(Pytorch) Why conv2d results are all different. Their data type is all integer, no float
I've tried and compared three different methods for convolution computation with a custom kernel in Pytorch. Their results are different but I don't understand why that is.
Setup code:
import torch
import torch.nn.functional as F

inp = torch.arange(3*500*700).reshape(1,3,500,700).to(dtype=torch.float32)
wgt = torch.ones((1,3,3,3)).to(dtype=torch.float32)
stride = 1
padding = 0
h = inp.shape[2] - wgt.shape[2] + 1
w = inp.shape[3] - wgt.shape[3] + 1

Method 1
out1 = torch.zeros((1,h,w)).to(dtype=torch.float32)
for o in range(1):
    for i in range(3):
        for j in range(h):
            for k in range(w):
                out1[o,j,k] = out1[o,j,k] + (inp[0, i, j*stride:j*stride+3, k*stride:k*stride+3] * wgt[0,i]).sum()
out1 = out1.to(dtype=torch.int)

Method 2
inp_unf = F.unfold(inp, (3,3))
out_unf = inp_unf.transpose(1,2).matmul(wgt.view(1,-1).t()).transpose(1,2)
out2 = F.fold(out_unf, (h,w), (1,1))
out2 = out2.to(dtype=torch.int)

Method 3
out3 = F.conv2d(inp, wgt, bias=None, stride=1, padding=0)
out3 = out3.to(dtype=torch.int)

And here are the results comparison:
&gt;&gt;&gt; h*w
347604

&gt;&gt;&gt; (out1==out2).sum().item()
327338
&gt;&gt;&gt; (out2 == out3).sum().item()
344026
&gt;&gt;&gt; (out1 == out3).sum().item()
330797

&gt;&gt;&gt; out1.shape
(1, 498, 698)
&gt;&gt;&gt; out2.shape 
(1, 1, 498, 698)
&gt;&gt;&gt; out3.shape
(1, 1, 498, 698)

Their data types are all int so floating point won't the result. When I use a squared input format such as h=500 and w=500, all three results are all matching. But not for non-squared inputs, such as the one above with h=500 and w=700. Any insight?

All three results are cast to integer data types but keep in mind their computation is done with float32... It is often preferred to check equality between two tensors using torch.isclose:
&gt;&gt;&gt; torch.isclose(out1, out2).float().mean()
tensor(1.)

&gt;&gt;&gt; torch.isclose(out2, out3).float().mean()
tensor(1.)

&gt;&gt;&gt; torch.isclose(out1, out3).float().mean()
tensor(1.)

