Error with provided pretrained models.

[Mauricio A Rovira Galvez]

Hello,

First of all, I would like to thank you for providing all of the data for your paper and congratulate you on the results. I followed the instructions for testing on my own sample images using the provided gfm_d121_tt.pth model. When loading, I get the following error:

Loading backbone: r34_2b
Loading rosta: TT
Loading model: models\gfm_d121_tt.pth
Predict choice: 3
Test strategy: HYBRID
Saving to the folder: 
Running on GPU with CUDA as True...
Traceback (most recent call last):
  File "core/test.py", line 312, in <module>
    load_model_and_deploy(args)
  File "core/test.py", line 295, in load_model_and_deploy
    model.load_state_dict(ckpt, strict=True)
  File "Anaconda3\envs\gfm\lib\site-packages\torch\nn\modules\module.py", line 1052, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for GFM:
	Missing key(s) in state_dict: "resnet.conv1.weight", "resnet.bn1.weight", "resnet.bn1.bias", "resnet.bn1.running_mean", "resnet.bn1.running_var", "resnet.layer1.0.conv1.weight", "resnet.layer1.0.bn1.weight", "resnet.layer1.0.bn1.bias", "resnet.layer1.0.bn1.running_mean", "resnet.layer1.0.bn1.running_var", "resnet.layer1.0.conv2.weight", "resnet.layer1.0.bn2.weight", "resnet.layer1.0.bn2.bias", "resnet.layer1.0.bn2.running_mean", "resnet.layer1.0.bn2.running_var", "resnet.layer1.1.conv1.weight", "resnet.layer1.1.bn1.weight", "resnet.layer1.1.bn1.bias", "resnet.layer1.1.bn1.running_mean", "resnet.layer1.1.bn1.running_var", "resnet.layer1.1.conv2.weight", "resnet.layer1.1.bn2.weight", "resnet.layer1.1.bn2.bias", "resnet.layer1.1.bn2.running_mean", "resnet.layer1.1.bn2.running_var", "resnet.layer1.2.conv1.weight", "resnet.layer1.2.bn1.weight", "resnet.layer1.2.bn1.bias", "resnet.layer1.2.bn1.running_mean", "resnet.layer1.2.bn1.running_var", "resnet.layer1.2.conv2.weight", "resnet.layer1.2.bn2.weight", "resnet.layer1.2.bn2.bias", "resnet.layer1.2.bn2.running_mean", "resnet.layer1.2.bn2.running_var", "resnet.layer2.0.conv1.weight", "resnet.layer2.0.bn1.weight", "resnet.layer2.0.bn1.bias", "resnet.layer2.0.bn1.running_mean", "resnet.layer2.0.bn1.running_var", "resnet.layer2.0.conv2.weight", "resnet.layer2.0.bn2.weight", "resnet.layer2.0.bn2.bias", "resnet.layer2.0.bn2.running_mean", "resnet.layer2.0.bn2.running_var", "resnet.layer2.0.downsample.0.weight", "resnet.layer2.0.downsample.1.weight", "resnet.layer2.0.downsample.1.bias", "resnet.layer2.0.downsample.1.running_mean", "resnet.layer2.0.downsample.1.running_var", "resnet.layer2.1.conv1.weight", "resnet.layer2.1.bn1.weight", "resnet.layer2.1.bn1.bias", "resnet.layer2.1.bn1.running_mean", "resnet.layer2.1.bn1.running_var", "resnet.layer2.1.conv2.weight", "resnet.layer2.1.bn2.weight", "resnet.layer2.1.bn2.bias", "resnet.layer2.1.bn2.running_mean", "resnet.layer2.1.bn2.running_var", "resnet.layer2.2.conv1.weight", "resnet.layer2.2.bn1.weight", "resnet.layer2.2.bn1.bias", "resnet.layer2.2.bn1.running_mean", "resnet.layer2.2.bn1.running_var", "resnet.layer2.2.conv2.weight", "resnet.layer2.2.bn2.weight", "resnet.layer2.2.bn2.bias", "resnet.layer2.2.bn2.running_mean", "resnet.layer2.2.bn2.running_var", "resnet.layer2.3.conv1.weight", "resnet.layer2.3.bn1.weight", "resnet.layer2.3.bn1.bias", "resnet.layer2.3.bn1.running_mean", "resnet.layer2.3.bn1.running_var", "resnet.layer2.3.conv2.weight", "resnet.layer2.3.bn2.weight", "resnet.layer2.3.bn2.bias", "resnet.layer2.3.bn2.running_mean", "resnet.layer2.3.bn2.running_var", "resnet.layer3.0.conv1.weight", "resnet.layer3.0.bn1.weight", "resnet.layer3.0.bn1.bias", "resnet.layer3.0.bn1.running_mean", "resnet.layer3.0.bn1.running_var", "resnet.layer3.0.conv2.weight", "resnet.layer3.0.bn2.weight", "resnet.layer3.0.bn2.bias", "resnet.layer3.0.bn2.running_mean", "resnet.layer3.0.bn2.running_var", "resnet.layer3.0.downsample.0.weight", "resnet.layer3.0.downsample.1.weight", "resnet.layer3.0.downsample.1.bias", "resnet.layer3.0.downsample.1.running_mean", "resnet.layer3.0.downsample.1.running_var", "resnet.layer3.1.conv1.weight", "resnet.layer3.1.bn1.weight", "resnet.layer3.1.bn1.bias", "resnet.layer3.1.bn1.running_mean", "resnet.layer3.1.bn1.running_var", "resnet.layer3.1.conv2.weight", "resnet.layer3.1.bn2.weight", "resnet.layer3.1.bn2.bias", "resnet.layer3.1.bn2.running_mean", "resnet.layer3.1.bn2.running_var", "resnet.layer3.2.conv1.weight", "resnet.layer3.2.bn1.weight", "resnet.layer3.2.bn1.bias", "resnet.layer3.2.bn1.running_mean", "resnet.layer3.2.bn1.running_var", "resnet.layer3.2.conv2.weight", "resnet.layer3.2.bn2.weight", "resnet.layer3.2.bn2.bias", "resnet.layer3.2.bn2.running_mean", "resnet.layer3.2.bn2.running_var", "resnet.layer3.3.conv1.weight", "resnet.layer3.3.bn1.weight", "resnet.layer3.3.bn1.bias", "resnet.layer3.3.bn1.running_mean", "resnet.layer3.3.bn1.running_var", "resnet.layer3.3.conv2.weight", "resnet.layer3.3.bn2.weight", "resnet.layer3.3.bn2.bias", "resnet.layer3.3.bn2.running_mean", "resnet.layer3.3.bn2.running_var", "resnet.layer3.4.conv1.weight", "resnet.layer3.4.bn1.weight", "resnet.layer3.4.bn1.bias", "resnet.layer3.4.bn1.running_mean", "resnet.layer3.4.bn1.running_var", "resnet.layer3.4.conv2.weight", "resnet.layer3.4.bn2.weight", "resnet.layer3.4.bn2.bias", "resnet.layer3.4.bn2.running_mean", "resnet.layer3.4.bn2.running_var", "resnet.layer3.5.conv1.weight", "resnet.layer3.5.bn1.weight", "resnet.layer3.5.bn1.bias", "resnet.layer3.5.bn1.running_mean", "resnet.layer3.5.bn1.running_var", "resnet.layer3.5.conv2.weight", "resnet.layer3.5.bn2.weight", "resnet.layer3.5.bn2.bias", "resnet.layer3.5.bn2.running_mean", "resnet.layer3.5.bn2.running_var", "resnet.layer4.0.conv1.weight", "resnet.layer4.0.bn1.weight", "resnet.layer4.0.bn1.bias", "resnet.layer4.0.bn1.running_mean", "resnet.layer4.0.bn1.running_var", "resnet.layer4.0.conv2.weight", "resnet.layer4.0.bn2.weight", "resnet.layer4.0.bn2.bias", "resnet.layer4.0.bn2.running_mean", "resnet.layer4.0.bn2.running_var", "resnet.layer4.0.downsample.0.weight", "resnet.layer4.0.downsample.1.weight", "resnet.layer4.0.downsample.1.bias", "resnet.layer4.0.downsample.1.running_mean", "resnet.layer4.0.downsample.1.running_var", "resnet.layer4.1.conv1.weight", "resnet.layer4.1.bn1.weight", "resnet.layer4.1.bn1.bias", "resnet.layer4.1.bn1.running_mean", "resnet.layer4.1.bn1.running_var", "resnet.layer4.1.conv2.weight", "resnet.layer4.1.bn2.weight", "resnet.layer4.1.bn2.bias", "resnet.layer4.1.bn2.running_mean", "resnet.layer4.1.bn2.running_var", "resnet.layer4.2.conv1.weight", "resnet.layer4.2.bn1.weight", "resnet.layer4.2.bn1.bias", "resnet.layer4.2.bn1.running_mean", "resnet.layer4.2.bn1.running_var", "resnet.layer4.2.conv2.weight", "resnet.layer4.2.bn2.weight", "resnet.layer4.2.bn2.bias", "resnet.layer4.2.bn2.running_mean", "resnet.layer4.2.bn2.running_var", "resnet.fc.weight", "resnet.fc.bias", "encoder0.0.bias", "encoder1.0.conv1.weight", "encoder1.0.bn1.weight", "encoder1.0.bn1.bias", "encoder1.0.bn1.running_mean", "encoder1.0.bn1.running_var", "encoder1.0.conv2.weight", "encoder1.0.bn2.weight", "encoder1.0.bn2.bias", "encoder1.0.bn2.running_mean", "encoder1.0.bn2.running_var", "encoder1.1.conv1.weight", "encoder1.1.bn1.weight", "encoder1.1.bn1.bias", "encoder1.1.bn1.running_mean", "encoder1.1.bn1.running_var", "encoder1.1.conv2.weight", "encoder1.1.bn2.weight", "encoder1.1.bn2.bias", "encoder1.1.bn2.running_mean", "encoder1.1.bn2.running_var", "encoder1.2.conv1.weight", "encoder1.2.bn1.weight", "encoder1.2.bn1.bias", "encoder1.2.bn1.running_mean", "encoder1.2.bn1.running_var", "encoder1.2.conv2.weight", "encoder1.2.bn2.weight", "encoder1.2.bn2.bias", "encoder1.2.bn2.running_mean", "encoder1.2.bn2.running_var", "encoder2.0.conv1.weight", "encoder2.0.bn1.weight", "encoder2.0.bn1.bias", "encoder2.0.bn1.running_mean", "encoder2.0.bn1.running_var", "encoder2.0.conv2.weight", "encoder2.0.bn2.weight", "encoder2.0.bn2.bias", "encoder2.0.bn2.running_mean", "encoder2.0.bn2.running_var", "encoder2.0.downsample.0.weight", "encoder2.0.downsample.1.weight", "encoder2.0.downsample.1.bias", "encoder2.0.downsample.1.running_mean", "encoder2.0.downsample.1.running_var", "encoder2.1.conv1.weight", "encoder2.1.bn1.weight", "encoder2.1.bn1.bias", "encoder2.1.bn1.running_mean", "encoder2.1.bn1.running_var", "encoder2.1.conv2.weight", "encoder2.1.bn2.weight", "encoder2.1.bn2.bias", "encoder2.1.bn2.running_mean", "encoder2.1.bn2.running_var", "encoder2.2.conv1.weight", "encoder2.2.bn1.weight", "encoder2.2.bn1.bias", "encoder2.2.bn1.running_mean", "encoder2.2.bn1.running_var", "encoder2.2.conv2.weight", "encoder2.2.bn2.weight", "encoder2.2.bn2.bias", "encoder2.2.bn2.running_mean", "encoder2.2.bn2.running_var", "encoder2.3.conv1.weight", "encoder2.3.bn1.weight", "encoder2.3.bn1.bias", "encoder2.3.bn1.running_mean", "encoder2.3.bn1.running_var", "encoder2.3.conv2.weight", "encoder2.3.bn2.weight", "encoder2.3.bn2.bias", "encoder2.3.bn2.running_mean", "encoder2.3.bn2.running_var", "encoder3.0.conv1.weight", "encoder3.0.bn1.weight", "encoder3.0.bn1.bias", "encoder3.0.bn1.running_mean", "encoder3.0.bn1.running_var", "encoder3.0.conv2.weight", "encoder3.0.bn2.weight", "encoder3.0.bn2.bias", "encoder3.0.bn2.running_mean", "encoder3.0.bn2.running_var", "encoder3.0.downsample.0.weight", "encoder3.0.downsample.1.weight", "encoder3.0.downsample.1.bias", "encoder3.0.downsample.1.running_mean", "encoder3.0.downsample.1.running_var", "encoder3.1.conv1.weight", "encoder3.1.bn1.weight", "encoder3.1.bn1.bias", "encoder3.1.bn1.running_mean", "encoder3.1.bn1.running_var", "encoder3.1.conv2.weight", "encoder3.1.bn2.weight", "encoder3.1.bn2.bias", "encoder3.1.bn2.running_mean", "encoder3.1.bn2.running_var", "encoder3.2.conv1.weight", "encoder3.2.bn1.weight", "encoder3.2.bn1.bias", "encoder3.2.bn1.running_mean", "encoder3.2.bn1.running_var", "encoder3.2.conv2.weight", "encoder3.2.bn2.weight", "encoder3.2.bn2.bias", "encoder3.2.bn2.running_mean", "encoder3.2.bn2.running_var", "encoder3.3.conv1.weight", "encoder3.3.bn1.weight", "encoder3.3.bn1.bias", "encoder3.3.bn1.running_mean", "encoder3.3.bn1.running_var", "encoder3.3.conv2.weight", "encoder3.3.bn2.weight", "encoder3.3.bn2.bias", "encoder3.3.bn2.running_mean", "encoder3.3.bn2.running_var", "encoder3.4.conv1.weight", "encoder3.4.bn1.weight", "encoder3.4.bn1.bias", "encoder3.4.bn1.running_mean", "encoder3.4.bn1.running_var", "encoder3.4.conv2.weight", "encoder3.4.bn2.weight", "encoder3.4.bn2.bias", "encoder3.4.bn2.running_mean", "encoder3.4.bn2.running_var", "encoder3.5.conv1.weight", "encoder3.5.bn1.weight", "encoder3.5.bn1.bias", "encoder3.5.bn1.running_mean", "encoder3.5.bn1.running_var", "encoder3.5.conv2.weight", "encoder3.5.bn2.weight", "encoder3.5.bn2.bias", "encoder3.5.bn2.running_mean", "encoder3.5.bn2.running_var", "encoder4.0.conv1.weight", "encoder4.0.bn1.weight", "encoder4.0.bn1.bias", "encoder4.0.bn1.running_mean", "encoder4.0.bn1.running_var", "encoder4.0.conv2.weight", "encoder4.0.bn2.weight", "encoder4.0.bn2.bias", "encoder4.0.bn2.running_mean", "encoder4.0.bn2.running_var", "encoder4.0.downsample.0.weight", "encoder4.0.downsample.1.weight", "encoder4.0.downsample.1.bias", "encoder4.0.downsample.1.running_mean", "encoder4.0.downsample.1.running_var", "encoder4.1.conv1.weight", "encoder4.1.bn1.weight", "encoder4.1.bn1.bias", "encoder4.1.bn1.running_mean", "encoder4.1.bn1.running_var", "encoder4.1.conv2.weight", "encoder4.1.bn2.weight", "encoder4.1.bn2.bias", "encoder4.1.bn2.running_mean", "encoder4.1.bn2.running_var", "encoder4.2.conv1.weight", "encoder4.2.bn1.weight", "encoder4.2.bn1.bias", "encoder4.2.bn1.running_mean", "encoder4.2.bn1.running_var", "encoder4.2.conv2.weight", "encoder4.2.bn2.weight", "encoder4.2.bn2.bias", "encoder4.2.bn2.running_mean", "encoder4.2.bn2.running_var", "encoder5.1.conv1.weight", "encoder5.1.bn1.weight", "encoder5.1.bn1.bias", "encoder5.1.bn1.running_mean", "encoder5.1.bn1.running_var", "encoder5.1.conv2.weight", "encoder5.1.bn2.weight", "encoder5.1.bn2.bias", "encoder5.1.bn2.running_mean", "encoder5.1.bn2.running_var", "encoder5.2.conv1.weight", "encoder5.2.bn1.weight", "encoder5.2.bn1.bias", "encoder5.2.bn1.running_mean", "encoder5.2.bn1.running_var", "encoder5.2.conv2.weight", "encoder5.2.bn2.weight", "encoder5.2.bn2.bias", "encoder5.2.bn2.running_mean", "encoder5.2.bn2.running_var", "encoder5.3.conv1.weight", "encoder5.3.bn1.weight", "encoder5.3.bn1.bias", "encoder5.3.bn1.running_mean", "encoder5.3.bn1.running_var", "encoder5.3.conv2.weight", "encoder5.3.bn2.weight", "encoder5.3.bn2.bias", "encoder5.3.bn2.running_mean", "encoder5.3.bn2.running_var", "encoder6.1.conv1.weight", "encoder6.1.bn1.weight", "encoder6.1.bn1.bias", "encoder6.1.bn1.running_mean", "encoder6.1.bn1.running_var", "encoder6.1.conv2.weight", "encoder6.1.bn2.weight", "encoder6.1.bn2.bias", "encoder6.1.bn2.running_mean", "encoder6.1.bn2.running_var", "encoder6.2.conv1.weight", "encoder6.2.bn1.weight", "encoder6.2.bn1.bias", "encoder6.2.bn1.running_mean", "encoder6.2.bn1.running_var", "encoder6.2.conv2.weight", "encoder6.2.bn2.weight", "encoder6.2.bn2.bias", "encoder6.2.bn2.running_mean", "encoder6.2.bn2.running_var", "encoder6.3.conv1.weight", "encoder6.3.bn1.weight", "encoder6.3.bn1.bias", "encoder6.3.bn1.running_mean", "encoder6.3.bn1.running_var", "encoder6.3.conv2.weight", "encoder6.3.bn2.weight", "encoder6.3.bn2.bias", "encoder6.3.bn2.running_mean", "encoder6.3.bn2.running_var", "psp6.0.weight", "psp6.0.bias", "psp6.1.weight", "psp6.1.bias", "psp6.1.running_mean", "psp6.1.running_var", "psp5.0.weight", "psp5.0.bias", "psp5.1.weight", "psp5.1.bias", "psp5.1.running_mean", "psp5.1.running_var", "decoder6_g.0.weight", "decoder6_g.0.bias", "decoder6_g.1.weight", "decoder6_g.1.bias", "decoder6_g.1.running_mean", "decoder6_g.1.running_var", "decoder6_g.3.weight", "decoder6_g.3.bias", "decoder6_g.4.weight", "decoder6_g.4.bias", "decoder6_g.4.running_mean", "decoder6_g.4.running_var", "decoder6_g.6.weight", "decoder6_g.6.bias", "decoder6_g.7.weight", "decoder6_g.7.bias", "decoder6_g.7.running_mean", "decoder6_g.7.running_var", "decoder5_g.0.weight", "decoder5_g.0.bias", "decoder5_g.1.weight", "decoder5_g.1.bias", "decoder5_g.1.running_mean", "decoder5_g.1.running_var", "decoder5_g.3.weight", "decoder5_g.3.bias", "decoder5_g.4.weight", "decoder5_g.4.bias", "decoder5_g.4.running_mean", "decoder5_g.4.running_var", "decoder5_g.6.weight", "decoder5_g.6.bias", "decoder5_g.7.weight", "decoder5_g.7.bias", "decoder5_g.7.running_mean", "decoder5_g.7.running_var", "decoder6_f.0.weight", "decoder6_f.0.bias", "decoder6_f.1.weight", "decoder6_f.1.bias", "decoder6_f.1.running_mean", "decoder6_f.1.running_var", "decoder6_f.3.weight", "decoder6_f.3.bias", "decoder6_f.4.weight", "decoder6_f.4.bias", "decoder6_f.4.running_mean", "decoder6_f.4.running_var", "decoder6_f.6.weight", "decoder6_f.6.bias", "decoder6_f.7.weight", "decoder6_f.7.bias", "decoder6_f.7.running_mean", "decoder6_f.7.running_var", "decoder5_f.0.weight", "decoder5_f.0.bias", "decoder5_f.1.weight", "decoder5_f.1.bias", "decoder5_f.1.running_mean", "decoder5_f.1.running_var", "decoder5_f.3.weight", "decoder5_f.3.bias", "decoder5_f.4.weight", "decoder5_f.4.bias", "decoder5_f.4.running_mean", "decoder5_f.4.running_var", "decoder5_f.6.weight", "decoder5_f.6.bias", "decoder5_f.7.weight", "decoder5_f.7.bias", "decoder5_f.7.running_mean", "decoder5_f.7.running_var". 
	Unexpected key(s) in state_dict: "densenet.features.conv0.weight", "densenet.features.norm0.weight", "densenet.features.norm0.bias", "densenet.features.norm0.running_mean", "densenet.features.norm0.running_var", "densenet.features.norm0.num_batches_tracked", "densenet.features.denseblock1.denselayer1.norm1.weight", "densenet.features.denseblock1.denselayer1.norm1.bias", "densenet.features.denseblock1.denselayer1.norm1.running_mean", "densenet.features.denseblock1.denselayer1.norm1.running_var", "densenet.features.denseblock1.denselayer1.norm1.num_batches_tracked", "densenet.features.denseblock1.denselayer1.conv1.weight", "densenet.features.denseblock1.denselayer1.norm2.weight", "densenet.features.denseblock1.denselayer1.norm2.bias", "densenet.features.denseblock1.denselayer1.norm2.running_mean", "densenet.features.denseblock1.denselayer1.norm2.running_var", "densenet.features.denseblock1.denselayer1.norm2.num_batches_tracked", "densenet.features.denseblock1.denselayer1.conv2.weight", 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"encoder4.0.denselayer15.norm2.running_mean", "encoder4.0.denselayer15.norm2.running_var", "encoder4.0.denselayer15.norm2.num_batches_tracked", "encoder4.0.denselayer15.conv2.weight", "encoder4.0.denselayer16.norm1.weight", "encoder4.0.denselayer16.norm1.bias", "encoder4.0.denselayer16.norm1.running_mean", "encoder4.0.denselayer16.norm1.running_var", "encoder4.0.denselayer16.norm1.num_batches_tracked", "encoder4.0.denselayer16.conv1.weight", "encoder4.0.denselayer16.norm2.weight", "encoder4.0.denselayer16.norm2.bias", "encoder4.0.denselayer16.norm2.running_mean", "encoder4.0.denselayer16.norm2.running_var", "encoder4.0.denselayer16.norm2.num_batches_tracked", "encoder4.0.denselayer16.conv2.weight", "encoder4.1.weight", "encoder4.1.bias", "encoder4.2.weight", "encoder4.2.bias", "encoder4.2.running_mean", "encoder4.2.running_var", "encoder4.2.num_batches_tracked", "decoder0_g.1.weight", "decoder0_g.1.bias", "decoder0_g.1.running_mean", "decoder0_g.1.running_var", "decoder0_g.1.num_batches_tracked", "decoder0_g.3.weight", "decoder0_g.3.bias", "decoder0_g.4.weight", "decoder0_g.4.bias", "decoder0_g.4.running_mean", "decoder0_g.4.running_var", "decoder0_g.4.num_batches_tracked", "decoder0_g.6.weight", "decoder0_g.6.bias", "decoder0_f.1.weight", "decoder0_f.1.bias", "decoder0_f.1.running_mean", "decoder0_f.1.running_var", "decoder0_f.1.num_batches_tracked", "decoder0_f.3.weight", "decoder0_f.3.bias", "decoder0_f.4.weight", "decoder0_f.4.bias", "decoder0_f.4.running_mean", "decoder0_f.4.running_var", "decoder0_f.4.num_batches_tracked", "decoder0_f.6.weight", "decoder0_f.6.bias". 
	size mismatch for encoder0.0.weight: copying a param with shape torch.Size([64, 3, 7, 7]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3]).
	size mismatch for decoder3_f.0.weight: copying a param with shape torch.Size([256, 768, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 3, 3]).
	size mismatch for decoder2_f.0.weight: copying a param with shape torch.Size([128, 384, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
	size mismatch for decoder1_f.0.weight: copying a param with shape torch.Size([64, 192, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 128, 3, 3]).
	size mismatch for decoder0_g.0.weight: copying a param with shape torch.Size([64, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([3, 64, 3, 3]).
	size mismatch for decoder0_g.0.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([3]).
	size mismatch for decoder0_f.0.weight: copying a param with shape torch.Size([64, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 64, 3, 3]).
	size mismatch for decoder0_f.0.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([1]).

I'm running the code in Windows using Anaconda 4.10.3. Would you be able to help me out with this? It feels like the model files and code are out of sync with each other.

[Jizhizi_Li]

Hi there,

Thanks for showing interest in our project. According to your output, the problem is caused by defining the wrong backbone argument in your script. Please note that models/gfm_d121_tt.pth is the model trained on densenet121 backbone, thus, you need to define backbone=d121 when using this pretrained model in your script, instead of defining backbone=r34_2b.

You can see each released model with its correct backbone, rosta and pred_choice in the comments in script scripts/test/test_dataset.sh as a reference. Let me know if you have further questions. :)

backbone: r34 | rosta: TT | pred_choice: 3 | model_path: models/pretrained/gfm_r34_tt.pth
backbone: r34 | rosta: FT | pred_choice: 3 | model_path: models/pretrained/gfm_r34_ft.pth
backbone: r34 | rosta: BT | pred_choice: 3 | model_path: models/pretrained/gfm_r34_bt.pth
backbone: r34_2b | rosta: TT | pred_choice: 3 | model_path: models/pretrained/gfm_r34_2b_tt.pth
backbone: d121 | rosta: TT | pred_choice: 3 | model_path: models/pretrained/gfm_d121_tt.pth
backbone: r101 | rosta: TT | pred_choice: 3 | model_path: models/pretrained/gfm_r101_tt.pth
backbone: d121 | rosta: RIM | pred_choice: 4 | model_path: models/pretrained/gfm_d121_rim.pth

[Mauricio A Rovira Galvez]

Hello,

Ah! I knew it was something obvious. I changed the backbone and I was able to run the model successfully. Thank you for the help!

[Mauricio A Rovira Galvez]

Hello,

I do have another question: whenever I try to train the model using scripts/train/train_ori.sh, I get a file not found error like this one:

Traceback (most recent call last):
  File "core/train.py", line 207, in <module>
    main()
  File "core/train.py", line 201, in main
    train(args, model, optimizer, train_loader, epoch)
  File "core/train.py", line 87, in train
    for iteration, batch in enumerate(train_loader, 1):
  File "miniconda3/envs/gfm/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
    data = self._next_data()
  File "miniconda3/envs/gfm/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1065, in _next_data
    return self._process_data(data)
  File "miniconda3/envs/gfm/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1111, in _process_data
    data.reraise()
  File "miniconda3/envs/gfm/lib/python3.7/site-packages/torch/_utils.py", line 428, in reraise
    raise self.exc_type(msg)
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 6.
Original Traceback (most recent call last):
  File "miniconda3/envs/gfm/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 198, in _worker_loop
    data = fetcher.fetch(index)
  File "miniconda3/envs/gfm/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "miniconda3/envs/gfm/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "GFM/core/data.py", line 92, in __getitem__
    ori = np.array(Image.open(ori_path))
  File "miniconda3/envs/gfm/lib/python3.7/site-packages/PIL/Image.py", line 2975, in open
    fp = builtins.open(filename, "rb")
FileNotFoundError: [Errno 2] No such file or directory: 'AM-2K/train/original/desktop.jpg'

It seems to be looking for a desktop.jpg image, but I can't figure out where it's supposed to be coming from. I've looked through the original dataset and there's no image by that name anywhere. Is this a bug or am I missing a file?

[Yurii Pashchenko]

Hello @JizhiziLi ,
I failed to download one of the models, it seems that link to GFM(r')-TT model from Google Drive is broken.
Could you please provide the correct link?

[Jacob Dallas]

Hello @JizhiziLi , I failed to download one of the models, it seems that link to GFM(r')-TT model from Google Drive is broken. Could you please provide the correct link?

^this

[Mauricio A Rovira Galvez]

I'm closing this issue as my both my original and follow up problems have been solved. Thanks once again for the great work!