import torch import torch.nn as nn from torch.nn import functional as F from nets.smpler_x import PositionNet, HandRotationNet, FaceRegressor, BoxNet, HandRoI, BodyRotationNet from nets.loss import CoordLoss, ParamLoss, CELoss from utils.human_models import smpl_x from utils.transforms import rot6d_to_axis_angle, restore_bbox from config import cfg import math import copy from mmpose.models import build_posenet from mmcv import Config class Model(nn.Module): def __init__(self, encoder, body_position_net, body_rotation_net, box_net, hand_position_net, hand_roi_net, hand_rotation_net, face_regressor): super(Model, self).__init__() # body self.encoder = encoder self.body_position_net = body_position_net self.body_regressor = body_rotation_net self.box_net = box_net # hand self.hand_roi_net = hand_roi_net self.hand_position_net = hand_position_net self.hand_regressor = hand_rotation_net # face self.face_regressor = face_regressor self.smplx_layer = copy.deepcopy(smpl_x.layer['neutral']).to(cfg.device) self.coord_loss = CoordLoss() self.param_loss = ParamLoss() self.ce_loss = CELoss() self.body_num_joints = len(smpl_x.pos_joint_part['body']) self.hand_joint_num = len(smpl_x.pos_joint_part['rhand']) self.neck = [self.box_net, self.hand_roi_net] self.head = [self.body_position_net, self.body_regressor, self.hand_position_net, self.hand_regressor, self.face_regressor] self.trainable_modules = [self.encoder, self.body_position_net, self.body_regressor, self.box_net, self.hand_position_net, self.hand_roi_net, self.hand_regressor, self.face_regressor] self.special_trainable_modules = [] # backbone: param_bb = sum(p.numel() for p in self.encoder.parameters() if p.requires_grad) # neck param_neck = 0 for module in self.neck: param_neck += sum(p.numel() for p in module.parameters() if p.requires_grad) # head param_head = 0 for module in self.head: param_head += sum(p.numel() for p in module.parameters() if p.requires_grad) param_net = param_bb + param_neck + param_head # print('#parameters:') # print(f'{param_bb}, {param_neck}, {param_head}, {param_net}') def get_camera_trans(self, cam_param): # camera translation t_xy = cam_param[:, :2] gamma = torch.sigmoid(cam_param[:, 2]) # apply sigmoid to make it positive k_value = torch.FloatTensor([math.sqrt(cfg.focal[0] * cfg.focal[1] * cfg.camera_3d_size * cfg.camera_3d_size / ( cfg.input_body_shape[0] * cfg.input_body_shape[1]))]).to(cfg.device).view(-1) t_z = k_value * gamma cam_trans = torch.cat((t_xy, t_z[:, None]), 1) return cam_trans def get_coord(self, root_pose, body_pose, lhand_pose, rhand_pose, jaw_pose, shape, expr, cam_trans, mode): batch_size = root_pose.shape[0] zero_pose = torch.zeros((1, 3)).float().to(cfg.device).repeat(batch_size, 1) # eye poses output = self.smplx_layer(betas=shape, body_pose=body_pose, global_orient=root_pose, right_hand_pose=rhand_pose, left_hand_pose=lhand_pose, jaw_pose=jaw_pose, leye_pose=zero_pose, reye_pose=zero_pose, expression=expr) # camera-centered 3D coordinate mesh_cam = output.vertices if mode == 'test' and cfg.testset == 'AGORA': # use 144 joints for AGORA evaluation joint_cam = output.joints else: joint_cam = output.joints[:, smpl_x.joint_idx, :] # project 3D coordinates to 2D space if mode == 'train' and len(cfg.trainset_3d) == 1 and cfg.trainset_3d[0] == 'AGORA' and len( cfg.trainset_2d) == 0: # prevent gradients from backpropagating to SMPLX paraemter regression module x = (joint_cam[:, :, 0].detach() + cam_trans[:, None, 0]) / ( joint_cam[:, :, 2].detach() + cam_trans[:, None, 2] + 1e-4) * cfg.focal[0] + cfg.princpt[0] y = (joint_cam[:, :, 1].detach() + cam_trans[:, None, 1]) / ( joint_cam[:, :, 2].detach() + cam_trans[:, None, 2] + 1e-4) * cfg.focal[1] + cfg.princpt[1] else: x = (joint_cam[:, :, 0] + cam_trans[:, None, 0]) / (joint_cam[:, :, 2] + cam_trans[:, None, 2] + 1e-4) * \ cfg.focal[0] + cfg.princpt[0] y = (joint_cam[:, :, 1] + cam_trans[:, None, 1]) / (joint_cam[:, :, 2] + cam_trans[:, None, 2] + 1e-4) * \ cfg.focal[1] + cfg.princpt[1] x = x / cfg.input_body_shape[1] * cfg.output_hm_shape[2] y = y / cfg.input_body_shape[0] * cfg.output_hm_shape[1] joint_proj = torch.stack((x, y), 2) # root-relative 3D coordinates root_cam = joint_cam[:, smpl_x.root_joint_idx, None, :] joint_cam = joint_cam - root_cam mesh_cam = mesh_cam + cam_trans[:, None, :] # for rendering joint_cam_wo_ra = joint_cam.clone() # left hand root (left wrist)-relative 3D coordinatese lhand_idx = smpl_x.joint_part['lhand'] lhand_cam = joint_cam[:, lhand_idx, :] lwrist_cam = joint_cam[:, smpl_x.lwrist_idx, None, :] lhand_cam = lhand_cam - lwrist_cam joint_cam = torch.cat((joint_cam[:, :lhand_idx[0], :], lhand_cam, joint_cam[:, lhand_idx[-1] + 1:, :]), 1) # right hand root (right wrist)-relative 3D coordinatese rhand_idx = smpl_x.joint_part['rhand'] rhand_cam = joint_cam[:, rhand_idx, :] rwrist_cam = joint_cam[:, smpl_x.rwrist_idx, None, :] rhand_cam = rhand_cam - rwrist_cam joint_cam = torch.cat((joint_cam[:, :rhand_idx[0], :], rhand_cam, joint_cam[:, rhand_idx[-1] + 1:, :]), 1) # face root (neck)-relative 3D coordinates face_idx = smpl_x.joint_part['face'] face_cam = joint_cam[:, face_idx, :] neck_cam = joint_cam[:, smpl_x.neck_idx, None, :] face_cam = face_cam - neck_cam joint_cam = torch.cat((joint_cam[:, :face_idx[0], :], face_cam, joint_cam[:, face_idx[-1] + 1:, :]), 1) return joint_proj, joint_cam, joint_cam_wo_ra, mesh_cam def generate_mesh_gt(self, targets, mode): if 'smplx_mesh_cam' in targets: return targets['smplx_mesh_cam'] nums = [3, 63, 45, 45, 3] accu = [] temp = 0 for num in nums: temp += num accu.append(temp) pose = targets['smplx_pose'] root_pose, body_pose, lhand_pose, rhand_pose, jaw_pose = \ pose[:, :accu[0]], pose[:, accu[0]:accu[1]], pose[:, accu[1]:accu[2]], pose[:, accu[2]:accu[3]], pose[:, accu[3]: accu[4]] # print(lhand_pose) shape = targets['smplx_shape'] expr = targets['smplx_expr'] cam_trans = targets['smplx_cam_trans'] # final output joint_proj, joint_cam, joint_cam_wo_ra, mesh_cam = self.get_coord(root_pose, body_pose, lhand_pose, rhand_pose, jaw_pose, shape, expr, cam_trans, mode) return mesh_cam def bbox_split(self, bbox): # bbox:[bs, 3, 3] lhand_bbox_center, rhand_bbox_center, face_bbox_center = \ bbox[:, 0, :2], bbox[:, 1, :2], bbox[:, 2, :2] return lhand_bbox_center, rhand_bbox_center, face_bbox_center def forward(self, inputs, targets, meta_info, mode): body_img = F.interpolate(inputs['img'], cfg.input_body_shape) # 1. Encoder img_feat, task_tokens = self.encoder(body_img) # task_token:[bs, N, c] shape_token, cam_token, expr_token, jaw_pose_token, hand_token, body_pose_token = \ task_tokens[:, 0], task_tokens[:, 1], task_tokens[:, 2], task_tokens[:, 3], task_tokens[:, 4:6], task_tokens[:, 6:] # 2. Body Regressor body_joint_hm, body_joint_img = self.body_position_net(img_feat) root_pose, body_pose, shape, cam_param, = self.body_regressor(body_pose_token, shape_token, cam_token, body_joint_img.detach()) root_pose = rot6d_to_axis_angle(root_pose) body_pose = rot6d_to_axis_angle(body_pose.reshape(-1, 6)).reshape(body_pose.shape[0], -1) # (N, J_R*3) cam_trans = self.get_camera_trans(cam_param) # 3. Hand and Face BBox Estimation lhand_bbox_center, lhand_bbox_size, rhand_bbox_center, rhand_bbox_size, face_bbox_center, face_bbox_size = self.box_net(img_feat, body_joint_hm.detach()) lhand_bbox = restore_bbox(lhand_bbox_center, lhand_bbox_size, cfg.input_hand_shape[1] / cfg.input_hand_shape[0], 2.0).detach() # xyxy in (cfg.input_body_shape[1], cfg.input_body_shape[0]) space rhand_bbox = restore_bbox(rhand_bbox_center, rhand_bbox_size, cfg.input_hand_shape[1] / cfg.input_hand_shape[0], 2.0).detach() # xyxy in (cfg.input_body_shape[1], cfg.input_body_shape[0]) space face_bbox = restore_bbox(face_bbox_center, face_bbox_size, cfg.input_face_shape[1] / cfg.input_face_shape[0], 1.5).detach() # xyxy in (cfg.input_body_shape[1], cfg.input_body_shape[0]) space # 4. Differentiable Feature-level Hand Crop-Upsample # hand_feat: list, [bsx2, c, cfg.output_hm_shape[1]*scale, cfg.output_hm_shape[2]*scale] hand_feat = self.hand_roi_net(img_feat, lhand_bbox, rhand_bbox) # hand_feat: flipped left hand + right hand # 5. Hand/Face Regressor # hand regressor _, hand_joint_img = self.hand_position_net(hand_feat) # (2N, J_P, 3) hand_pose = self.hand_regressor(hand_feat, hand_joint_img.detach()) hand_pose = rot6d_to_axis_angle(hand_pose.reshape(-1, 6)).reshape(hand_feat.shape[0], -1) # (2N, J_R*3) # restore flipped left hand joint coordinates batch_size = hand_joint_img.shape[0] // 2 lhand_joint_img = hand_joint_img[:batch_size, :, :] lhand_joint_img = torch.cat((cfg.output_hand_hm_shape[2] - 1 - lhand_joint_img[:, :, 0:1], lhand_joint_img[:, :, 1:]), 2) rhand_joint_img = hand_joint_img[batch_size:, :, :] # restore flipped left hand joint rotations batch_size = hand_pose.shape[0] // 2 lhand_pose = hand_pose[:batch_size, :].reshape(-1, len(smpl_x.orig_joint_part['lhand']), 3) lhand_pose = torch.cat((lhand_pose[:, :, 0:1], -lhand_pose[:, :, 1:3]), 2).view(batch_size, -1) rhand_pose = hand_pose[batch_size:, :] # hand regressor expr, jaw_pose = self.face_regressor(expr_token, jaw_pose_token) jaw_pose = rot6d_to_axis_angle(jaw_pose) # final output joint_proj, joint_cam, joint_cam_wo_ra, mesh_cam = self.get_coord(root_pose, body_pose, lhand_pose, rhand_pose, jaw_pose, shape, expr, cam_trans, mode) pose = torch.cat((root_pose, body_pose, lhand_pose, rhand_pose, jaw_pose), 1) joint_img = torch.cat((body_joint_img, lhand_joint_img, rhand_joint_img), 1) if mode == 'test' and 'smplx_pose' in targets: mesh_pseudo_gt = self.generate_mesh_gt(targets, mode) if mode == 'train': # loss functions loss = {} smplx_kps_3d_weight = getattr(cfg, 'smplx_kps_3d_weight', 1.0) smplx_kps_3d_weight = getattr(cfg, 'smplx_kps_weight', smplx_kps_3d_weight) # old config smplx_kps_2d_weight = getattr(cfg, 'smplx_kps_2d_weight', 1.0) net_kps_2d_weight = getattr(cfg, 'net_kps_2d_weight', 1.0) smplx_pose_weight = getattr(cfg, 'smplx_pose_weight', 1.0) smplx_shape_weight = getattr(cfg, 'smplx_loss_weight', 1.0) # smplx_orient_weight = getattr(cfg, 'smplx_orient_weight', smplx_pose_weight) # if not specified, use the same weight as pose # do not supervise root pose if original agora json is used if getattr(cfg, 'agora_fix_global_orient_transl', False): # loss['smplx_pose'] = self.param_loss(pose, targets['smplx_pose'], meta_info['smplx_pose_valid'])[:, 3:] * smplx_pose_weight if hasattr(cfg, 'smplx_orient_weight'): smplx_orient_weight = getattr(cfg, 'smplx_orient_weight') loss['smplx_orient'] = self.param_loss(pose, targets['smplx_pose'], meta_info['smplx_pose_valid'])[:, :3] * smplx_orient_weight loss['smplx_pose'] = self.param_loss(pose, targets['smplx_pose'], meta_info['smplx_pose_valid']) * smplx_pose_weight else: loss['smplx_pose'] = self.param_loss(pose, targets['smplx_pose'], meta_info['smplx_pose_valid'])[:, 3:] * smplx_pose_weight loss['smplx_shape'] = self.param_loss(shape, targets['smplx_shape'], meta_info['smplx_shape_valid'][:, None]) * smplx_shape_weight loss['smplx_expr'] = self.param_loss(expr, targets['smplx_expr'], meta_info['smplx_expr_valid'][:, None]) # supervision for keypoints3d wo/ ra loss['joint_cam'] = self.coord_loss(joint_cam_wo_ra, targets['joint_cam'], meta_info['joint_valid'] * meta_info['is_3D'][:, None, None]) * smplx_kps_3d_weight # supervision for keypoints3d w/ ra loss['smplx_joint_cam'] = self.coord_loss(joint_cam, targets['smplx_joint_cam'], meta_info['smplx_joint_valid']) * smplx_kps_3d_weight if not (meta_info['lhand_bbox_valid'] == 0).all(): loss['lhand_bbox'] = (self.coord_loss(lhand_bbox_center, targets['lhand_bbox_center'], meta_info['lhand_bbox_valid'][:, None]) + self.coord_loss(lhand_bbox_size, targets['lhand_bbox_size'], meta_info['lhand_bbox_valid'][:, None])) if not (meta_info['rhand_bbox_valid'] == 0).all(): loss['rhand_bbox'] = (self.coord_loss(rhand_bbox_center, targets['rhand_bbox_center'], meta_info['rhand_bbox_valid'][:, None]) + self.coord_loss(rhand_bbox_size, targets['rhand_bbox_size'], meta_info['rhand_bbox_valid'][:, None])) if not (meta_info['face_bbox_valid'] == 0).all(): loss['face_bbox'] = (self.coord_loss(face_bbox_center, targets['face_bbox_center'], meta_info['face_bbox_valid'][:, None]) + self.coord_loss(face_bbox_size, targets['face_bbox_size'], meta_info['face_bbox_valid'][:, None])) # if (meta_info['face_bbox_valid'] == 0).all(): # out = {} targets['original_joint_img'] = targets['joint_img'].clone() targets['original_smplx_joint_img'] = targets['smplx_joint_img'].clone() # out['original_joint_proj'] = joint_proj.clone() if not (meta_info['lhand_bbox_valid'] + meta_info['rhand_bbox_valid'] == 0).all(): # change hand target joint_img and joint_trunc according to hand bbox (cfg.output_hm_shape -> downsampled hand bbox space) for part_name, bbox in (('lhand', lhand_bbox), ('rhand', rhand_bbox)): for coord_name, trunc_name in (('joint_img', 'joint_trunc'), ('smplx_joint_img', 'smplx_joint_trunc')): x = targets[coord_name][:, smpl_x.joint_part[part_name], 0] y = targets[coord_name][:, smpl_x.joint_part[part_name], 1] z = targets[coord_name][:, smpl_x.joint_part[part_name], 2] trunc = meta_info[trunc_name][:, smpl_x.joint_part[part_name], 0] x -= (bbox[:, None, 0] / cfg.input_body_shape[1] * cfg.output_hm_shape[2]) x *= (cfg.output_hand_hm_shape[2] / ( (bbox[:, None, 2] - bbox[:, None, 0]) / cfg.input_body_shape[1] * cfg.output_hm_shape[ 2])) y -= (bbox[:, None, 1] / cfg.input_body_shape[0] * cfg.output_hm_shape[1]) y *= (cfg.output_hand_hm_shape[1] / ( (bbox[:, None, 3] - bbox[:, None, 1]) / cfg.input_body_shape[0] * cfg.output_hm_shape[ 1])) z *= cfg.output_hand_hm_shape[0] / cfg.output_hm_shape[0] trunc *= ((x >= 0) * (x < cfg.output_hand_hm_shape[2]) * (y >= 0) * ( y < cfg.output_hand_hm_shape[1])) coord = torch.stack((x, y, z), 2) trunc = trunc[:, :, None] targets[coord_name] = torch.cat((targets[coord_name][:, :smpl_x.joint_part[part_name][0], :], coord, targets[coord_name][:, smpl_x.joint_part[part_name][-1] + 1:, :]), 1) meta_info[trunc_name] = torch.cat((meta_info[trunc_name][:, :smpl_x.joint_part[part_name][0], :], trunc, meta_info[trunc_name][:, smpl_x.joint_part[part_name][-1] + 1:, :]), 1) # change hand projected joint coordinates according to hand bbox (cfg.output_hm_shape -> hand bbox space) for part_name, bbox in (('lhand', lhand_bbox), ('rhand', rhand_bbox)): x = joint_proj[:, smpl_x.joint_part[part_name], 0] y = joint_proj[:, smpl_x.joint_part[part_name], 1] x -= (bbox[:, None, 0] / cfg.input_body_shape[1] * cfg.output_hm_shape[2]) x *= (cfg.output_hand_hm_shape[2] / ( (bbox[:, None, 2] - bbox[:, None, 0]) / cfg.input_body_shape[1] * cfg.output_hm_shape[2])) y -= (bbox[:, None, 1] / cfg.input_body_shape[0] * cfg.output_hm_shape[1]) y *= (cfg.output_hand_hm_shape[1] / ( (bbox[:, None, 3] - bbox[:, None, 1]) / cfg.input_body_shape[0] * cfg.output_hm_shape[1])) coord = torch.stack((x, y), 2) trans = [] for bid in range(coord.shape[0]): mask = meta_info['joint_trunc'][bid, smpl_x.joint_part[part_name], 0] == 1 if torch.sum(mask) == 0: trans.append(torch.zeros((2)).float().to(cfg.device)) else: trans.append((-coord[bid, mask, :2] + targets['joint_img'][:, smpl_x.joint_part[part_name], :][ bid, mask, :2]).mean(0)) trans = torch.stack(trans)[:, None, :] coord = coord + trans # global translation alignment joint_proj = torch.cat((joint_proj[:, :smpl_x.joint_part[part_name][0], :], coord, joint_proj[:, smpl_x.joint_part[part_name][-1] + 1:, :]), 1) if not (meta_info['face_bbox_valid'] == 0).all(): # change face projected joint coordinates according to face bbox (cfg.output_hm_shape -> face bbox space) coord = joint_proj[:, smpl_x.joint_part['face'], :] trans = [] for bid in range(coord.shape[0]): mask = meta_info['joint_trunc'][bid, smpl_x.joint_part['face'], 0] == 1 if torch.sum(mask) == 0: trans.append(torch.zeros((2)).float().to(cfg.device)) else: trans.append((-coord[bid, mask, :2] + targets['joint_img'][:, smpl_x.joint_part['face'], :][bid, mask, :2]).mean(0)) trans = torch.stack(trans)[:, None, :] coord = coord + trans # global translation alignment joint_proj = torch.cat((joint_proj[:, :smpl_x.joint_part['face'][0], :], coord, joint_proj[:, smpl_x.joint_part['face'][-1] + 1:, :]), 1) loss['joint_proj'] = self.coord_loss(joint_proj, targets['joint_img'][:, :, :2], meta_info['joint_trunc']) * smplx_kps_2d_weight loss['joint_img'] = self.coord_loss(joint_img, smpl_x.reduce_joint_set(targets['joint_img']), smpl_x.reduce_joint_set(meta_info['joint_trunc']), meta_info['is_3D']) * net_kps_2d_weight loss['smplx_joint_img'] = self.coord_loss(joint_img, smpl_x.reduce_joint_set(targets['smplx_joint_img']), smpl_x.reduce_joint_set(meta_info['smplx_joint_trunc'])) * net_kps_2d_weight return loss else: # change hand output joint_img according to hand bbox for part_name, bbox in (('lhand', lhand_bbox), ('rhand', rhand_bbox)): joint_img[:, smpl_x.pos_joint_part[part_name], 0] *= ( ((bbox[:, None, 2] - bbox[:, None, 0]) / cfg.input_body_shape[1] * cfg.output_hm_shape[2]) / cfg.output_hand_hm_shape[2]) joint_img[:, smpl_x.pos_joint_part[part_name], 0] += ( bbox[:, None, 0] / cfg.input_body_shape[1] * cfg.output_hm_shape[2]) joint_img[:, smpl_x.pos_joint_part[part_name], 1] *= ( ((bbox[:, None, 3] - bbox[:, None, 1]) / cfg.input_body_shape[0] * cfg.output_hm_shape[1]) / cfg.output_hand_hm_shape[1]) joint_img[:, smpl_x.pos_joint_part[part_name], 1] += ( bbox[:, None, 1] / cfg.input_body_shape[0] * cfg.output_hm_shape[1]) # change input_body_shape to input_img_shape for bbox in (lhand_bbox, rhand_bbox, face_bbox): bbox[:, 0] *= cfg.input_img_shape[1] / cfg.input_body_shape[1] bbox[:, 1] *= cfg.input_img_shape[0] / cfg.input_body_shape[0] bbox[:, 2] *= cfg.input_img_shape[1] / cfg.input_body_shape[1] bbox[:, 3] *= cfg.input_img_shape[0] / cfg.input_body_shape[0] # test output out = {} out['img'] = inputs['img'] out['joint_img'] = joint_img out['smplx_joint_proj'] = joint_proj out['smplx_mesh_cam'] = mesh_cam out['smplx_root_pose'] = root_pose out['smplx_body_pose'] = body_pose out['smplx_lhand_pose'] = lhand_pose out['smplx_rhand_pose'] = rhand_pose out['smplx_jaw_pose'] = jaw_pose out['smplx_shape'] = shape out['smplx_expr'] = expr out['cam_trans'] = cam_trans out['lhand_bbox'] = lhand_bbox out['rhand_bbox'] = rhand_bbox out['face_bbox'] = face_bbox if 'smplx_shape' in targets: out['smplx_shape_target'] = targets['smplx_shape'] if 'img_path' in meta_info: out['img_path'] = meta_info['img_path'] if 'smplx_pose' in targets: out['smplx_mesh_cam_pseudo_gt'] = mesh_pseudo_gt if 'smplx_mesh_cam' in targets: out['smplx_mesh_cam_target'] = targets['smplx_mesh_cam'] if 'smpl_mesh_cam' in targets: out['smpl_mesh_cam_target'] = targets['smpl_mesh_cam'] if 'bb2img_trans' in meta_info: out['bb2img_trans'] = meta_info['bb2img_trans'] if 'gt_smplx_transl' in meta_info: out['gt_smplx_transl'] = meta_info['gt_smplx_transl'] return out def init_weights(m): try: if type(m) == nn.ConvTranspose2d: nn.init.normal_(m.weight, std=0.001) elif type(m) == nn.Conv2d: nn.init.normal_(m.weight, std=0.001) nn.init.constant_(m.bias, 0) elif type(m) == nn.BatchNorm2d: nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif type(m) == nn.Linear: nn.init.normal_(m.weight, std=0.01) nn.init.constant_(m.bias, 0) except AttributeError: pass def get_model(mode): # body vit_cfg = Config.fromfile(cfg.encoder_config_file) vit = build_posenet(vit_cfg.model) body_position_net = PositionNet('body', feat_dim=cfg.feat_dim) body_rotation_net = BodyRotationNet(feat_dim=cfg.feat_dim) box_net = BoxNet(feat_dim=cfg.feat_dim) # hand hand_position_net = PositionNet('hand', feat_dim=cfg.feat_dim) hand_roi_net = HandRoI(feat_dim=cfg.feat_dim, upscale=cfg.upscale) hand_rotation_net = HandRotationNet('hand', feat_dim=cfg.feat_dim) # face face_regressor = FaceRegressor(feat_dim=cfg.feat_dim) if mode == 'train': # body if not getattr(cfg, 'random_init', False): encoder_pretrained_model = torch.load(cfg.encoder_pretrained_model_path)['state_dict'] vit.load_state_dict(encoder_pretrained_model, strict=False) print(f"Initialize encoder from {cfg.encoder_pretrained_model_path}") else: print('Random init!!!!!!!') body_position_net.apply(init_weights) body_rotation_net.apply(init_weights) box_net.apply(init_weights) # hand hand_position_net.apply(init_weights) hand_roi_net.apply(init_weights) hand_rotation_net.apply(init_weights) # face face_regressor.apply(init_weights) encoder = vit.backbone model = Model(encoder, body_position_net, body_rotation_net, box_net, hand_position_net, hand_roi_net, hand_rotation_net, face_regressor) return model