Comments (3)
I encountered the same problem.
And sadly, there seems to be no pre-trained model provided here.
from highres-net.
Did someone solve the problem?
from highres-net.
I thought that here it was enough to remove the transpose, because it looked like it disturbed the dimensionality, but nevertheless it did nothing :/.
def transform(self, theta, I, device="cpu"):
'''
Shifts images I by theta with Lanczos interpolation.
Args:
theta : tensor (B, 2), translation params
I : tensor (B, C_in, H, W), input images
Returns:
out: tensor (B, C_in, W, H), shifted images
'''
self.theta = theta
new_I = lanczos.lanczos_shift(img=I.transpose(0, 1), #delate transpose
shift=self.theta.flip(-1), # (dx, dy) from register_batch -> flip
a=3, p=5)[:, None]
return new_I
from highres-net.
Related Issues (10)
- RuntimeError: CUDA out of memory HOT 7
- Shared representation in fusion block
- Expected 2D (unbatched) or 3D (batched) input to conv1d, but got input of size: [1, 4, 202, 202] HOT 6
- Calculation of val_score
- torch_mask 192 vs 384 HOT 3
- Pretrain-model HOT 1
- training problem because of the different sizes HOT 8
- ShiftNet loss HOT 1
- predic HOT 5
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from highres-net.