Whoops sorry about the confusion - I didn’t realize either that the manner in which anchors are created changed between our notebook and the official one. Will be more careful going forward to check the diff.

Update: To pinpoint and clarify exactly how we went off-track…

**Our version:**

```
anc_x = np.concatenate([np.tile(np.linspace(ao, 1-ao, ag), ag)
for ao,ag in zip(anc_offsets,anc_grids)])
anc_y = np.concatenate([np.repeat(np.linspace(ao, 1-ao, ag), ag)
for ao,ag in zip(anc_offsets,anc_grids)])
```

for anc_grids=2 and k=1, produces anchor_cnr:

```
Variable containing:
0.0000 0.0000 0.5000 0.5000
0.5000 0.0000 1.0000 0.5000
0.0000 0.5000 0.5000 1.0000
0.5000 0.5000 1.0000 1.0000
[torch.cuda.FloatTensor of size 4x4 (GPU 0)]
```

which draws the anchor boxes going top–>down each column (note the number of each box: 0, 1, 2, 3)

**Official pascal-multi version:**

```
anc_x = np.concatenate([np.repeat(np.linspace(ao, 1-ao, ag), ag)
for ao,ag in zip(anc_offsets,anc_grids)])
anc_y = np.concatenate([np.tile(np.linspace(ao, 1-ao, ag), ag)
for ao,ag in zip(anc_offsets,anc_grids)])
```

produces anchor_cnr:

```
Variable containing:
0.0000 0.0000 0.5000 0.5000
0.0000 0.5000 0.5000 1.0000
0.5000 0.0000 1.0000 0.5000
0.5000 0.5000 1.0000 1.0000
[torch.cuda.FloatTensor of size 4x4 (GPU 0)]
```

which draws the boxes going left–>right and then next row down:

This is the correct arrangement of boxes that lines up with how the receptive fields are ordered (left–>right and then next row)

The only difference between the two versions is the order in which we use np.repeat and np.tile for anc_x and anc_y. Given [0,1] and repeats=2:

- np.repeat makes [0,0,1,1]
- np.tile makes [0,1,0,1]

So switching the order of the functions as applied to anc_x and anc_y flips the x,y coordinates (0,1) <–> (1,0) and transposes the ordering of the anchor boxes.

Subtle yet important difference! as I’ve unwittingly discovered…