| import torch |
| import torch.nn as nn |
| import trimesh |
| import numpy as np |
| from .TEHNet import TEHNet |
| from .utils import create_mano_layers |
| from settings import MANO_PATH, MANO_CMPS |
|
|
|
|
| class TEHNetWrapper(): |
| def state_dict(self): |
| return self.net.state_dict() |
|
|
| def load_state_dict(self, params, *args, **kwargs): |
| modified_params = dict() |
|
|
| for k, v in params.items(): |
| if k.startswith('module.'): |
| k = k[len('module.'):] |
| |
| modified_params[k] = v |
| |
| self.net.load_state_dict(modified_params, *args, **kwargs) |
|
|
| def parameters(self): |
| return self.net.parameters() |
|
|
| def train(self): |
| self.training = True |
| return self.net.train() |
|
|
| def eval(self): |
| self.training = False |
| return self.net.eval() |
|
|
| def P3dtoP2d(self, j3d, scale, translation): |
| B, N = j3d.shape[:2] |
|
|
| homogeneous_j3d = torch.cat([j3d, torch.ones(B, N, 1, device=j3d.device)], 2) |
| homogeneous_j3d = homogeneous_j3d @ self.rot.detach() |
|
|
| translation = translation.unsqueeze(1) |
| scale = scale.unsqueeze(1) |
|
|
| j2d = torch.zeros(B, N, 2, device=j3d.device) |
| j2d[:, :, 0] = translation[:, :, 0] + scale[:, :, 0] * homogeneous_j3d[:, :, 0] |
| j2d[:, :, 1] = translation[:, :, 1] + scale[:, :, 1] * homogeneous_j3d[:, :, 1] |
|
|
| return j2d |
|
|
| def __init__(self, device): |
| net = TEHNet(n_pose_params=MANO_CMPS).to(device) |
|
|
| self.net = net |
| self.training = False |
| |
| self.hands = create_mano_layers(MANO_PATH, device, MANO_CMPS) |
|
|
| self.rot = torch.tensor(trimesh.transformations.rotation_matrix(np.radians(180), [1, 0, 0]), device=device).float() |
|
|
| def __call__(self, inp): |
| outputs = self.net(inp, self.hands) |
|
|
| return outputs |
|
|
