Closed youdutaidi closed 2 years ago
Hi! Our models are loaded with the official open source checkpoints, and it can be found in link1 and link2. You can check whether the pretrained models are loaded correctly.
Yes, I loaded the pre-trained model in link and test it ,but the validation result performs poorly, I print the pred and target lable (where the original code is :
accuracy(output, target, topk=(1,)):
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
print("pred: ",pred)
print("target: ",target.reshape(1, -1).expand_as(pred))
correct = pred.eq(target.reshape(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
And the printed results are like this :
pred: tensor([[171, 819, 672, 30, 688, 549, 549, 591, 591, 145, 591, 622, 591, 745, 688, 557, 688, 591, 978, 591, 492, 591, 492, 622, 549, 549, 688, 591, 492, 492, 622, 688, 688, 688, 591, 492, 152, 171, 557, 688, 145, 616, 978, 978, 591, 591, 591, 591, 492, 622, 591, 622, 978, 745, 492, 591, 591, 492, 591, 336, 622, 846, 492, 740, 336, 492, 745, 492, 336, 688, 688, 30, 928, 978, 30, 624, 152, 591, 727, 622, 688, 549, 692, 492, 928, 622, 624, 492, 549, 756, 492, 622, 853, 688, 492, 591, 156, 727, 745, 688], [622, 150, 591, 688, 907, 941, 745, 152, 740, 557, 740, 171, 740, 492, 513, 745, 591, 941, 427, 549, 169, 152, 745, 171, 745, 492, 672, 688, 622, 622, 492, 740, 740, 740, 740, 622, 591, 622, 571, 591, 622, 614, 853, 744, 672, 798, 152, 783, 622, 492, 549, 145, 728, 370, 145, 152, 152, 745, 744, 624, 492, 549, 549, 591, 624, 745, 549, 622, 624, 30, 907, 688, 688, 819, 688, 169, 331, 30, 624, 492, 30, 591, 744, 923, 911, 492, 336, 557, 745, 273, 622, 171, 978, 692, 273, 740, 461, 336, 61, 740], [196, 336, 30, 591, 928, 798, 657, 549, 672, 745, 692, 492, 672, 663, 672, 492, 740, 978, 672, 152, 387, 928, 549, 196, 657, 745, 30, 740, 923, 727, 145, 309, 591, 30, 783, 145, 692, 492, 745, 740, 492, 269, 941, 819, 740, 152, 549, 346, 923, 145, 740, 196, 672, 61, 336, 740, 187, 557, 672, 387, 557, 609, 387, 688, 169, 557, 464, 923, 907, 740, 882, 513, 911, 624, 513, 196, 675, 740, 614, 196, 740, 798, 549, 387, 291, 557, 907, 571, 387, 111, 557, 530, 728, 907, 418, 152, 170, 492, 370, 455], [492, 978, 874, 513, 740, 591, 492, 798, 337, 492, 152, 196, 688, 464, 874, 418, 30, 187, 707, 740, 846, 549, 387, 492, 387, 387, 874, 152, 145, 196, 196, 672, 30, 591, 152, 196, 662, 145, 145, 672, 557, 203, 591, 218, 337, 928, 928, 672, 846, 557, 152, 171, 688, 273, 557, 928, 692, 61, 218, 853, 196, 387, 27, 30, 907, 528, 663, 452, 513, 591, 797, 907, 246, 150, 591, 259, 591, 688, 702, 145, 874, 152, 381, 622, 688, 145, 513, 622, 492, 928, 418, 203, 688, 152, 448, 549, 530, 923, 273, 337], [978, 387, 978, 874, 591, 745, 692, 740, 30, 571, 898, 978, 152, 549, 728, 622, 289, 629, 153, 978, 549, 740, 923, 145, 635, 635, 337, 30, 387, 495, 495, 106, 672, 672, 346, 171, 675, 196, 492, 30, 875, 336, 549, 672, 30, 549, 798, 619, 387, 196, 783, 492, 513, 657, 622, 688, 740, 273, 819, 367, 145, 306, 325, 672, 622, 61, 61, 196, 853, 672, 600, 728, 907, 387, 129, 609, 372, 672, 907, 557, 513, 692, 306, 336, 692, 196, 387, 145, 635, 370, 923, 978, 30, 591, 370, 692, 962, 387, 464, 106]], device='cuda:0') target: tensor([[14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15], [14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15], [14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15], [14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15], [14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]], device='cuda:0')
@youdutaidi Hi, sorry for that, and can u provide your command and the code (if the code were modified). I will check it.
@youdutaidi Hi, sorry for that, and can u provide your command and the code (if the code were modified). I will check it.
Thank you very much for your friendly help. I didn't modify the original code ,and the command is :
CUDA_VISIBLE_DEVICES=1 python test_quant.py deit_small /home/dataset/cailingling/ImageNet/ --quant --ptf --lis --quant-method minmax
result is :
Please first verify your dataset and float model is right?
CUDA_VISIBLE_DEVICES=1 python test_quant.py deit_small /home/dataset/cailingling/ImageNet/
Thank you for sharing your PTQ code but I test Deit-Small with min-max quant technic, top@1 accuracy in validation dataset is only 0.11, can not match the papers 75.05%,is there anything wrong?