What are the differences between 'Your_ResNet' and MMCV's ResNet vb/vc/vd about vitae-transformer-remote-sensing HOT 4 OPEN

@sherwincn Actually they are no difference. Initially, we directly transfer the code used in classification to segmentation, thus we rename it our_resnet. When we use the mmcv's resnet for detection, we find the pretrained weights can also be loaded. You can refer to config of rsp_resnet50_dota

from vitae-transformer-remote-sensing.

@sherwincn Actually they are no difference. Initially, we directly transfer the code used in classification to segmentation, thus we rename it our_resnet. When we use the mmcv's resnet for detection, we find the pretrained weights can also be loaded. You can refer to config of rsp_resnet50_dota

Thanks for your reply. I use the pretrained-model in mmsegmentation and load the params in mmcv's resnet without any changes. But your model's state_dict_keys aren't suitable with mmcv'resnet.
I get the log as follow:
missing keys in source state_dict: conv1.weight, bn1.weight, bn1.bias, bn1.running_mean, bn1.running_var, layer1.0.conv1.weight, layer1.0.bn1.weight, layer1.0.bn1.bias, layer1.0.bn1.running_mean, layer1.0.bn1.running_var, layer1.0.conv2.weight, layer1.0.bn2.weight, layer1.0.bn2.bias, layer1.0.bn2.running_mean, layer1.0.bn2.running_var, layer1.0.conv3.weight, layer1.0.bn3.weight, layer1.0.bn3.bias, layer1.0.bn3.running_mean, layer1.0.bn3.running_var, layer1.0.downsample.0.weight, layer1.0.downsample.1.weight, layer1.0.downsample.1.bias, layer1.0.downsample.1.running_mean, layer1.0.downsample.1.running_var, layer1.1.conv1.weight, layer1.1.bn1.weight, layer1.1.bn1.bias, layer1.1.bn1.running_mean, layer1.1.bn1.running_var, layer1.1.conv2.weight, layer1.1.bn2.weight, layer1.1.bn2.bias, layer1.1.bn2.running_mean, layer1.1.bn2.running_var, layer1.1.conv3.weight, layer1.1.bn3.weight, layer1.1.bn3.bias, layer1.1.bn3.running_mean, layer1.1.bn3.running_var, layer1.2.conv1.weight, layer1.2.bn1.weight, layer1.2.bn1.bias, layer1.2.bn1.running_mean, layer1.2.bn1.running_var, layer1.2.conv2.weight, layer1.2.bn2.weight, layer1.2.bn2.bias, layer1.2.bn2.running_mean, layer1.2.bn2.running_var, layer1.2.conv3.weight, layer1.2.bn3.weight, layer1.2.bn3.bias, layer1.2.bn3.running_mean, layer1.2.bn3.running_var, layer2.0.conv1.weight, layer2.0.bn1.weight, layer2.0.bn1.bias, layer2.0.bn1.running_mean, layer2.0.bn1.running_var, layer2.0.conv2.weight, layer2.0.bn2.weight, layer2.0.bn2.bias, layer2.0.bn2.running_mean, layer2.0.bn2.running_var, layer2.0.conv3.weight, layer2.0.bn3.weight, layer2.0.bn3.bias, layer2.0.bn3.running_mean, layer2.0.bn3.running_var, layer2.0.downsample.0.weight, layer2.0.downsample.1.weight, layer2.0.downsample.1.bias, layer2.0.downsample.1.running_mean, layer2.0.downsample.1.running_var, layer2.1.conv1.weight, layer2.1.bn1.weight, layer2.1.bn1.bias, layer2.1.bn1.running_mean, layer2.1.bn1.running_var, layer2.1.conv2.weight, layer2.1.bn2.weight, layer2.1.bn2.bias, layer2.1.bn2.running_mean, layer2.1.bn2.running_var, layer2.1.conv3.weight, layer2.1.bn3.weight, layer2.1.bn3.bias, layer2.1.bn3.running_mean, layer2.1.bn3.running_var, layer2.2.conv1.weight, layer2.2.bn1.weight, layer2.2.bn1.bias, layer2.2.bn1.running_mean, layer2.2.bn1.running_var, layer2.2.conv2.weight, layer2.2.bn2.weight, layer2.2.bn2.bias, layer2.2.bn2.running_mean, layer2.2.bn2.running_var, layer2.2.conv3.weight, layer2.2.bn3.weight, layer2.2.bn3.bias, layer2.2.bn3.running_mean, layer2.2.bn3.running_var, layer2.3.conv1.weight, layer2.3.bn1.weight, layer2.3.bn1.bias, layer2.3.bn1.running_mean, layer2.3.bn1.running_var, layer2.3.conv2.weight, layer2.3.bn2.weight, layer2.3.bn2.bias, layer2.3.bn2.running_mean, layer2.3.bn2.running_var, layer2.3.conv3.weight, layer2.3.bn3.weight, layer2.3.bn3.bias, layer2.3.bn3.running_mean, layer2.3.bn3.running_var, layer3.0.conv1.weight, layer3.0.bn1.weight, layer3.0.bn1.bias, layer3.0.bn1.running_mean, layer3.0.bn1.running_var, layer3.0.conv2.weight, layer3.0.bn2.weight, layer3.0.bn2.bias, layer3.0.bn2.running_mean, layer3.0.bn2.running_var, layer3.0.conv3.weight, layer3.0.bn3.weight, layer3.0.bn3.bias, layer3.0.bn3.running_mean, layer3.0.bn3.running_var, layer3.0.downsample.0.weight, layer3.0.downsample.1.weight, layer3.0.downsample.1.bias, layer3.0.downsample.1.running_mean, layer3.0.downsample.1.running_var, layer3.1.conv1.weight, layer3.1.bn1.weight, layer3.1.bn1.bias, layer3.1.bn1.running_mean, layer3.1.bn1.running_var, layer3.1.conv2.weight, layer3.1.bn2.weight, layer3.1.bn2.bias, layer3.1.bn2.running_mean, layer3.1.bn2.running_var, layer3.1.conv3.weight, layer3.1.bn3.weight, layer3.1.bn3.bias, layer3.1.bn3.running_mean, layer3.1.bn3.running_var, layer3.2.conv1.weight, layer3.2.bn1.weight, layer3.2.bn1.bias, layer3.2.bn1.running_mean, layer3.2.bn1.running_var, layer3.2.conv2.weight, layer3.2.bn2.weight, layer3.2.bn2.bias, layer3.2.bn2.running_mean, layer3.2.bn2.running_var, layer3.2.conv3.weight, layer3.2.bn3.weight, layer3.2.bn3.bias, layer3.2.bn3.running_mean, layer3.2.bn3.running_var, layer3.3.conv1.weight, layer3.3.bn1.weight, layer3.3.bn1.bias, layer3.3.bn1.running_mean, layer3.3.bn1.running_var, layer3.3.conv2.weight, layer3.3.bn2.weight, layer3.3.bn2.bias, layer3.3.bn2.running_mean, layer3.3.bn2.running_var, layer3.3.conv3.weight, layer3.3.bn3.weight, layer3.3.bn3.bias, layer3.3.bn3.running_mean, layer3.3.bn3.running_var, layer3.4.conv1.weight, layer3.4.bn1.weight, layer3.4.bn1.bias, layer3.4.bn1.running_mean, layer3.4.bn1.running_var, layer3.4.conv2.weight, layer3.4.bn2.weight, layer3.4.bn2.bias, layer3.4.bn2.running_mean, layer3.4.bn2.running_var, layer3.4.conv3.weight, layer3.4.bn3.weight, layer3.4.bn3.bias, layer3.4.bn3.running_mean, layer3.4.bn3.running_var, layer3.5.conv1.weight, layer3.5.bn1.weight, layer3.5.bn1.bias, layer3.5.bn1.running_mean, layer3.5.bn1.running_var, layer3.5.conv2.weight, layer3.5.bn2.weight, layer3.5.bn2.bias, layer3.5.bn2.running_mean, layer3.5.bn2.running_var, layer3.5.conv3.weight, layer3.5.bn3.weight, layer3.5.bn3.bias, layer3.5.bn3.running_mean, layer3.5.bn3.running_var, layer4.0.conv1.weight, layer4.0.bn1.weight, layer4.0.bn1.bias, layer4.0.bn1.running_mean, layer4.0.bn1.running_var, layer4.0.conv2.weight, layer4.0.bn2.weight, layer4.0.bn2.bias, layer4.0.bn2.running_mean, layer4.0.bn2.running_var, layer4.0.conv3.weight, layer4.0.bn3.weight, layer4.0.bn3.bias, layer4.0.bn3.running_mean, layer4.0.bn3.running_var, layer4.0.downsample.0.weight, layer4.0.downsample.1.weight, layer4.0.downsample.1.bias, layer4.0.downsample.1.running_mean, layer4.0.downsample.1.running_var, layer4.1.conv1.weight, layer4.1.bn1.weight, layer4.1.bn1.bias, layer4.1.bn1.running_mean, layer4.1.bn1.running_var, layer4.1.conv2.weight, layer4.1.bn2.weight, layer4.1.bn2.bias, layer4.1.bn2.running_mean, layer4.1.bn2.running_var, layer4.1.conv3.weight, layer4.1.bn3.weight, layer4.1.bn3.bias, layer4.1.bn3.running_mean, layer4.1.bn3.running_var, layer4.2.conv1.weight, layer4.2.bn1.weight, layer4.2.bn1.bias, layer4.2.bn1.running_mean, layer4.2.bn1.running_var, layer4.2.conv2.weight, layer4.2.bn2.weight, layer4.2.bn2.bias, layer4.2.bn2.running_mean, layer4.2.bn2.running_var, layer4.2.conv3.weight, layer4.2.bn3.weight, layer4.2.bn3.bias, layer4.2.bn3.running_mean, layer4.2.bn3.running_var

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@sherwincn Can you print the keys of two dicts separately?

from vitae-transformer-remote-sensing.

@sherwincn In resnet_in_det, we extra add a function def init_weights(self, pretrained=None):. In our_resnet_seg, we do a similar operation. So if you want to use resnet_seg, you also should remove unnecessary prefixes such as 'backbone' or 'module'

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