KeyError: 'module.weight'

Describe the bug I am trying to convert a simple Pytorch Gender Classifier to Keras (model is provided below). Whenever I run model = pytorch_to_keras(model, orig_img, [np.shape(orig_img.cpu().numpy())], names='short'), I get

Traceback (most recent call last): File “main.py”, line 154, in <module> main(args) File “main.py”, line 92, in main model = pytorch_to_keras(model, orig_img, [np.shape(orig_img.cpu().numpy())], names=‘short’) File “/mnt/c/Users/Yannis/CIFAR10S/real_world_experiments/CEM/venv/lib/python3.6/site-packages/pytorch2keras/converter.py”, line 325, in pytorch_to_keras names File “/mnt/c/Users/Yannis/CIFAR10S/real_world_experiments/CEM/venv/lib/python3.6/site-packages/pytorch2keras/convolution_layers.py”, line 35, in convert_conv if len(weights[weights_name].numpy().shape) == 5: # 3D conv KeyError: ‘module.weight’

To Reproduce Here is my pytorch model:

class resnet_modified_small(nn.Module):
    def base_size(self): return 512
    def rep_size(self): return 1024

    def __init__(self, n_classes):
        super(resnet_modified_small, self).__init__()
        self.resnet = tv.models.resnet34(pretrained=True)

        # define layers
        self.n_classes = n_classes
        self.linear = nn.Linear(7 * 7 * self.base_size(), self.rep_size())
        self.cls = nn.Linear(self.rep_size(), self.n_classes)
        self.dropout2d = nn.Dropout2d(.5)
        self.dropout = nn.Dropout(.5)
        self.relu = nn.LeakyReLU()
        initLinear(self.linear)

    def forward(self, out0):
        x = self.resnet.conv1(out0)
        x = self.resnet.bn1(x)
        x = self.resnet.relu(x)
        out1 = self.resnet.maxpool(x)

        out2 = self.resnet.layer1(out1)
        out3 = self.resnet.layer2(out2)
        out4 = self.resnet.layer3(out3)
        out5 = self.resnet.layer4(out4)
     
        x = self.dropout2d(out5)

        features = self.dropout(self.relu(self.linear(x.view(-1, 7*7*self.base_size()))))
        cls_scores = self.cls(features)
        return [out0, out1, out2, out3, out4, out5, features, cls_scores]

Environment:

• OS: Bash for Windows
• Python 3.6
• Pytorch 0.4.1

[gmalivenko]

I’ve just converted the model without a problem. So, have you tried the latest converter version? Please, upgrade the package with pip install --upgrade pytorch2keras, I published the release 5 minutes ago. I hope it will help.

[gmalivenko]

Hi, @I-C-Karakozis. Here are many problems in the converter related to your model. All nested modules (ModuleList, big submodules, etc) are very complex to trace. Actually I plan to refactor the tracing part, but a bit later. So, If you want to convert your model right now:

1. Set resnet layers as a submodules
2. Don’t return an input.

class resnet_modified_small(nn.Module):
    def base_size(self): return 512
    def rep_size(self): return 1024

    def __init__(self, n_classes):
        super(resnet_modified_small, self).__init__()
        self.resnet = tv.models.resnet34(pretrained=True)
        self.conv1 = self.resnet.conv1
        self.bn1 = self.resnet.bn1
        self.layer1 = self.resnet.layer1
        self.layer2 = self.resnet.layer2
        self.layer3 = self.resnet.layer3
        self.layer4 = self.resnet.layer4

        # define layers
        self.n_classes = n_classes
        self.linear = nn.Linear(7 * 7 * self.base_size(), self.rep_size())
        self.cls = nn.Linear(self.rep_size(), self.n_classes)
        self.dropout2d = nn.Dropout2d(.5)
        self.dropout = nn.Dropout(.5)
        self.relu = nn.LeakyReLU()

    def forward(self, out0):
        x = self.conv1(out0)
        x = self.bn1(x)
        x = self.resnet.relu(x)
        out1 = self.resnet.maxpool(x)

        out2 = self.layer1(out1)
        out3 = self.layer2(out2)
        out4 = self.layer3(out3)
        out5 = self.layer4(out4)
     
        x = self.dropout2d(out5)

        features_ = self.relu(self.linear(x.view(-1, 7*7*self.base_size())))
        features = self.dropout(features_.clone())
        cls_scores = self.cls(features.clone())
        return [out1, out2, out3, out4, out5, features, cls_scores]