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import torch |
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import torch.nn as nn |
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import numpy as np |
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import os |
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from util import * |
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class Face_3DMM(nn.Module): |
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def __init__(self, modelpath, id_dim, exp_dim, tex_dim, point_num): |
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super(Face_3DMM, self).__init__() |
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self.point_num = point_num |
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DMM_info = np.load( |
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os.path.join(modelpath, "3DMM_info.npy"), allow_pickle=True |
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).item() |
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base_id = DMM_info["b_shape"][:id_dim, :] |
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mu_id = DMM_info["mu_shape"] |
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base_exp = DMM_info["b_exp"][:exp_dim, :] |
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mu_exp = DMM_info["mu_exp"] |
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mu = mu_id + mu_exp |
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mu = mu.reshape(-1, 3) |
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for i in range(3): |
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mu[:, i] -= np.mean(mu[:, i]) |
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mu = mu.reshape(-1) |
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self.base_id = torch.as_tensor(base_id).cuda() / 100000.0 |
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self.base_exp = torch.as_tensor(base_exp).cuda() / 100000.0 |
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self.mu = torch.as_tensor(mu).cuda() / 100000.0 |
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base_tex = DMM_info["b_tex"][:tex_dim, :] |
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mu_tex = DMM_info["mu_tex"] |
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self.base_tex = torch.as_tensor(base_tex).cuda() |
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self.mu_tex = torch.as_tensor(mu_tex).cuda() |
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sig_id = DMM_info["sig_shape"][:id_dim] |
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sig_tex = DMM_info["sig_tex"][:tex_dim] |
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sig_exp = DMM_info["sig_exp"][:exp_dim] |
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self.sig_id = torch.as_tensor(sig_id).cuda() |
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self.sig_tex = torch.as_tensor(sig_tex).cuda() |
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self.sig_exp = torch.as_tensor(sig_exp).cuda() |
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keys_info = np.load( |
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os.path.join(modelpath, "keys_info.npy"), allow_pickle=True |
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).item() |
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self.keyinds = torch.as_tensor(keys_info["keyinds"]).cuda() |
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self.left_contours = torch.as_tensor(keys_info["left_contour"]).cuda() |
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self.right_contours = torch.as_tensor(keys_info["right_contour"]).cuda() |
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self.rigid_ids = torch.as_tensor(keys_info["rigid_ids"]).cuda() |
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def get_3dlandmarks(self, id_para, exp_para, euler_angle, trans, focal_length, cxy): |
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id_para = id_para * self.sig_id |
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exp_para = exp_para * self.sig_exp |
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batch_size = id_para.shape[0] |
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num_per_contour = self.left_contours.shape[1] |
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left_contours_flat = self.left_contours.reshape(-1) |
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right_contours_flat = self.right_contours.reshape(-1) |
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sel_index = torch.cat( |
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( |
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3 * left_contours_flat.unsqueeze(1), |
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3 * left_contours_flat.unsqueeze(1) + 1, |
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3 * left_contours_flat.unsqueeze(1) + 2, |
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), |
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dim=1, |
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).reshape(-1) |
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left_geometry = ( |
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torch.mm(id_para, self.base_id[:, sel_index]) |
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+ torch.mm(exp_para, self.base_exp[:, sel_index]) |
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+ self.mu[sel_index] |
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) |
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left_geometry = left_geometry.view(batch_size, -1, 3) |
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proj_x = forward_transform( |
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left_geometry, euler_angle, trans, focal_length, cxy |
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)[:, :, 0] |
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proj_x = proj_x.reshape(batch_size, 8, num_per_contour) |
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arg_min = proj_x.argmin(dim=2) |
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left_geometry = left_geometry.view(batch_size * 8, num_per_contour, 3) |
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left_3dlands = left_geometry[ |
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torch.arange(batch_size * 8), arg_min.view(-1), : |
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].view(batch_size, 8, 3) |
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sel_index = torch.cat( |
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( |
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3 * right_contours_flat.unsqueeze(1), |
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3 * right_contours_flat.unsqueeze(1) + 1, |
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3 * right_contours_flat.unsqueeze(1) + 2, |
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), |
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dim=1, |
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).reshape(-1) |
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right_geometry = ( |
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torch.mm(id_para, self.base_id[:, sel_index]) |
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+ torch.mm(exp_para, self.base_exp[:, sel_index]) |
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+ self.mu[sel_index] |
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) |
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right_geometry = right_geometry.view(batch_size, -1, 3) |
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proj_x = forward_transform( |
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right_geometry, euler_angle, trans, focal_length, cxy |
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)[:, :, 0] |
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proj_x = proj_x.reshape(batch_size, 8, num_per_contour) |
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arg_max = proj_x.argmax(dim=2) |
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right_geometry = right_geometry.view(batch_size * 8, num_per_contour, 3) |
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right_3dlands = right_geometry[ |
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torch.arange(batch_size * 8), arg_max.view(-1), : |
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].view(batch_size, 8, 3) |
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sel_index = torch.cat( |
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( |
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3 * self.keyinds.unsqueeze(1), |
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3 * self.keyinds.unsqueeze(1) + 1, |
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3 * self.keyinds.unsqueeze(1) + 2, |
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), |
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dim=1, |
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).reshape(-1) |
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geometry = ( |
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torch.mm(id_para, self.base_id[:, sel_index]) |
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+ torch.mm(exp_para, self.base_exp[:, sel_index]) |
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+ self.mu[sel_index] |
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) |
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lands_3d = geometry.view(-1, self.keyinds.shape[0], 3) |
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lands_3d[:, :8, :] = left_3dlands |
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lands_3d[:, 9:17, :] = right_3dlands |
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return lands_3d |
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def forward_geo_sub(self, id_para, exp_para, sub_index): |
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id_para = id_para * self.sig_id |
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exp_para = exp_para * self.sig_exp |
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sel_index = torch.cat( |
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( |
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3 * sub_index.unsqueeze(1), |
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3 * sub_index.unsqueeze(1) + 1, |
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3 * sub_index.unsqueeze(1) + 2, |
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), |
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dim=1, |
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).reshape(-1) |
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geometry = ( |
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torch.mm(id_para, self.base_id[:, sel_index]) |
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+ torch.mm(exp_para, self.base_exp[:, sel_index]) |
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+ self.mu[sel_index] |
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) |
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return geometry.reshape(-1, sub_index.shape[0], 3) |
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def forward_geo(self, id_para, exp_para): |
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id_para = id_para * self.sig_id |
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exp_para = exp_para * self.sig_exp |
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geometry = ( |
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torch.mm(id_para, self.base_id) |
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+ torch.mm(exp_para, self.base_exp) |
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+ self.mu |
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) |
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return geometry.reshape(-1, self.point_num, 3) |
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def forward_tex(self, tex_para): |
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tex_para = tex_para * self.sig_tex |
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texture = torch.mm(tex_para, self.base_tex) + self.mu_tex |
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return texture.reshape(-1, self.point_num, 3) |
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