Differentiable Spatial to Numerical Transform

An unofficial Tensorflow implementation of differentiable spatial to numerical (DSNT) layer.

Code in this project implements ideas presented in the research paper ""Numerical Coordinate Regression with Convolutional Neural Networks" by Nibali et al". If you use it in your own research project, please be sure to cite the original paper appropriately.

Also included is a small Sonnet module wrapper around the DSNT layer.

Provided Files:

dsnt.py - The layer implementation and its supporting functions
dsnt_snt.py - A Sonnet module wrapping the layer
DSNT_sample.ipynb - A notebook demonstrating the usage of the DSNT layer.

Example usage:

The instructions vary slightly depending on if Sonnet or raw Tensorflow is used.

Begin by importing the module:

Raw Tensorflow:

import dsnt

Sonnet:

import dsnt
from dsnt_snt import DSNT

Insert the layer

The layer can be inserted at the end of a stack of convolutional layers, where the final tensor shape is [batch, height, width, 1]. The function's input tensor will be rectified, then passed through the transform. dsnt.dsnt returns the rectified input heatmaps and the produced coordinates tensor of shape [batch, x, y]:

Raw Tensorflow

norm_heatmaps, coords = dsnt.dsnt(my_tensor)

Sonnet:

norm_heatmaps, coords = DSNT()(my_tensor)

There are different rectification methods available, which can be provided as an additional argument, e.g: dsnt.dsnt(my_tensor, method='relu')

Add the loss terms

The loss function must be composed of two components. Mean-Squared-Error or similar for the coordinate regression, and Jensen-Shannon Divergence for regularization.

# Coordinate regression loss
loss_1 = tf.losses.mean_squared_error(targets, coords)
# Regularization loss
loss_2 = dsnt.js_reg_loss(norm_heatmaps, targets)

loss = loss_1 + loss_2