我在 中训练了一个QAT基于（量化感知训练）的模型Pytorch，训练进行得很顺利。然而，当我尝试将权重加载到融合模型中并在更宽的数据集上运行测试时，我遇到了很多错误：
(base) marian@u04-2:/mnt/s3user/Pytorch_Retinaface_quantized# python test_widerface.py --trained_model ./weights/mobilenet0.25_Final_quantized.pth --network mobile0.25layers:
Loading pretrained model from ./weights/mobilenet0.25_Final_quantized.pth
remove prefix 'module.'
Missing keys:235
Unused checkpoint keys:171
Used keys:65
Traceback (most recent call last):
File "/root/.vscode/extensions/ms-python.python-2020.1.58038/pythonFiles/ptvsd_launcher.py", line 43, in <module>
main(ptvsdArgs)
File "/root/.vscode/extensions/ms-python.python-2020.1.58038/pythonFiles/lib/python/old_ptvsd/ptvsd/__main__.py", line 432, in main
run()
File "/root/.vscode/extensions/ms-python.python-2020.1.58038/pythonFiles/lib/python/old_ptvsd/ptvsd/__main__.py", line 316, in run_file
runpy.run_path(target, run_name='__main__')
File "/root/anaconda3/lib/python3.7/runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "/root/anaconda3/lib/python3.7/runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "/root/anaconda3/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/mnt/f3user/Pytorch_Retinaface_quantized/test_widerface.py", line 114, in <module>
net = load_model(net, args.trained_model, args.cpu)
File "/mnt/f3user/Pytorch_Retinaface_quantized/test_widerface.py", line 95, in load_model
model.load_state_dict(pretrained_dict, strict=False)
File "/root/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 830, in load_state_dict
self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for RetinaFace:
While copying the parameter named "ssh1.conv3X3.0.weight", whose dimensions in the model are torch.Size([32, 64, 3, 3]) and whose dimensions in the checkpoint are torch.Size([32, 64, 3, 3]).
While copying the parameter named "ssh1.conv5X5_2.0.weight", whose dimensions in the model are torch.Size([16, 16, 3, 3]) and whose dimensions in the checkpoint are torch.Size([16, 16, 3, 3]).
While copying the parameter named "ssh1.conv7x7_3.0.weight", whose dimensions in the model are torch.Size([16, 16, 3, 3]) and whose dimensions in the checkpoint are torch.Size([16, 16, 3, 3]).
While copying the parameter named "ssh2.conv3X3.0.weight", whose dimensions in the model are torch.Size([32, 64, 3, 3]) and whose dimensions in the checkpoint are torch.Size([32, 64, 3, 3]).
While copying the parameter named "ssh2.conv5X5_2.0.weight", whose dimensions in the model are torch.Size([16, 16, 3, 3]) and whose dimensions in the checkpoint are torch.Size([16, 16, 3, 3]).
.....
完整列表可以在这里找到。
基本上找不到权重。加上融合模型中缺少的比例和零点。
如果重要，以下代码段是用于训练和保存模型的实际训练循环：
if __name__ == '__main__':
# train()
...
net = RetinaFace(cfg=cfg)
print("Printing net...")
print(net)
net.fuse_model()
...
net.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')
torch.quantization.prepare_qat(net, inplace=True)
print(f'quantization preparation done.')
...
quantized_model = net
for i in range(max_epoch):
net = net.to(device)
train_one_epoch(net, data_loader, optimizer, criterion, cfg, gamma, i, step_index, device)
if i in stepvalues:
step_index += 1
if i > 3 :
net.apply(torch.quantization.disable_observer)
if i > 2 :
net.apply(torch.nn.intrinsic.qat.freeze_bn_stats)
net=net.cpu()
quantized_model = torch.quantization.convert(net.eval(), inplace=False)
quantized_model.eval()
# evaluate on test set ?!
torch.save(net.state_dict(), save_folder + cfg['name'] + '_Final.pth')
torch.save(quantized_model.state_dict(), save_folder + cfg['name'] + '_Final_quantized.pth')
#torch.jit.save(torch.jit.script(quantized_model), save_folder + cfg['name'] + '_Final_quantized_jit.pth')
用于测试test_widerface.py使用的可以在此处访问您可以在此处
查看密钥
为什么会这样？这应该如何处理？
更新
我检查了名称，并创建了一个新的 state_dict 字典，并使用下面的代码片段插入了检查点和模型中的 112 个键：
new_state_dict = {}
checkpoint_state_dict = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
for (ck, cp) in checkpoint_state_dict.items():
for (mk, mp) in model.state_dict().items():
kname,kext = os.path.splitext(ck)
mname,mext = os.path.splitext(mk)
# check the two parameter and see if they are the same
# then use models key naming scheme and use checkpoints weights
if kname+kext == mname+mext or kname+'.0'+kext == mname+mext:
new_state_dict[mname+mext] = cp
else:
if kext in ('.scale','.zero_point'):
new_state_dict[ck] = cp
然后使用这个新的 state_dict！但是我得到了完全相同的错误！意思是这样的错误：
RuntimeError: Error(s) in loading state_dict for RetinaFace:
While copying the parameter named "ssh1.conv3X3.0.weight", whose dimensions in the model are torch.Size([32, 64, 3, 3]) and whose dimensions in the checkpoint are torch.Size([32, 64, 3, 3]).
这真的很令人沮丧，并且没有关于此的文档！我在这里完全一无所知。