Problem on using vgg11_bn to run `run_test.py`

[chilldenaya]

Can vgg11_bn be used instead of vgg16_bn in the code?

I attempted to change the model to the pre-trained vgg11_bn model provided by PyTorch, but I encountered an error. The error message states that some keys are missing in the state_dict for the P2PNet model, such as backbone.body1.5.weight, backbone.body1.5.bias, backbone.body1.5.running_mean, backbone.body1.5.running_var, and many others. In addition, there are unexpected keys in the state_dict, such as backbone.body1.10.weight, backbone.body1.10.bias, backbone.body1.11.weight, backbone.body1.11.bias, and so on. There is also a size mismatch for some of the parameters, such as backbone.body1.4.weight and backbone.body1.4.bias.

I found this error:

RuntimeError: Error(s) in loading state_dict for P2PNet:
        Missing key(s) in state_dict: "backbone.body1.5.weight", "backbone.body1.5.bias", "backbone.body1.5.running_mean", "backbone.body1.5.running_var", "backbone.body2.0.weight", "backbone.body2.0.bias", "backbone.body2.0.running_mean", "backbone.body2.0.running_var", "backbone.body2.3.weight", "backbone.body2.3.bias", "backbone.body2.3.running_mean", "backbone.body2.3.running_var", "backbone.body2.6.weight", "backbone.body2.6.bias", "backbone.body3.0.weight", "backbone.body3.0.bias", "backbone.body3.0.running_mean", "backbone.body3.0.running_var", "backbone.body3.3.weight", "backbone.body3.3.bias", "backbone.body3.3.running_mean", "backbone.body3.3.running_var", "backbone.body3.6.weight", "backbone.body3.6.bias", "backbone.body4.0.weight", "backbone.body4.0.bias", "backbone.body4.0.running_mean", "backbone.body4.0.running_var", "backbone.body4.3.weight", "backbone.body4.3.bias", "backbone.body4.3.running_mean", "backbone.body4.3.running_var". 
        Unexpected key(s) in state_dict: "backbone.body1.10.weight", "backbone.body1.10.bias", "backbone.body1.11.weight", "backbone.body1.11.bias", "backbone.body1.11.running_mean", "backbone.body1.11.running_var", "backbone.body1.11.num_batches_tracked", "backbone.body1.3.weight", "backbone.body1.3.bias", "backbone.body1.4.running_mean", "backbone.body1.4.running_var", "backbone.body1.4.num_batches_tracked", "backbone.body1.7.weight", "backbone.body1.7.bias", "backbone.body1.8.running_mean", "backbone.body1.8.running_var", "backbone.body1.8.num_batches_tracked", "backbone.body2.7.weight", "backbone.body2.7.bias", "backbone.body2.8.weight", "backbone.body2.8.bias", "backbone.body2.8.running_mean", "backbone.body2.8.running_var", "backbone.body2.8.num_batches_tracked", "backbone.body2.1.weight", "backbone.body2.1.bias", "backbone.body2.2.running_mean", "backbone.body2.2.running_var", "backbone.body2.2.num_batches_tracked", "backbone.body2.4.weight", "backbone.body2.4.bias", "backbone.body2.5.weight", "backbone.body2.5.bias", "backbone.body2.5.running_mean", "backbone.body2.5.running_var", "backbone.body2.5.num_batches_tracked", "backbone.body3.7.weight", "backbone.body3.7.bias", "backbone.body3.8.weight", "backbone.body3.8.bias", "backbone.body3.8.running_mean", "backbone.body3.8.running_var", "backbone.body3.8.num_batches_tracked", "backbone.body3.1.weight", "backbone.body3.1.bias", "backbone.body3.2.running_mean", "backbone.body3.2.running_var", "backbone.body3.2.num_batches_tracked", "backbone.body3.4.weight", "backbone.body3.4.bias", "backbone.body3.5.weight", "backbone.body3.5.bias", "backbone.body3.5.running_mean", "backbone.body3.5.running_var", "backbone.body3.5.num_batches_tracked", "backbone.body4.7.weight", "backbone.body4.7.bias", "backbone.body4.8.weight", "backbone.body4.8.bias", "backbone.body4.8.running_mean", "backbone.body4.8.running_var", "backbone.body4.8.num_batches_tracked", "backbone.body4.1.weight", "backbone.body4.1.bias", "backbone.body4.2.running_mean", "backbone.body4.2.running_var", "backbone.body4.2.num_batches_tracked", "backbone.body4.4.weight", "backbone.body4.4.bias", "backbone.body4.5.weight", "backbone.body4.5.bias", "backbone.body4.5.running_mean", "backbone.body4.5.running_var", "backbone.body4.5.num_batches_tracked". 
        size mismatch for backbone.body1.4.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128, 64, 3, 3]).
        size mismatch for backbone.body1.4.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for backbone.body1.8.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256, 128, 3, 3]).
        size mismatch for backbone.body1.8.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for backbone.body2.2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
        size mismatch for backbone.body3.2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3]).
        size mismatch for backbone.body4.2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3]).

I would greatly appreciate any response. Thank you!

[chilldenaya]

I attempted to include a conditional statement within the __init__method of BackboneBase_VGG class as follows:

            elif name == "vgg11_bn":
                self.body1 = nn.Sequential(*features[:7])
                self.body2 = nn.Sequential(*features[7:14])
                self.body3 = nn.Sequential(*features[14:21])
                self.body4 = nn.Sequential(*features[21:28])

However, I encountered another error that was similar in nature.

RuntimeError: Error(s) in loading state_dict for P2PNet:
        Missing key(s) in state_dict: "backbone.body1.5.weight", "backbone.body1.5.bias", "backbone.body1.5.running_mean", "backbone.body1.5.running_var". 
        Unexpected key(s) in state_dict: "backbone.body1.7.weight", "backbone.body1.7.bias", "backbone.body1.8.weight", "backbone.body1.8.bias", "backbone.body1.8.running_mean", "backbone.body1.8.running_var", "backbone.body1.8.num_batches_tracked", "backbone.body1.10.weight", "backbone.body1.10.bias", "backbone.body1.11.weight", "backbone.body1.11.bias", "backbone.body1.11.running_mean", "backbone.body1.11.running_var", "backbone.body1.11.num_batches_tracked", "backbone.body1.3.weight", "backbone.body1.3.bias", "backbone.body1.4.running_mean", "backbone.body1.4.running_var", "backbone.body1.4.num_batches_tracked", "backbone.body2.7.weight", "backbone.body2.7.bias", "backbone.body2.8.weight", "backbone.body2.8.bias", "backbone.body2.8.running_mean", "backbone.body2.8.running_var", "backbone.body2.8.num_batches_tracked", "backbone.body3.7.weight", "backbone.body3.7.bias", "backbone.body3.8.weight", "backbone.body3.8.bias", "backbone.body3.8.running_mean", "backbone.body3.8.running_var", "backbone.body3.8.num_batches_tracked", "backbone.body4.7.weight", "backbone.body4.7.bias", "backbone.body4.8.weight", "backbone.body4.8.bias", "backbone.body4.8.running_mean", "backbone.body4.8.running_var", "backbone.body4.8.num_batches_tracked". 
        size mismatch for backbone.body1.4.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128, 64, 3, 3]).
        size mismatch for backbone.body1.4.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).