Readme.md

Errata

In the chapter Class_activation_maps
Originally the functions were

im2fmap = nn.Sequential(*(list(model.model[:5].children()) + list(model.model[5][:2].children())))
def im2gradCAM(x):
    model.eval()
    logits = model(x)
    heatmaps = []
    activations = im2fmap(x)
    print(activations.shape)
    pred = logits.max(-1)[-1]
    # get the model's prediction
    model.zero_grad()
    # compute gradients with respect to model's most confident logit
    logits[0,pred].backward(retain_graph=True)
    # get the gradients at the required featuremap location
    # and take the avg gradient for every featuremap
    pooled_grads = model.model[-7][1].weight.grad.data.mean((0,2,3))
    # multiply each activation map with corresponding gradient average
    for i in range(activations.shape[1]):
        activations[:,i,:,:] *= pooled_grads[i]
    # take the mean of all weighted activation maps
    # (that has been weighted by avg. grad at each fmap)
    heatmap = torch.mean(activations, dim=1)[0].cpu().detach()
    return heatmap, 'Uninfected' if pred.item() else 'Parasitized'

The paper assumes we get the activations and pooled_grads from the same convolution layer, but im2fmap and pooled_grads were pointing to different layers in the Resnet. The correction is simple - point the pooled_grads layer to the layer corresponding to im2fmap, i.e., - model.model[-6][1].weight.grad.data.mean((1,2,3)) Here's the correct version.

def im2gradCAM(x):
    model.eval()
    logits = model(x)
    heatmaps = []
    activations = im2fmap(x)
    print(activations.shape)
    pred = logits.max(-1)[-1]
    # get the model's prediction
    model.zero_grad()
    # compute gradients with respect to model's most confident logit
    logits[0,pred].backward(retain_graph=True)
    # get the gradients at the required featuremap location
    # and take the avg gradient for every featuremap
    pooled_grads = model.model[-6][1].weight.grad.data.mean((1,2,3))
    # multiply each activation map with corresponding gradient average
    for i in range(activations.shape[1]):
        activations[:,i,:,:] *= pooled_grads[i]
    # take the mean of all weighted activation maps
    # (that has been weighted by avg. grad at each fmap)
    heatmap = torch.mean(activations, dim=1)[0].cpu().detach()
    return heatmap, 'Uninfected' if pred.item() else 'Parasitized'

This is now incorporated into the notebook