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抱歉这么久打扰,为什么提供的CRNN模型里只有一个可以用呢? RuntimeError: Error(s) in loading state_dict for CRNN: Missing key(s) in state_dict: "conv1.weight", "conv1.bias", "conv2.weight", "conv2.bias", "conv3_1.weight", "conv3_1.bias", "bn3.weight", "bn3.bias", "bn3.running_mean", "bn3.running_var", "conv3_2.weight", "conv3_2.bias", "conv4_1.weight", "conv4_1.bias", "bn4.weight", "bn4.bias", "bn4.running_mean", "bn4.running_var", "conv4_2.weight", "conv4_2.bias", "conv5.weight", "conv5.bias", "bn5.weight", "bn5.bias", "bn5.running_mean", "bn5.running_var". Unexpected 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.batchnorm2.num_batches_tracked", "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.batchnorm4.num_batches_tracked", "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", "cnn.batchnorm6.num_batches_tracked". size mismatch for rnn.1.embedding.weight: copying a param with shape torch.Size([5997, 512]) from checkpoint, the shape in current model is torch.Size([5835, 512]). size mismatch for rnn.1.embedding.bias: copying a param with shape torch.Size([5997]) from checkpoint, the shape in current
The text was updated successfully, but these errors were encountered:
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抱歉这么久打扰,为什么提供的CRNN模型里只有一个可以用呢?
RuntimeError: Error(s) in loading state_dict for CRNN:
Missing key(s) in state_dict: "conv1.weight", "conv1.bias", "conv2.weight", "conv2.bias", "conv3_1.weight", "conv3_1.bias", "bn3.weight", "bn3.bias", "bn3.running_mean", "bn3.running_var", "conv3_2.weight", "conv3_2.bias", "conv4_1.weight", "conv4_1.bias", "bn4.weight", "bn4.bias", "bn4.running_mean", "bn4.running_var", "conv4_2.weight", "conv4_2.bias", "conv5.weight", "conv5.bias", "bn5.weight", "bn5.bias", "bn5.running_mean", "bn5.running_var".
Unexpected 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.batchnorm2.num_batches_tracked", "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.batchnorm4.num_batches_tracked", "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", "cnn.batchnorm6.num_batches_tracked".
size mismatch for rnn.1.embedding.weight: copying a param with shape torch.Size([5997, 512]) from checkpoint, the shape in current model is torch.Size([5835, 512]).
size mismatch for rnn.1.embedding.bias: copying a param with shape torch.Size([5997]) from checkpoint, the shape in current
The text was updated successfully, but these errors were encountered: