UniverseFan
commented
4 months ago @houjunlin hello, is it solved.
Open houjunlin opened 7 months ago
UniverseFan
commented
4 months ago @houjunlin hello, is it solved.
Dear author, Thank you so much for providing the pretrained weights. I tried to load the pretrained weights (i.e. bat_1_1_0_e6_loss_0_aug_1 and bat_valid_1_1_0_e6_loss_0_aug_1 ) to test images and met the following issue. I wonder which model do these weights corresponding to? The xboundformer model or other models like deeplabv3? Thanks!
RuntimeError: Error(s) in loading state_dict for _SimpleSegmentationModel: Missing key(s) in state_dict: "backbone.patch_embed1.proj.weight", "backbone.patch_embed1.proj.bias", "backbone.patch_embed1.norm.weight", "backbone.patch_embed1.norm.bias", "backbone.patch_embed2.proj.weight", "backbone.patch_embed2.proj.bias", "backbone.patch_embed2.norm.weight", "backbone.patch_embed2.norm.bias", "backbone.patch_embed3.proj.weight", "backbone.patch_embed3.proj.bias", "backbone.patch_embed3.norm.weight", "backbone.patch_embed3.norm.bias", "backbone.patch_embed4.proj.weight", "backbone.patch_embed4.proj.bias", "backbone.patch_embed4.norm.weight", "backbone.patch_embed4.norm.bias", "backbone.block1.0.norm1.weight", "backbone.block1.0.norm1.bias", "backbone.block1.0.attn.q.weight", "backbone.block1.0.attn.q.bias", "backbone.block1.0.attn.kv.weight", "backbone.block1.0.attn.kv.bias", "backbone.block1.0.attn.proj.weight", "backbone.block1.0.attn.proj.bias", "backbone.block1.0.attn.sr.weight", 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Unexpected key(s) in state_dict: "query_positions", "row_embedding", "column_embedding", "deeplab.resnet.resnet.0.weight", "deeplab.resnet.resnet.1.weight", "deeplab.resnet.resnet.1.bias", "deeplab.resnet.resnet.1.running_mean", "deeplab.resnet.resnet.1.running_var", "deeplab.resnet.resnet.1.num_batches_tracked", "deeplab.resnet.resnet.4.0.conv1.weight", "deeplab.resnet.resnet.4.0.bn1.weight", "deeplab.resnet.resnet.4.0.bn1.bias", "deeplab.resnet.resnet.4.0.bn1.running_mean", "deeplab.resnet.resnet.4.0.bn1.running_var", "deeplab.resnet.resnet.4.0.bn1.num_batches_tracked", "deeplab.resnet.resnet.4.0.conv2.weight", "deeplab.resnet.resnet.4.0.bn2.weight", "deeplab.resnet.resnet.4.0.bn2.bias", "deeplab.resnet.resnet.4.0.bn2.running_mean", "deeplab.resnet.resnet.4.0.bn2.running_var", "deeplab.resnet.resnet.4.0.bn2.num_batches_tracked", "deeplab.resnet.resnet.4.0.conv3.weight", "deeplab.resnet.resnet.4.0.bn3.weight", "deeplab.resnet.resnet.4.0.bn3.bias", "deeplab.resnet.resnet.4.0.bn3.running_mean", "deeplab.resnet.resnet.4.0.bn3.running_var", "deeplab.resnet.resnet.4.0.bn3.num_batches_tracked", "deeplab.resnet.resnet.4.0.downsample.0.weight", "deeplab.resnet.resnet.4.0.downsample.1.weight", "deeplab.resnet.resnet.4.0.downsample.1.bias", "deeplab.resnet.resnet.4.0.downsample.1.running_mean", "deeplab.resnet.resnet.4.0.downsample.1.running_var", "deeplab.resnet.resnet.4.0.downsample.1.num_batches_tracked", "deeplab.resnet.resnet.4.1.conv1.weight", "deeplab.resnet.resnet.4.1.bn1.weight", "deeplab.resnet.resnet.4.1.bn1.bias", "deeplab.resnet.resnet.4.1.bn1.running_mean", "deeplab.resnet.resnet.4.1.bn1.running_var", "deeplab.resnet.resnet.4.1.bn1.num_batches_tracked", "deeplab.resnet.resnet.4.1.conv2.weight", "deeplab.resnet.resnet.4.1.bn2.weight", "deeplab.resnet.resnet.4.1.bn2.bias", "deeplab.resnet.resnet.4.1.bn2.running_mean", "deeplab.resnet.resnet.4.1.bn2.running_var", "deeplab.resnet.resnet.4.1.bn2.num_batches_tracked", "deeplab.resnet.resnet.4.1.conv3.weight", "deeplab.resnet.resnet.4.1.bn3.weight", "deeplab.resnet.resnet.4.1.bn3.bias", "deeplab.resnet.resnet.4.1.bn3.running_mean", "deeplab.resnet.resnet.4.1.bn3.running_var", "deeplab.resnet.resnet.4.1.bn3.num_batches_tracked", "deeplab.resnet.resnet.4.2.conv1.weight", "deeplab.resnet.resnet.4.2.bn1.weight", "deeplab.resnet.resnet.4.2.bn1.bias", "deeplab.resnet.resnet.4.2.bn1.running_mean", "deeplab.resnet.resnet.4.2.bn1.running_var", "deeplab.resnet.resnet.4.2.bn1.num_batches_tracked", "deeplab.resnet.resnet.4.2.conv2.weight", "deeplab.resnet.resnet.4.2.bn2.weight", "deeplab.resnet.resnet.4.2.bn2.bias", "deeplab.resnet.resnet.4.2.bn2.running_mean", "deeplab.resnet.resnet.4.2.bn2.running_var", "deeplab.resnet.resnet.4.2.bn2.num_batches_tracked", "deeplab.resnet.resnet.4.2.conv3.weight", "deeplab.resnet.resnet.4.2.bn3.weight", "deeplab.resnet.resnet.4.2.bn3.bias", "deeplab.resnet.resnet.4.2.bn3.running_mean", "deeplab.resnet.resnet.4.2.bn3.running_var", "deeplab.resnet.resnet.4.2.bn3.num_batches_tracked", "deeplab.resnet.resnet.5.0.conv1.weight", "deeplab.resnet.resnet.5.0.bn1.weight", "deeplab.resnet.resnet.5.0.bn1.bias", "deeplab.resnet.resnet.5.0.bn1.running_mean", "deeplab.resnet.resnet.5.0.bn1.running_var", "deeplab.resnet.resnet.5.0.bn1.num_batches_tracked", "deeplab.resnet.resnet.5.0.conv2.weight", "deeplab.resnet.resnet.5.0.bn2.weight", "deeplab.resnet.resnet.5.0.bn2.bias", "deeplab.resnet.resnet.5.0.bn2.running_mean", "deeplab.resnet.resnet.5.0.bn2.running_var", "deeplab.resnet.resnet.5.0.bn2.num_batches_tracked", "deeplab.resnet.resnet.5.0.conv3.weight", "deeplab.resnet.resnet.5.0.bn3.weight", "deeplab.resnet.resnet.5.0.bn3.bias", "deeplab.resnet.resnet.5.0.bn3.running_mean", "deeplab.resnet.resnet.5.0.bn3.running_var", "deeplab.resnet.resnet.5.0.bn3.num_batches_tracked", "deeplab.resnet.resnet.5.0.downsample.0.weight", "deeplab.resnet.resnet.5.0.downsample.1.weight", 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