Feature Extraction with Attention Module

[jawi289o]

@rwightman thank you for providing so many pre-trained backbone networks.
I am using resnet34 as a feature extractor with an image size of 256x256:
model = timm.create_model('resnet34', pretrained=True, features_only=True)

It gives the following blocks:
Feature from block 0: torch.Size([1, 64, 128, 128])
Feature from block 1: torch.Size([1, 64, 64, 64])
Feature from block 2: torch.Size([1, 128, 32, 32])
Feature from block 3: torch.Size([1, 256, 16, 16])
Feature from block 4: torch.Size([1, 512, 8, 8])

How can I apply ScSE attention modules after each block as shown in the below architecture and pass it to the decoder?

[jawi289o]

@rwightman waiting for your response