Utilizing resnet_50.pth for 3D Feature Map Extraction

[aeinkoupaei]

Hi, I want to use resnet_50.pth pre-trained encoder to extract 3D feature maps from medical images. Is the following method correct? It seems strange that the parameters of width, height, depth and number of channels can be adjusted manually. Isn't it the case that the resnet_50.pth pre-trained model is trained with a specific architecture, length, width, height, and channel? Therefore, shouldn't the input of the trained model for extracting 3D feature maps have the same dimensions as inputs of the model in the training phase?

resnet50 = resnet50(
sample_input_D=32,
sample_input_H=256,
sample_input_W=256,
shortcut_type='B',
no_cuda=True,
num_seg_classes=1
)
pretrain = torch.load("pretrain/resnet_50.pth") # Load the weights from the pretrained file
pretrained_dict = pretrain['state_dict']
new_state_dict = OrderedDict()
for k, v in pretrained_dict.items():
name = k[7:] # Remove 'module.'
new_state_dict[name] = v
resnet10.load_state_dict(new_state_dict, strict=False)

A_img_feature_map = resnet50(A_img)

[aeinkoupaei]

Hi @Ram2314,

To use a pre-trained ResNet model for extracting 3D feature maps, you'll need to focus on the ResNet class within the resnet.py file. Here's what to change:
1- Modifying the init function of ResNet class: Delete the entire self.conv_seg block. This removes the unnecessary layers for our feature extraction task.
2- Modifying the forward function of ResNet class: Delete this line: x = self.conv_seg(x). This ensures the model doesn't perform the final segmentation prediction, but instead outputs the feature map before that stage.

Here's an example of how to use a pre-trained ResNet-10 model for feature extraction:

resnet_10 = resnet10(shortcut_type='B', no_cuda=True)
pretrain = torch.load("resnet_10_23dataset.pth")
pretrained_dict = pretrain['state_dict']
new_state_dict = OrderedDict()
for k, v in pretrained_dict.items():
name = k[7:]
new_state_dict[name] = v

resnet_10.load_state_dict(new_state_dict)