hourglass-facekeypoints-detection
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raymon-tian
training is good, eval is worse
When I trained hg model using 300w datasets, and the training result showed is good, but is worse when I use eval mode. Maybe it's because of the bn layer. Have you met this problem? What should I do for it?
@ailias No, I didn't meet the problem. Did you set net.eval() before evaluation？
@ailias ，I meet the same problem just like you. After finishing training, if I set the net to eval, then output of the net seems to be much more worse than the train mode:(
I have met the same problem when training the hg model on MPII dataset. I'm training on a Titan Xp GPU, Pytorch 0.4.1. Changing hg.py, line 22-23
self.res2 = Residual(128, 128)
self.res3 = Residual(128, self._nFeats)
to
self.res2 = Residual(128, self._nFeats)
self.res3 = Residual(self._nFeats, self._nFeats)
solved my problem. Hope it will help you too.
When I trained hg model using 300w datasets, and the training result showed is good, but is worse when I use eval mode. Maybe it's because of the bn layer. Have you met this problem? What should I do for it?
Can you tell me how to run the code ? python train.py
Traceback (most recent call last):
File "train.py", line 123, in
TypeError: new() received an invalid combination of arguments - got (float, int, int, int), but expected one of:
- (torch.device device)
- (torch.Storage storage)
- (Tensor other)
- (tuple of ints size, torch.device device)
- (object data, torch.device device)
Thank you very much . Best wishes.
When I trained hg model using 300w datasets, and the training result showed is good, but is worse when I use eval mode. Maybe it's because of the bn layer. Have you met this problem? What should I do for it?
Can you tell me how to run the code ?
python train.py Traceback (most recent call last): File "train.py", line 123, in net = KFSGNet()
TypeError: new() received an invalid combination of arguments - got (float, int, int, int), but expected one of:
- (torch.device device)
- (torch.Storage storage)
- (Tensor other)
- (tuple of ints size, torch.device device)
- (object data, torch.device device)
Thank you very much . Best wishes.
have you solve this problem?
in models.py, from line 64, should be modified: nn.Conv2d(ins,int(outs//2),1), nn.BatchNorm2d(int(outs//2)), nn.ReLU(inplace=True), nn.Conv2d(int(outs//2),int(outs//2),3,1,1), nn.BatchNorm2d(int(outs//2)), nn.ReLU(inplace=True), nn.Conv2d(int(outs//2),outs,1)
