.conv6.weight", "module.cnn.conv6.bias", "module.cnn.batchnorm6.weight", "module.cnn.batchnorm6.bias", "module.cnn.batchnorm6.running_mean", "module.cnn.batchnorm6.running_var", "module.cnn.batchnorm6.num_batches_tracked", "module.rnn.0.rnn.weight_ih_l0", "module.rnn.0.rnn.weight_hh_l0", "module.rnn.0.rnn.bias_ih_l0", "module.rnn.0.rnn.bias_hh_l0", "module.rnn.0.rnn.weight_ih_l0_reverse", "module.rnn.0.rnn.weight_hh_l0_reverse", "module.rnn.0.rnn.bias_ih_l0_reverse", "module.rnn.0.rnn.bias_hh_l0_reverse", "module.rnn.0.embedding.weight", "module.rnn.0.embedding.bias".

[liushuchun]

hi,应该是单卡，多卡模型保存不一致导致的，加上这个试试。
model = nn.DataParallel(model)
cudnn.benchmark = True

[Dyna2015]

我也出现了这个问题，请问你是怎么解决的？

[a741419244]

model = nn.DataParallel(model)
model.load_state_dict(torch.load(model_path),strict=False)