_pickle.UnpicklingError: invalid load key, '<'.

Question

_pickle.UnpicklingError: invalid load key, '<'.

Lzshanshan opened this issue 2 years ago · 5 comments

Hello,

When I run python test.py --pretrained scannet --architecture BN on Win10, it shows this:

loading checkpoint... ./checkpoints/scannet.pt
Loading base model ()...Using cache found in C:\Users\LZS/.cache\torch\hub\rwightman_gen-efficientnet-pytorch_master
Done.
Removing last two layers (global_pool & classifier).
Traceback (most recent call last):
File "test.py", line 97, in
model = utils.load_checkpoint(checkpoint, model)
File "F:\surface_normal_uncertainty\utils\utils.py", line 57, in load_checkpoint
ckpt = torch.load(fpath, map_location=lambda storage, loc: storage)['model']
File "F:\Anaconda3\lib\site-packages\torch\serialization.py", line 608, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "F:\Anaconda3\lib\site-packages\torch\serialization.py", line 777, in _legacy_load
magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: invalid load key, '<'.

I have tried directly downloading the model file, tf_efficientnet_b5_ap-9e82fae8.pth, from https://zzun.app/repo/rwightman-pytorch-image-models-python-deep-learning#releases, and use it to replace the original one.

I checked their file size and they are all over 100M.

Could you please give me some idea?

Answer 1 · 2022-04-22T20:31:48.000Z

Hi, can you tell me what version of PyTorch you are using?

Answer 2 · 2022-04-23T05:59:22.000Z

Thank you for reply.

Pytorch version is 1.9.1.

I also tried run it on Ubuntu18, and the version of Pytorch is 1.6.0. The ERROR is same:
loading checkpoint... ./checkpoints/scannet.pt
Using cache found in /home/lzs/.cache/torch/hub/rwightman_gen-efficientnet-pytorch_master
Loading base model ()...Done.
Removing last two layers (global_pool & classifier).
Traceback (most recent call last):
File "test.py", line 97, in
model = utils.load_checkpoint(checkpoint, model)
File "/home/lzs/surface_normal_uncertainty/utils/utils.py", line 56, in load_checkpoint
ckpt = torch.load(fpath, map_location='cpu')['model']
File "/home/lzs/anaconda3/envs/surface_normal_estimation/lib/python3.6/site-packages/torch/serialization.py", line 585, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/home/lzs/anaconda3/envs/surface_normal_estimation/lib/python3.6/site-packages/torch/serialization.py", line 755, in _legacy_load
magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: invalid load key, '<'.

Now, I try to directly load checkpoint in test.py like this:

load checkpoint

""" checkpoint = './checkpoints/%s.pt' % args.pretrained
print('loading checkpoint... {}'.format(checkpoint)) """
checkpoint = 'tf_efficientnet_b5_ap-9e82fae8.pth'
model = NNET(args).to(device)
model = utils.load_checkpoint(checkpoint, model)
model.eval()
print('loading checkpoint... / done')

And, I modify the load_checkpoint(fpath, model) like this:

load model

def load_checkpoint(fpath, model):
ckpt = torch.load(fpath)
model.load_state_dict(ckpt)
""" ckpt = torch.load(fpath, map_location=torch.device('cpu'))['model']
load_dict = {}
for k, v in ckpt.items():
if k.startswith('module.'):
k_ = k.replace('module.', '')
load_dict[k_] = v
else:
load_dict[k] = v
model.load_state_dict(load_dict) """
return model

I get the output:
Loading base model ()...Using cache found in C:\Users\LZS/.cache\torch\hub\rwightman_gen-efficientnet-pytorch_master
Done.
Removing last two layers (global_pool & classifier).
Traceback (most recent call last):
File "test.py", line 98, in
model = utils.load_checkpoint(checkpoint, model)
File "F:\Concordia\Depth\Depth Completion\Boundary_and_Surface_Normal\Surface Normal\Estimating_and_Exploiting_the_Aleatoric_Uncertainty\surface_normal_uncertainty\utils\utils.py", line 57, in load_checkpoint
model.load_state_dict(ckpt)
File "F:\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1406, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for NNET:
Missing key(s) in state_dict: "encoder.original_model.conv_stem.weight", "encoder.original_model.bn1.weight", "encoder.original_model.bn1.bias", "encoder.original_model.bn1.running_mean", "encoder.original_model.bn1.running_var", "encoder.original_model.blocks.0.0.conv_dw.weight", "encoder.original_model.blocks.0.0.bn1.weight", "encoder.original_model.blocks.0.0.bn1.bias", "encoder.original_model.blocks.0.0.bn1.running_mean", "encoder.original_model.blocks.0.0.bn1.running_var", "encoder.original_model.blocks.0.0.se.conv_reduce.weight", "encoder.original_model.blocks.0.0.se.conv_reduce.bias", "encoder.original_model.blocks.0.0.se.conv_expand.weight", "encoder.original_model.blocks.0.0.se.conv_expand.bias", "encoder.original_model.blocks.0.0.conv_pw.weight", "encoder.original_model.blocks.0.0.bn2.weight", "encoder.original_model.blocks.0.0.bn2.bias", "encoder.original_model.blocks.0.0.bn2.running_mean", "encoder.original_model.blocks.0.0.bn2.running_var", 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"encoder.original_model.blocks.6.2.conv_pw.weight", "encoder.original_model.blocks.6.2.bn1.weight", "encoder.original_model.blocks.6.2.bn1.bias", "encoder.original_model.blocks.6.2.bn1.running_mean", "encoder.original_model.blocks.6.2.bn1.running_var", "encoder.original_model.blocks.6.2.conv_dw.weight", "encoder.original_model.blocks.6.2.bn2.weight", "encoder.original_model.blocks.6.2.bn2.bias", "encoder.original_model.blocks.6.2.bn2.running_mean", "encoder.original_model.blocks.6.2.bn2.running_var", "encoder.original_model.blocks.6.2.se.conv_reduce.weight", "encoder.original_model.blocks.6.2.se.conv_reduce.bias", "encoder.original_model.blocks.6.2.se.conv_expand.weight", "encoder.original_model.blocks.6.2.se.conv_expand.bias", "encoder.original_model.blocks.6.2.conv_pwl.weight", "encoder.original_model.blocks.6.2.bn3.weight", "encoder.original_model.blocks.6.2.bn3.bias", "encoder.original_model.blocks.6.2.bn3.running_mean", "encoder.original_model.blocks.6.2.bn3.running_var", "encoder.original_model.conv_head.weight", "encoder.original_model.bn2.weight", "encoder.original_model.bn2.bias", "encoder.original_model.bn2.running_mean", "encoder.original_model.bn2.running_var", "decoder.conv2.weight", "decoder.conv2.bias", "decoder.up1._net.0.weight", "decoder.up1._net.0.bias", "decoder.up1._net.1.weight", "decoder.up1._net.1.bias", "decoder.up1._net.1.running_mean", "decoder.up1._net.1.running_var", "decoder.up1._net.3.weight", "decoder.up1._net.3.bias", "decoder.up1._net.4.weight", "decoder.up1._net.4.bias", "decoder.up1._net.4.running_mean", "decoder.up1._net.4.running_var", "decoder.up2._net.0.weight", "decoder.up2._net.0.bias", "decoder.up2._net.1.weight", "decoder.up2._net.1.bias", "decoder.up2._net.1.running_mean", "decoder.up2._net.1.running_var", "decoder.up2._net.3.weight", "decoder.up2._net.3.bias", "decoder.up2._net.4.weight", "decoder.up2._net.4.bias", "decoder.up2._net.4.running_mean", "decoder.up2._net.4.running_var", "decoder.up3._net.0.weight", "decoder.up3._net.0.bias", "decoder.up3._net.1.weight", "decoder.up3._net.1.bias", "decoder.up3._net.1.running_mean", "decoder.up3._net.1.running_var", "decoder.up3._net.3.weight", "decoder.up3._net.3.bias", "decoder.up3._net.4.weight", "decoder.up3._net.4.bias", "decoder.up3._net.4.running_mean", "decoder.up3._net.4.running_var", "decoder.up4._net.0.weight", "decoder.up4._net.0.bias", "decoder.up4._net.1.weight", "decoder.up4._net.1.bias", "decoder.up4._net.1.running_mean", "decoder.up4._net.1.running_var", "decoder.up4._net.3.weight", "decoder.up4._net.3.bias", "decoder.up4._net.4.weight", "decoder.up4._net.4.bias", "decoder.up4._net.4.running_mean", "decoder.up4._net.4.running_var", "decoder.out_conv_res8.weight", "decoder.out_conv_res8.bias", "decoder.out_conv_res4.0.weight", "decoder.out_conv_res4.0.bias", "decoder.out_conv_res4.2.weight", "decoder.out_conv_res4.2.bias", "decoder.out_conv_res4.4.weight", "decoder.out_conv_res4.4.bias", "decoder.out_conv_res4.6.weight", "decoder.out_conv_res4.6.bias", "decoder.out_conv_res2.0.weight", "decoder.out_conv_res2.0.bias", "decoder.out_conv_res2.2.weight", "decoder.out_conv_res2.2.bias", "decoder.out_conv_res2.4.weight",
"decoder.out_conv_res2.4.bias", "decoder.out_conv_res2.6.weight", "decoder.out_conv_res2.6.bias", "decoder.out_conv_res1.0.weight", "decoder.out_conv_res1.0.bias", "decoder.out_conv_res1.2.weight", "decoder.out_conv_res1.2.bias", "decoder.out_conv_res1.4.weight", "decoder.out_conv_res1.4.bias", "decoder.out_conv_res1.6.weight", "decoder.out_conv_res1.6.bias".
Unexpected key(s) in state_dict: "conv_stem.weight", "bn1.weight", "bn1.bias", "bn1.running_mean", "bn1.running_var", "bn1.num_batches_tracked", "blocks.0.0.conv_dw.weight", "blocks.0.0.bn1.weight", "blocks.0.0.bn1.bias", "blocks.0.0.bn1.running_mean", "blocks.0.0.bn1.running_var", "blocks.0.0.bn1.num_batches_tracked", "blocks.0.0.se.conv_reduce.weight", "blocks.0.0.se.conv_reduce.bias", "blocks.0.0.se.conv_expand.weight", "blocks.0.0.se.conv_expand.bias", "blocks.0.0.conv_pw.weight", "blocks.0.0.bn2.weight", "blocks.0.0.bn2.bias", "blocks.0.0.bn2.running_mean", "blocks.0.0.bn2.running_var", "blocks.0.0.bn2.num_batches_tracked", "blocks.0.1.conv_dw.weight", "blocks.0.1.bn1.weight", "blocks.0.1.bn1.bias", "blocks.0.1.bn1.running_mean", "blocks.0.1.bn1.running_var", "blocks.0.1.bn1.num_batches_tracked", "blocks.0.1.se.conv_reduce.weight", "blocks.0.1.se.conv_reduce.bias", "blocks.0.1.se.conv_expand.weight", "blocks.0.1.se.conv_expand.bias", "blocks.0.1.conv_pw.weight", "blocks.0.1.bn2.weight", "blocks.0.1.bn2.bias", "blocks.0.1.bn2.running_mean", "blocks.0.1.bn2.running_var", "blocks.0.1.bn2.num_batches_tracked", "blocks.0.2.conv_dw.weight", "blocks.0.2.bn1.weight", "blocks.0.2.bn1.bias", "blocks.0.2.bn1.running_mean", "blocks.0.2.bn1.running_var", "blocks.0.2.bn1.num_batches_tracked", "blocks.0.2.se.conv_reduce.weight", "blocks.0.2.se.conv_reduce.bias", "blocks.0.2.se.conv_expand.weight", "blocks.0.2.se.conv_expand.bias", "blocks.0.2.conv_pw.weight", "blocks.0.2.bn2.weight", "blocks.0.2.bn2.bias", "blocks.0.2.bn2.running_mean", "blocks.0.2.bn2.running_var", "blocks.0.2.bn2.num_batches_tracked", "blocks.1.0.conv_pw.weight", "blocks.1.0.bn1.weight", "blocks.1.0.bn1.bias", "blocks.1.0.bn1.running_mean", "blocks.1.0.bn1.running_var", "blocks.1.0.bn1.num_batches_tracked", "blocks.1.0.conv_dw.weight", "blocks.1.0.bn2.weight", "blocks.1.0.bn2.bias", "blocks.1.0.bn2.running_mean", "blocks.1.0.bn2.running_var", "blocks.1.0.bn2.num_batches_tracked", "blocks.1.0.se.conv_reduce.weight", "blocks.1.0.se.conv_reduce.bias", "blocks.1.0.se.conv_expand.weight", "blocks.1.0.se.conv_expand.bias", "blocks.1.0.conv_pwl.weight", "blocks.1.0.bn3.weight", "blocks.1.0.bn3.bias", "blocks.1.0.bn3.running_mean", "blocks.1.0.bn3.running_var", "blocks.1.0.bn3.num_batches_tracked", "blocks.1.1.conv_pw.weight", "blocks.1.1.bn1.weight", "blocks.1.1.bn1.bias", "blocks.1.1.bn1.running_mean", "blocks.1.1.bn1.running_var", "blocks.1.1.bn1.num_batches_tracked", "blocks.1.1.conv_dw.weight", "blocks.1.1.bn2.weight", "blocks.1.1.bn2.bias", "blocks.1.1.bn2.running_mean", "blocks.1.1.bn2.running_var", "blocks.1.1.bn2.num_batches_tracked", "blocks.1.1.se.conv_reduce.weight", "blocks.1.1.se.conv_reduce.bias", "blocks.1.1.se.conv_expand.weight", "blocks.1.1.se.conv_expand.bias", "blocks.1.1.conv_pwl.weight", "blocks.1.1.bn3.weight", "blocks.1.1.bn3.bias", "blocks.1.1.bn3.running_mean", "blocks.1.1.bn3.running_var", "blocks.1.1.bn3.num_batches_tracked", "blocks.1.2.conv_pw.weight", "blocks.1.2.bn1.weight", "blocks.1.2.bn1.bias", "blocks.1.2.bn1.running_mean", "blocks.1.2.bn1.running_var", "blocks.1.2.bn1.num_batches_tracked", "blocks.1.2.conv_dw.weight", "blocks.1.2.bn2.weight", "blocks.1.2.bn2.bias", "blocks.1.2.bn2.running_mean", "blocks.1.2.bn2.running_var", "blocks.1.2.bn2.num_batches_tracked", "blocks.1.2.se.conv_reduce.weight", "blocks.1.2.se.conv_reduce.bias", "blocks.1.2.se.conv_expand.weight", "blocks.1.2.se.conv_expand.bias", "blocks.1.2.conv_pwl.weight", "blocks.1.2.bn3.weight", "blocks.1.2.bn3.bias", "blocks.1.2.bn3.running_mean", "blocks.1.2.bn3.running_var", "blocks.1.2.bn3.num_batches_tracked", "blocks.1.3.conv_pw.weight", "blocks.1.3.bn1.weight", "blocks.1.3.bn1.bias", "blocks.1.3.bn1.running_mean", "blocks.1.3.bn1.running_var", "blocks.1.3.bn1.num_batches_tracked", "blocks.1.3.conv_dw.weight", "blocks.1.3.bn2.weight", "blocks.1.3.bn2.bias", "blocks.1.3.bn2.running_mean", "blocks.1.3.bn2.running_var", "blocks.1.3.bn2.num_batches_tracked", "blocks.1.3.se.conv_reduce.weight", "blocks.1.3.se.conv_reduce.bias", "blocks.1.3.se.conv_expand.weight", "blocks.1.3.se.conv_expand.bias", "blocks.1.3.conv_pwl.weight", "blocks.1.3.bn3.weight", "blocks.1.3.bn3.bias", "blocks.1.3.bn3.running_mean", "blocks.1.3.bn3.running_var", "blocks.1.3.bn3.num_batches_tracked", "blocks.1.4.conv_pw.weight", "blocks.1.4.bn1.weight", "blocks.1.4.bn1.bias", "blocks.1.4.bn1.running_mean", "blocks.1.4.bn1.running_var", "blocks.1.4.bn1.num_batches_tracked", "blocks.1.4.conv_dw.weight", "blocks.1.4.bn2.weight", "blocks.1.4.bn2.bias", "blocks.1.4.bn2.running_mean", "blocks.1.4.bn2.running_var", "blocks.1.4.bn2.num_batches_tracked", "blocks.1.4.se.conv_reduce.weight", "blocks.1.4.se.conv_reduce.bias", "blocks.1.4.se.conv_expand.weight", "blocks.1.4.se.conv_expand.bias", "blocks.1.4.conv_pwl.weight", "blocks.1.4.bn3.weight", "blocks.1.4.bn3.bias", "blocks.1.4.bn3.running_mean", "blocks.1.4.bn3.running_var", 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"blocks.2.1.bn2.running_var", "blocks.2.1.bn2.num_batches_tracked", "blocks.2.1.se.conv_reduce.weight", "blocks.2.1.se.conv_reduce.bias", "blocks.2.1.se.conv_expand.weight", "blocks.2.1.se.conv_expand.bias", "blocks.2.1.conv_pwl.weight", "blocks.2.1.bn3.weight", "blocks.2.1.bn3.bias", "blocks.2.1.bn3.running_mean", "blocks.2.1.bn3.running_var", "blocks.2.1.bn3.num_batches_tracked", "blocks.2.2.conv_pw.weight", "blocks.2.2.bn1.weight", "blocks.2.2.bn1.bias", "blocks.2.2.bn1.running_mean", "blocks.2.2.bn1.running_var",
"blocks.2.2.bn1.num_batches_tracked", "blocks.2.2.conv_dw.weight", "blocks.2.2.bn2.weight", "blocks.2.2.bn2.bias", "blocks.2.2.bn2.running_mean", "blocks.2.2.bn2.running_var", "blocks.2.2.bn2.num_batches_tracked", "blocks.2.2.se.conv_reduce.weight", "blocks.2.2.se.conv_reduce.bias", "blocks.2.2.se.conv_expand.weight", "blocks.2.2.se.conv_expand.bias", "blocks.2.2.conv_pwl.weight", "blocks.2.2.bn3.weight", "blocks.2.2.bn3.bias", "blocks.2.2.bn3.running_mean", "blocks.2.2.bn3.running_var", "blocks.2.2.bn3.num_batches_tracked", "blocks.2.3.conv_pw.weight", "blocks.2.3.bn1.weight", "blocks.2.3.bn1.bias", "blocks.2.3.bn1.running_mean", "blocks.2.3.bn1.running_var", "blocks.2.3.bn1.num_batches_tracked", "blocks.2.3.conv_dw.weight", "blocks.2.3.bn2.weight", "blocks.2.3.bn2.bias", "blocks.2.3.bn2.running_mean", "blocks.2.3.bn2.running_var", "blocks.2.3.bn2.num_batches_tracked", "blocks.2.3.se.conv_reduce.weight", "blocks.2.3.se.conv_reduce.bias", "blocks.2.3.se.conv_expand.weight", "blocks.2.3.se.conv_expand.bias", "blocks.2.3.conv_pwl.weight", "blocks.2.3.bn3.weight", "blocks.2.3.bn3.bias", "blocks.2.3.bn3.running_mean", "blocks.2.3.bn3.running_var", "blocks.2.3.bn3.num_batches_tracked",
"blocks.2.4.conv_pw.weight", "blocks.2.4.bn1.weight", "blocks.2.4.bn1.bias", "blocks.2.4.bn1.running_mean", "blocks.2.4.bn1.running_var", "blocks.2.4.bn1.num_batches_tracked", "blocks.2.4.conv_dw.weight", "blocks.2.4.bn2.weight", "blocks.2.4.bn2.bias", "blocks.2.4.bn2.running_mean", "blocks.2.4.bn2.running_var", "blocks.2.4.bn2.num_batches_tracked", "blocks.2.4.se.conv_reduce.weight", "blocks.2.4.se.conv_reduce.bias", "blocks.2.4.se.conv_expand.weight", "blocks.2.4.se.conv_expand.bias", "blocks.2.4.conv_pwl.weight", "blocks.2.4.bn3.weight", "blocks.2.4.bn3.bias", "blocks.2.4.bn3.running_mean", "blocks.2.4.bn3.running_var", "blocks.2.4.bn3.num_batches_tracked", "blocks.3.0.conv_pw.weight", "blocks.3.0.bn1.weight", "blocks.3.0.bn1.bias", "blocks.3.0.bn1.running_mean", "blocks.3.0.bn1.running_var", "blocks.3.0.bn1.num_batches_tracked", "blocks.3.0.conv_dw.weight", "blocks.3.0.bn2.weight", "blocks.3.0.bn2.bias", "blocks.3.0.bn2.running_mean", "blocks.3.0.bn2.running_var", "blocks.3.0.bn2.num_batches_tracked", "blocks.3.0.se.conv_reduce.weight", "blocks.3.0.se.conv_reduce.bias", "blocks.3.0.se.conv_expand.weight", "blocks.3.0.se.conv_expand.bias", "blocks.3.0.conv_pwl.weight", "blocks.3.0.bn3.weight", "blocks.3.0.bn3.bias", "blocks.3.0.bn3.running_mean", "blocks.3.0.bn3.running_var", "blocks.3.0.bn3.num_batches_tracked", "blocks.3.1.conv_pw.weight", "blocks.3.1.bn1.weight", "blocks.3.1.bn1.bias", "blocks.3.1.bn1.running_mean", "blocks.3.1.bn1.running_var", "blocks.3.1.bn1.num_batches_tracked", "blocks.3.1.conv_dw.weight", "blocks.3.1.bn2.weight", "blocks.3.1.bn2.bias", "blocks.3.1.bn2.running_mean", "blocks.3.1.bn2.running_var", "blocks.3.1.bn2.num_batches_tracked", "blocks.3.1.se.conv_reduce.weight", "blocks.3.1.se.conv_reduce.bias", "blocks.3.1.se.conv_expand.weight", "blocks.3.1.se.conv_expand.bias", "blocks.3.1.conv_pwl.weight", "blocks.3.1.bn3.weight", "blocks.3.1.bn3.bias", "blocks.3.1.bn3.running_mean", "blocks.3.1.bn3.running_var", "blocks.3.1.bn3.num_batches_tracked", "blocks.3.2.conv_pw.weight", "blocks.3.2.bn1.weight", "blocks.3.2.bn1.bias", "blocks.3.2.bn1.running_mean", "blocks.3.2.bn1.running_var", "blocks.3.2.bn1.num_batches_tracked", "blocks.3.2.conv_dw.weight", "blocks.3.2.bn2.weight", "blocks.3.2.bn2.bias", "blocks.3.2.bn2.running_mean", "blocks.3.2.bn2.running_var", "blocks.3.2.bn2.num_batches_tracked", "blocks.3.2.se.conv_reduce.weight", "blocks.3.2.se.conv_reduce.bias", "blocks.3.2.se.conv_expand.weight", "blocks.3.2.se.conv_expand.bias", "blocks.3.2.conv_pwl.weight", "blocks.3.2.bn3.weight", "blocks.3.2.bn3.bias", "blocks.3.2.bn3.running_mean", "blocks.3.2.bn3.running_var", "blocks.3.2.bn3.num_batches_tracked", "blocks.3.3.conv_pw.weight", "blocks.3.3.bn1.weight", "blocks.3.3.bn1.bias", "blocks.3.3.bn1.running_mean", "blocks.3.3.bn1.running_var", "blocks.3.3.bn1.num_batches_tracked", "blocks.3.3.conv_dw.weight", "blocks.3.3.bn2.weight", "blocks.3.3.bn2.bias", "blocks.3.3.bn2.running_mean", 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"blocks.5.5.bn3.running_mean", "blocks.5.5.bn3.running_var", "blocks.5.5.bn3.num_batches_tracked", "blocks.5.6.conv_pw.weight", "blocks.5.6.bn1.weight", "blocks.5.6.bn1.bias", "blocks.5.6.bn1.running_mean", "blocks.5.6.bn1.running_var", "blocks.5.6.bn1.num_batches_tracked", "blocks.5.6.conv_dw.weight", "blocks.5.6.bn2.weight", "blocks.5.6.bn2.bias", "blocks.5.6.bn2.running_mean", "blocks.5.6.bn2.running_var", "blocks.5.6.bn2.num_batches_tracked", "blocks.5.6.se.conv_reduce.weight", "blocks.5.6.se.conv_reduce.bias", "blocks.5.6.se.conv_expand.weight", "blocks.5.6.se.conv_expand.bias", "blocks.5batches_tracked", "blocks.5.7.conv_pw.weight", "blocks.5.7.bn1.weight", "blocks.5.7.bn1.bias", "blocks.5.7.bn1.running_mean", "blocks.5.7.bn1.running_var", "blocks.5.7.bn1.num_batches_tracked", "blocks.5.7.conv_dw.weight", "blocks.5.7.bn2.weight", "blocks.5.7.bn2.bias", "blocks.5.7.bn2.running_mean"bn3.running_mean", "blocks.5.7.bn3.running_var", "blocks.5.7.bn3.num_batches_tracked", "blocks.5.8.conv_pw.weight", "blocks.5.8.bn1.weight", "blocks.55.8.bn2.weight", "blocks.5.8.bn2.bias", "blocks.5.8.bn2.running_mean", "blocks.5.8.bn2.running_var", "blocks.5.8.bn2.num_batches_tracked", "blocks.5.8.se.conv_reduce.weight", "blocks.5.8.se.conv_reduce.bias", "blocks.5.8.se.conv_expand.weight", "blocks.5.8.se.conv_expand.bias", "blocks.5.8.conv_pwl.weight", "blocks.5.8.bn3.weight", "blocks.5.8.bn3.bias", "blocks.5.8.bn3.running_mean", "blocks.5.8.bn3.running_var", "blocks.5.8.bn3.num_batches_tracked", "blocks.6.0.conv_pw.weight", "blocks.6.0.bn1.weight", "blocks.6.0.bn1.bias", "blocks.6.0.bn1.running_mean", "blocks.6.0.bn1.running_var", "blocks.6.0.bn1.num_batches_tracked", "blocks.6.0.conv_dw.weight", "blocks.6.0.bn2.weight", "blocks.6.0.bn2.bias", "blocks.6.0.bn2.running_mean", "blocks.6.0.bn2.running_var", "blocks.6.0.bn2.num_batches_tracked", "blocks.6.0.se.conv_reduce.weight", "blocks.6.0.se.conv_reduce.bias", "blocks.6.0.se.conv_expand.weight", "blocks.6.0.se.conv_expand.bias", "blocks.6.0.conv_pwl.weight", "blocks.6.0.bn3.weight", "blocks.6.0.bn3.bias", "blocks.6.0.bn3.running_mean", "blocks.6.0.bn3.running_var", "blocks.6.0.bn3.num_batches_tracked", "blocks.6.1.conv_pw.weight", "blocks.6.1.bn1.weight", "blocks.6.1.bn1.bias", "blocks.6.1.bn1.running_mean", "blocks.6.1.bn1.running_var", "blocks.6.1.bn1.num_batches_tracked", "blocks.6.1.conv_dw.weight", "blocks.6.1.bn2.weight", "blocks.6.1.bn2.bias", "blocks.6.1.bn2.running_mean", "blocks.6.1.bn2.running_var", "blocks.6.1.bn2.num_batches_tracked", "blocks.6.1.se.conv_reduce.weight", "blocks.6.1.se.conv_reduce.bias", "blocks.6.1.se.conv_expand.weight", "blocks.6.1.se.conv_expand.bias", "blocks.6.1.conv_pwl.weight", "blocks.6.1.bn3.weight", "blocks.6.1.bn3.bias", "blocks.6.1.bn3.running_mean", "blocks.6.1.bn3.running_var", "blocks.6.1.bn3.num_batches_tracked", "blocks.6.2.conv_pw.weight", "blocks.6.2.bn1.weight", "blocks.6.2.bn1.bias", "blocks.6.2.bn1.running_mean", "blocks.6.2.bn1.running_var", "blocks.6.2.bn1.num_batches_tracked", "blocks.6.2.conv_dw.weight", "blocks.6.2.bn2.weight", "blocks.6.2.bn2.bias", "blocks.6.2.bn2.running_mean", "blocks.6.2.bn2.running_var", "blocks.6.2.bn2.num_batches_tracked", "blocks.6.2.se.conv_reduce.weight", "blocks.6.2.se.conv_reduce.bias", "blocks.6.2.se.conv_expand.weight", "blocks.6.2.se.conv_expand.bias", "blocks.6.2.conv_pwl.weight", "blocks.6.2.bn3.weight", "blocks.6.2.bn3.bias", "blocks.6.2.bn3.running_mean", "blocks.6.2.bn3.running_var", "blocks.6.2.bn3.num_batches_tracked", "conv_head.weight", "bn2.weight", "bn2.bias", "bn2.running_mean", "bn2.running_var", "bn2.num_batches_tracked", "classifier.weight", "classifier.bias".

Therefore, I modify the keys' names of ckpt directly to match NNET. Then, Encoder part is correct but Decoder is also wrong:
Loading base model ()...Using cache found in C:\Users\LZS/.cache\torch\hub\rwightman_gen-efficientnet-pytorch_master
Done.
Removing last two layers (global_pool & classifier).
Traceback (most recent call last):
File "test.py", line 98, in
model = utils.load_checkpoint(checkpoint, model)
File "F:\Concordia\Depth\Depth Completion\Boundary_and_Surface_Normal\Surface Normal\Estimating_and_Exploiting_the_Aleatoric_Uncertainty\surface_normal_uncertainty\utils\utils.py", line 71, in load_checkpoint
model.load_state_dict(load_dict)
File "F:\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1406, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for NNET:
Missing key(s) in state_dict: "decoder.conv2.weight", "decoder.conv2.bias", "decoder.up1._net.0.weight", "decoder.up1._net.0.bias", "decoder.up1._net.1.weight", "decoder.up1._net.1.bias", "decoder.up1._net.1.running_mean", "decoder.up1._net.1.running_var", "decoder.up1._net.3.weight", "decoder.up1._net.3.bias", "decoder.up1._net.4.weight", "decoder.up1._net.4.bias", "decoder.up1._net.4.running_mean", "decoder.up1._net.4.running_var", "decoder.up2._net.0.weight", "decoder.up2._net.0.bias", "decoder.up2._net.1.weight", "decoder.up2._net.1.bias", "decoder.up2._net.1.running_mean", "decoder.up2._net.1.running_var", "decoder.up2._net.3.weight", "decoder.up2._net.3.bias", "decoder.up2._net.4.weight", "decoder.up2._net.4.bias", "decoder.up2._net.4.running_mean", "decoder.up2._net.4.running_var", "decoder.up3._net.0.weight", "decoder.up3._net.0.bias", "decoder.up3._net.1.weight", "decoder.up3._net.1.bias", "decoder.up3._net.1.running_mean", "decoder.up3._net.1.running_var", "decoder.up3._net.3.weight", "decoder.up3._net.3.bias", "decoder.up3._net.4.weight", "decoder.up3._net.4.bias", "decoder.up3._net.4.running_mean", "decoder.up3._net.4.running_var", "decoder.up4._net.0.weight", "decoder.up4._net.0.bias", "decoder.up4._net.1.weight", "decoder.up4._net.1.bias", "decoder.up4._net.1.running_mean", "decoder.up4._net.1.running_var", "decoder.up4._net.3.weight", "decoder.up4._net.3.bias", "decoder.up4._net.4.weight", "decoder.up4._net.4.bias", "decoder.up4._net.4.running_mean", "decoder.up4._net.4.running_var", "decoder.out_conv_res8.weight", "decoder.out_conv_res8.bias", "decoder.out_conv_res4.0.weight", "decoder.out_conv_res4.0.bias", "decoder.out_conv_res4.2.weight", "decoder.out_conv_res4.2.bias", "decoder.out_conv_res4.4.weight", "decoder.out_conv_res4.4.bias", "decoder.out_conv_res4.6.weight", "decoder.out_conv_res4.6.bias", "decoder.out_conv_res2.0.weight", "decoder.out_conv_res2.0.bias", "decoder.out_conv_res2.2.weight", "decoder.out_conv_res2.2.bias", "decoder.out_conv_res2.4.weight", "decoder.out_conv_res2.4.bias", "decoder.out_conv_res2.6.weight", "decoder.out_conv_res2.6.bias", "decoder.out_conv_res1.0.weight", "decoder.out_conv_res1.0.bias", "decoder.out_conv_res1.2.weight", "decoder.out_conv_res1.2.bias", "decoder.out_conv_res1.4.weight", "decoder.out_conv_res1.4.bias", "decoder.out_conv_res1.6.weight", "decoder.out_conv_res1.6.bias".
Unexpected key(s) in state_dict: "encoder.original_model.classifier.weight", "encoder.original_model.classifier.bias".

Answer 3 · 2022-04-25T08:35:53.000Z

I've encountered the same issue. It has to do with the download script. Maybe Google updated something which broke the script. Basically what's happening is that you do not download the model but an HTML document stating a warning that this file is too large for Google's anti-virus scan. You can bypass this by just downloading model directly from https://drive.google.com/open?id=X and by replacing X with the id from the download.py file. Hope this helps.

Answer 4 · 2022-04-25T08:40:24.000Z

@a1ex90 Thank you for the comment. It seems like download.py file is not working anymore. @Earmus Please try downloading the models and images from the following link and let me know if this fixes the issue.

https://drive.google.com/drive/folders/1Ku25Am69h_HrbtcCptXn4aetjo7sB33F?usp=sharing

Answer 5 · 2022-04-25T15:44:52.000Z

@a1ex90 Thank you for your suggestion!

@baegwangbin Thank you for the download link! It works!