Hi,
The pretrained model seems not the vgg16 model as described in the paper and source code.
The loaded state_dict keys is as following:
odict_keys(['features.0.weight', 'features.0.bias', 'features.0.running_mean', 'features.0.running_var', 'features.0.num_batches_tracked', 'features.3.0.conv1.weight', 'features.3.0.bn1.weight', 'features.3.0.bn1.bias', 'features.3.0.bn1.running_mean', 'features.3.0.bn1.running_var', 'features.3.0.bn1.num_batches_tracked', 'features.3.0.conv2.weight', 'features.3.0.bn2.weight', 'features.3.0.bn2.bias', 'features.3.0.bn2.running_mean', 'features.3.0.bn2.running_var', 'features.3.0.bn2.num_batches_tracked', 'features.3.0.conv3.weight', 'features.3.0.bn3.weight', 'features.3.0.bn3.bias', 'features.3.0.bn3.running_mean', 'features.3.0.bn3.running_var', 'features.3.0.bn3.num_batches_tracked', 'features.3.0.downsample.0.weight', 'features.3.0.downsample.1.weight', 'features.3.0.downsample.1.bias', 'features.3.0.downsample.1.running_mean', 'features.3.0.downsample.1.running_var', 'features.3.0.downsample.1.num_batches_tracked', 'features.3.1.conv1.weight', 'features.3.1.bn1.weight', 'features.3.1.bn1.bias', 'features.3.1.bn1.running_mean', 'features.3.1.bn1.running_var', 'features.3.1.bn1.num_batches_tracked', 'features.3.1.conv2.weight', 'features.3.1.bn2.weight', 'features.3.1.bn2.bias', 'features.3.1.bn2.running_mean', 'features.3.1.bn2.running_var', 'features.3.1.bn2.num_batches_tracked', 'features.3.1.conv3.weight', 'features.3.1.bn3.weight', 'features.3.1.bn3.bias', 'features.3.1.bn3.running_mean', 'features.3.1.bn3.running_var', 'features.3.1.bn3.num_batches_tracked', 'features.3.2.conv1.weight', 'features.3.2.bn1.weight', 'features.3.2.bn1.bias', 'features.3.2.bn1.running_mean', 'features.3.2.bn1.running_var', 'features.3.2.bn1.num_batches_tracked', 'features.3.2.conv2.weight', 'features.3.2.bn2.weight', 'features.3.2.bn2.bias', 'features.3.2.bn2.running_mean', 'features.3.2.bn2.running_var', 'features.3.2.bn2.num_batches_tracked', 'features.3.2.conv3.weight', 'features.3.2.bn3.weight', 'features.3.2.bn3.bias', 'features.3.2.bn3.running_mean', 'features.3.2.bn3.running_var', 'features.3.2.bn3.num_batches_tracked', 'features.4.0.conv1.weight', 'features.4.0.bn1.weight', 'features.4.0.bn1.bias', 'features.4.0.bn1.running_mean', 'features.4.0.bn1.running_var', 'features.4.0.bn1.num_batches_tracked', 'features.4.0.conv2.weight', 'features.4.0.bn2.weight', 'features.4.0.bn2.bias', 'features.4.0.bn2.running_mean', 'features.4.0.bn2.running_var', 'features.4.0.bn2.num_batches_tracked', 'features.4.0.conv3.weight', 'features.4.0.bn3.weight', 'features.4.0.bn3.bias', 'features.4.0.bn3.running_mean', 'features.4.0.bn3.running_var', 'features.4.0.bn3.num_batches_tracked', 'features.4.0.downsample.0.weight', 'features.4.0.downsample.1.weight', 'features.4.0.downsample.1.bias', 'features.4.0.downsample.1.running_mean', 'features.4.0.downsample.1.running_var', 'features.4.0.downsample.1.num_batches_tracked', 'features.4.1.conv1.weight', 'features.4.1.bn1.weight', 'features.4.1.bn1.bias', 'features.4.1.bn1.running_mean', 'features.4.1.bn1.running_var', 'features.4.1.bn1.num_batches_tracked', 'features.4.1.conv2.weight', 'features.4.1.bn2.weight', 'features.4.1.bn2.bias', 'features.4.1.bn2.running_mean', 'features.4.1.bn2.running_var', 'features.4.1.bn2.num_batches_tracked', 'features.4.1.conv3.weight', 'features.4.1.bn3.weight', 'features.4.1.bn3.bias', 'features.4.1.bn3.running_mean', 'features.4.1.bn3.running_var', 'features.4.1.bn3.num_batches_tracked', 'features.4.2.conv1.weight', 'features.4.2.bn1.weight', 'features.4.2.bn1.bias', 'features.4.2.bn1.running_mean', 'features.4.2.bn1.running_var', 'features.4.2.bn1.num_batches_tracked', 'features.4.2.conv2.weight', 'features.4.2.bn2.weight', 'features.4.2.bn2.bias', 'features.4.2.bn2.running_mean', 'features.4.2.bn2.running_var', 'features.4.2.bn2.num_batches_tracked', 'features.4.2.conv3.weight', 'features.4.2.bn3.weight', 'features.4.2.bn3.bias', 'features.4.2.bn3.running_mean', 'features.4.2.bn3.running_var', 'features.4.2.bn3.num_batches_tracked', 'features.4.3.conv1.weight', 'features.4.3.bn1.weight', 'features.4.3.bn1.bias', 'features.4.3.bn1.running_mean', 'features.4.3.bn1.running_var', 'features.4.3.bn1.num_batches_tracked', 'features.4.3.conv2.weight', 'features.4.3.bn2.weight', 'features.4.3.bn2.bias', 'features.4.3.bn2.running_mean', 'features.4.3.bn2.running_var', 'features.4.3.bn2.num_batches_tracked', 'features.4.3.conv3.weight', 'features.4.3.bn3.weight', 'features.4.3.bn3.bias', 'features.4.3.bn3.running_mean', 'features.4.3.bn3.running_var', 'features.4.3.bn3.num_batches_tracked', 'features.5.0.conv1.weight', 'features.5.0.bn1.weight', 'features.5.0.bn1.bias', 'features.5.0.bn1.running_mean', 'features.5.0.bn1.running_var', 'features.5.0.bn1.num_batches_tracked', 'features.5.0.conv2.weight', 'features.5.0.bn2.weight', 'features.5.0.bn2.bias', 'features.5.0.bn2.running_mean', 'features.5.0.bn2.running_var', 'features.5.0.bn2.num_batches_tracked', 'features.5.0.conv3.weight', 'features.5.0.bn3.weight', 'features.5.0.bn3.bias', 'features.5.0.bn3.running_mean', 'features.5.0.bn3.running_var', 'features.5.0.bn3.num_batches_tracked', 'features.5.0.downsample.0.weight', 'features.5.0.downsample.1.weight', 'features.5.0.downsample.1.bias', 'features.5.0.downsample.1.running_mean', 'features.5.0.downsample.1.running_var', 'features.5.0.downsample.1.num_batches_tracked', 'features.5.1.conv1.weight', 'features.5.1.bn1.weight', 'features.5.1.bn1.bias', 'features.5.1.bn1.running_mean', 'features.5.1.bn1.running_var', 'features.5.1.bn1.num_batches_tracked', 'features.5.1.conv2.weight', 'features.5.1.bn2.weight', 'features.5.1.bn2.bias', 'features.5.1.bn2.running_mean', 'features.5.1.bn2.running_var', 'features.5.1.bn2.num_batches_tracked', 'features.5.1.conv3.weight', 'features.5.1.bn3.weight', 'features.5.1.bn3.bias', 'features.5.1.bn3.running_mean', 'features.5.1.bn3.running_var', 'features.5.1.bn3.num_batches_tracked', 'features.5.2.conv1.weight', 'features.5.2.bn1.weight', 'features.5.2.bn1.bias', 'features.5.2.bn1.running_mean', 'features.5.2.bn1.running_var', 'features.5.2.bn1.num_batches_tracked', 'features.5.2.conv2.weight', 'features.5.2.bn2.weight', 'features.5.2.bn2.bias', 'features.5.2.bn2.running_mean', 'features.5.2.bn2.running_var', 'features.5.2.bn2.num_batches_tracked', 'features.5.2.conv3.weight', 'features.5.2.bn3.weight', 'features.5.2.bn3.bias', 'features.5.2.bn3.running_mean', 'features.5.2.bn3.running_var', 'features.5.2.bn3.num_batches_tracked', 'features.5.3.conv1.weight', 'features.5.3.bn1.weight', 'features.5.3.bn1.bias', 'features.5.3.bn1.running_mean', 'features.5.3.bn1.running_var', 'features.5.3.bn1.num_batches_tracked', 'features.5.3.conv2.weight', 'features.5.3.bn2.weight', 'features.5.3.bn2.bias', 'features.5.3.bn2.running_mean', 'features.5.3.bn2.running_var', 'features.5.3.bn2.num_batches_tracked', 'features.5.3.conv3.weight', 'features.5.3.bn3.weight', 'features.5.3.bn3.bias', 'features.5.3.bn3.running_mean', 'features.5.3.bn3.running_var', 'features.5.3.bn3.num_batches_tracked', 'features.5.4.conv1.weight', 'features.5.4.bn1.weight', 'features.5.4.bn1.bias', 'features.5.4.bn1.running_mean', 'features.5.4.bn1.running_var', 'features.5.4.bn1.num_batches_tracked', 'features.5.4.conv2.weight', 'features.5.4.bn2.weight', 'features.5.4.bn2.bias', 'features.5.4.bn2.running_mean', 'features.5.4.bn2.running_var', 'features.5.4.bn2.num_batches_tracked', 'features.5.4.conv3.weight', 'features.5.4.bn3.weight', 'features.5.4.bn3.bias', 'features.5.4.bn3.running_mean', 'features.5.4.bn3.running_var', 'features.5.4.bn3.num_batches_tracked', 'features.5.5.conv1.weight', 'features.5.5.bn1.weight', 'features.5.5.bn1.bias', 'features.5.5.bn1.running_mean', 'features.5.5.bn1.running_var', 'features.5.5.bn1.num_batches_tracked', 'features.5.5.conv2.weight', 'features.5.5.bn2.weight', 'features.5.5.bn2.bias', 'features.5.5.bn2.running_mean', 'features.5.5.bn2.running_var', 'features.5.5.bn2.num_batches_tracked', 'features.5.5.conv3.weight', 'features.5.5.bn3.weight', 'features.5.5.bn3.bias', 'features.5.5.bn3.running_mean', 'features.5.5.bn3.running_var', 'features.5.5.bn3.num_batches_tracked', 'features.5.6.conv1.weight', 'features.5.6.bn1.weight', 'features.5.6.bn1.bias', 'features.5.6.bn1.running_mean', 'features.5.6.bn1.running_var', 'features.5.6.bn1.num_batches_tracked', 'features.5.6.conv2.weight', 'features.5.6.bn2.weight', 'features.5.6.bn2.bias', 'features.5.6.bn2.running_mean', 'features.5.6.bn2.running_var', 'features.5.6.bn2.num_batches_tracked', 'features.5.6.conv3.weight', 'features.5.6.bn3.weight', 'features.5.6.bn3.bias', 'features.5.6.bn3.running_mean', 'features.5.6.bn3.running_var', 'features.5.6.bn3.num_batches_tracked', 'features.5.7.conv1.weight', 'features.5.7.bn1.weight', 'features.5.7.bn1.bias', 'features.5.7.bn1.running_mean', 'features.5.7.bn1.running_var', 'features.5.7.bn1.num_batches_tracked', 'features.5.7.conv2.weight', 'features.5.7.bn2.weight', 'features.5.7.bn2.bias', 'features.5.7.bn2.running_mean', 'features.5.7.bn2.running_var', 'features.5.7.bn2.num_batches_tracked', 'features.5.7.conv3.weight', 'features.5.7.bn3.weight', 'features.5.7.bn3.bias', 'features.5.7.bn3.running_mean', 'features.5.7.bn3.running_var', 'features.5.7.bn3.num_batches_tracked', 'features.5.8.conv1.weight', 'features.5.8.bn1.weight', 'features.5.8.bn1.bias', 'features.5.8.bn1.running_mean', 'features.5.8.bn1.running_var', 'features.5.8.bn1.num_batches_tracked', 'features.5.8.conv2.weight', 'features.5.8.bn2.weight', 'features.5.8.bn2.bias', 'features.5.8.bn2.running_mean', 'features.5.8.bn2.running_var', 'features.5.8.bn2.num_batches_tracked', 'features.5.8.conv3.weight', 'features.5.8.bn3.weight', 'features.5.8.bn3.bias', 'features.5.8.bn3.running_mean', 'features.5.8.bn3.running_var', 'features.5.8.bn3.num_batches_tracked', 'features.5.9.conv1.weight', 'features.5.9.bn1.weight', 'features.5.9.bn1.bias', 'features.5.9.bn1.running_mean', 'features.5.9.bn1.running_var', 'features.5.9.bn1.num_batches_tracked', 'features.5.9.conv2.weight', 'features.5.9.bn2.weight', 'features.5.9.bn2.bias', 'features.5.9.bn2.running_mean', 'features.5.9.bn2.running_var', 'features.5.9.bn2.num_batches_tracked', 'features.5.9.conv3.weight', 'features.5.9.bn3.weight', 'features.5.9.bn3.bias', 'features.5.9.bn3.running_mean', 'features.5.9.bn3.running_var', 'features.5.9.bn3.num_batches_tracked', 'features.5.10.conv1.weight', 'features.5.10.bn1.weight', 'features.5.10.bn1.bias', 'features.5.10.bn1.running_mean', 'features.5.10.bn1.running_var', 'features.5.10.bn1.num_batches_tracked', 'features.5.10.conv2.weight', 'features.5.10.bn2.weight', 'features.5.10.bn2.bias', 'features.5.10.bn2.running_mean', 'features.5.10.bn2.running_var', 'features.5.10.bn2.num_batches_tracked', 'features.5.10.conv3.weight', 'features.5.10.bn3.weight', 'features.5.10.bn3.bias', 'features.5.10.bn3.running_mean', 'features.5.10.bn3.running_var', 'features.5.10.bn3.num_batches_tracked', 'features.5.11.conv1.weight', 'features.5.11.bn1.weight', 'features.5.11.bn1.bias', 'features.5.11.bn1.running_mean', 'features.5.11.bn1.running_var', 'features.5.11.bn1.num_batches_tracked', 'features.5.11.conv2.weight', 'features.5.11.bn2.weight', 'features.5.11.bn2.bias', 'features.5.11.bn2.running_mean', 'features.5.11.bn2.running_var', 'features.5.11.bn2.num_batches_tracked', 'features.5.11.conv3.weight', 'features.5.11.bn3.weight', 'features.5.11.bn3.bias', 'features.5.11.bn3.running_mean', 'features.5.11.bn3.running_var', 'features.5.11.bn3.num_batches_tracked', 'features.5.12.conv1.weight', 'features.5.12.bn1.weight', 'features.5.12.bn1.bias', 'features.5.12.bn1.running_mean', 'features.5.12.bn1.running_var', 'features.5.12.bn1.num_batches_tracked', 'features.5.12.conv2.weight', 'features.5.12.bn2.weight', 'features.5.12.bn2.bias', 'features.5.12.bn2.running_mean', 'features.5.12.bn2.running_var', 'features.5.12.bn2.num_batches_tracked', 'features.5.12.conv3.weight', 'features.5.12.bn3.weight', 'features.5.12.bn3.bias', 'features.5.12.bn3.running_mean', 'features.5.12.bn3.running_var', 'features.5.12.bn3.num_batches_tracked', 'features.5.13.conv1.weight', 'features.5.13.bn1.weight', 'features.5.13.bn1.bias', 'features.5.13.bn1.running_mean', 'features.5.13.bn1.running_var', 'features.5.13.bn1.num_batches_tracked', 'features.5.13.conv2.weight', 'features.5.13.bn2.weight', 'features.5.13.bn2.bias', 'features.5.13.bn2.running_mean', 'features.5.13.bn2.running_var', 'features.5.13.bn2.num_batches_tracked', 'features.5.13.conv3.weight', 'features.5.13.bn3.weight', 'features.5.13.bn3.bias', 'features.5.13.bn3.running_mean', 'features.5.13.bn3.running_var', 'features.5.13.bn3.num_batches_tracked', 'features.5.14.conv1.weight', 'features.5.14.bn1.weight', 'features.5.14.bn1.bias', 'features.5.14.bn1.running_mean', 'features.5.14.bn1.running_var', 'features.5.14.bn1.num_batches_tracked', 'features.5.14.conv2.weight', 'features.5.14.bn2.weight', 'features.5.14.bn2.bias', 'features.5.14.bn2.running_mean', 'features.5.14.bn2.running_var', 'features.5.14.bn2.num_batches_tracked', 'features.5.14.conv3.weight', 'features.5.14.bn3.weight', 'features.5.14.bn3.bias', 'features.5.14.bn3.running_mean', 'features.5.14.bn3.running_var', 'features.5.14.bn3.num_batches_tracked', 'features.5.15.conv1.weight', 'features.5.15.bn1.weight', 'features.5.15.bn1.bias', 'features.5.15.bn1.running_mean', 'features.5.15.bn1.running_var', 'features.5.15.bn1.num_batches_tracked', 'features.5.15.conv2.weight', 'features.5.15.bn2.weight', 'features.5.15.bn2.bias', 'features.5.15.bn2.running_mean', 'features.5.15.bn2.running_var', 'features.5.15.bn2.num_batches_tracked', 'features.5.15.conv3.weight', 'features.5.15.bn3.weight', 'features.5.15.bn3.bias', 'features.5.15.bn3.running_mean', 'features.5.15.bn3.running_var', 'features.5.15.bn3.num_batches_tracked', 'features.5.16.conv1.weight', 'features.5.16.bn1.weight', 'features.5.16.bn1.bias', 'features.5.16.bn1.running_mean', 'features.5.16.bn1.running_var', 'features.5.16.bn1.num_batches_tracked', 'features.5.16.conv2.weight', 'features.5.16.bn2.weight', 'features.5.16.bn2.bias', 'features.5.16.bn2.running_mean', 'features.5.16.bn2.running_var', 'features.5.16.bn2.num_batches_tracked', 'features.5.16.conv3.weight', 'features.5.16.bn3.weight', 'features.5.16.bn3.bias', 'features.5.16.bn3.running_mean', 'features.5.16.bn3.running_var', 'features.5.16.bn3.num_batches_tracked', 'features.5.17.conv1.weight', 'features.5.17.bn1.weight', 'features.5.17.bn1.bias', 'features.5.17.bn1.running_mean', 'features.5.17.bn1.running_var', 'features.5.17.bn1.num_batches_tracked', 'features.5.17.conv2.weight', 'features.5.17.bn2.weight', 'features.5.17.bn2.bias', 'features.5.17.bn2.running_mean', 'features.5.17.bn2.running_var', 'features.5.17.bn2.num_batches_tracked', 'features.5.17.conv3.weight', 'features.5.17.bn3.weight', 'features.5.17.bn3.bias', 'features.5.17.bn3.running_mean', 'features.5.17.bn3.running_var', 'features.5.17.bn3.num_batches_tracked', 'features.5.18.conv1.weight', 'features.5.18.bn1.weight', 'features.5.18.bn1.bias', 'features.5.18.bn1.running_mean', 'features.5.18.bn1.running_var', 'features.5.18.bn1.num_batches_tracked', 'features.5.18.conv2.weight', 'features.5.18.bn2.weight', 'features.5.18.bn2.bias', 'features.5.18.bn2.running_mean', 'features.5.18.bn2.running_var', 'features.5.18.bn2.num_batches_tracked', 'features.5.18.conv3.weight', 'features.5.18.bn3.weight', 'features.5.18.bn3.bias', 'features.5.18.bn3.running_mean', 'features.5.18.bn3.running_var', 'features.5.18.bn3.num_batches_tracked', 'features.5.19.conv1.weight', 'features.5.19.bn1.weight', 'features.5.19.bn1.bias', 'features.5.19.bn1.running_mean', 'features.5.19.bn1.running_var', 'features.5.19.bn1.num_batches_tracked', 'features.5.19.conv2.weight', 'features.5.19.bn2.weight', 'features.5.19.bn2.bias', 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I also printed the shapes of those conv layers in features.n.k.weights, there are blocks consists of 1x1,3x3,1x1 conv layers and batchnorm layers. Could you share the network architecture or upload another vgg16 pretrained weights for testing?
Thank you.