Hey Frank,


The term “confidence map” (together with the use of Softmax)
suggests to me that you might be dealing with probability-like
values - Yes,  I am want the probability of a finding a keypoint in a heatmap


I mention 16 confidence maps instead of 17 because I am using 16 keypoints - 1 per channel


Regarding the input output shape of images they look like below:
Input shape  = torch.rand(1,16,2,256,256) # 2 refers to the depth (in my case images)
model output = torch.rand(1,16,1,256,256)
target shape = torch.rand(1,16,1,256,256)


Considering the last layer of my model is not a softmax layer but a convtranspose layer with no subsequent activation. Does the implementation look right? The values looks large
def softXEnt (input, target):
    logprobs = torch.nn.functional.log_softmax (input, dim = 1)
    return  -(target * logprobs).sum() / input.shape[0]

input  = torch.rand(1, 16, 1, 256, 256)
target = torch.rand(1, 16, 1, 256, 256)
softXEnt(input, target)

output:tensor(1474121.)