Default MaxPoolingOp only supports NHWC on device type CPU

[anaamorim90]

How i can resolve this?

tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError:  Default MaxPoolingOp only supports NHWC on device type CPU
         [[node FaceNetModel/max_pooling2d/MaxPool (defined at C:\Users\Ana\Documents\Projetos\face_compare\face_compare\model.py:253) ]] [Op:__inference_predict_function_4833]

Function call stack:
predict_function

pip show intel-tensorflow
Name: intel-tensorflow
Version: 0.0.1
Summary: Intel Optimized Tensorflow with MKL
Home-page: https://github.com/IntelAI
Author: Intel Tensorflow optimization team
Author-email: intelnervana@intel.com
License: UNKNOWN
Location: c:\users\ana\appdata\local\programs\python\python38\lib\site-packages
Requires: pip
Required-by:

[m-lyon]

I'm not sure why installing intel-tensorflow hasn't fixed this, as it did for me. I can point you to https://software.intel.com/content/www/us/en/develop/articles/intel-optimization-for-tensorflow-installation-guide.html to troubleshoot the installation (perhaps there is a different version you need). May seem like a silly question but, are you sure you have an Intel CPU?

[anaamorim90]

yes, i'm sure.
Processador: Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz 2.90GHz

[xfliu1998]

I have met the same question with you, How did you solve it?

[msj121]

@anaamorim90 @xfliu1998 I also have this issue. Any solution? Are you running on macos?

[msj121]

I think that intel-tensorflow only has been updated and has wheels for linux/windows. Not sure if it can be compiled for macos (perhaps m1 chip meant intel would stop supporting mac - reasonable). There is tensorflow-macos & tensorflow-metal, but those did not work.

@anaamorim90
Based off of "intel-tensorflow" version 0.0.1 I think that is likely your issue since you don't have the up to date version.

[xahiru]

I have the same issue on MacOs. My processor is 2.7 GHz Dual-Core Intel Core i5.

[cian0]

which GPUs are supported? I've tried on runpod with 30xx and A500 with no success, still prompts the same error

[m-lyon]

TensorFlow 2.5.x, which is what this project uses, should definitely support NVIDIA RTX 30XX's, and also A5000. What kind of error are you getting? Because the aforementioned error specifically mentions CPU usage, so if you're getting that error then you won't have the GPU properly configured.

[xfliu1998]

I think it's a problem that the tensorflow and Nvidia versions are inconsistent, you can try it out on a computer with a GPU to install the corresponding version of tensorflow.

[cian0]

I get the same exact error as above.. Weird since I am using stable diffusion (AI images that uses tensorflow) in the same machine with no issues.. i don't have access to physical machines though

[EFNFCTN]

@m-lyon which tensorflow and intel-tensorflow version are you using? thanks in advance

[m-lyon]

@EFNFCTN I'm not using tensorflow==2.9.3, face-compare==1.1.4, and no intel-tensorflow as my PC does not have an intel CPU. Sorry I can't be of much help with regards to the intel-tensorflow.

I've just moved this project to tensorflow==2.9.3 so perhaps this change may fix your issues.

[FurkanGozukara]

@m-lyon i have this issue
windows 10
RTX 3090 NVIDIA GPU
intel core i7 10700 cpu

Installed on Python 3.10.9 venv

installed tensorflow is 2.9.3

Here full error message can you help me?

Graph execution error:
Detected at node 'FaceNetModel/max_pooling2d/MaxPool' defined at (most recent call last):
    File "C:\Python3108\lib\runpy.py", line 196, in _run_module_as_main
      return _run_code(code, main_globals, None,
    File "C:\Python3108\lib\runpy.py", line 86, in _run_code
      exec(code, run_globals)
    File "c:\program files\microsoft visual studio\2022\community\common7\ide\extensions\microsoft\python\core\debugpy__main__.py", line 39, in <module>
      cli.main()
    File "c:\program files\microsoft visual studio\2022\community\common7\ide\extensions\microsoft\python\core\debugpy/..\debugpy\server\cli.py", line 430, in main
      run()
    File "c:\program files\microsoft visual studio\2022\community\common7\ide\extensions\microsoft\python\core\debugpy/..\debugpy\server\cli.py", line 284, in run_file
      runpy.run_path(target, run_name="__main__")
    File "c:\program files\microsoft visual studio\2022\community\common7\ide\extensions\microsoft\python\core\debugpy_vendored\pydevd_pydevd_bundle\pydevd_runpy.py", line 321, in run_path
      return _run_module_code(code, init_globals, run_name,
    File "c:\program files\microsoft visual studio\2022\community\common7\ide\extensions\microsoft\python\core\debugpy_vendored\pydevd_pydevd_bundle\pydevd_runpy.py", line 135, in _run_module_code
      _run_code(code, mod_globals, init_globals,
    File "c:\program files\microsoft visual studio\2022\community\common7\ide\extensions\microsoft\python\core\debugpy_vendored\pydevd_pydevd_bundle\pydevd_runpy.py", line 124, in _run_code
      exec(code, run_globals)
    File "D:\86 se courses youtube kanali\09 face compare_09_face_compare.py", line 68, in <module>
      run("1.png", "1.jpg", "D:/86 se courses youtube kanali/09 face compare/dest/")
    File "D:\86 se courses youtube kanali\09 face compare_09_face_compare.py", line 40, in run
      embedding_one = img_to_encoding(face_one, model)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\face_compare\model.py", line 259, in img_to_encoding
      embedding = model.predict_on_batch(x_train)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\engine\training.py", line 2230, in predict_on_batch
      outputs = self.predict_function(iterator)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\engine\training.py", line 1845, in predict_function
      return step_function(self, iterator)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\engine\training.py", line 1834, in step_function
      outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\engine\training.py", line 1823, in run_step
      outputs = model.predict_step(data)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\engine\training.py", line 1791, in predict_step
      return self(x, training=False)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
      return fn(*kwargs)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\engine\training.py", line 490, in __call__
      return super().__call__(*kwargs)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
      return fn(*kwargs)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\engine\base_layer.py", line 1014, in __call__
      outputs = call_fn(inputs, *kwargs)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\utils\traceback_utils.py", line 92, in error_handler
      return fn(*kwargs)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\engine\functional.py", line 458, in call
      return self._run_internal_graph(
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\engine\functional.py", line 596, in _run_internal_graph
      outputs = node.layer(*kwargs)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
      return fn(*kwargs)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\engine\base_layer.py", line 1014, in __call__
      outputs = call_fn(inputs, *kwargs)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\utils\traceback_utils.py", line 92, in error_handler
      return fn(*kwargs)
    File "D:\86 se courses youtube kanali\09 face compare\env\lib\site-packages\keras\layers\pooling\base_pooling2d.py", line 73, in call
      outputs = self.pool_function(
Node: 'FaceNetModel/max_pooling2d/MaxPool'
Default MaxPoolingOp only supports NHWC on device type CPU
	 [[{{node FaceNetModel/max_pooling2d/MaxPool}}]] [Op:__inference_predict_function_3570]

[FurkanGozukara]

@Kotesitory @PoeticDeath @saharmor do you know how to fix above error?