"compute_scale_and_shift" can be found in Loss.py and is based on least square loss.
Given that MiDaS-v2 was trained using the median + MAD and MiDaS-v1 was trained using the least squares-based loss (https://arxiv.org/abs/1907.01341v1)
def compute_scale_and_shift(prediction, target, mask):
# system matrix: A = [[a_00, a_01], [a_10, a_11]]
a_00 = torch.sum(mask * prediction * prediction, (1, 2))
a_01 = torch.sum(mask * prediction, (1, 2))
a_11 = torch.sum(mask, (1, 2))
# right hand side: b = [b_0, b_1]
b_0 = torch.sum(mask * prediction * target, (1, 2))
b_1 = torch.sum(mask * target, (1, 2))
# solution: x = A^-1 . b = [[a_11, -a_01], [-a_10, a_00]] / (a_00 * a_11 - a_01 * a_10) . b
x_0 = torch.zeros_like(b_0)
x_1 = torch.zeros_like(b_1)
det = a_00 * a_11 - a_01 * a_01
valid = det.nonzero()
x_0[valid] = (a_11[valid] * b_0[valid] - a_01[valid] * b_1[valid]) / det[valid]
x_1[valid] = (-a_01[valid] * b_0[valid] + a_00[valid] * b_1[valid]) / det[valid]
return x_0, x_1
"compute_scale_and_shift" can be found in Loss.py and is based on least square loss.
Given that MiDaS-v2 was trained using the median + MAD and MiDaS-v1 was trained using the least squares-based loss (https://arxiv.org/abs/1907.01341v1)
Have median + MAD been implemented yet?
https://gist.github.com/dvdhfnr/732c26b61a0e63a0abc8a5d769dbebd0
https://arxiv.org/pdf/1907.01341v3.pdf