About the differences on the training strategies when "_pt" in model name or not.

[xiaovhua]

About the differences on the training strategies when "_pt" in model name or not.

[aywi]

It is kind of our fault for not cleaning up these code, since these options are defined for models that are deleted in the final paper.

1. "_pt" means "pre-trained" models that we tested before, which are 2D & 3D mixed models where their 2D layers are pre-trained with Imagenet1k. Since we test pure 3D models in the final paper, these models are deleted for fair comparisons. You can also see that in the final experiments we choose feature layers all with "_ri", which means "randomly initialized" (pre-trained versions are ignored since we only use these 2D backbones from torchvision as sketches to build 3D feature layers):
   

   mprotonet/src/models.py

   Lines 27 to 47 in 4565e22

   	def features_imagenet1k(features):
   	if features == 'resnet18':
   	return build_resnet_features(vision_models.resnet18(weights='IMAGENET1K_V1'))
   	elif features == 'resnet18_ri':
   	return build_resnet_features(vision_models.resnet18())
   	elif features == 'resnet34':
   	return build_resnet_features(vision_models.resnet34(weights='IMAGENET1K_V1'))
   	elif features == 'resnet34_ri':
   	return build_resnet_features(vision_models.resnet34())
   	elif features == 'resnet50':
   	return build_resnet_features(vision_models.resnet50(weights='IMAGENET1K_V2'))
   	elif features == 'resnet50_ri':
   	return build_resnet_features(vision_models.resnet50())
   	elif features == 'resnet101':
   	return build_resnet_features(vision_models.resnet101(weights='IMAGENET1K_V2'))
   	elif features == 'resnet101_ri':
   	return build_resnet_features(vision_models.resnet101())
   	elif features == 'resnet152':
   	return build_resnet_features(vision_models.resnet152(weights='IMAGENET1K_V2'))
   	elif features == 'resnet152_ri':
   	return build_resnet_features(vision_models.resnet152())

2. "fixed" is a much older option when I tested the pre-trained models without a fixed training period in the beginning (yes, the correct name should be "not fixed").
3. Vanilla ProtoPNet has a fixed training period because it is a 2D model pre-trained with Imagenet1k. Since we test pure 3D models that are randomly initialized in the final paper, this period becomes useless and only wastes training time.

So, in a word, just ignore them. I will add a commit later to remove these code.

[xiaovhua]

It is kind of our fault for not cleaning up these code, since these options are defined for models that are deleted in the final paper.

1. "_pt" means "pre-trained" models that we tested before, which are 2D & 3D mixed models where their 2D layers are pre-trained with Imagenet1k. Since we test pure 3D models in the final paper, these models are deleted for fair comparisons. You can also see that in the final experiments we choose feature layers all with "_ri", which means "randomly initialized" (pre-trained versions are ignored since we only use these 2D backbones from torchvision as sketches to build 3D feature layers):
   

   mprotonet/src/models.py

   Lines 27 to 47 in 4565e22

   	def features_imagenet1k(features):
   	if features == 'resnet18':
   	return build_resnet_features(vision_models.resnet18(weights='IMAGENET1K_V1'))
   	elif features == 'resnet18_ri':
   	return build_resnet_features(vision_models.resnet18())
   	elif features == 'resnet34':
   	return build_resnet_features(vision_models.resnet34(weights='IMAGENET1K_V1'))
   	elif features == 'resnet34_ri':
   	return build_resnet_features(vision_models.resnet34())
   	elif features == 'resnet50':
   	return build_resnet_features(vision_models.resnet50(weights='IMAGENET1K_V2'))
   	elif features == 'resnet50_ri':
   	return build_resnet_features(vision_models.resnet50())
   	elif features == 'resnet101':
   	return build_resnet_features(vision_models.resnet101(weights='IMAGENET1K_V2'))
   	elif features == 'resnet101_ri':
   	return build_resnet_features(vision_models.resnet101())
   	elif features == 'resnet152':
   	return build_resnet_features(vision_models.resnet152(weights='IMAGENET1K_V2'))
   	elif features == 'resnet152_ri':
   	return build_resnet_features(vision_models.resnet152())

2. "fixed" is a much older option when I tested the pre-trained models without a fixed training period in the beginning (yes, the correct name should be "not fixed").
3. Vanilla ProtoPNet has a fixed training period because it is a 2D model pre-trained with Imagenet1k. Since we test pure 3D models that are randomly initialized in the final paper, this period becomes useless and only wastes training time.

So, in a word, just ignore them. I will add a commit later to remove these code.

I get it! Thank you so much!