Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

bias not found in checkpoint #14

Open
Banhalmi opened this issue Mar 28, 2019 · 5 comments
Open

bias not found in checkpoint #14

Banhalmi opened this issue Mar 28, 2019 · 5 comments

Comments

@Banhalmi
Copy link

model: models/pwcnet-sm-6-2-multisteps-chairsthingsmix/pwcnet.ckpt-592000
gpu_devices = []
controller = '/device:CPU:0'
windows 8.1
python 3.6
tensorflow 1.13
running: pwcnet_predict_from_img_pairs.py

full error output:
tensorflow.python.framework.errors_impl.NotFoundError: Restoring from checkpoint failed. This is most likely due to a Variable name or other graph key that is missing from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:

Key pwcnet/ctxt/dc_conv31/bias not found in checkpoint
[[node save/RestoreV2 (defined at C:\PROJECTS\SASMOB - hídas projekt\optical_flow\tfoptflow-master\tfoptflow\model_base.py:119) ]]

Caused by op 'save/RestoreV2', defined at:
File "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\Common7\IDE\Extensions\Microsoft\Python\Core\ptvsd_launcher.py", line 89, in
vspd.debug(filename, port_num, debug_id, debug_options, run_as)
File "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\Common7\IDE\Extensions\Microsoft\Python\Core\ptvsd\debugger.py", line 2631, in debug
exec_file(file, globals_obj)
File "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\Common7\IDE\Extensions\Microsoft\Python\Core\ptvsd\util.py", line 119, in exec_file
exec_code(code, file, global_variables)
File "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\Common7\IDE\Extensions\Microsoft\Python\Core\ptvsd\util.py", line 95, in exec_code
exec(code_obj, global_variables)
File "C:\PROJECTS\SASMOB - hídas projekt\optical_flow\tfoptflow-master\tfoptflow\pwcnet_predict_from_img_pairs.py", line 58, in
nn = ModelPWCNet(mode='test', options=nn_opts)
File "C:\PROJECTS\SASMOB - hídas projekt\optical_flow\tfoptflow-master\tfoptflow\model_pwcnet.py", line 231, in init
super().init(name, mode, session, options)
File "C:\PROJECTS\SASMOB - hídas projekt\optical_flow\tfoptflow-master\tfoptflow\model_base.py", line 66, in init
self.build_graph()
File "C:\PROJECTS\SASMOB - hídas projekt\optical_flow\tfoptflow-master\tfoptflow\model_base.py", line 266, in build_graph
self.init_saver()
File "C:\PROJECTS\SASMOB - hídas projekt\optical_flow\tfoptflow-master\tfoptflow\model_base.py", line 119, in init_saver
self.saver = tf.train.Saver()
File "C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 832, in init
self.build()
File "C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 844, in build
self._build(self._filename, build_save=True, build_restore=True)
File "C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 881, in _build
build_save=build_save, build_restore=build_restore)
File "C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 513, in _build_internal
restore_sequentially, reshape)
File "C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 332, in _AddRestoreOps
restore_sequentially)
File "C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 580, in bulk_restore
return io_ops.restore_v2(filename_tensor, names, slices, dtypes)
File "C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_io_ops.py", line 1655, in restore_v2
name=name)
File "C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
return func(*kwargs)
File "C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3300, in create_op
op_def=op_def)
File "C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1801, in init
self._traceback = tf_stack.extract_stack()

NotFoundError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a Variable name or other graph key that is missing from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:

Key pwcnet/ctxt/dc_conv31/bias not found in checkpoint
[[node save/RestoreV2 (defined at C:\PROJECTS\SASMOB - hídas projekt\optical_flow\tfoptflow-master\tfoptflow\model_base.py:119) ]]

output until error:
C:\Users\BAndras\Anaconda3\lib\site-packages\h5py_init_.py:34: FutureWarning:
Conversion of the second argument of issubdtype from float to np.floating i
s deprecated. In future, it will be treated as np.float64 == np.dtype(float).ty pe.
from ._conv import register_converters as _register_converters
Building model...
WARNING:tensorflow:From C:\PROJECTS\SASMOB - hídas projekt\optical_flow\tfoptflo
w-master\tfoptflow\model_pwcnet.py:1094: conv2d (from tensorflow.python.layers.c
onvolutional) is deprecated and will be removed in a future version.
Instructions for updating:
Use keras.layers.conv2d instead.
WARNING:tensorflow:From C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow
python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.fr
amework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From C:\PROJECTS\SASMOB - hídas projekt\optical_flow\tfoptflo
w-master\tfoptflow\model_pwcnet.py:1221: conv2d_transpose (from tensorflow.pytho
n.layers.convolutional) is deprecated and will be removed in a future version.
Instructions for updating:
Use keras.layers.conv2d_transpose instead.
... model built.
Loading model checkpoint c:/PROJECTS/SASMOB - hídas projekt/optical_flow/tfoptfl
ow-master/tfoptflow/models/pwcnet-sm-6-2-multisteps-chairsthingsmix/pwcnet.ckpt-
592000 for eval or testing...

WARNING:tensorflow:From C:\Users\BAndras\Anaconda3\lib\site-packages\tensorflow
python\training\saver.py:1266: checkpoint_exists (from tensorflow.python.trainin
g.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
INFO:tensorflow:Restoring parameters from c:/PROJECTS/SASMOB - hídas projekt/opt
ical_flow/tfoptflow-master/tfoptflow/models/pwcnet-sm-6-2-multisteps-chairsthing
smix/pwcnet.ckpt-592000
2019-03-28 12:13:28.455200: W tensorflow/core/framework/op_kernel.cc:1401] OP_RE
QUIRES failed at save_restore_v2_ops.cc:184 : Not found: Key pwcnet/ctxt/dc_conv
31/bias not found in checkpoint

@apxlwl
Copy link

apxlwl commented Aug 23, 2019

@Banhalmi Have you solved the problem? I met the same problem.

@dagongji10
Copy link

@Banhalmi @wlguan When I use the pretrained model sm,I got the same error. So I change the model to lg, it can run normally. Maybr there is something wrong in sm model.

Can I ask you the speed when run lg model? I use the image with (436, 1024) , each pair cost about 0.1 s; when image reduce to (256, 340), it cost 8.5s for 150 pairs. How about yours?

@liyunfei1994
Copy link

@Banhalmi @philferriere Have you solved this problem? I met the same problem when I restore the model. What is strange is that the model can run and be restored normally a few months ago, but now it can not be restored.

@jeffbaena
Copy link

jeffbaena commented Sep 11, 2019

Same problem here, I am using tensorflow 1.10.0 installed thorugh conda.

this might be useful, resnet 101 has the same problem, some people solved by deleting the folder and saving on clean folder tensorflow/models#5003 (comment) .EDIT: I tried and it seems it is not the case for this small model.

@yacaeh
Copy link

yacaeh commented Sep 19, 2019

Well I've found solution, but seems like lg model is better for me.
it took 0.08 sec for lg model with [384, 512] size, 0.06 for sm model with the same size, but the inference result was not satisfying.

If you still want to use sm model, you have to change the following nn_opts

nn_opts['use_dense_cx'] = False,
nn_opts['use_res_cx'] = False

It should be True for lg model, and False for sm model

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

6 participants