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""" |
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This part reuses code from https://github.com/MandyMo/pytorch_HMR/blob/master/src/util.py |
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which is part of a PyTorch port of SMPL. |
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Thanks to Zhang Xiong (MandyMo) for making this great code available on github ! |
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""" |
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import argparse |
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from torch.autograd import gradcheck |
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import torch |
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from torch.autograd import Variable |
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from custom_manopth import argutils |
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def quat2mat(quat): |
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"""Convert quaternion coefficients to rotation matrix. |
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Args: |
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quat: size = [batch_size, 4] 4 <===>(w, x, y, z) |
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Returns: |
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Rotation matrix corresponding to the quaternion -- size = [batch_size, 3, 3] |
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""" |
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norm_quat = quat |
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norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True) |
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w, x, y, z = norm_quat[:, 0], norm_quat[:, 1], norm_quat[:, |
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2], norm_quat[:, |
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3] |
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batch_size = quat.size(0) |
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w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2) |
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wx, wy, wz = w * x, w * y, w * z |
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xy, xz, yz = x * y, x * z, y * z |
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rotMat = torch.stack([ |
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w2 + x2 - y2 - z2, 2 * xy - 2 * wz, 2 * wy + 2 * xz, 2 * wz + 2 * xy, |
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w2 - x2 + y2 - z2, 2 * yz - 2 * wx, 2 * xz - 2 * wy, 2 * wx + 2 * yz, |
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w2 - x2 - y2 + z2 |
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], |
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dim=1).view(batch_size, 3, 3) |
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return rotMat |
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def batch_rodrigues(axisang): |
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axisang_norm = torch.norm(axisang + 1e-8, p=2, dim=1) |
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angle = torch.unsqueeze(axisang_norm, -1) |
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axisang_normalized = torch.div(axisang, angle) |
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angle = angle * 0.5 |
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v_cos = torch.cos(angle) |
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v_sin = torch.sin(angle) |
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quat = torch.cat([v_cos, v_sin * axisang_normalized], dim=1) |
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rot_mat = quat2mat(quat) |
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rot_mat = rot_mat.view(rot_mat.shape[0], 9) |
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return rot_mat |
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def th_get_axis_angle(vector): |
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angle = torch.norm(vector, 2, 1) |
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axes = vector / angle.unsqueeze(1) |
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return axes, angle |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--batch_size', default=1, type=int) |
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parser.add_argument('--cuda', action='store_true') |
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args = parser.parse_args() |
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argutils.print_args(args) |
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n_components = 6 |
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rot = 3 |
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inputs = torch.rand(args.batch_size, rot) |
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inputs_var = Variable(inputs.double(), requires_grad=True) |
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if args.cuda: |
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inputs = inputs.cuda() |
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test_function = gradcheck(batch_rodrigues, (inputs_var, )) |
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print('batch test passed !') |
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inputs = torch.rand(rot) |
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inputs_var = Variable(inputs.double(), requires_grad=True) |
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test_function = gradcheck(th_cv2_rod_sub_id.apply, (inputs_var, )) |
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print('th_cv2_rod test passed') |
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inputs = torch.rand(rot) |
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inputs_var = Variable(inputs.double(), requires_grad=True) |
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test_th = gradcheck(th_cv2_rod.apply, (inputs_var, )) |
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print('th_cv2_rod_id test passed !') |
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