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| import torch | |
| def area(a, b, c): | |
| return (c[1] - a[1]) * (b[0] - a[0]) - (b[1] - a[1]) * (c[0] - a[0]) | |
| def triangle_area(A, B, C): | |
| out = (C - A).flip([-1]) * (B - A) | |
| out = out[..., 1] - out[..., 0] | |
| return out | |
| def compute_sine_theta(s1, s2): # s1 and s2 aret two segments to be uswed | |
| # s1, s2 (2, 2) | |
| v1 = s1[1, :] - s1[0, :] | |
| v2 = s2[1, :] - s2[0, :] | |
| # print(v1, v2) | |
| sine_theta = (v1[0] * v2[1] - v1[1] * v2[0]) / (torch.norm(v1) * torch.norm(v2)) | |
| return sine_theta | |
| def xing_loss_fn(x_list, scale=1e-3): # x[npoints, 2] | |
| loss = 0. | |
| # print(f"points_len: {len(x_list)}") | |
| for x in x_list: | |
| # print(f"x: {x}") | |
| seg_loss = 0. | |
| N = x.size()[0] | |
| assert N % 3 == 0, f'The segment number ({N}) is not correct!' | |
| x = torch.cat([x, x[0, :].unsqueeze(0)], dim=0) # (N+1,2) | |
| segments = torch.cat([x[:-1, :].unsqueeze(1), x[1:, :].unsqueeze(1)], dim=1) # (N, start/end, 2) | |
| segment_num = int(N / 3) | |
| for i in range(segment_num): | |
| cs1 = segments[i * 3, :, :] # start control segs | |
| cs2 = segments[i * 3 + 1, :, :] # middle control segs | |
| cs3 = segments[i * 3 + 2, :, :] # end control segs | |
| # print('the direction of the vectors:') | |
| # print(compute_sine_theta(cs1, cs2)) | |
| direct = (compute_sine_theta(cs1, cs2) >= 0).float() | |
| opst = 1 - direct # another direction | |
| sina = compute_sine_theta(cs1, cs3) # the angle between cs1 and cs3 | |
| seg_loss += direct * torch.relu(- sina) + opst * torch.relu(sina) | |
| # print(direct, opst, sina) | |
| seg_loss /= segment_num | |
| templ = seg_loss | |
| loss += templ * scale # area_loss * scale | |
| return loss / (len(x_list)) | |
| if __name__ == "__main__": | |
| # x = torch.rand([6, 2]) | |
| # x = torch.tensor([[0,0], [1,1], [2,1], [1.5,0]]) | |
| x = torch.tensor([[0, 0], [1, 1], [2, 1], [0.5, 0]]) | |
| # x = torch.tensor([[1,0], [2,1], [0,1], [2,0]]) | |
| scale = 1 # 0.5 | |
| y = xing_loss_fn([x], scale) | |
| print(y) | |
| x = torch.tensor([[0, 0], [1, 1], [2, 1], [2., 0]]) | |
| # x = torch.tensor([[1,0], [2,1], [0,1], [2,0]]) | |
| scale = 1 # 0.5 | |
| y = xing_loss_fn([x], scale) | |
| print(y) | |