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Keras implementation of Non-local Neural Networks
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| 2026-08-05 | 288 |
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# Keras Non-Local Neural Networks Keras implementation of Non-local blocks from [[1]](https://arxiv.org/abs/1711.07971). - Support for `"Gaussian"`, `"Embedded Gaussian"` and `"Dot"` instantiations of the Non-Local block. - Support for variable shielded computation mode (reduces computation by N**2 x, where N is default to 2) - Support for `"Concatenation"` instantiation will be supported when authors release their code. # Usage Templates The script `non_local.py` contains the `NonLocalBlock` instance which takes in an input tensor and wraps a non-local block around it. ```python from non_local import NonLocalBlock from tensorflow.keras.layers import Input, Conv1D, Conv2D, Conv3D ip = Input(shape=(...)) # input tensor with an "N" rank order of 3, 4 or 5 x = ConvND(...) # convolution operation with aforementioned rank ... non_local_block = NonLocalBlock(intermediate_dim=None, compression=2, mode='embedded', add_residual=True) x = non_local_block(x) ... ``` The script `non_local_layerstyle.py` contains the `NonLocalBlock` **layer** which takes in an input tensor and wraps a non-local block around it. Made to facilitate the neural network builder using the Sequential method. ```python from non_local_layerstyle import NonLocalBlock from tensorflow.keras.layers import Input, Conv1D, Conv2D, Conv3D # Define the input shape input_shape = (...) # shape of input tensor model = Sequential() model.add(ConvND(...)) # convolution operation with an "N" rank order of 3, 4 or 5 ... model.add(NonLocalBlock(intermediate_dim=None, compression=2, mode='embedded', add_residual=True)) ... ``` # Basic block From [[1]](https://arxiv.org/abs/1711.07971), a basic Non-Local block with the Embedded Gaussian instantiation has the below logic: <center><img src="https://github.com/titu1994/keras-non-local-nets/blob/master/images/non-local-block.PNG?raw=true" width=50% ></center> 1. Xiaolong Wang, Ross Girshick, Abhinav Gupta, Kaiming He. "Non-local Neural Networks." arXiv:1711.07971 [cs.CV], 21 Nov 2017. [Link](https://arxiv.org/abs/1711.07971)
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