Visualization for neural networks
The aim of the library is to provide a simple interface to visualize the feature representation of Convolutional Neural Networks. Works with PyTorch models.
To obtain GradCam visualization for a single image use grad_cam_viz method
grad_cam_viz(model, layer, x, y, proc_x=None, unproc_x=None):
'''
model (PyTorch Module) - PyTorch model whose features needs to be visualized
layer (list) - a sequence of layer names representing the heirarchy to reach the layer inside the model
whose visualization needs to be done.
Ex: ['5', '7'] represents that the desired layer can be found using model._modules['5']._modules['7']
x (PyTorch rank 3 tensor) - the input image. Expected to be of shape (3, h, w) where h is the height and w is the width
y (PyTorch rank 0 tensor i.e. a scalar) - the true class of "x". The expectation is that model(x)[y] represents the
score for the true class
proc_x (function): proc_x will be passed "x" as the first and only parameter. Any preprosessing can be done here before
the score for x is evaluated
unproc_x (function): function should take "x" and return the values back to image space i.e. remove any preprocessing steps
performed either in "proc_x" or before calling "grad_cam_viz" method
'''
Example result after applying grad_cam_viz to a Pneumonia classification problem (y is class 'Pneumonia')-
To visualize both the positive and negative contributions towards the specified class, use "grad_cam_no_relu_viz" method
Result obtained by visualizing both positive and negative contributions towards class "pneumonia" (same example as shown above) -
Saliency Confusion Matrix provides a visualization of the positive and negative contributions for both the predicted class as well as the ground truth class. Use the method "grad_cam_saliency_confusion_map" to obtain the Saliency Confusion Matrix.
