Error(s) in loading state_dict

1_ne: <p>model.load_state_dict(checkpoint[‘model’])</p>
<p>Error(s) in loading state_dict for Cnn14:<br>
Unexpected key(s) in state_dict: “spectrogram_extractor.stft.conv_real.weight”, “spectrogram_extractor.stft.conv_imag.weight”, “logmel_extractor.melW”.</p>
<pre><code class="lang-auto">class Cnn14(nn.Module):
    def __init__(self, classes_num=527):

        super(Cnn14, self).__init__()

        self.bn0 = nn.BatchNorm2d(64)

        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
        self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
        self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)

        self.fc1 = nn.Linear(2048, 2048, bias=True)
        self.fc_audioset = nn.Linear(2048, classes_num, bias=True)

        self.init_weight()

    def init_weight(self):
        init_bn(self.bn0)

    def forward(self, input):

        x = input.unsqueeze(1)   # (batch_size, 1, time_steps, mel_bins)

        x = x.transpose(1, 3)
        x = self.bn0(x)
        x = x.transpose(1, 3)

        x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
        x = F.dropout(x, p=0.2, training=self.training)
        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
        x = F.dropout(x, p=0.2, training=self.training)
        x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
        x = F.dropout(x, p=0.2, training=self.training)
        x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
        x = F.dropout(x, p=0.2, training=self.training)
        x = self.conv_block5(x, pool_size=(1, 1), pool_type='avg')
        x = F.dropout(x, p=0.2, training=self.training)
        x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg')
        x = F.dropout(x, p=0.2, training=self.training)     #(batch_size, 2048, T/16, mel_bins/16)

        '''
        x = torch.mean(x, dim=3)
        (x1, _) = torch.max(x, dim=2)
        x2 = torch.mean(x, dim=2)
        x = x1 + x2
        x = F.dropout(x, p=0.5, training=self.training)
        x = F.relu_(self.fc1(x))
        embedding = F.dropout(x, p=0.5, training=self.training)
        clipwise_output = torch.sigmoid(self.fc_audioset(x))
        output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding}
        '''

        return x

</code></pre>
<pre><code class="lang-auto">print(checkpoint['model'])

'spectrogram_extractor.stft.conv_real.weight',
              tensor([[[ 0.0000e+00,  9.4124e-06,  3.7649e-05,  ...,  8.4709e-05,
                         3.7649e-05,  9.4124e-06]],
              
                      [[ 0.0000e+00,  9.4122e-06,  3.7646e-05,  ...,  8.4695e-05,
                         3.7646e-05,  9.4122e-06]],
              
                      [[ 0.0000e+00,  9.4117e-06,  3.7638e-05,  ...,  8.4652e-05,
                         3.7638e-05,  9.4117e-06]],
              
                      ...,
              
                      [[ 0.0000e+00, -9.4117e-06,  3.7638e-05,  ..., -8.4652e-05,
                         3.7638e-05, -9.4117e-06]],
              
                      [[ 0.0000e+00, -9.4122e-06,  3.7646e-05,  ..., -8.4695e-05,
                         3.7646e-05, -9.4122e-06]],
              
                      [[ 0.0000e+00, -9.4124e-06,  3.7649e-05,  ..., -8.4709e-05,
                         3.7649e-05, -9.4124e-06]]], device='cuda:0')),
             ('spectrogram_extractor.stft.conv_imag.weight',
</code></pre>
<p>I faced a problem when I was transfer learning. There is an error that does not match the number of weights of pre-train model with the number of weights of the cnn14 model to be learned. Should the weight be the same when I do transfer learning?</p>
<p>The following is the structure of the model called up by the torch.load command:</p>
<pre><code class="lang-auto">class Cnn14(nn.Module):
    def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, 
        fmax, classes_num):
        
        super(Cnn14, self).__init__()

        window = 'hann'
        center = True
        pad_mode = 'reflect'
        ref = 1.0
        amin = 1e-10
        top_db = None

        # Spectrogram extractor
        self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, 
            win_length=window_size, window=window, center=center, pad_mode=pad_mode, 
            freeze_parameters=True)

        # Logmel feature extractor
        self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, 
            n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, 
            freeze_parameters=True)

        # Spec augmenter
        self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, 
            freq_drop_width=8, freq_stripes_num=2)

        self.bn0 = nn.BatchNorm2d(64)

        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
        self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
        self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)

        self.fc1 = nn.Linear(2048, 2048, bias=True)
        self.fc_audioset = nn.Linear(2048, classes_num, bias=True)
        
        self.init_weight()

    def init_weight(self):
        init_bn(self.bn0)
        init_layer(self.fc1)
        init_layer(self.fc_audioset)
 
    def forward(self, input, mixup_lambda=None):
        """
        Input: (batch_size, data_length)"""

        x = self.spectrogram_extractor(input)   # (batch_size, 1, time_steps, freq_bins)
        x = self.logmel_extractor(x)    # (batch_size, 1, time_steps, mel_bins)

        x = x.transpose(1, 3)
        x = self.bn0(x)
        x = x.transpose(1, 3)
        
        if self.training:
            x = self.spec_augmenter(x)

        # Mixup on spectrogram
        if self.training and mixup_lambda is not None:
            x = do_mixup(x, mixup_lambda)

        x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
        x = F.dropout(x, p=0.2, training=self.training)
        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
        x = F.dropout(x, p=0.2, training=self.training)
        x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
        x = F.dropout(x, p=0.2, training=self.training)
        x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
        x = F.dropout(x, p=0.2, training=self.training)
        x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg')
        x = F.dropout(x, p=0.2, training=self.training)
        x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg')
        x = F.dropout(x, p=0.2, training=self.training)
        x = torch.mean(x, dim=3)
        
        (x1, _) = torch.max(x, dim=2)
        x2 = torch.mean(x, dim=2)
        x = x1 + x2
        x = F.dropout(x, p=0.5, training=self.training)
        x = F.relu_(self.fc1(x))
        embedding = F.dropout(x, p=0.5, training=self.training)
        clipwise_output = torch.sigmoid(self.fc_audioset(x))
        
        output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding}

        return output_dict
</code></pre>
<p>Help me <img src="https://discuss.pytorch.org/images/emoji/apple/joy.png?v=9" title=":joy:" class="emoji" alt=":joy:"> <img src="https://discuss.pytorch.org/images/emoji/apple/joy.png?v=9" title=":joy:" class="emoji" alt=":joy:"></p>

I-Love-U: <p>Maybe you should load the parameters like this:</p>
<pre><code class="lang-python">def load_pretrained_params(model, model_state_path: str):
    pretrained_dict = torch.load(model_state_path, map_location="cpu")
    model_dict = model.state_dict()
    # 1. filter out unnecessary keys
    if list(pretrained_dict.keys())[0].startswith("module."):
        pretrained_dict = {k[7:]: v for k, v in pretrained_dict.items() if k[7:] in model_dict}
    else:
        pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
    # 2. overwrite entries in the existing state dict
    model_dict.update(pretrained_dict)
    # 3. load the new state dict
    model.load_state_dict(model_dict)
</code></pre>