The Tensorflow, Keras implementation of U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET with optional ImageNet-trained backbones.
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Possible bug in number of tensors for layer fusion #60
The figure below shows that the value of a decoder neuron is based on 5 other values:
When trying to reproduce this in the code, I get the following:
The number of tensors is equal to 4 instead of 5. This suggest there is a bug in the code around the following lines:
https://github.com/yingkaisha/keras-unet-collection/blob/d30f14a259656d2f26ea11ed978255d6a7d0ce37/keras_unet_collection/_model_unet_3plus_2d.py#L142-L150
I think the second for loop should have
depth_decode+1
as the upper boundary in the range. Is this indeed a bug?