From the code, I know the structure of discriminator used fully convolution network(like discriminator in DCGAN), but when we input some any size self-information map , I(x),we can't fix the output shape of discriminator to (B, C, 1, 1)， maybe we get a output whose shape is (B, 1, 4, 4) and then create a ground truth tensor whose all elements is 1 or 0 (source or target) to calculate BCE loss.
I can't know why the output shape of discriminator don't have to be (B, 1, 1, 1), and we can directly use them for BCE loss.
Thank you!
From the code, I know the structure of discriminator used fully convolution network(like discriminator in DCGAN), but when we input some any size self-information map , I(x),we can't fix the output shape of discriminator to (B, C, 1, 1)， maybe we get a output whose shape is (B, 1, 4, 4) and then create a ground truth tensor whose all elements is 1 or 0 (source or target) to calculate BCE loss. I can't know why the output shape of discriminator don't have to be (B, 1, 1, 1), and we can directly use them for BCE loss. Thank you!