SMPLer-X / main /SMPLer_X.py
onescotch
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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