search

wuhaozhe / pytorch-nicp

GPU Accelerated Non-rigid ICP for surface registration
MIT License
243 stars 31 forks source link

Hi, with landmarks: `landmarks = torch.from_numpy(np.array(landmarks)).to(device).long()`, maybe you can reshape landmarks from torch.Size([1, 1, 68, 2]) to torch.Size([1, 68, 2]) #5

Open alicedingyueming opened 2 years ago

alicedingyueming commented 2 years ago

Hi, with landmarks: landmarks = torch.from_numpy(np.array(landmarks)).to(device).long(), maybe you can reshape landmarks from torch.Size([1, 1, 68, 2]) to torch.Size([1, 68, 2])

Originally posted by @wuhaozhe in https://github.com/wuhaozhe/pytorch-nicp/issues/3#issuecomment-971453681 hi！I got output as torch.Size([1, 68, 512, 3]) torch.Size([1, 68, 2]) torch.Size([1, 512, 512, 3]) I think the shape of following tensors are right, but I meet the same problem. lm_vertex = torch.gather(lm_vertex, 2, column_index) RuntimeError: CUDA error: device-side assert triggered

landmarks = torch.from_numpy(np.array(landmarks)).to(device).long()

row_index = landmarks[:, :, 1].view(landmarks.shape[0], -1)
column_index = landmarks[:, :, 0].view(landmarks.shape[0], -1)
row_index = row_index.unsqueeze(2).unsqueeze(3).expand(landmarks.shape[0], landmarks.shape[1], shape_img.shape[2], shape_img.shape[3])
column_index = column_index.unsqueeze(1).unsqueeze(3).expand(landmarks.shape[0], landmarks.shape[1], landmarks.shape[1], shape_img.shape[3])
print(row_index.shape, landmarks.shape, shape_img.shape)
alicedingyueming commented 2 years ago

I see! when the row_index = 512(the size of my rendered image), the error will be reported. it seems that the index should minus one. besides, the error can be found when running on cpu.