I am very confused with this parameter in pytroch document. According to wiki

https://en.wikipedia.org/wiki/Bilinear_interpolation, the bilinear interpolation formula result is consistent with

align_corners =True. which is defatult before pytorch 0.4.0.

I want to know when should use align_corners=False ??

# What we should use align_corners = False

same problem here.

Someone can help?

Have you seen the note and examples under `Upsample`

? I think they do a great job in explaining why.

Yeah, I check the example under `Upsample`

. I don’t get it.

```
>>> input_3x3 = torch.zeros(3, 3).view(1, 1, 3, 3)
>>> input_3x3[:, :, :2, :2].copy_(input)
tensor([[[[ 1., 2.],
[ 3., 4.]]]])
>>> input_3x3
tensor([[[[ 1., 2., 0.],
[ 3., 4., 0.],
[ 0., 0., 0.]]]])
>>> m = nn.Upsample(scale_factor=2, mode='bilinear') # align_corners=False
>>> # Notice that values in top left corner are the same with the small input (except at boundary)
>>> m(input_3x3)
tensor([[[[ 1.0000, 1.2500, 1.7500, 1.5000, 0.5000, 0.0000],
[ 1.5000, 1.7500, 2.2500, 1.8750, 0.6250, 0.0000],
[ 2.5000, 2.7500, 3.2500, 2.6250, 0.8750, 0.0000],
[ 2.2500, 2.4375, 2.8125, 2.2500, 0.7500, 0.0000],
[ 0.7500, 0.8125, 0.9375, 0.7500, 0.2500, 0.0000],
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]]])
>>> m = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
>>> # Notice that values in top left corner are now changed
>>> m(input_3x3)
tensor([[[[ 1.0000, 1.4000, 1.8000, 1.6000, 0.8000, 0.0000],
[ 1.8000, 2.2000, 2.6000, 2.2400, 1.1200, 0.0000],
[ 2.6000, 3.0000, 3.4000, 2.8800, 1.4400, 0.0000],
[ 2.4000, 2.7200, 3.0400, 2.5600, 1.2800, 0.0000],
[ 1.2000, 1.3600, 1.5200, 1.2800, 0.6400, 0.0000],
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]]])
```

Whether `align_corners`

is `False`

or `True`

, the left top corner is always 1.

Oh, my mistake. Now I get it.

I will show you a 1-dimension example.

Suppose that you want to resize tensor [0, 1] to [?, ?, ?, ?], so the factor=2.

Now we only care about coordinates.

For mode=‘bilinear’ and align_corners=False, the result is the same with opencv and other popular image processing libraries (I guess). Corresponding coordinates are [-0.25, 0.25, 0.75, 1.25] which are calculate by x_original = (x_upsamle + 0.5) / 2 - 0.5. Then you can these coordinates to interpolate.

For mode=‘bilinear’ and align_corners=True, corresponding coordinates are [0, 1/3, 2/3, 1]. From this, you can see why this is called align_corners=True.

I will be very happy if you find this answer useful.

Talk is cheap, show you the code!

```
# align_corners = False
# x_ori is the coordinate in original image
# x_up is the coordinate in the upsampled image
x_ori = (x_up + 0.5) / factor - 0.5
```

```
# align_corners = True
# h_ori is the height in original image
# h_up is the height in the upsampled image
stride = (h_ori - 1) / (h_up - 1)
x_ori_list = []
# append the first coordinate
x_ori_list.append(0)
for i in range(1, h_up - 1):
x_ori_list.append(0 + i * stride)
# append the last coordinate
x_ori_list.append(h_ori - 1)
```

I have the same doubt. The corners are the same, what is the difference?

Here is a simple illustration I made showing how a 4x4 image is upsampled to 8x8.

When `align_corners=True`

, pixels are regarded as a grid of points. Points at the corners are aligned.

When `align_corners=False`

, pixels are regarded as 1x1 areas. Area boundaries, rather than their centers, are aligned.

Thanks for your image! Do you know in semantic segmentation task should we use `align_corners=True`

or `align_corners=False`

?