I am debugging a neural network which has a torch.nn.functional.grid.sample operator inside. Using the Pycharm IDE, I can watch the values during debugging. My grid is a 1*15*2 tensor, here are the values in the first batch
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My input is a 1*128*16*16 tensor, here are the values in the first channel of the first batch:.
My output is 1*128*1*15 tensor, here are the values in the first channel of the first batch.
align_corners = False, mode = 'bilinear', padding_mode = 'zero'.
For gird coordinates (-1,-1), I can understand that the value(-4.74179) is sampled from 4 values on the top-left corner with 3 of them being the padded '0's and 1 of them being the value '-18.96716'.(-18.96716/4 = -4.74179).
But for other grid coordinates, I am confused. Taking the value '84.65594' for example, it's corresponding grid coordinate is (-0.45302, 0.53659). I firstly convert them from (-1,1) to (0,15) by adding 1 and then dividing by 2 and then multiplying 15(see official implementation). The converted coordinate is then (4.10235, 11.524425), Upon which I see the four values that should be sampled from are :
(x)44.20010---0.10235---------(y)26.68777 | | | | | | 0.524425---(a,b)-------------------- | | | | | | (w)102.18765---------------------(z)30.03996 here are my calculation by hand step, Let:
a = 0.10235 b = 0.524425 x = 44.20010 y = 26.68777 z = 30.03996 w = 102.18765 The interpolated value should then be:
output = a*b*z + (1 - a)*(1 - b)*x + (1 - a)*b*w + (1-b)*a*y = 0.10235*0.524425*30.03996 + (1-0.10235)*(1-0.524425)*44.20010 + (1- 0.10235)*0.524425*102.18765 + (1-0.524425)*0.10235*26.68777 = 69.8852865171 which isn't 84.65594, I cant't figure out how the value '84.65594' in the output is calculated, please help!
1 Answer
I answer my own question, it turns out that the inconsistency is due to the 'align_corners' flag. My way of calculation is actually under the case when 'align_corners' is true while in the program, this flag is set to be false. For how to calculate sample coordinates, please see this
