Open sankexin opened 1 year ago
I have solved by this way:
lkpt += kpt_loss_factor((1 - torch.exp(-d/(2(s*sigmas)*2+1e-9)))kpt_mask).mean() lkpt += (self.kptloss(tkpt[i][:, 0::2], pkpt_x, kpt_mask) + self.kptloss(tkpt[i][:, 1::2], pkpt_y, kpt_mask)) / 2 reference: https://github.com/qinggangwu/yolov7-pose_Npoint_Ncla/blob/db5ebf25060f723cce084bdaafd277847ff38d27/utils/loss_Ncla.py
then train it myself.
I have solved by this way:
lkpt += kpt_loss_factor((1 - torch.exp(-d/(s(4*sigmas*2)+1e-9)))kpt_mask).mean() # not correct equation
lkpt += kpt_loss_factor((1 - torch.exp(-d/(2(s*sigmas)*2+1e-9)))kpt_mask).mean() lkpt += (self.kptloss(tkpt[i][:, 0::2], pkpt_x, kpt_mask) + self.kptloss(tkpt[i][:, 1::2], pkpt_y, kpt_mask)) / 2 reference: https://github.com/qinggangwu/yolov7-pose_Npoint_Ncla/blob/db5ebf25060f723cce084bdaafd277847ff38d27/utils/loss_Ncla.py
then train it myself.
Hi! @sankexin
Are you using both of these equations?
lkpt += kpt_loss_factor*((1 - torch.exp(-d/(2*(s*sigmas)**2+1e-9)))*kpt_mask).mean()
lkpt += (self.kptloss(tkpt[i][:, 0::2], pkpt_x, kpt_mask) + self.kptloss(tkpt[i][:, 1::2], pkpt_y, kpt_mask)) / 2
Or just the one at the bottom?
这是来自QQ邮箱的假期自动回复邮件。 您好,我最近正在休假中,无法亲自回复您的邮件。我将在假期结束后,尽快给您回复。
I have solved by this way:
lkpt += kpt_loss_factor((1 - torch.exp(-d/(s(4*sigmas*2)+1e-9)))kpt_mask).mean() # not correct equation
lkpt += kpt_loss_factor((1 - torch.exp(-d/(2(s*sigmas)*2+1e-9)))kpt_mask).mean() lkpt += (self.kptloss(tkpt[i][:, 0::2], pkpt_x, kpt_mask) + self.kptloss(tkpt[i][:, 1::2], pkpt_y, kpt_mask)) / 2 reference: https://github.com/qinggangwu/yolov7-pose_Npoint_Ncla/blob/db5ebf25060f723cce084bdaafd277847ff38d27/utils/loss_Ncla.py then train it myself.
Hi! @sankexin
Are you using both of these equations?
lkpt += kpt_loss_factor*((1 - torch.exp(-d/(2*(s*sigmas)**2+1e-9)))*kpt_mask).mean()
lkpt += (self.kptloss(tkpt[i][:, 0::2], pkpt_x, kpt_mask) + self.kptloss(tkpt[i][:, 1::2], pkpt_y, kpt_mask)) / 2
Or just the one at the bottom?
hello! Are you work out this problem?
lkpt += kptloss
这是来自QQ邮箱的假期自动回复邮件。 您好,我最近正在休假中,无法亲自回复您的邮件。我将在假期结束后,尽快给您回复。
你好,请问这个问题你解决了吗
detect by this weight: result: I was beginning to think it was a post-process error,but when I print the pred of detect.py,the y of keypoint of the person is Almost the same size: tensor([[219.79369, 225.33395, 226.03517, 225.67001, 220.89984, 223.56926, 219.32716, 228.19868, 227.60158, 224.10791, 220.28212, 227.59915, 225.78040, 223.75421, 224.11528, 226.45985, 228.28322], [411.84937, 418.37204, 417.93945, 417.42044, 412.42886, 414.72122, 411.45743, 419.45306, 419.69476, 416.37424, 412.08868, 420.05914, 417.24695, 415.01300, 415.85199, 418.92923, 420.43335]]) 640x640 2 persons, Done. (0.047s) so, the newest Pre-training weights are also not correct! could you find find any reason and update zhe correct one? and will you update yolov5(6.0) ?there is no focus. and then I test Yolov5s6_pose_640_ti_lite, there is some bug: yolov5-pose/utils/plots.py, line 118, in plot_skeleton_kpts pos1 = (int(kpts[(sk[0]-1)steps]), int(kpts[(sk[0]-1)steps+1])) IndexError: index 45 is out of bounds for dimension 0 with size 0