Comments (11)
goncalomordido
commented on May 20, 2024
63
You can also manually updated the dic. Like this:
state_dict =checkpoint['state_dict']
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
if 'module' not in k:
k = 'module.'+k
else:
k = k.replace('features.module.', 'module.features.')
new_state_dict[k]=v
model.load_state_dict(new_state_dict)
from pytorch-classification.
bilalghanem
commented on May 20, 2024
16
change:
model.load_state_dict(torch.load(path + '/pytorch_model.pt'))
to
model.load_state_dict(torch.load(path + '/pytorch_model.pt'), strict=False)
from pytorch-classification.
imirzadeh
commented on May 20, 2024
12
Hi,
The problem is the module is load with dataparallel activated and you are trying to load it without data parallel. That's why there's an extra module at the beginning of each key!
Refer to this link for more information:
https://discuss.pytorch.org/t/missing-keys-unexpected-keys-in-state-dict-when-loading-self-trained-model/22379
from pytorch-classification.
mgrachten
commented on May 20, 2024
2
change:
model.load_state_dict(torch.load(path + '/pytorch_model.pt'))tomodel.load_state_dict(torch.load(path + '/pytorch_model.pt'), strict=False)
Although it will make the RuntimeError go away, don't do this unless you know what you are doing. It will leave any parameters it can't find in the checkpoint with random values. That's not what you want if the issue is caused by a mix-up of parameter names, as was the case for the issue reporter.
from pytorch-classification.
xjock
commented on May 20, 2024
1
The mutiple GPUs usage in pytorch is a little difficult.
In TF, you just set the os.environ["CUDA_VISIBLE_DEVICES"]='0,1,2,3'
from pytorch-classification.
raghav1810
commented on May 20, 2024
Am getting an error similar to this one
This is what I am running:
python cifar.py -a preresnet --depth 110 --epochs 3 --schedule 81 122 --gamma 0.1 --wd 1e-4 --checkpoint checkpoints/cifar10/preresnet-110 --resume 'checkpoint.pth.tar'
('checkpoint.pth.tar' is from the onedrive folder)
RuntimeError: Error(s) in loading state_dict for DataParallel:
Missing key(s) in state_dict: "module.bn.weight", "module.bn.bias", "module.bn.running_mean", "module.bn.running_var".
Unexpected key(s) in state_dict: "module.bn1.weight", "module.bn1.bias", "module.bn1.running_mean", "module.bn1.running_var", "module.layer1.0.conv3.weight", "module.layer1.0.bn3.weight", "module.layer1.0.bn3.bias", "module.layer1.0.bn3.running_mean", "module.layer1.0.bn3.running_var", "module.layer1.0.downsample.0.weight", "module.layer1.0.downsample.1.weight", "module.layer1.0.downsample.1.bias", "module.layer1.0.downsample.1.running_mean", "module.layer1.0.downsample.1.running_var", "module.layer1.1.conv3.weight", "module.layer1.1.bn3.weight", "module.layer1.1.bn3.bias", "module.layer1.1.bn3.running_mean", "module.layer1.1.bn3.running_var", "module.layer1.2.conv3.weight", "module.layer1.2.bn3.weight", "module.layer1.2.bn3.bias", "module.layer1.2.bn3.running_mean", "module.layer1.2.bn3.running_var", "module.layer1.3.conv3.weight", "module.layer1.3.bn3.weight", "module.layer1.3.bn3.bias", "module.layer1.3.bn3.running_mean", "module.layer1.3.bn3.running_var", "module.layer1.4.conv3.weight", "module.layer1.4.bn3.weight", "module.layer1.4.bn3.bias", "module.layer1.4.bn3.running_mean", "module.layer1.4.bn3.running_var", "module.layer1.5.conv3.weight", "module.layer1.5.bn3.weight", "module.layer1.5.bn3.bias", "module.layer1.5.bn3.running_mean", "module.layer1.5.bn3.running_var", "module.layer1.6.conv3.weight", "module.layer1.6.bn3.weight", "module.layer1.6.bn3.bias", "module.layer1.6.bn3.running_mean", "module.layer1.6.bn3.running_var", "module.layer1.7.conv3.weight", "module.layer1.7.bn3.weight", "module.layer1.7.bn3.bias", "module.layer1.7.bn3.running_mean", "module.layer1.7.bn3.running_var", "module.layer1.8.conv3.weight", "module.layer1.8.bn3.weight", "module.layer1.8.bn3.bias", "module.layer1.8.bn3.running_mean", "module.layer1.8.bn3.running_var", "module.layer1.9.conv3.weight", "module.layer1.9.bn3.weight", "module.layer1.9.bn3.bias", "module.layer1.9.bn3.running_mean", "module.layer1.9.bn3.running_var", "module.layer1.10.conv3.weight", "module.layer1.10.bn3.weight", "module.layer1.10.bn3.bias", "module.layer1.10.bn3.running_mean", "module.layer1.10.bn3.running_var", "module.layer1.11.conv3.weight", "module.layer1.11.bn3.weight", "module.layer1.11.bn3.bias", "module.layer1.11.bn3.running_mean", "module.layer1.11.bn3.running_var", "module.layer1.12.conv3.weight", "module.layer1.12.bn3.weight", "module.layer1.12.bn3.bias", "module.layer1.12.bn3.running_mean", "module.layer1.12.bn3.running_var", "module.layer1.13.conv3.weight", "module.layer1.13.bn3.weight", "module.layer1.13.bn3.bias", "module.layer1.13.bn3.running_mean", "module.layer1.13.bn3.running_var", "module.layer1.14.conv3.weight", "module.layer1.14.bn3.weight", "module.layer1.14.bn3.bias", "module.layer1.14.bn3.running_mean", "module.layer1.14.bn3.running_var", "module.layer1.15.conv3.weight", "module.layer1.15.bn3.weight", "module.layer1.15.bn3.bias", "module.layer1.15.bn3.running_mean", "module.layer1.15.bn3.running_var", "module.layer1.16.conv3.weight", "module.layer1.16.bn3.weight", "module.layer1.16.bn3.bias", "module.layer1.16.bn3.running_mean", "module.layer1.16.bn3.running_var", "module.layer1.17.conv3.weight", "module.layer1.17.bn3.weight", "module.layer1.17.bn3.bias", "module.layer1.17.bn3.running_mean", "module.layer1.17.bn3.running_var", "module.layer2.0.conv3.weight", "module.layer2.0.bn3.weight", "module.layer2.0.bn3.bias", "module.layer2.0.bn3.running_mean", "module.layer2.0.bn3.running_var", "module.layer2.0.downsample.1.weight", "module.layer2.0.downsample.1.bias", "module.layer2.0.downsample.1.running_mean", "module.layer2.0.downsample.1.running_var", "module.layer2.1.conv3.weight", "module.layer2.1.bn3.weight", "module.layer2.1.bn3.bias", "module.layer2.1.bn3.running_mean", "module.layer2.1.bn3.running_var", "module.layer2.2.conv3.weight", "module.layer2.2.bn3.weight", "module.layer2.2.bn3.bias", "module.layer2.2.bn3.running_mean", "module.layer2.2.bn3.running_var", "module.layer2.3.conv3.weight", "module.layer2.3.bn3.weight", "module.layer2.3.bn3.bias", "module.layer2.3.bn3.running_mean", "module.layer2.3.bn3.running_var", "module.layer2.4.conv3.weight", "module.layer2.4.bn3.weight", "module.layer2.4.bn3.bias", "module.layer2.4.bn3.running_mean", "module.layer2.4.bn3.running_var", "module.layer2.5.conv3.weight", "module.layer2.5.bn3.weight", "module.layer2.5.bn3.bias", "module.layer2.5.bn3.running_mean", "module.layer2.5.bn3.running_var", "module.layer2.6.conv3.weight", "module.layer2.6.bn3.weight", "module.layer2.6.bn3.bias", "module.layer2.6.bn3.running_mean", "module.layer2.6.bn3.running_var", "module.layer2.7.conv3.weight", "module.layer2.7.bn3.weight", "module.layer2.7.bn3.bias", "module.layer2.7.bn3.running_mean", "module.layer2.7.bn3.running_var", "module.layer2.8.conv3.weight", "module.layer2.8.bn3.weight", "module.layer2.8.bn3.bias", "module.layer2.8.bn3.running_mean", "module.layer2.8.bn3.running_var", "module.layer2.9.conv3.weight", "module.layer2.9.bn3.weight", "module.layer2.9.bn3.bias", "module.layer2.9.bn3.running_mean", "module.layer2.9.bn3.running_var", "module.layer2.10.conv3.weight", "module.layer2.10.bn3.weight", "module.layer2.10.bn3.bias", "module.layer2.10.bn3.running_mean", "module.layer2.10.bn3.running_var", "module.layer2.11.conv3.weight", "module.layer2.11.bn3.weight", "module.layer2.11.bn3.bias", "module.layer2.11.bn3.running_mean", "module.layer2.11.bn3.running_var", "module.layer2.12.conv3.weight", "module.layer2.12.bn3.weight", "module.layer2.12.bn3.bias", "module.layer2.12.bn3.running_mean", "module.layer2.12.bn3.running_var", "module.layer2.13.conv3.weight", "module.layer2.13.bn3.weight", "module.layer2.13.bn3.bias", "module.layer2.13.bn3.running_mean", "module.layer2.13.bn3.running_var", "module.layer2.14.conv3.weight", "module.layer2.14.bn3.weight", "module.layer2.14.bn3.bias", "module.layer2.14.bn3.running_mean", "module.layer2.14.bn3.running_var", "module.layer2.15.conv3.weight", "module.layer2.15.bn3.weight", "module.layer2.15.bn3.bias", "module.layer2.15.bn3.running_mean", "module.layer2.15.bn3.running_var", "module.layer2.16.conv3.weight", "module.layer2.16.bn3.weight", "module.layer2.16.bn3.bias", "module.layer2.16.bn3.running_mean", "module.layer2.16.bn3.running_var", "module.layer2.17.conv3.weight", "module.layer2.17.bn3.weight", "module.layer2.17.bn3.bias", "module.layer2.17.bn3.running_mean", "module.layer2.17.bn3.running_var", "module.layer3.0.conv3.weight", "module.layer3.0.bn3.weight", "module.layer3.0.bn3.bias", "module.layer3.0.bn3.running_mean", "module.layer3.0.bn3.running_var", "module.layer3.0.downsample.1.weight", "module.layer3.0.downsample.1.bias", "module.layer3.0.downsample.1.running_mean", "module.layer3.0.downsample.1.running_var", "module.layer3.1.conv3.weight", "module.layer3.1.bn3.weight", "module.layer3.1.bn3.bias", "module.layer3.1.bn3.running_mean", "module.layer3.1.bn3.running_var", "module.layer3.2.conv3.weight", "module.layer3.2.bn3.weight", "module.layer3.2.bn3.bias", "module.layer3.2.bn3.running_mean", "module.layer3.2.bn3.running_var", "module.layer3.3.conv3.weight", "module.layer3.3.bn3.weight", "module.layer3.3.bn3.bias", "module.layer3.3.bn3.running_mean", "module.layer3.3.bn3.running_var", "module.layer3.4.conv3.weight", "module.layer3.4.bn3.weight", "module.layer3.4.bn3.bias", "module.layer3.4.bn3.running_mean", "module.layer3.4.bn3.running_var", "module.layer3.5.conv3.weight", "module.layer3.5.bn3.weight", "module.layer3.5.bn3.bias", "module.layer3.5.bn3.running_mean", "module.layer3.5.bn3.running_var", "module.layer3.6.conv3.weight", "module.layer3.6.bn3.weight", "module.layer3.6.bn3.bias", "module.layer3.6.bn3.running_mean", "module.layer3.6.bn3.running_var", "module.layer3.7.conv3.weight", "module.layer3.7.bn3.weight", "module.layer3.7.bn3.bias", "module.layer3.7.bn3.running_mean", "module.layer3.7.bn3.running_var", "module.layer3.8.conv3.weight", "module.layer3.8.bn3.weight", "module.layer3.8.bn3.bias", "module.layer3.8.bn3.running_mean", "module.layer3.8.bn3.running_var", "module.layer3.9.conv3.weight", "module.layer3.9.bn3.weight", "module.layer3.9.bn3.bias", "module.layer3.9.bn3.running_mean", "module.layer3.9.bn3.running_var", "module.layer3.10.conv3.weight", "module.layer3.10.bn3.weight", "module.layer3.10.bn3.bias", "module.layer3.10.bn3.running_mean", "module.layer3.10.bn3.running_var", "module.layer3.11.conv3.weight", "module.layer3.11.bn3.weight", "module.layer3.11.bn3.bias", "module.layer3.11.bn3.running_mean", "module.layer3.11.bn3.running_var", "module.layer3.12.conv3.weight", "module.layer3.12.bn3.weight", "module.layer3.12.bn3.bias", "module.layer3.12.bn3.running_mean", "module.layer3.12.bn3.running_var", "module.layer3.13.conv3.weight", "module.layer3.13.bn3.weight", "module.layer3.13.bn3.bias", "module.layer3.13.bn3.running_mean", "module.layer3.13.bn3.running_var", "module.layer3.14.conv3.weight", "module.layer3.14.bn3.weight", "module.layer3.14.bn3.bias", "module.layer3.14.bn3.running_mean", "module.layer3.14.bn3.running_var", "module.layer3.15.conv3.weight", "module.layer3.15.bn3.weight", "module.layer3.15.bn3.bias", "module.layer3.15.bn3.running_mean", "module.layer3.15.bn3.running_var", "module.layer3.16.conv3.weight", "module.layer3.16.bn3.weight", "module.layer3.16.bn3.bias", "module.layer3.16.bn3.running_mean", "module.layer3.16.bn3.running_var", "module.layer3.17.conv3.weight", "module.layer3.17.bn3.weight", "module.layer3.17.bn3.bias", "module.layer3.17.bn3.running_mean", "module.layer3.17.bn3.running_var".
size mismatch for module.layer1.0.conv1.weight: copying a param with shape torch.Size([16, 16, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.1.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.2.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.3.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.4.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.5.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.6.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.7.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.8.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.9.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.10.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.11.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.12.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.13.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.14.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.15.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.16.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.17.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer2.0.bn1.weight: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]).
size mismatch for module.layer2.0.bn1.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]).
size mismatch for module.layer2.0.bn1.running_mean: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]).
size mismatch for module.layer2.0.bn1.running_var: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]).
size mismatch for module.layer2.0.conv1.weight: copying a param with shape torch.Size([32, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 16, 3, 3]).
size mismatch for module.layer2.0.downsample.0.weight: copying a param with shape torch.Size([128, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 16, 1, 1]).
size mismatch for module.layer2.1.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.2.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.3.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.4.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.5.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.6.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.7.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.8.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.9.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.10.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.11.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.12.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.13.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.14.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.15.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.16.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.17.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer3.0.bn1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for module.layer3.0.bn1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for module.layer3.0.bn1.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for module.layer3.0.bn1.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for module.layer3.0.conv1.weight: copying a param with shape torch.Size([64, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 32, 3, 3]).
size mismatch for module.layer3.0.downsample.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 32, 1, 1]).
size mismatch for module.layer3.1.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.2.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.3.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.4.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.5.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.6.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.7.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.8.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.9.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.10.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.11.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.12.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.13.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.14.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.15.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.16.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.17.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.fc.weight: copying a param with shape torch.Size([100, 256]) from checkpoint, the shape in current model is torch.Size([10, 64]).
size mismatch for module.fc.bias: copying a param with shape torch.Size([100]) from checkpoint, the shape in current model is torch.Size([10]).
from pytorch-classification.
jiteshm17
commented on May 20, 2024
You can also manually updated the dic. Like this:
state_dict =checkpoint['state_dict'] from collections import OrderedDict new_state_dict = OrderedDict() for k, v in state_dict.items(): if 'module' not in k: k = 'module.'+k else: k = k.replace('features.module.', 'module.features.') new_state_dict[k]=v model.load_state_dict(new_state_dict)
I suppose this issue can be closed as the referenced post mentions the cause of the error and offers a solution
from pytorch-classification.
yustiks
commented on May 20, 2024
You can also manually updated the dic. Like this:
state_dict =checkpoint['state_dict'] from collections import OrderedDict new_state_dict = OrderedDict() for k, v in state_dict.items(): if 'module' not in k: k = 'module.'+k else: k = k.replace('features.module.', 'module.features.') new_state_dict[k]=v model.load_state_dict(new_state_dict)
life saver!
from pytorch-classification.
ersamo
commented on May 20, 2024
Am getting an error similar to this one
This is what I am running:
python cifar.py -a preresnet --depth 110 --epochs 3 --schedule 81 122 --gamma 0.1 --wd 1e-4 --checkpoint checkpoints/cifar10/preresnet-110 --resume 'checkpoint.pth.tar'
('checkpoint.pth.tar' is from the onedrive folder)RuntimeError: Error(s) in loading state_dict for DataParallel:
Missing key(s) in state_dict: "module.bn.weight", "module.bn.bias", "module.bn.running_mean", "module.bn.running_var".
Unexpected key(s) in state_dict: "module.bn1.weight", "module.bn1.bias", "module.bn1.running_mean", "module.bn1.running_var", "module.layer1.0.conv3.weight", "module.layer1.0.bn3.weight", "module.layer1.0.bn3.bias", "module.layer1.0.bn3.running_mean", "module.layer1.0.bn3.running_var", "module.layer1.0.downsample.0.weight", "module.layer1.0.downsample.1.weight", "module.layer1.0.downsample.1.bias", "module.layer1.0.downsample.1.running_mean", "module.layer1.0.downsample.1.running_var", "module.layer1.1.conv3.weight", "module.layer1.1.bn3.weight", "module.layer1.1.bn3.bias", "module.layer1.1.bn3.running_mean", "module.layer1.1.bn3.running_var", "module.layer1.2.conv3.weight", "module.layer1.2.bn3.weight", "module.layer1.2.bn3.bias", "module.layer1.2.bn3.running_mean", "module.layer1.2.bn3.running_var", "module.layer1.3.conv3.weight", "module.layer1.3.bn3.weight", "module.layer1.3.bn3.bias", "module.layer1.3.bn3.running_mean", "module.layer1.3.bn3.running_var", "module.layer1.4.conv3.weight", "module.layer1.4.bn3.weight", "module.layer1.4.bn3.bias", "module.layer1.4.bn3.running_mean", "module.layer1.4.bn3.running_var", "module.layer1.5.conv3.weight", "module.layer1.5.bn3.weight", "module.layer1.5.bn3.bias", "module.layer1.5.bn3.running_mean", "module.layer1.5.bn3.running_var", "module.layer1.6.conv3.weight", "module.layer1.6.bn3.weight", "module.layer1.6.bn3.bias", "module.layer1.6.bn3.running_mean", "module.layer1.6.bn3.running_var", "module.layer1.7.conv3.weight", "module.layer1.7.bn3.weight", "module.layer1.7.bn3.bias", "module.layer1.7.bn3.running_mean", "module.layer1.7.bn3.running_var", "module.layer1.8.conv3.weight", "module.layer1.8.bn3.weight", "module.layer1.8.bn3.bias", "module.layer1.8.bn3.running_mean", "module.layer1.8.bn3.running_var", "module.layer1.9.conv3.weight", "module.layer1.9.bn3.weight", "module.layer1.9.bn3.bias", "module.layer1.9.bn3.running_mean", "module.layer1.9.bn3.running_var", "module.layer1.10.conv3.weight", "module.layer1.10.bn3.weight", "module.layer1.10.bn3.bias", "module.layer1.10.bn3.running_mean", "module.layer1.10.bn3.running_var", "module.layer1.11.conv3.weight", "module.layer1.11.bn3.weight", "module.layer1.11.bn3.bias", "module.layer1.11.bn3.running_mean", "module.layer1.11.bn3.running_var", "module.layer1.12.conv3.weight", "module.layer1.12.bn3.weight", "module.layer1.12.bn3.bias", "module.layer1.12.bn3.running_mean", "module.layer1.12.bn3.running_var", "module.layer1.13.conv3.weight", "module.layer1.13.bn3.weight", "module.layer1.13.bn3.bias", "module.layer1.13.bn3.running_mean", "module.layer1.13.bn3.running_var", "module.layer1.14.conv3.weight", "module.layer1.14.bn3.weight", "module.layer1.14.bn3.bias", "module.layer1.14.bn3.running_mean", "module.layer1.14.bn3.running_var", "module.layer1.15.conv3.weight", "module.layer1.15.bn3.weight", "module.layer1.15.bn3.bias", "module.layer1.15.bn3.running_mean", "module.layer1.15.bn3.running_var", "module.layer1.16.conv3.weight", "module.layer1.16.bn3.weight", "module.layer1.16.bn3.bias", "module.layer1.16.bn3.running_mean", "module.layer1.16.bn3.running_var", "module.layer1.17.conv3.weight", "module.layer1.17.bn3.weight", "module.layer1.17.bn3.bias", "module.layer1.17.bn3.running_mean", "module.layer1.17.bn3.running_var", "module.layer2.0.conv3.weight", "module.layer2.0.bn3.weight", "module.layer2.0.bn3.bias", "module.layer2.0.bn3.running_mean", "module.layer2.0.bn3.running_var", "module.layer2.0.downsample.1.weight", "module.layer2.0.downsample.1.bias", "module.layer2.0.downsample.1.running_mean", "module.layer2.0.downsample.1.running_var", "module.layer2.1.conv3.weight", "module.layer2.1.bn3.weight", "module.layer2.1.bn3.bias", "module.layer2.1.bn3.running_mean", "module.layer2.1.bn3.running_var", "module.layer2.2.conv3.weight", "module.layer2.2.bn3.weight", "module.layer2.2.bn3.bias", "module.layer2.2.bn3.running_mean", "module.layer2.2.bn3.running_var", "module.layer2.3.conv3.weight", "module.layer2.3.bn3.weight", "module.layer2.3.bn3.bias", "module.layer2.3.bn3.running_mean", "module.layer2.3.bn3.running_var", "module.layer2.4.conv3.weight", "module.layer2.4.bn3.weight", "module.layer2.4.bn3.bias", "module.layer2.4.bn3.running_mean", "module.layer2.4.bn3.running_var", "module.layer2.5.conv3.weight", "module.layer2.5.bn3.weight", "module.layer2.5.bn3.bias", "module.layer2.5.bn3.running_mean", "module.layer2.5.bn3.running_var", "module.layer2.6.conv3.weight", "module.layer2.6.bn3.weight", "module.layer2.6.bn3.bias", "module.layer2.6.bn3.running_mean", "module.layer2.6.bn3.running_var", "module.layer2.7.conv3.weight", "module.layer2.7.bn3.weight", "module.layer2.7.bn3.bias", "module.layer2.7.bn3.running_mean", "module.layer2.7.bn3.running_var", "module.layer2.8.conv3.weight", "module.layer2.8.bn3.weight", "module.layer2.8.bn3.bias", "module.layer2.8.bn3.running_mean", "module.layer2.8.bn3.running_var", "module.layer2.9.conv3.weight", "module.layer2.9.bn3.weight", "module.layer2.9.bn3.bias", "module.layer2.9.bn3.running_mean", "module.layer2.9.bn3.running_var", "module.layer2.10.conv3.weight", "module.layer2.10.bn3.weight", "module.layer2.10.bn3.bias", "module.layer2.10.bn3.running_mean", "module.layer2.10.bn3.running_var", "module.layer2.11.conv3.weight", "module.layer2.11.bn3.weight", "module.layer2.11.bn3.bias", "module.layer2.11.bn3.running_mean", "module.layer2.11.bn3.running_var", "module.layer2.12.conv3.weight", "module.layer2.12.bn3.weight", "module.layer2.12.bn3.bias", "module.layer2.12.bn3.running_mean", "module.layer2.12.bn3.running_var", "module.layer2.13.conv3.weight", "module.layer2.13.bn3.weight", "module.layer2.13.bn3.bias", "module.layer2.13.bn3.running_mean", "module.layer2.13.bn3.running_var", "module.layer2.14.conv3.weight", "module.layer2.14.bn3.weight", "module.layer2.14.bn3.bias", "module.layer2.14.bn3.running_mean", "module.layer2.14.bn3.running_var", "module.layer2.15.conv3.weight", "module.layer2.15.bn3.weight", "module.layer2.15.bn3.bias", "module.layer2.15.bn3.running_mean", "module.layer2.15.bn3.running_var", "module.layer2.16.conv3.weight", "module.layer2.16.bn3.weight", "module.layer2.16.bn3.bias", "module.layer2.16.bn3.running_mean", "module.layer2.16.bn3.running_var", "module.layer2.17.conv3.weight", "module.layer2.17.bn3.weight", "module.layer2.17.bn3.bias", "module.layer2.17.bn3.running_mean", "module.layer2.17.bn3.running_var", "module.layer3.0.conv3.weight", "module.layer3.0.bn3.weight", "module.layer3.0.bn3.bias", "module.layer3.0.bn3.running_mean", "module.layer3.0.bn3.running_var", "module.layer3.0.downsample.1.weight", "module.layer3.0.downsample.1.bias", "module.layer3.0.downsample.1.running_mean", "module.layer3.0.downsample.1.running_var", "module.layer3.1.conv3.weight", "module.layer3.1.bn3.weight", "module.layer3.1.bn3.bias", "module.layer3.1.bn3.running_mean", "module.layer3.1.bn3.running_var", "module.layer3.2.conv3.weight", "module.layer3.2.bn3.weight", "module.layer3.2.bn3.bias", "module.layer3.2.bn3.running_mean", "module.layer3.2.bn3.running_var", "module.layer3.3.conv3.weight", "module.layer3.3.bn3.weight", "module.layer3.3.bn3.bias", "module.layer3.3.bn3.running_mean", "module.layer3.3.bn3.running_var", "module.layer3.4.conv3.weight", "module.layer3.4.bn3.weight", "module.layer3.4.bn3.bias", "module.layer3.4.bn3.running_mean", "module.layer3.4.bn3.running_var", "module.layer3.5.conv3.weight", "module.layer3.5.bn3.weight", "module.layer3.5.bn3.bias", "module.layer3.5.bn3.running_mean", "module.layer3.5.bn3.running_var", "module.layer3.6.conv3.weight", "module.layer3.6.bn3.weight", "module.layer3.6.bn3.bias", "module.layer3.6.bn3.running_mean", "module.layer3.6.bn3.running_var", "module.layer3.7.conv3.weight", "module.layer3.7.bn3.weight", "module.layer3.7.bn3.bias", "module.layer3.7.bn3.running_mean", "module.layer3.7.bn3.running_var", "module.layer3.8.conv3.weight", "module.layer3.8.bn3.weight", "module.layer3.8.bn3.bias", "module.layer3.8.bn3.running_mean", "module.layer3.8.bn3.running_var", "module.layer3.9.conv3.weight", "module.layer3.9.bn3.weight", "module.layer3.9.bn3.bias", "module.layer3.9.bn3.running_mean", "module.layer3.9.bn3.running_var", "module.layer3.10.conv3.weight", "module.layer3.10.bn3.weight", "module.layer3.10.bn3.bias", "module.layer3.10.bn3.running_mean", "module.layer3.10.bn3.running_var", "module.layer3.11.conv3.weight", "module.layer3.11.bn3.weight", "module.layer3.11.bn3.bias", "module.layer3.11.bn3.running_mean", "module.layer3.11.bn3.running_var", "module.layer3.12.conv3.weight", "module.layer3.12.bn3.weight", "module.layer3.12.bn3.bias", "module.layer3.12.bn3.running_mean", "module.layer3.12.bn3.running_var", "module.layer3.13.conv3.weight", "module.layer3.13.bn3.weight", "module.layer3.13.bn3.bias", "module.layer3.13.bn3.running_mean", "module.layer3.13.bn3.running_var", "module.layer3.14.conv3.weight", "module.layer3.14.bn3.weight", "module.layer3.14.bn3.bias", "module.layer3.14.bn3.running_mean", "module.layer3.14.bn3.running_var", "module.layer3.15.conv3.weight", "module.layer3.15.bn3.weight", "module.layer3.15.bn3.bias", "module.layer3.15.bn3.running_mean", "module.layer3.15.bn3.running_var", "module.layer3.16.conv3.weight", "module.layer3.16.bn3.weight", "module.layer3.16.bn3.bias", "module.layer3.16.bn3.running_mean", "module.layer3.16.bn3.running_var", "module.layer3.17.conv3.weight", "module.layer3.17.bn3.weight", "module.layer3.17.bn3.bias", "module.layer3.17.bn3.running_mean", "module.layer3.17.bn3.running_var".
size mismatch for module.layer1.0.conv1.weight: copying a param with shape torch.Size([16, 16, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.1.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.2.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.3.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.4.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.5.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.6.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.7.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.8.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.9.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.10.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.11.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.12.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.13.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.14.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.15.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.16.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer1.17.conv1.weight: copying a param with shape torch.Size([16, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).
size mismatch for module.layer2.0.bn1.weight: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]).
size mismatch for module.layer2.0.bn1.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]).
size mismatch for module.layer2.0.bn1.running_mean: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]).
size mismatch for module.layer2.0.bn1.running_var: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([16]).
size mismatch for module.layer2.0.conv1.weight: copying a param with shape torch.Size([32, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 16, 3, 3]).
size mismatch for module.layer2.0.downsample.0.weight: copying a param with shape torch.Size([128, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 16, 1, 1]).
size mismatch for module.layer2.1.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.2.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.3.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.4.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.5.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.6.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.7.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.8.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.9.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.10.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.11.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.12.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.13.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.14.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.15.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.16.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer2.17.conv1.weight: copying a param with shape torch.Size([32, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for module.layer3.0.bn1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for module.layer3.0.bn1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for module.layer3.0.bn1.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for module.layer3.0.bn1.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for module.layer3.0.conv1.weight: copying a param with shape torch.Size([64, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 32, 3, 3]).
size mismatch for module.layer3.0.downsample.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 32, 1, 1]).
size mismatch for module.layer3.1.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.2.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.3.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.4.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.5.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.6.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.7.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.8.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.9.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.10.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.11.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.12.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.13.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.14.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.15.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.16.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.layer3.17.conv1.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for module.fc.weight: copying a param with shape torch.Size([100, 256]) from checkpoint, the shape in current model is torch.Size([10, 64]).
size mismatch for module.fc.bias: copying a param with shape torch.Size([100]) from checkpoint, the shape in current model is torch.Size([10]).
did you solve it please ?
from pytorch-classification.
Kin-Zhang
commented on May 20, 2024
state_dict =checkpoint['state_dict'] from collections import OrderedDict new_state_dict = OrderedDict() for k, v in state_dict.items(): if 'module' not in k: k = 'module.'+k else: k = k.replace('features.module.', 'module.features.') new_state_dict[k]=v model.load_state_dict(new_state_dict)
This is the solution!!!! Thanks!!!!!
from pytorch-classification.
Chenny0808
commented on May 20, 2024
Use model.module.state_dict() instead of model.state_dict() in DP mode
from pytorch-classification.
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from pytorch-classification.