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model.py
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model.py
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import torch
import torch.nn as nn
from torch.nn import functional as F
from nets.resnet import ResNetBackbone
from nets.module import Pose2Feat, PositionNet, RotationNet, Vposer
from nets.loss import CoordLoss, ParamLoss, NormalVectorLoss, EdgeLengthLoss
from utils.smpl import SMPL
from utils.mano import MANO
from config import cfg
from contextlib import nullcontext
import math
from utils.transforms import rot6d_to_axis_angle
class Model(nn.Module):
def __init__(self, backbone, pose2feat, position_net, rotation_net, vposer):
super(Model, self).__init__()
self.backbone = backbone
self.pose2feat = pose2feat
self.position_net = position_net
self.rotation_net = rotation_net
self.vposer = vposer
if 'FreiHAND' in cfg.trainset_3d + cfg.trainset_2d + [cfg.testset]:
self.human_model = MANO()
self.human_model_layer = self.human_model.layer.cuda()
else:
self.human_model = SMPL()
self.human_model_layer = self.human_model.layer['neutral'].cuda()
self.root_joint_idx = self.human_model.root_joint_idx
self.mesh_face = self.human_model.face
self.joint_regressor = self.human_model.joint_regressor
self.coord_loss = CoordLoss()
self.param_loss = ParamLoss()
def get_camera_trans(self, cam_param, meta_info, is_render):
# 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)
if is_render:
bbox = meta_info['bbox']
k_value = k_value * math.sqrt(cfg.input_img_shape[0]*cfg.input_img_shape[1]) / (bbox[:, 2]*bbox[:, 3]).sqrt()
t_z = k_value * gamma
cam_trans = torch.cat((t_xy, t_z[:,None]),1)
return cam_trans
def make_2d_gaussian_heatmap(self, joint_coord_img):
x = torch.arange(cfg.output_hm_shape[2])
y = torch.arange(cfg.output_hm_shape[1])
yy, xx = torch.meshgrid(y, x)
xx = xx[None, None, :, :].cuda().float();
yy = yy[None, None, :, :].cuda().float();
x = joint_coord_img[:, :, 0, None, None];
y = joint_coord_img[:, :, 1, None, None];
heatmap = torch.exp(
-(((xx - x) / cfg.sigma) ** 2) / 2 - (((yy - y) / cfg.sigma) ** 2) / 2)
return heatmap
def get_coord(self, smpl_pose, smpl_shape, smpl_trans):
batch_size = smpl_pose.shape[0]
mesh_cam, _ = self.human_model_layer(smpl_pose, smpl_shape, smpl_trans)
# camera-centered 3D coordinate
joint_cam = torch.bmm(torch.from_numpy(self.joint_regressor).cuda()[None,:,:].repeat(batch_size,1,1), mesh_cam)
root_joint_idx = self.human_model.root_joint_idx
# project 3D coordinates to 2D space
x = joint_cam[:,:,0] / (joint_cam[:,:,2] + 1e-4) * cfg.focal[0] + cfg.princpt[0]
y = joint_cam[:,:,1] / (joint_cam[:,:,2] + 1e-4) * cfg.focal[1] + cfg.princpt[1]
x = x / cfg.input_img_shape[1] * cfg.output_hm_shape[2]
y = y / cfg.input_img_shape[0] * cfg.output_hm_shape[1]
joint_proj = torch.stack((x,y),2)
mesh_cam_render = mesh_cam.clone()
# root-relative 3D coordinates
root_cam = joint_cam[:,root_joint_idx,None,:]
joint_cam = joint_cam - root_cam
mesh_cam = mesh_cam - root_cam
return joint_proj, joint_cam, mesh_cam, mesh_cam_render
def forward(self, inputs, targets, meta_info, mode):
early_img_feat = self.backbone(inputs['img']) #pose_guided_img_feat
# get pose gauided image feature
joint_coord_img = inputs['joints']
with torch.no_grad():
joint_heatmap = self.make_2d_gaussian_heatmap(joint_coord_img.detach())
# remove blob centered at (0,0) == invalid ones
joint_heatmap = joint_heatmap * inputs['joints_mask'][:,:,:,None]
pose_img_feat = self.pose2feat(early_img_feat, joint_heatmap)
pose_guided_img_feat = self.backbone(pose_img_feat, skip_early=True) # 2048 x 8 x 8
joint_img, joint_score = self.position_net(pose_guided_img_feat) # refined 2D pose or 3D pose
# estimate model parameters
root_pose_6d, z, shape_param, cam_param = self.rotation_net(pose_guided_img_feat, joint_img.detach(), joint_score.detach())
# change root pose 6d + latent code -> axis angles
root_pose = rot6d_to_axis_angle(root_pose_6d)
pose_param = self.vposer(z)
cam_trans = self.get_camera_trans(cam_param, meta_info, is_render=(cfg.render and (mode == 'test')))
pose_param = pose_param.view(-1, self.human_model.orig_joint_num - 1, 3)
pose_param = torch.cat((root_pose[:, None, :], pose_param), 1).view(-1, self.human_model.orig_joint_num * 3)
joint_proj, joint_cam, mesh_cam, mesh_cam_render = self.get_coord(pose_param, shape_param, cam_trans)
if mode == 'train':
# loss functions
loss = {}
# joint_img: 0~8, joint_proj: 0~64, target: 0~64
loss['body_joint_img'] = (1/8) * self.coord_loss(joint_img*8, self.human_model.reduce_joint_set(targets['orig_joint_img']), self.human_model.reduce_joint_set(meta_info['orig_joint_trunc']), meta_info['is_3D'])
loss['smpl_joint_img'] = (1/8) * self.coord_loss(joint_img*8, self.human_model.reduce_joint_set(targets['fit_joint_img']),
self.human_model.reduce_joint_set(meta_info['fit_joint_trunc']) * meta_info['is_valid_fit'][:, None, None])
loss['smpl_pose'] = self.param_loss(pose_param, targets['pose_param'], meta_info['fit_param_valid'] * meta_info['is_valid_fit'][:, None])
loss['smpl_shape'] = self.param_loss(shape_param, targets['shape_param'], meta_info['is_valid_fit'][:, None])
loss['body_joint_proj'] = (1/8) * self.coord_loss(joint_proj, targets['orig_joint_img'][:, :, :2], meta_info['orig_joint_trunc'])
loss['body_joint_cam'] = self.coord_loss(joint_cam, targets['orig_joint_cam'], meta_info['orig_joint_valid'] * meta_info['is_3D'][:, None, None])
loss['smpl_joint_cam'] = self.coord_loss(joint_cam, targets['fit_joint_cam'], meta_info['is_valid_fit'][:, None, None])
return loss
else:
# test output
out = {'cam_param': cam_param}
# out['input_joints'] = joint_coord_img
out['joint_img'] = joint_img * 8
out['joint_proj'] = joint_proj
out['joint_score'] = joint_score
out['smpl_mesh_cam'] = mesh_cam
out['smpl_pose'] = pose_param
out['smpl_shape'] = shape_param
out['mesh_cam_render'] = mesh_cam_render
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 'img2bb_trans' in meta_info:
out['img2bb_trans'] = meta_info['img2bb_trans']
if 'bbox' in meta_info:
out['bbox'] = meta_info['bbox']
if 'tight_bbox' in meta_info:
out['tight_bbox'] = meta_info['tight_bbox']
if 'aid' in meta_info:
out['aid'] = meta_info['aid']
return out
def init_weights(m):
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)
def get_model(vertex_num, joint_num, mode):
backbone = ResNetBackbone(cfg.resnet_type)
pose2feat = Pose2Feat(joint_num)
position_net = PositionNet()
rotation_net = RotationNet()
vposer = Vposer()
if mode == 'train':
backbone.init_weights()
pose2feat.apply(init_weights)
position_net.apply(init_weights)
rotation_net.apply(init_weights)
model = Model(backbone, pose2feat, position_net, rotation_net, vposer)
return model