File size: 26,156 Bytes
2de1f98
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
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