64 lines
2.7 KiB
Python
64 lines
2.7 KiB
Python
'''
|
|
@ Date: 2020-11-19 17:46:04
|
|
@ Author: Qing Shuai
|
|
@ LastEditors: Qing Shuai
|
|
@ LastEditTime: 2021-01-14 15:02:39
|
|
@ FilePath: /EasyMocap/code/pyfitting/lossfactory.py
|
|
'''
|
|
import torch
|
|
from .operation import projection, batch_rodrigues
|
|
|
|
def ReprojectionLoss(keypoints3d, keypoints2d, K, Rc, Tc, inv_bbox_sizes):
|
|
img_points = projection(keypoints3d, K, Rc, Tc)
|
|
residual = (img_points - keypoints2d[:, :, :2]) * keypoints2d[:, :, 2:3]
|
|
squared_res = (residual ** 2) * inv_bbox_sizes
|
|
return torch.sum(squared_res)
|
|
|
|
class SMPLAngleLoss:
|
|
def __init__(self, keypoints):
|
|
use_feet = keypoints[:, [19, 20, 21, 22, 23, 24], -1].sum() > 0.1
|
|
use_head = keypoints[:, [15, 16, 17, 18], -1].sum() > 0.1
|
|
SMPL_JOINT_ZERO_IDX = [3, 6, 9, 13, 14, 20, 21, 22, 23]
|
|
if not use_feet:
|
|
SMPL_JOINT_ZERO_IDX.extend([7, 8])
|
|
if not use_head:
|
|
SMPL_JOINT_ZERO_IDX.extend([12, 15])
|
|
SMPL_POSES_ZERO_IDX = [[j for j in range(3*i, 3*i+3)] for i in SMPL_JOINT_ZERO_IDX]
|
|
SMPL_POSES_ZERO_IDX = sum(SMPL_POSES_ZERO_IDX, [])
|
|
self.idx = SMPL_POSES_ZERO_IDX
|
|
|
|
def loss(self, poses):
|
|
return torch.sum(torch.abs(poses[:, self.idx]))
|
|
|
|
def SmoothLoss(body_params, keys, weight_loss, span=4):
|
|
spans = [i for i in range(1, span)]
|
|
span_weights = {i:1/i for i in range(1, span)}
|
|
span_weights = {key: i/sum(span_weights) for key, i in span_weights.items()}
|
|
loss_dict = {}
|
|
nFrames = body_params['poses'].shape[0]
|
|
for key in ['poses', 'Th']:
|
|
k = 'smooth_' + key
|
|
if k in weight_loss.keys() and weight_loss[k] > 0.:
|
|
loss_dict[k] = 0.
|
|
for span in spans:
|
|
val = torch.sum((body_params[key][span:, :] - body_params[key][:nFrames-span, :])**2)
|
|
loss_dict[k] += span_weights[span] * val
|
|
# smooth rotation
|
|
rot = batch_rodrigues(body_params['Rh'])
|
|
key, k = 'Rh', 'smooth_Rh'
|
|
if k in weight_loss.keys() and weight_loss[k] > 0.:
|
|
loss_dict[k] = 0.
|
|
for span in spans:
|
|
val = torch.sum((rot[span:, :] - rot[:nFrames-span, :])**2)
|
|
loss_dict[k] += span_weights[span] * val
|
|
return loss_dict
|
|
|
|
def RegularizationLoss(body_params, body_params_init, weight_loss):
|
|
loss_dict = {}
|
|
for key in ['poses', 'shapes', 'Th']:
|
|
if 'init_'+key in weight_loss.keys() and weight_loss['init_'+key] > 0.:
|
|
loss_dict['init_'+key] = torch.sum((body_params[key] - body_params_init[key])**2)
|
|
for key in ['poses', 'shapes']:
|
|
if 'reg_'+key in weight_loss.keys() and weight_loss['reg_'+key] > 0.:
|
|
loss_dict['reg_'+key] = torch.sum((body_params[key])**2)
|
|
return loss_dict |