def _simple_roialign(self, img, box, resolution, aligned=True):
"""
RoiAlign with scale 1.0 and 0 sample ratio.
"""
if isinstance(resolution, int):
resolution = (resolution, resolution)
op = ROIAlign(resolution, 1.0, 0, aligned=aligned)
input = torch.from_numpy(img[None, None, :, :].astype("float32"))
rois = [0] + list(box)
rois = torch.from_numpy(np.asarray(rois)[None, :].astype("float32"))
output = op.forward(input, rois)
if torch.cuda.is_available():
output_cuda = op.forward(input.cuda(), rois.cuda()).cpu()
self.assertTrue(torch.allclose(output, output_cuda))
return output[0, 0]
def _simple_roialign_with_grad(self, img, box, resolution, device):
if isinstance(resolution, int):
resolution = (resolution, resolution)
op = ROIAlign(resolution, 1.0, 0, aligned=True)
input = torch.from_numpy(img[None, None, :, :].astype("float32"))
rois = [0] + list(box)
rois = torch.from_numpy(np.asarray(rois)[None, :].astype("float32"))
input = input.to(device=device)
rois = rois.to(device=device)
input.requires_grad = True
output = op.forward(input, rois)
return input, output
def test_empty_batch(self):
input = torch.zeros(0, 3, 10, 10, dtype=torch.float32)
rois = torch.zeros(0, 5, dtype=torch.float32)
op = ROIAlign((7, 7), 1.0, 0, aligned=True)
output = op.forward(input, rois)
self.assertTrue(output.shape == (0, 3, 7, 7))