Now updated for TF2.0 (and tf.keras).

1. Layers
2. ResBlock
    a. SkipInit Initialization
3. Spatial Tranformer
    a. Usage

Layers:

• ResBlock
  • Versions : 1D, 2D, 3D, 2DTranspose/Deconv, 3DTranspose/Deconv
  • Basic blocks are a separate class
• DenseBlock
  • Versions : 1D, 2D, 3D
  • Composite Function Block as a separate class
• SpatialTransformer

Very basic tests on Cifar10 are provided in /tests for ResBlock and DenseBlock.

Original paper (ResNet):

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

Currently implemented paper (SkipInit):

@article{de2020batch,
    title={Batch normalization biases deep residual networks towards shallow paths},
    author={De, Soham and Smith, Samuel L},
    journal={arXiv preprint arXiv:2002.10444},
    year={2020}
}

Original paper :

@inproceedings{jaderberg2015spatial,
  title={Spatial transformer networks},
  author={Jaderberg, Max and Simonyan, Karen and Zisserman, Andrew and others},
  booktitle={Advances in neural information processing systems},
  pages={2017--2025},
  year={2015}
}

You must provide a Localisation network (Layer or Model) to the Spatial Transformer.

If the dimension of the output of the Localisation network if not 6, the Spatial Transformer will add one dense layer with an output of dimension 6 (and flatten its inputs).

Why 6 ? It is the dimension of theta, the affine 2x3 transformation matrix used to transform the input.

The transformation is performed through differentiable bilinear sampling.

Here is a small example:

from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Input
from CustomKerasLayers import SpatialTransformer

# Build
input_shape = (128, 128, 3)
encoder_input_shape = (64, 64, 3)

localisation_net = Sequential(...)
spatial_transformer = SpatialTransformer(localisation_net,
                                         output_size=encoder_input_shape)
encoder = Sequential(...)
...

# Call
input_layer = Input(input_shape)

output_theta = False
if output_theta:
    layer, theta = spatial_transformer(input_layer, output_theta=True)
else:
    layer = spatial_transformer(input_layer)
   
layer = encoder(layer)
...