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])

[alicedingyueming]

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 #3 (comment)
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]

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.