Tensorflow 2 / tf.keras port for keras-gcnn

A tf.keras port of keras-gcnn, a library for p4 and p4m-equivariant networks. Includes some minor bug fixes for group batch normalization (correctly handling train/test modes, making affine scaling and shifting optional). Depends on tf2-GrouPy.

To install, run:

conda install tensorflow-gpu=2.2.0  # or your preferred way of installing tf2
pip install git+https://github.com/neel-dey/tf2-GrouPy#egg=GrouPy -e git+https://github.com/neel-dey/tf2-keras-gcnn.git#egg=keras_gcnn

Currently, the primary functionality including the GConv2D, GBatchNorm, and GroupPool layers work. The remaining low-priority tasks include:

1. Transposed GConv support. (side note: you could just use UpSampling layers instead)
2. Updating the tests.
3. Updating gdensenet to tf keras.

If there's interest in these, please open an issue.

Minimal working example with an equivariance-test:

import numpy as np
import tensorflow as tf

from tensorflow.keras import Model
from tensorflow.keras.layers import Input, Activation, MaxPooling2D

from keras_gcnn.layers import GConv2D, GBatchNorm, GroupPool

# Define model with functional API:
ip = Input(shape=(128, 128, 3))
x = GConv2D(8, h_input='Z2', h_output='D4', kernel_size=5, padding='same')(ip)
x = Activation('relu')(x)
x = MaxPooling2D()(x)

x = GConv2D(16, h_input='D4', h_output='D4', kernel_size=3, padding='same')(x)
x = GBatchNorm(h='D4')(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)

x = GConv2D(32, h_input='D4', h_output='D4', kernel_size=3, padding='same')(x)
x = GBatchNorm(h='D4')(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)

x = GConv2D(1, h_input='D4', h_output='D4', kernel_size=3, padding='same')(x)
x = GroupPool('D4')(x)
model = Model(inputs=ip, outputs=x)

# Generate random test image:
img = np.random.randn(128, 128, 3)

# Run a forward pass through the model with the image and transformed images:
res = model.predict(
    np.stack([img, np.rot90(img), np.rot90(np.fliplr(img), 2)]),
    batch_size=1,
)

# Test that activations are the same:
assert np.allclose(res[0], np.rot90(res[1], 3), rtol=1e-5, atol=1e-3)
assert np.allclose(res[0], np.flipud(res[2]), rtol=1e-5, atol=1e-3)

Original README follows below,

Group-Equivariant Convolutional Neural networks for Keras: keras_gcnn

Straight-forward keras implementations for 90-degree roto-reflections equivariant CNNs. See a working example.

Install: pip install git+https://github.com/neel-dey/tf2-GrouPy#egg=GrouPy -e git+https://github.com/neel-dey/tf-keras-gcnn.git#egg=keras_gcnn

Requires python 3, up to date keras and a tensorflow backend. Please report any problems in the issues.

About Group-equivariance

Conventional fully-convolutional NNs are 'equivariant' to translation: as the input shifts in the spatial plane, the output shifts accordingly. This can be extended to include other forms of transformations such as 90 degree rotations and reflection. This is formalized by [2].

Citing

If you use these implementations in your work, we appreciate a citation to our paper:

[1] B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling. "Rotation Equivariant CNNs for Digital Pathology". arXiv:1806.03962

Biblatex entry:

@ARTICLE{Veeling2018-qh,
  title         = "Rotation Equivariant {CNNs} for Digital Pathology",
  author        = "Veeling, Bastiaan S and Linmans, Jasper and Winkens, Jim and
                   Cohen, Taco and Welling, Max",
  month         =  jun,
  year          =  2018,
  archivePrefix = "arXiv",
  primaryClass  = "cs.CV",
  eprint        = "1806.03962"
}

GDensenet

We provide a Group-equivariant version of DenseNet [3] as proposed in [1].

Recipe for building equivariant networks:

• Decide on a group to use, currently D4 (roto-reflection) and C4 (rotations) are supported.
• All convolutional layers with kernels larger than 1 should be replaced with group-equivariant layers.
  • The first layer transforms the input from Z2 to D4, by setting h_input='Z2' and h_output='C4' or 'D4'.
  • Follow up layers live on the chosen group and have h_input=h_output='D4' (or 'C4').
• Operations that learn parameters per feature-map should be replaced with group versions, including:
  • BatchNormalization becomes GBatchNorm.
• To create a model invariant to rotations, use GroupPool followed by a global spatial pooling layer such as GlobalAveragePooling.

References

• [1] B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling. "Rotation Equivariant CNNs for Digital Pathology". arXiv [cs.CV] (2018), (available at http://arxiv.org/abs/1806.03962).
• [2] Cohen, Taco, and Max Welling. "Group equivariant convolutional networks." International Conference on Machine Learning. 2016.
• [3] Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. Vol. 1. No. 2. 2017.