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import torch.nn as nn
from torch.nn import functional as F
from nets.smpler_x import PositionNet, HandRotationNet, FaceRegressor, BoxNet, HandRoI, BodyRotationNet, BodyTokenNet, GlobalHandPositionNet, HandTokenRegressor
from tokenhmr.lib.models.heads.token_head import SMPLTokenDecoderHead
from nets.loss import CoordLoss, ParamLoss, CELoss
from utils.human_models import smpl_x, smpl
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
from FishEyeCalibrated import FishEyeCameraCalibrated
import numpy as np
import cv2
import matplotlib.pyplot as plt
from pdb import set_trace
# from common.utils.vis import render_mesh_fisheye
from scipy.spatial import cKDTree
from data import humandata
import os
from omegaconf import OmegaConf
from DPoserX.run.tester.wholebody.smplify import DPoser
from torch.autograd import Function
# from main.transformer_utils.mmpose.models.backbones.ViT_DINO import vit_large
# from main.transformer_utils.mmpose.models.heads.RAFTDepthNormalDPTDecoder5 import RAFTDepthNormalDPT5
# from mmengine.config import Config
class GradReverse(Function):
@staticmethod
def forward(ctx, x, lambd):
ctx.lambd = float(lambd)
return x.view_as(x)
@staticmethod
def backward(ctx, grad_output):
return grad_output.neg() * ctx.lambd, None
bone_pairs = [
[0, 1], [1, 2], [2, 3], # right arm
[0, 4], [4, 5], [5, 6], # left arm
[1, 7], [7, 8], [8, 9], [9,10], # right leg
[4,11], [11,12], [12,13], [13,14], # left leg
]
mo2cap2_to_smplx = {
"neck": 7,
"right_shoulder": 9,
"right_elbow": 11,
"right_wrist": 13,
"left_shoulder": 8,
"left_elbow": 10,
"left_wrist": 12,
"right_hip": 2,
"right_knee": 4,
"right_ankle": 6,
"right_foot": 19,
"left_hip": 1,
"left_knee": 3,
"left_ankle": 5,
"left_foot": 16,
}
mo2cap2_joint_names = [
"neck","right_shoulder","right_elbow","right_wrist",
"left_shoulder","left_elbow","left_wrist",
"right_hip","right_knee","right_ankle","right_foot",
"left_hip","left_knee","left_ankle","left_foot",
]
mo2cap2_chain = [
[0, 1, 2, 3],
[0, 4, 5, 6],
[1, 7, 8, 9, 10],
[4, 11, 12, 13, 14],
[7, 11],
]
class Model(nn.Module):
def __init__(self, encoder, token_decoder, body_position_net, body_rotation_net, box_net, hand_position_net, hand_roi_net,
hand_rotation_net, face_regressor, mode):
super(Model, self).__init__()
if getattr(cfg, 'fisheye_camera_path', False):
fisheye_camera_path = cfg.fisheye_camera_path
else:
fisheye_camera_path = '/home/cv1/works/SMPLer-X/main/fisheye.calibration_01_12.json'
self.fisheye_camera = FishEyeCameraCalibrated(fisheye_camera_path)
# body
self.encoder = encoder
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
self.neck = [self.box_net, self.hand_roi_net]
# face
self.face_regressor = face_regressor
self.smplx_layer = copy.deepcopy(smpl_x.layer['neutral']).cuda()
self.smpl_layer = copy.deepcopy(smpl.layer['neutral']).cuda() # SMPL(6890 verts)
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.body_position_net = body_position_net
if getattr(cfg, 'use_token_decoder', False):
if getattr(cfg, 'use_smpl', False):
self.token_decoder = token_decoder
self.head = [self.token_decoder, self.body_position_net,
self.hand_position_net, self.hand_regressor,
self.face_regressor]
self.trainable_modules = [self.encoder, self.token_decoder, self.body_position_net]
else:
self.token_decoder = token_decoder
self.head = [self.token_decoder, self.body_position_net,
self.hand_position_net, self.hand_regressor,
self.face_regressor]
self.trainable_modules = [self.encoder, self.token_decoder, self.body_position_net,
self.box_net, self.hand_position_net,
self.hand_roi_net, self.hand_regressor, self.face_regressor]
else:
if getattr(cfg, 'use_smpl', False):
self.body_regressor = body_rotation_net
self.head = [self.body_position_net, self.body_regressor,
self.hand_position_net, self.hand_regressor,
self.face_regressor]
if getattr(cfg, 'use_dposerx', False):
if mode == 'train':
self.dposer_x = DPoser(batch_size=cfg.train_batch_size, config_path=cfg.dposerx_cfg_path).cuda()
self.trainable_modules = [self.encoder, self.dposer_x, self.body_position_net, self.body_regressor]
#self.trainable_modules = [self.encoder, self.body_position_net, self.body_regressor]
else:
self.dposer_x = DPoser(batch_size=cfg.test_batch_size, config_path=cfg.dposerx_cfg_path).cuda()
else:
self.trainable_modules = [self.encoder, self.body_position_net, self.body_regressor]
else:
self.body_regressor = body_rotation_net
self.head = [self.body_position_net, self.body_regressor,
self.hand_position_net, self.hand_regressor,
self.face_regressor]
if getattr(cfg, 'use_dposerx', False):
if mode == 'train':
self.dposer_x = DPoser(batch_size=cfg.train_batch_size, config_path=cfg.dposerx_cfg_path).cuda()
self.trainable_modules = [self.encoder, self.dposer_x, self.body_position_net, self.body_regressor,
self.box_net, self.hand_position_net,
self.hand_roi_net, self.hand_regressor]
else:
self.dposer_x = DPoser(batch_size=cfg.test_batch_size, config_path=cfg.dposerx_cfg_path).cuda()
else:
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 = []
# --- ID adversarial head (optional) ---
self.id_adv_lambda = float(getattr(cfg, 'id_adv_lambda', 0.0)) # e.g., 0.5
self.num_subjects = int(getattr(cfg, 'num_subjects', 0)) # ID 클래스 수
if self.id_adv_lambda > 0 and self.num_subjects > 0:
# img_feat의 채널 차원 = encoder 출력 채널(보통 cfg.feat_dim과 일치)
self.id_head = nn.Linear(cfg.feat_dim, self.num_subjects)
self.id_loss_fn = nn.BCEWithLogitsLoss()
# 학습 모듈에 포함(선택)
self.trainable_modules.append(self.id_head)
self.head.append(self.id_head)
# 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
# ---- 파라미터 개수 출력 ----
total_params = sum(p.numel() for p in self.parameters())
total_trainable = sum(p.numel() for p in self.parameters() if p.requires_grad)
def _m(x):
return x / 1e6 # million 단위
print("========== SMPLer-X Parameter Counts ==========")
print(f"Backbone (encoder, trainable) : {param_bb:,} ({_m(param_bb):.3f} M)")
print(f"Neck (box + hand_roi) : {param_neck:,} ({_m(param_neck):.3f} M)")
print(f"Head (body/hand/face 등) : {param_head:,} ({_m(param_head):.3f} M)")
print("----------------------------------------------")
print(f"Total trainable : {param_net:,} ({_m(param_net):.3f} M)")
print(f"Total (all params) : {total_params:,} ({_m(total_params):.3f} M)")
print("==============================================")
self.undist_w, self.undist_h = 1280, 1024
fov_deg = 120
fov_rad = np.deg2rad(fov_deg)
x = np.linspace(-1, 1, self.undist_w)
y = np.linspace(-1, 1, self.undist_h)
xx, yy = np.meshgrid(x, y)
z = 1.0 / np.tan(fov_rad / 2)
rays = np.stack([xx, yy, np.full_like(xx, z)], axis=-1)
rays /= np.linalg.norm(rays, axis=-1, keepdims=True)
rays_flat = rays.reshape(-1, 3)
mapped_pixels = []
for r in rays_flat:
try:
uv = self.fisheye_camera.world2camera(np.array([r]))
mapped_pixels.append(uv[0])
except:
mapped_pixels.append([-1, -1])
self.mapped_pixels = np.array(mapped_pixels, dtype=np.float32).reshape(self.undist_h, self.undist_w, 2)
def _get_subject_scene_dir(self, img_path):
"""
img_path: .../{subject}/{scene}/imgs/filename 형태라고 가정하고
마지막 'imgs' 기준으로 바로 앞의 두 디렉토리(subject, scene)를 리턴.
실패하면 빈 문자열 반환.
"""
if not isinstance(img_path, str):
return ""
path = img_path.replace("\\", "/")
parts = path.split("/")
# 마지막 'imgs' 위치 찾기
idx = None
for j in range(len(parts) - 1, -1, -1):
if parts[j] == "imgs":
idx = j
break
if idx is None or idx < 2:
return ""
subject = parts[idx - 2]
scene = parts[idx - 1]
return os.path.join(subject, scene)
def _save_activation_heatmap(
self,
img_3chw: torch.Tensor,
feat_3chw: torch.Tensor,
save_path: str,
alpha: float = 0.45,
# 새 인자들: 원래 토큰 그리드와 잘려나간 칸 수
orig_grid_hw=(16, 16), # (H0, W0) = 원래 16x16
crop=(0, 2, 0, 2), # (top, right, bottom, left) = 좌우 2칸
upsample: str = "nearest" # 토큰 경계 또렷: 'nearest' 권장
):
"""
img_3chw : [3,H,W]
feat_3chw: [C,h,w] (현재 16x12 토큰 그리드로 재배치된 feature)
"""
import os
import matplotlib.pyplot as plt
import torch.nn.functional as F
# 1) 채널 평균 → (h,w)
hm = feat_3chw.detach().float().mean(0) # [h,w] (※ .values 쓰지 마세요)
# 2) (선택) 간단 정규화
hm = (hm - hm.min()) / (hm.max() - hm.min() + 1e-6)
# 3) 잘려나간 칸만큼 패딩해서 원래 16x16 위치로 복원 + per-pixel alpha 마스크
H0, W0 = orig_grid_hw # ex) 16, 16
t, r, b, l = crop # ex) (0,2,0,2)
cam_pad = torch.zeros((H0, W0), dtype=hm.dtype, device=hm.device)
alpha_pad = torch.zeros_like(cam_pad) # 패딩 영역은 투명(0)
cam_pad[t:H0-b, l:W0-r] = hm
alpha_pad[t:H0-b, l:W0-r] = 1.0
# 4) 이미지 해상도로 업샘플 (히트맵/알파)
H_img, W_img = img_3chw.shape[-2], img_3chw.shape[-1]
cam_up = F.interpolate(cam_pad[None, None], size=(H_img, W_img),
mode=upsample, align_corners=False if upsample!="nearest" else None)[0, 0].cpu().numpy()
a_up = F.interpolate(alpha_pad[None, None], size=(H_img, W_img),
mode="nearest")[0, 0].cpu().numpy() # 알파는 nearest 권장
# 5) 원본 오버레이 (잘려나간 영역은 자동으로 투명)
img = img_3chw.detach().cpu().float().clamp(0, 1).permute(1, 2, 0).numpy()
plt.figure(figsize=(6, 4.5))
plt.imshow(img)
plt.imshow(cam_up, cmap="viridis", alpha=alpha * a_up) # per-pixel alpha
plt.axis("off"); plt.tight_layout()
os.makedirs(os.path.dirname(save_path) or ".", exist_ok=True)
plt.savefig(save_path, dpi=200, bbox_inches="tight")
plt.close()
def visualize_hand_bboxes_on_input(self, img_batch, lhand_bbox, rhand_bbox,
save_dir, meta_info=None):
"""
img_batch : [B, 3, H, W] (inputs['img_ori'])
lhand_bbox, rhand_bbox : [B, 4] (xyxy, input_img_shape 기준)
"""
os.makedirs(save_dir, exist_ok=True)
# [B, H, W, C] 로 변환
img_np = img_batch.detach().cpu().numpy().transpose(0, 2, 3, 1)
B = img_np.shape[0]
for i in range(B):
img = img_np[i]
# 이미지 값 범위에 따라 0~255 uint8 로 변환
if img.max() <= 1.0:
img = (img * 255.0).astype(np.uint8)
else:
img = img.astype(np.uint8)
vis = img.copy()
h, w = vis.shape[:2]
def _draw_box(box, color, label_text=None):
box = box.detach().cpu().numpy()
x1, y1, x2, y2 = box
x1 = int(np.clip(x1, 0, w - 1))
x2 = int(np.clip(x2, 0, w - 1))
y1 = int(np.clip(y1, 0, h - 1))
y2 = int(np.clip(y2, 0, h - 1))
cv2.rectangle(vis, (x1, y1), (x2, y2), color, 2)
if label_text is not None:
cv2.putText(vis, label_text, (x1, max(0, y1 - 5)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1,
lineType=cv2.LINE_AA)
# 왼손: 초록, 오른손: 파랑
_draw_box(lhand_bbox[i], (0, 255, 0), 'L-hand')
_draw_box(rhand_bbox[i], (255, 0, 0), 'R-hand')
# # 파일 이름 결정 (img_path 있으면 거기서 베이스 이름 사용)
# if meta_info is not None and 'img_path' in meta_info:
# base = meta_info['img_path'][i]
# if not isinstance(base, str):
# base = f"{i:06d}.png"
# name = os.path.splitext(os.path.basename(base))[0]
# else:
# name = f"{i:06d}"
# out_path = os.path.join(save_dir, f"{name}_hand_bbox.png")
# # OpenCV는 BGR 을 기대하므로 RGB -> BGR 로 뒤집어서 저장
# cv2.imwrite(out_path, vis[:, :, ::-1])
img_path_i = None
sub_rel = ""
if meta_info is not None and 'img_path' in meta_info:
img_path_i = meta_info['img_path'][i]
if isinstance(img_path_i, str):
sub_rel = self._get_subject_scene_dir(img_path_i)
name = os.path.splitext(os.path.basename(img_path_i))[0]
else:
name = f"{i:06d}"
else:
name = f"{i:06d}"
if sub_rel:
cur_dir = os.path.join(save_dir, sub_rel)
else:
cur_dir = save_dir
os.makedirs(cur_dir, exist_ok=True)
out_path = os.path.join(cur_dir, f"{name}_hand_bbox.png")
cv2.imwrite(out_path, vis[:, :, ::-1])
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_img_shape[0] * cfg.input_img_shape[1]))]).cuda().view(-1)
t_z = k_value * gamma
cam_trans = torch.cat((t_xy, t_z[:, None]), 1)
return cam_trans
def undistort_tensor_image(self, tensor_img, fisheye_camera: FishEyeCameraCalibrated):
"""
tensor_img: [B, C, H, W] - torch.Tensor (range 0~1 or 0~255)
return: undistorted image in torch.Tensor [B, C, H, W]
"""
device = tensor_img.device
B, C, H, W = tensor_img.shape
tensor_img_np = tensor_img.detach().cpu().numpy().transpose(0, 2, 3, 1) # [B, H, W, C]
undistorted_np = []
for i in range(B):
img = (tensor_img_np[i] * 255).astype(np.uint8) if tensor_img_np[i].max() <= 1 else tensor_img_np[i].astype(np.uint8)
# grid 생성
x = np.linspace(-1, 1, W)
y = np.linspace(-1, 1, H)
xx, yy = np.meshgrid(x, y)
z = 1.0 # FOV에 해당하는 focal length 기준
rays = np.stack([xx, yy, np.full_like(xx, z)], axis=-1)
rays /= np.linalg.norm(rays, axis=-1, keepdims=True)
rays_flat = rays.reshape(-1, 3)
mapped_pixels = []
for r in rays_flat:
try:
uv = self.fisheye_camera.world2camera(np.array([r])) # (1, 2)
mapped_pixels.append(uv[0])
except:
mapped_pixels.append([-1, -1])
mapped_pixels = np.array(mapped_pixels, dtype=np.float32).reshape(H, W, 2)
undistorted_img = cv2.remap(
img,
mapped_pixels[:, :, 0],
mapped_pixels[:, :, 1],
interpolation=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=0
)
undistorted_np.append(undistorted_img)
undistorted_np = np.stack(undistorted_np).astype(np.float32) / 255.0
undistorted_tensor = torch.from_numpy(undistorted_np).permute(0, 3, 1, 2).to(device)
return undistorted_tensor
def visualize_body_img_comparison(self, original, undistorted):
"""
original, undistorted: [B, C, H, W] torch.Tensor
"""
import matplotlib.pyplot as plt
B = original.shape[0]
for i in range(B):
orig_np = original[i].detach().cpu().permute(1, 2, 0).numpy()
undist_np = undistorted[i].detach().cpu().permute(1, 2, 0).numpy()
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.imshow(np.clip(orig_np, 0, 1))
plt.title('Original (Fisheye)')
plt.axis('off')
plt.subplot(1, 2, 2)
plt.imshow(np.clip(undist_np, 0, 1))
plt.title('Undistorted')
plt.axis('off')
plt.tight_layout()
plt.show()
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]
# set_trace()
zero_pose = torch.zeros((1, 3)).float().cuda().repeat(batch_size, 1) # eye poses
# if getattr(cfg, 'use_token_decoder', False):
# pad = torch.zeros(body_pose.size(0), 6, dtype=body_pose.dtype, device=body_pose.device) # (B, 2, 3)
# body_pose = torch.cat((body_pose, pad), 1)
# output = self.smpl_layer(betas=shape, body_pose=body_pose, global_orient=root_pose)
# else:
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)
mesh_cam = output.vertices + cam_trans[:, None, :] # ← 최종 mesh_cam은 SMPL 기준
# smplx decoder 사용
# 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, :]
# set_trace()
B, J, _ = joint_cam.shape
# set_trace()
joint_cam_translation =joint_cam.detach() + cam_trans[:, None, :]
joint_cam_flat = (joint_cam_translation).view(-1, 3) # (B*J, 3)
# (B*J, 3) → (B*J, 2): fisheye projection (image 좌표계)
joint_proj_flat = self.fisheye_camera.world2camera_pytorch(joint_cam_flat) # (B*J, 2)
# (B*J, 2) → (B, J, 2)
joint_proj = joint_proj_flat.view(B, J, 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()
# 2. heatmap 좌표계로 변환
joint_proj[..., 0] = joint_proj[..., 0] / cfg.input_img_shape[1] * cfg.output_hm_shape[2] # x: w 기준
joint_proj[..., 1] = joint_proj[..., 1] / cfg.input_img_shape[0] * cfg.output_hm_shape[1] # y: h 기준
# 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 build_forward_map(self,mapped_pixels):
"""
mapped_pixels: (H, W, 2) — dst(i,j) 위치에서 원본 좌표를 가져오는 역방향 맵
반환: KDTree와 보정 이미지 좌표 (dst_x, dst_y)
"""
H, W = mapped_pixels.shape[:2]
src_points = mapped_pixels.reshape(-1, 2) # (H*W, 2): 원본 좌표계 기준
dst_coords = np.stack(np.meshgrid(np.arange(W), np.arange(H)), axis=-1).reshape(-1, 2) # (H*W, 2)
# 유효한 원본 좌표만 사용
valid_mask = np.all((src_points >= 0) & (src_points < np.array([W, H])), axis=1)
src_points = src_points[valid_mask]
dst_coords = dst_coords[valid_mask]
tree = cKDTree(src_points)
return tree, dst_coords
def remap_joints_forward(self, joint_orig, tree, dst_coords):
"""
joint_orig: (N, 2) — 원본 이미지 기준 joint
tree: KDTree of mapped_pixels (src → dst)
dst_coords: 보정된 이미지 좌표
반환: 보정된 joint 위치 (N, 2)
"""
dist, idx = tree.query(joint_orig, k=1)
joint_remapped = dst_coords[idx]
return joint_remapped.astype(np.float32)
def draw_joint_lines(self, img_np, joints_2d, joint_names, chains, color=(0,255,0)):
"""
img_np: (H,W,3) numpy array (uint8)
joints_2d: (N,2) float (xy)
joint_names: 이름 리스트
chains: 각 limb의 joint 인덱스 chain(순서대로 연결)
color: 선 색
"""
# joint 찍기
for x, y in joints_2d:
x_int, y_int = int(round(x)), int(round(y))
if 0 <= x_int < img_np.shape[1] and 0 <= y_int < img_np.shape[0]:
cv2.circle(img_np, (x_int, y_int), 3, color, -1)
# 선 그리기
for chain in chains:
for i in range(len(chain)-1):
idx1, idx2 = chain[i], chain[i+1]
x1, y1 = joints_2d[idx1]
x2, y2 = joints_2d[idx2]
pt1 = (int(round(x1)), int(round(y1)))
pt2 = (int(round(x2)), int(round(y2)))
if all(0 <= pt < img_np.shape[1] for pt in pt1) and all(0 <= pt < img_np.shape[0] for pt in pt1) and \
all(0 <= pt < img_np.shape[1] for pt in pt2) and all(0 <= pt < img_np.shape[0] for pt in pt2):
cv2.line(img_np, pt1, pt2, (255, 128, 0), 2) # 선 색은 예시 (주황)
return img_np
def visualize_mesh_on_image(self, img_np, mesh_cam, color=(0, 255, 0)):
"""
img_np: (H, W, 3) numpy array (uint8)
mesh_cam: (N, 3) torch.Tensor - 카메라 좌표계의 mesh vertices
"""
# numpy로 변환
mesh_cam_np = mesh_cam.detach().cpu().numpy()
# 3D mesh를 fisheye projection을 통해 2D로 투영
mesh_proj_2d = self.fisheye_camera.world2camera_pytorch(
torch.from_numpy(mesh_cam_np).float().cuda()
).detach().cpu().numpy() # (N, 2)
# 각 vertex를 이미지 위에 표시
for x, y in mesh_proj_2d:
x_int, y_int = int(round(x)), int(round(y))
if 0 <= x_int < img_np.shape[1] and 0 <= y_int < img_np.shape[0]:
cv2.circle(img_np, (x_int, y_int), 1, color, -1)
return img_np
def forward(self, inputs, targets, meta_info, mode):
# body_img = F.interpolate(inputs['img_ori'], cfg.input_body_shape)
# body_img = inputs['img']
# # 0. Fisheye 보정 수행
# # 0.5 시각화 (선택)
# self.visualize_body_img_comparison(body_img, body_img_undistorted)
body_img = inputs['img_ori']
# set_trace()
# 1. Encoder
## img_feat: [bs, 1280, 16, 12], task_tokens: [bs, 31, 1280]
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:]
# 1-1. ID Regressor
# person_id = self.id_regressor(id_token)
# set_trace()
# 2. Body Regressor
if not getattr(cfg, 'use_token_decoder', False):
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())
else:
body_joint_hm, body_joint_img = self.body_position_net(img_feat)
# set_trace()
smpl_param = self.token_decoder(img_feat)
# set_trace()
root_pose = smpl_param['root_pose']
body_pose = smpl_param['body_pose']
shape = smpl_param['betas']
cam_param = smpl_param['cam_param']
# set_trace()
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)
# set_trace()
# 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:, :]
# # 5. Hand regressor without ROI
# hand_joint_hm, hand_joint_coord_global = self.hand_position_net(img_feat) # (B, 2J_all, 3)
# B = img_feat.size(0)
# J_all = self.hand_joint_num # 20 (keypoints/joints)
# J_p = len(smpl_x.orig_joint_part['lhand']) # 보통 15 (pose parameter joints)
# # split left/right (each is (B, J_all, 3))
# lhand_joint_img = hand_joint_coord_global[:, :J_all, :]
# rhand_joint_img = hand_joint_coord_global[:, J_all:, :]
# # --- 중요: pose에 쓰는 joint만 선택 ---
# # 대부분 ordering이 [wrist(0), finger 15개(1~15), tips 4개(16~19)] 라서
# # wrist/tips 제외하고 finger 15개만 쓰면 됨.
# lhand_joint_img_pose = lhand_joint_img[:, 1:1+J_p, :] # (B, J_p, 3)
# rhand_joint_img_pose = rhand_joint_img[:, 1:1+J_p, :] # (B, J_p, 3)
# hand_joint_coord = torch.stack([lhand_joint_img_pose, rhand_joint_img_pose], dim=1) # (B, 2, J_p, 3)
# token+joint -> 6D (B, 2, J_p, 6)
# hand_pose_6d = self.hand_regressor(hand_token, hand_joint_coord.detach())
# 6D -> axis-angle (B, 2, J_p, 3)
# hand_pose_aa = rot6d_to_axis_angle(hand_pose_6d.reshape(-1, 6)).view(B, 2, J_p, 3)
# (B, J_p*3) == (B, 45) 로 맞춰짐
# lhand_pose = hand_pose_aa[:, 0].reshape(B, -1)
# rhand_pose = hand_pose_aa[:, 1].reshape(B, -1)
# face 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_param, mode)
pose = torch.cat((root_pose, body_pose, lhand_pose, rhand_pose, jaw_pose), 1)
# set_trace()
# set_trace()
B = body_pose.size(0)
device = body_pose.device
dtype = body_pose.dtype
expr_zeros = torch.zeros(B, 100, device=device, dtype=dtype)
pose_experssion = torch.cat((body_pose, lhand_pose, rhand_pose, jaw_pose, expr_zeros), 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 = {}
if mode == 'train' and getattr(self, 'id_head', None) is not None:
id_loss_weight = getattr(cfg, 'id_loss_weight', 1.0)
# img_feat: [B, C, H, W] -> GAP로 [B, C]
g = F.adaptive_avg_pool2d(img_feat, 1).flatten(1)
# GRL 통과(부호 반전 + 스케일링)
# g_rev = GradReverse.apply(g, self.id_adv_lambda)
# set_trace()
id_logits = self.id_head(g) # [B, num_subjects]
# 타깃: 정수 인덱스 형태 권장 (DataLoader에서 meta_info['id_idx']로 전달)
id_idx = meta_info['id_idx'].long() # [B]
id_target = F.one_hot(id_idx, num_classes=self.num_subjects).float() # [B, K]
loss['id_adv'] = self.id_loss_fn(id_logits, id_target) * id_loss_weight
# loss functions
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)
dposer_x_weight = getattr(cfg, 'dposer_x_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
### SMPL parameter regression ###
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
# loss['smplx_lhand_pose'] = self.param_loss(lhand_pose, targets['smplx_lhand_pose'], meta_info['smplx_pose_valid'][:, 70:116]) * smplx_pose_weight
# loss['smplx_lhand_pose'] = self.param_loss(lhand_pose, targets['smplx_lhand_pose'], meta_info['smplx_pose_valid'][:, 116:162]) * smplx_pose_weight
else:
loss['smplx_pose'] = self.param_loss(pose, targets['smplx_pose'], meta_info['smplx_pose_valid']) * smplx_pose_weight
# loss['smplx_lhand_pose'] = self.param_loss(lhand_pose, targets['smplx_lhand_pose'], meta_info['smplx_pose_valid'][:, 70:116]) * smplx_pose_weight
# loss['smplx_lhand_pose'] = self.param_loss(lhand_pose, targets['smplx_lhand_pose'], meta_info['smplx_pose_valid'][:, 116:162]) * 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])
loss['dposerx'] = self.dposer_x(pose_experssion) * dposer_x_weight
# 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
# loss['bone_length'] = self.compute_bone_length_loss(
# joint_cam[:, [mo2cap2_to_smplx[name] for name in mo2cap2_joint_names], :],
# targets['smplx_joint_cam'][:, [mo2cap2_to_smplx[name] for name in mo2cap2_joint_names], :],
# bone_pairs,
# weight=bone_loss_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().cuda())
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().cuda())
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)
# print("joint_proj:", joint_proj.shape)
# print("coord_gt :", targets['joint_img'][:, :, :2].shape)
# print("valid :", meta_info['joint_trunc'].shape)
# set_trace()
loss['joint_proj'] = self.coord_loss(joint_proj, targets['smplx_joint_img'][:, :, :2], meta_info['smplx_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
# set_trace()
# PositionNet
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:
save_dir = os.path.join(cfg.result_dir, 'vis_test_result')
os.makedirs(save_dir, exist_ok=True)
B = body_img.shape[0]
if getattr(cfg, 'vis_feature', False):
base_feat_dir = os.path.join(cfg.result_dir, 'vis_test_result', 'feature')
B = body_img.shape[0]
for i in range(B):
img_path_i = None
if 'img_path' in meta_info:
img_path_i = meta_info['img_path'][i]
# dataset/.../{subject}/{scene}/imgs/... 에서 subject/scene 뽑기
sub_rel = self._get_subject_scene_dir(img_path_i) if isinstance(img_path_i, str) else ""
if sub_rel:
feat_dir = os.path.join(base_feat_dir, sub_rel)
else:
feat_dir = base_feat_dir
os.makedirs(feat_dir, exist_ok=True)
if isinstance(img_path_i, str):
base = os.path.basename(img_path_i)
name = os.path.splitext(base)[0]
else:
name = f"{i:06d}"
save_path = os.path.join(feat_dir, f"{name}_feat.png")
# body_img: [B,3,H,W], img_feat: [B,C,h,w]
self._save_activation_heatmap(body_img[i], img_feat[i], save_path)
# 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]
if getattr(cfg, 'vis_hand_bbox', False):
hand_bbox_dir = os.path.join(save_dir, 'hand_bbox')
os.makedirs(hand_bbox_dir, exist_ok=True)
self.visualize_hand_bboxes_on_input(
body_img, lhand_bbox, rhand_bbox,
hand_bbox_dir, meta_info
)
# for i in range(B):
# set_trace()
# img_np = inputs['img_vis'][i].detach().cpu().numpy() # [H, W, C]
# img_np = np.clip(img_np, 0, 1) * 255.0
# img_np = img_np.astype(np.uint8).copy()
# selected_joint_indices = list(mo2cap2_to_smplx.values())
# joints_2d = joint_proj[i][selected_joint_indices].detach().cpu().numpy() # [N_valid, 2]
# for x, y in joints_2d:
# x_int, y_int = int(round(x)), int(round(y))
# if 0 <= x_int < img_np.shape[1] and 0 <= y_int < img_np.shape[0]:
# cv2.circle(img_np, (x_int, y_int), 3, (0, 255, 0), -1)
# joint 시각화
# img_np = self.draw_joint_lines(img_np, joints_2d, mo2cap2_joint_names, mo2cap2_chain)
# mesh 시각화
# img_np = self.visualize_mesh_on_image(img_np, mesh_cam[i])
# save_path = os.path.join(save_dir, f'proj_{i:03d}.png')
# cv2.imwrite(save_path, img_np[:, :, ::-1])
# test output
out = {}
out['img'] = inputs['img_ori']
# out['img_vis'] = inputs['img_vis']
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 'joint_cam' in meta_info:
out['gt_joint'] = meta_info['joint_cam']
# 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 reinit_cam_out(m):
"""cam_out만 랜덤 초기화"""
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, std=0.01)
nn.init.constant_(m.bias, 0)
def get_model(mode):
# body
vit_cfg = Config.fromfile(cfg.encoder_config_file)
vit = build_posenet(vit_cfg.model)
token_decoder = None
if getattr(cfg, 'use_token_decoder', False):
token_cfg_path = cfg.token_cfg
token_cfg = OmegaConf.load(token_cfg_path)
token_decoder = SMPLTokenDecoderHead(token_cfg)
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
## no-ROI
# hand_position_net = GlobalHandPositionNet(feat_dim = cfg.feat_dim)
# hand_rotation_net = HandTokenRegressor()
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!!!!!!!')
if getattr(cfg, 'use_smpl', False):
body_position_net.apply(init_weights)
body_rotation_net.apply(init_weights)
else:
body_position_net.apply(init_weights)
body_rotation_net.apply(init_weights)
box_net.apply(init_weights)
# if getattr(cfg, 'use_token_decoder', False):
# set_trace()
# for name, child in token_decoder.named_children():
# if name == 'tokenizer':
# continue
# child.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, token_decoder, body_position_net, body_rotation_net, box_net, hand_position_net, hand_roi_net, hand_rotation_net,
face_regressor, mode)
return model
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