import torch import torch.nn as nn from torch.nn import functional as F from nets.layer import make_conv_layers, make_linear_layers, make_deconv_layers from utils.transforms import sample_joint_features, soft_argmax_2d, soft_argmax_3d from utils.human_models import smpl_x from config import cfg from mmcv.ops.roi_align import roi_align class PositionNet(nn.Module): def __init__(self, part, feat_dim=768): super(PositionNet, self).__init__() if part == 'body': self.joint_num = len(smpl_x.pos_joint_part['body']) self.hm_shape = cfg.output_hm_shape elif part == 'hand': self.joint_num = len(smpl_x.pos_joint_part['rhand']) self.hm_shape = cfg.output_hand_hm_shape self.conv = make_conv_layers([feat_dim, self.joint_num * self.hm_shape[0]], kernel=1, stride=1, padding=0, bnrelu_final=False) def forward(self, img_feat): joint_hm = self.conv(img_feat).view(-1, self.joint_num, self.hm_shape[0], self.hm_shape[1], self.hm_shape[2]) joint_coord = soft_argmax_3d(joint_hm) joint_hm = F.softmax(joint_hm.view(-1, self.joint_num, self.hm_shape[0] * self.hm_shape[1] * self.hm_shape[2]), 2) joint_hm = joint_hm.view(-1, self.joint_num, self.hm_shape[0], self.hm_shape[1], self.hm_shape[2]) return joint_hm, joint_coord class HandRotationNet(nn.Module): def __init__(self, part, feat_dim = 768): super(HandRotationNet, self).__init__() self.part = part self.joint_num = len(smpl_x.pos_joint_part['rhand']) self.hand_conv = make_conv_layers([feat_dim, 512], kernel=1, stride=1, padding=0) self.hand_pose_out = make_linear_layers([self.joint_num * 515, len(smpl_x.orig_joint_part['rhand']) * 6], relu_final=False) self.feat_dim = feat_dim def forward(self, img_feat, joint_coord_img): batch_size = img_feat.shape[0] img_feat = self.hand_conv(img_feat) img_feat_joints = sample_joint_features(img_feat, joint_coord_img[:, :, :2]) feat = torch.cat((img_feat_joints, joint_coord_img), 2) # batch_size, joint_num, 512+3 hand_pose = self.hand_pose_out(feat.view(batch_size, -1)) return hand_pose class BodyRotationNet(nn.Module): def __init__(self, feat_dim = 768): super(BodyRotationNet, self).__init__() self.joint_num = len(smpl_x.pos_joint_part['body']) self.body_conv = make_linear_layers([feat_dim, 512], relu_final=False) self.root_pose_out = make_linear_layers([self.joint_num * (512+3), 6], relu_final=False) self.body_pose_out = make_linear_layers( [self.joint_num * (512+3), (len(smpl_x.orig_joint_part['body']) - 1) * 6], relu_final=False) # without root self.shape_out = make_linear_layers([feat_dim, smpl_x.shape_param_dim], relu_final=False) self.cam_out = make_linear_layers([feat_dim, 3], relu_final=False) self.feat_dim = feat_dim def forward(self, body_pose_token, shape_token, cam_token, body_joint_img): batch_size = body_pose_token.shape[0] # shape parameter shape_param = self.shape_out(shape_token) # camera parameter cam_param = self.cam_out(cam_token) # body pose parameter body_pose_token = self.body_conv(body_pose_token) body_pose_token = torch.cat((body_pose_token, body_joint_img), 2) root_pose = self.root_pose_out(body_pose_token.view(batch_size, -1)) body_pose = self.body_pose_out(body_pose_token.view(batch_size, -1)) return root_pose, body_pose, shape_param, cam_param class FaceRegressor(nn.Module): def __init__(self, feat_dim=768): super(FaceRegressor, self).__init__() self.expr_out = make_linear_layers([feat_dim, smpl_x.expr_code_dim], relu_final=False) self.jaw_pose_out = make_linear_layers([feat_dim, 6], relu_final=False) def forward(self, expr_token, jaw_pose_token): expr_param = self.expr_out(expr_token) # expression parameter jaw_pose = self.jaw_pose_out(jaw_pose_token) # jaw pose parameter return expr_param, jaw_pose class BoxNet(nn.Module): def __init__(self, feat_dim=768): super(BoxNet, self).__init__() self.joint_num = len(smpl_x.pos_joint_part['body']) self.deconv = make_deconv_layers([feat_dim + self.joint_num * cfg.output_hm_shape[0], 256, 256, 256]) self.bbox_center = make_conv_layers([256, 3], kernel=1, stride=1, padding=0, bnrelu_final=False) self.lhand_size = make_linear_layers([256, 256, 2], relu_final=False) self.rhand_size = make_linear_layers([256, 256, 2], relu_final=False) self.face_size = make_linear_layers([256, 256, 2], relu_final=False) def forward(self, img_feat, joint_hm): joint_hm = joint_hm.view(joint_hm.shape[0], joint_hm.shape[1] * cfg.output_hm_shape[0], cfg.output_hm_shape[1], cfg.output_hm_shape[2]) img_feat = torch.cat((img_feat, joint_hm), 1) img_feat = self.deconv(img_feat) # bbox center bbox_center_hm = self.bbox_center(img_feat) bbox_center = soft_argmax_2d(bbox_center_hm) lhand_center, rhand_center, face_center = bbox_center[:, 0, :], bbox_center[:, 1, :], bbox_center[:, 2, :] # bbox size lhand_feat = sample_joint_features(img_feat, lhand_center[:, None, :].detach())[:, 0, :] lhand_size = self.lhand_size(lhand_feat) rhand_feat = sample_joint_features(img_feat, rhand_center[:, None, :].detach())[:, 0, :] rhand_size = self.rhand_size(rhand_feat) face_feat = sample_joint_features(img_feat, face_center[:, None, :].detach())[:, 0, :] face_size = self.face_size(face_feat) lhand_center = lhand_center / 8 rhand_center = rhand_center / 8 face_center = face_center / 8 return lhand_center, lhand_size, rhand_center, rhand_size, face_center, face_size class BoxSizeNet(nn.Module): def __init__(self): super(BoxSizeNet, self).__init__() self.lhand_size = make_linear_layers([256, 256, 2], relu_final=False) self.rhand_size = make_linear_layers([256, 256, 2], relu_final=False) self.face_size = make_linear_layers([256, 256, 2], relu_final=False) def forward(self, box_fea): # box_fea: [bs, 3, C] lhand_size = self.lhand_size(box_fea[:, 0]) rhand_size = self.rhand_size(box_fea[:, 1]) face_size = self.face_size(box_fea[:, 2]) return lhand_size, rhand_size, face_size class HandRoI(nn.Module): def __init__(self, feat_dim=768, upscale=4): super(HandRoI, self).__init__() self.upscale = upscale if upscale==1: self.deconv = make_conv_layers([feat_dim, feat_dim], kernel=1, stride=1, padding=0, bnrelu_final=False) self.conv = make_conv_layers([feat_dim, feat_dim], kernel=1, stride=1, padding=0, bnrelu_final=False) elif upscale==2: self.deconv = make_deconv_layers([feat_dim, feat_dim//2]) self.conv = make_conv_layers([feat_dim//2, feat_dim], kernel=1, stride=1, padding=0, bnrelu_final=False) elif upscale==4: self.deconv = make_deconv_layers([feat_dim, feat_dim//2, feat_dim//4]) self.conv = make_conv_layers([feat_dim//4, feat_dim], kernel=1, stride=1, padding=0, bnrelu_final=False) elif upscale==8: self.deconv = make_deconv_layers([feat_dim, feat_dim//2, feat_dim//4, feat_dim//8]) self.conv = make_conv_layers([feat_dim//8, feat_dim], kernel=1, stride=1, padding=0, bnrelu_final=False) def forward(self, img_feat, lhand_bbox, rhand_bbox): lhand_bbox = torch.cat((torch.arange(lhand_bbox.shape[0]).float().cuda()[:, None], lhand_bbox), 1) # batch_idx, xmin, ymin, xmax, ymax rhand_bbox = torch.cat((torch.arange(rhand_bbox.shape[0]).float().cuda()[:, None], rhand_bbox), 1) # batch_idx, xmin, ymin, xmax, ymax img_feat = self.deconv(img_feat) lhand_bbox_roi = lhand_bbox.clone() lhand_bbox_roi[:, 1] = lhand_bbox_roi[:, 1] / cfg.input_body_shape[1] * cfg.output_hm_shape[2] * self.upscale lhand_bbox_roi[:, 2] = lhand_bbox_roi[:, 2] / cfg.input_body_shape[0] * cfg.output_hm_shape[1] * self.upscale lhand_bbox_roi[:, 3] = lhand_bbox_roi[:, 3] / cfg.input_body_shape[1] * cfg.output_hm_shape[2] * self.upscale lhand_bbox_roi[:, 4] = lhand_bbox_roi[:, 4] / cfg.input_body_shape[0] * cfg.output_hm_shape[1] * self.upscale assert (cfg.output_hm_shape[1]*self.upscale, cfg.output_hm_shape[2]*self.upscale) == (img_feat.shape[2], img_feat.shape[3]) lhand_img_feat = roi_align(img_feat, lhand_bbox_roi, (cfg.output_hand_hm_shape[1], cfg.output_hand_hm_shape[2]), 1.0, 0, 'avg', False) lhand_img_feat = torch.flip(lhand_img_feat, [3]) # flip to the right hand rhand_bbox_roi = rhand_bbox.clone() rhand_bbox_roi[:, 1] = rhand_bbox_roi[:, 1] / cfg.input_body_shape[1] * cfg.output_hm_shape[2] * self.upscale rhand_bbox_roi[:, 2] = rhand_bbox_roi[:, 2] / cfg.input_body_shape[0] * cfg.output_hm_shape[1] * self.upscale rhand_bbox_roi[:, 3] = rhand_bbox_roi[:, 3] / cfg.input_body_shape[1] * cfg.output_hm_shape[2] * self.upscale rhand_bbox_roi[:, 4] = rhand_bbox_roi[:, 4] / cfg.input_body_shape[0] * cfg.output_hm_shape[1] * self.upscale rhand_img_feat = roi_align(img_feat, rhand_bbox_roi, (cfg.output_hand_hm_shape[1], cfg.output_hand_hm_shape[2]), 1.0, 0, 'avg', False) hand_img_feat = torch.cat((lhand_img_feat, rhand_img_feat)) # [bs, c, cfg.output_hand_hm_shape[2]*scale, cfg.output_hand_hm_shape[1]*scale] hand_img_feat = self.conv(hand_img_feat) return hand_img_feat