Keras2onnx error with EfficientNet - _remove_unused_nodes and  input_1:01 is disconnected

Dear keras2onnx mantainers,
I am trying to convert a keras model to onnx format using your library, but I am having trouble. I first tried with my model, then I tried just running the example notebook and it failed. the error on my model is the following:
 ---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-8-548b1d6326b3> in <module>
----> 1 onnx_model = keras2onnx.convert_keras(inf_model,inf_model.name)

~\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\keras2onnx\main.py in convert_keras(model, name,    doc_string, target_opset, initial_types, channel_first_inputs, debug_mode, custom_op_conversions)
    100     topology.compile()
    101
   --> 102     return convert_topology(topology, name, doc_string, target_opset, channel_first_inputs)
    103
    104

~\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\keras2onnx\topology.py in convert_topology(topology,     model_name, doc_string, target_opset, channel_first_inputs)
    312     graph = None
    313     extra_inputs = _build_extra_inputs(container)
    --> 314     nodes = _remove_unused_nodes(container.nodes, container.inputs + extra_inputs, container.outputs)
    315
    316     if not topology.debug_mode:

~\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\keras2onnx\topology.py in _remove_unused_nodes(nodes, inputs, outputs)
    217             else:
    218                 assert in_ == '' or in_ in input_dict, \
    --> 219                     "{} is disconnected, check the parsing log for more details.".format(in_)
    220
    221     return [nd_ for nd_ in nodes if id(nd_) in nodes_to_keep]

 AssertionError: input_1:01 is disconnected, check the parsing log for more details.`

Which is essentially the same as the one I found running the example notebook in here :
`---------------------------------------------------------------------------
 AssertionError                            Traceback (most recent call last)
 <ipython-input-3-f65dad0d3c63> in <module>
      1 import keras2onnx
      2 output_model_path = "keras_efficientNet.onnx"
      ----> 3 onnx_model = keras2onnx.convert_keras(model, model.name)
      4 keras2onnx.save_model(onnx_model, output_model_path)

    ~\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\keras2onnx\main.py in convert_keras(model, name, doc_string, target_opset, initial_types, channel_first_inputs, debug_mode, custom_op_conversions)
      100     topology.compile()
      101
      --> 102     return convert_topology(topology, name, doc_string, target_opset, channel_first_inputs)
       103
       104

   ~\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\keras2onnx\topology.py in convert_topology(topology, model_name, doc_string, target_opset, channel_first_inputs)
       312     graph = None
       313     extra_inputs = _build_extra_inputs(container)
      --> 314     nodes = _remove_unused_nodes(container.nodes, container.inputs + extra_inputs, container.outputs)
       315
       316     if not topology.debug_mode:

      ~\AppData\Local\Continuum\anaconda3\envs\tf-gpu\lib\site-packages\keras2onnx\topology.py in _remove_unused_nodes(nodes, inputs, outputs)
         217             else:
         218                 assert in_ == '' or in_ in input_dict, \
        --> 219                     "{} is disconnected, check the parsing log for more details.".format(in_)
          220
           221     return [nd_ for nd_ in nodes if id(nd_) in nodes_to_keep]

          AssertionError: input_1:01 is disconnected, check the parsing log for more details.`

I have tried with tensorflow 2.4.0 and 2.1.0, the later recommended in the notebook.
I don't really know how to proceed from here, if you have any suggestion it would be greatly appreciated.
Best regards,
José hugo Elsas

I am also facing an issue with the conversion of the EfficientNet model.
ValueError: Unable to find out a correct type for tensor type = 20 of top_bn/ReadVariableOp/resource:0

Sorry to ask, but, have any mantainers seen this issue?

Hi @jhelsas, I have tried a slightly different approach to convert Keras model to Onnx. To give an overview, I have done transfer learning on the Keras EfficientNet model.
First, I convert my Keras model to Tensorflow Model using:
from tensorflow import keras
model = keras.models.load_model('./saveModel/model.hdf5')
model.save('./saveModel/optimised/', save_format="tf")

Then I use tf2onnx to convert my Tensorflow model to .onnx format using:
python -m tf2onnx.convert \
        --saved-model ./saveModel/optimised \
        --output ./saveModel/model.onnx \
        --opset 13

When converting the EfficientModel is gives several gradient warning (reference here)  Importing a function function-name with ops with custom gradients. Will likely fail if a gradient is requested.. But the model successfully gets converted into the onnx format.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,223 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,223 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,223 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,224 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,224 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,225 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,225 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,226 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,226 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,226 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,226 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,227 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,228 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,228 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,228 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,229 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,229 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,229 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,230 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,230 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,231 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,231 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,232 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,232 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,233 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,233 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,233 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,234 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,234 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,234 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,235 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,235 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,236 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,236 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,236 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,237 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,237 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,238 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,238 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,238 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,239 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,239 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,240 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,240 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,240 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,241 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,241 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,241 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,242 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,242 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,243 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,243 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,244 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,244 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,245 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,245 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,245 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,246 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,246 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,246 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,247 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,247 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,248 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,248 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,248 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,249 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,249 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,250 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,250 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,251 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,251 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,251 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,252 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,252 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,253 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,253 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,253 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,254 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,254 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,255 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,255 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,256 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,256 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,256 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,257 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,257 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,257 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:09,258 - WARNING - Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_129988) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block2g_expand_activation_layer_call_and_return_conditional_losses_144769) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:10,523 - WARNING - Importing a function (__inference_block2g_expand_activation_layer_call_and_return_conditional_losses_144769) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block3b_se_reduce_layer_call_and_return_conditional_losses_146007) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:10,561 - WARNING - Importing a function (__inference_block3b_se_reduce_layer_call_and_return_conditional_losses_146007) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block2e_expand_activation_layer_call_and_return_conditional_losses_143663) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:10,584 - WARNING - Importing a function (__inference_block2e_expand_activation_layer_call_and_return_conditional_losses_143663) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5g_se_reduce_layer_call_and_return_conditional_losses_158079) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:10,591 - WARNING - Importing a function (__inference_block5g_se_reduce_layer_call_and_return_conditional_losses_158079) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5f_expand_activation_layer_call_and_return_conditional_losses_157347) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:10,659 - WARNING - Importing a function (__inference_block5f_expand_activation_layer_call_and_return_conditional_losses_157347) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6i_activation_layer_call_and_return_conditional_losses_164628) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:10,740 - WARNING - Importing a function (__inference_block6i_activation_layer_call_and_return_conditional_losses_164628) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block2f_se_reduce_layer_call_and_return_conditional_losses_56481) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:10,766 - WARNING - Importing a function (__inference_block2f_se_reduce_layer_call_and_return_conditional_losses_56481) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block4d_expand_activation_layer_call_and_return_conditional_losses_150758) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:10,780 - WARNING - Importing a function (__inference_block4d_expand_activation_layer_call_and_return_conditional_losses_150758) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block4a_se_reduce_layer_call_and_return_conditional_losses_60390) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:10,798 - WARNING - Importing a function (__inference_block4a_se_reduce_layer_call_and_return_conditional_losses_60390) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block3a_activation_layer_call_and_return_conditional_losses_57315) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:10,838 - WARNING - Importing a function (__inference_block3a_activation_layer_call_and_return_conditional_losses_57315) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5a_se_reduce_layer_call_and_return_conditional_losses_64737) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:10,899 - WARNING - Importing a function (__inference_block5a_se_reduce_layer_call_and_return_conditional_losses_64737) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block2f_activation_layer_call_and_return_conditional_losses_144355) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:10,906 - WARNING - Importing a function (__inference_block2f_activation_layer_call_and_return_conditional_losses_144355) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5a_expand_activation_layer_call_and_return_conditional_losses_154629) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,030 - WARNING - Importing a function (__inference_block5a_expand_activation_layer_call_and_return_conditional_losses_154629) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block2g_activation_layer_call_and_return_conditional_losses_144908) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,032 - WARNING - Importing a function (__inference_block2g_activation_layer_call_and_return_conditional_losses_144908) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block2c_activation_layer_call_and_return_conditional_losses_142696) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,076 - WARNING - Importing a function (__inference_block2c_activation_layer_call_and_return_conditional_losses_142696) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5d_expand_activation_layer_call_and_return_conditional_losses_65865) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,112 - WARNING - Importing a function (__inference_block5d_expand_activation_layer_call_and_return_conditional_losses_65865) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6g_se_reduce_layer_call_and_return_conditional_losses_71672) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,138 - WARNING - Importing a function (__inference_block6g_se_reduce_layer_call_and_return_conditional_losses_71672) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6m_expand_activation_layer_call_and_return_conditional_losses_74173) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,206 - WARNING - Importing a function (__inference_block6m_expand_activation_layer_call_and_return_conditional_losses_74173) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5b_expand_activation_layer_call_and_return_conditional_losses_155135) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,214 - WARNING - Importing a function (__inference_block5b_expand_activation_layer_call_and_return_conditional_losses_155135) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5d_activation_layer_call_and_return_conditional_losses_156380) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,239 - WARNING - Importing a function (__inference_block5d_activation_layer_call_and_return_conditional_losses_156380) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block4g_expand_activation_layer_call_and_return_conditional_losses_152417) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,264 - WARNING - Importing a function (__inference_block4g_expand_activation_layer_call_and_return_conditional_losses_152417) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6h_se_reduce_layer_call_and_return_conditional_losses_72112) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,268 - WARNING - Importing a function (__inference_block6h_se_reduce_layer_call_and_return_conditional_losses_72112) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block2d_se_reduce_layer_call_and_return_conditional_losses_55601) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,278 - WARNING - Importing a function (__inference_block2d_se_reduce_layer_call_and_return_conditional_losses_55601) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block4h_expand_activation_layer_call_and_return_conditional_losses_152970) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,309 - WARNING - Importing a function (__inference_block4h_expand_activation_layer_call_and_return_conditional_losses_152970) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5i_expand_activation_layer_call_and_return_conditional_losses_159006) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,316 - WARNING - Importing a function (__inference_block5i_expand_activation_layer_call_and_return_conditional_losses_159006) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6a_activation_layer_call_and_return_conditional_losses_160251) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,348 - WARNING - Importing a function (__inference_block6a_activation_layer_call_and_return_conditional_losses_160251) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block4b_se_reduce_layer_call_and_return_conditional_losses_60777) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,380 - WARNING - Importing a function (__inference_block4b_se_reduce_layer_call_and_return_conditional_losses_60777) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block4i_se_reduce_layer_call_and_return_conditional_losses_153702) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,393 - WARNING - Importing a function (__inference_block4i_se_reduce_layer_call_and_return_conditional_losses_153702) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block2g_activation_layer_call_and_return_conditional_losses_56874) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,482 - WARNING - Importing a function (__inference_block2g_activation_layer_call_and_return_conditional_losses_56874) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block3g_activation_layer_call_and_return_conditional_losses_59902) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,547 - WARNING - Importing a function (__inference_block3g_activation_layer_call_and_return_conditional_losses_59902) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5j_activation_layer_call_and_return_conditional_losses_159698) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,557 - WARNING - Importing a function (__inference_block5j_activation_layer_call_and_return_conditional_losses_159698) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block3d_expand_activation_layer_call_and_return_conditional_losses_146934) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,656 - WARNING - Importing a function (__inference_block3d_expand_activation_layer_call_and_return_conditional_losses_146934) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block1c_se_reduce_layer_call_and_return_conditional_losses_53561) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,697 - WARNING - Importing a function (__inference_block1c_se_reduce_layer_call_and_return_conditional_losses_53561) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6g_se_reduce_layer_call_and_return_conditional_losses_163562) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,703 - WARNING - Importing a function (__inference_block6g_se_reduce_layer_call_and_return_conditional_losses_163562) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5h_se_reduce_layer_call_and_return_conditional_losses_158632) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,717 - WARNING - Importing a function (__inference_block5h_se_reduce_layer_call_and_return_conditional_losses_158632) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6h_expand_activation_layer_call_and_return_conditional_losses_163936) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,758 - WARNING - Importing a function (__inference_block6h_expand_activation_layer_call_and_return_conditional_losses_163936) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block1d_se_reduce_layer_call_and_return_conditional_losses_141124) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,809 - WARNING - Importing a function (__inference_block1d_se_reduce_layer_call_and_return_conditional_losses_141124) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6j_expand_activation_layer_call_and_return_conditional_losses_72853) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,903 - WARNING - Importing a function (__inference_block6j_expand_activation_layer_call_and_return_conditional_losses_72853) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block4j_expand_activation_layer_call_and_return_conditional_losses_154076) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,920 - WARNING - Importing a function (__inference_block4j_expand_activation_layer_call_and_return_conditional_losses_154076) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5h_expand_activation_layer_call_and_return_conditional_losses_158453) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,944 - WARNING - Importing a function (__inference_block5h_expand_activation_layer_call_and_return_conditional_losses_158453) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6a_se_reduce_layer_call_and_return_conditional_losses_69085) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,958 - WARNING - Importing a function (__inference_block6a_se_reduce_layer_call_and_return_conditional_losses_69085) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block7d_expand_activation_layer_call_and_return_conditional_losses_168866) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,964 - WARNING - Importing a function (__inference_block7d_expand_activation_layer_call_and_return_conditional_losses_168866) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6c_activation_layer_call_and_return_conditional_losses_161310) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,972 - WARNING - Importing a function (__inference_block6c_activation_layer_call_and_return_conditional_losses_161310) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block7a_expand_activation_layer_call_and_return_conditional_losses_74613) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,975 - WARNING - Importing a function (__inference_block7a_expand_activation_layer_call_and_return_conditional_losses_74613) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block4b_expand_activation_layer_call_and_return_conditional_losses_60638) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,980 - WARNING - Importing a function (__inference_block4b_expand_activation_layer_call_and_return_conditional_losses_60638) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block4e_activation_layer_call_and_return_conditional_losses_151450) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,991 - WARNING - Importing a function (__inference_block4e_activation_layer_call_and_return_conditional_losses_151450) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block3b_expand_activation_layer_call_and_return_conditional_losses_57610) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:11,993 - WARNING - Importing a function (__inference_block3b_expand_activation_layer_call_and_return_conditional_losses_57610) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5h_expand_activation_layer_call_and_return_conditional_losses_67625) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:12,054 - WARNING - Importing a function (__inference_block5h_expand_activation_layer_call_and_return_conditional_losses_67625) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block4d_se_reduce_layer_call_and_return_conditional_losses_150937) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:12,075 - WARNING - Importing a function (__inference_block4d_se_reduce_layer_call_and_return_conditional_losses_150937) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block5j_activation_layer_call_and_return_conditional_losses_68597) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:12,114 - WARNING - Importing a function (__inference_block5j_activation_layer_call_and_return_conditional_losses_68597) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block2d_activation_layer_call_and_return_conditional_losses_143249) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:12,119 - WARNING - Importing a function (__inference_block2d_activation_layer_call_and_return_conditional_losses_143249) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block2e_expand_activation_layer_call_and_return_conditional_losses_55902) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:12,136 - WARNING - Importing a function (__inference_block2e_expand_activation_layer_call_and_return_conditional_losses_55902) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6g_activation_layer_call_and_return_conditional_losses_163522) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:12,237 - WARNING - Importing a function (__inference_block6g_activation_layer_call_and_return_conditional_losses_163522) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6f_activation_layer_call_and_return_conditional_losses_71185) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:12,274 - WARNING - Importing a function (__inference_block6f_activation_layer_call_and_return_conditional_losses_71185) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6e_activation_layer_call_and_return_conditional_losses_70745) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:12,295 - WARNING - Importing a function (__inference_block6e_activation_layer_call_and_return_conditional_losses_70745) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block3d_activation_layer_call_and_return_conditional_losses_147073) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:14,505 - WARNING - Importing a function (__inference_block3d_activation_layer_call_and_return_conditional_losses_147073) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_block6m_se_reduce_layer_call_and_return_conditional_losses_166880) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:17:17,083 - WARNING - Importing a function (__inference_block6m_se_reduce_layer_call_and_return_conditional_losses_166880) with ops with custom gradients. Will likely fail if a gradient is requested.
2021-05-21 23:24:33,675 - INFO - Signatures found in model: [serving_default].
2021-05-21 23:24:33,676 - WARNING - '--signature_def' not specified, using first signature: serving_default
2021-05-21 23:24:33,677 - INFO - Output names: ['output_layer']
2021-05-21 23:24:33.759229: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1
2021-05-21 23:24:33.759413: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session
2021-05-21 23:24:33.760503: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0001:00:00.0 name: NVIDIA Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2021-05-21 23:24:33.760554: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-05-21 23:24:33.760596: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2021-05-21 23:24:33.760623: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2021-05-21 23:24:33.760649: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2021-05-21 23:24:33.760684: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2021-05-21 23:24:33.760721: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2021-05-21 23:24:33.760758: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2021-05-21 23:24:33.762181: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-05-21 23:24:33.762236: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-05-21 23:24:33.762261: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0
2021-05-21 23:24:33.762281: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N
2021-05-21 23:24:33.763722: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10626 MB memory) -> physical GPU (device: 0, name: NVIDIA Tesla K80, pci bus id: 0001:00:00.0, compute capability: 3.7)
2021-05-21 23:24:33.982306: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:816] Optimization results for grappler item: graph_to_optimize
2021-05-21 23:24:33.982391: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:818]   function_optimizer: Graph size after: 5341 nodes (4295), 5714 edges (4668), time = 128.069ms.
2021-05-21 23:24:33.982400: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:818]   function_optimizer: function_optimizer did nothing. time = 2.858ms.
2021-05-21 23:24:54.524694: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0001:00:00.0 name: NVIDIA Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2021-05-21 23:24:54.524793: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-05-21 23:24:54.524843: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2021-05-21 23:24:54.524867: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2021-05-21 23:24:54.524890: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2021-05-21 23:24:54.524913: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2021-05-21 23:24:54.524936: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2021-05-21 23:24:54.524960: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2021-05-21 23:24:54.526407: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-05-21 23:24:54.526455: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-05-21 23:24:54.526469: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0
2021-05-21 23:24:54.526479: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N
2021-05-21 23:24:54.527915: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10626 MB memory) -> physical GPU (device: 0, name: NVIDIA Tesla K80, pci bus id: 0001:00:00.0, compute capability: 3.7)
WARNING:tensorflow:From /anaconda/envs/azureml_py38/lib/python3.8/site-packages/tf2onnx/tf_loader.py:621: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.graph_util.extract_sub_graph`
2021-05-21 23:24:58,098 - WARNING - From /anaconda/envs/azureml_py38/lib/python3.8/site-packages/tf2onnx/tf_loader.py:621: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.graph_util.extract_sub_graph`
2021-05-21 23:24:58.276560: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1
2021-05-21 23:24:58.276787: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session
2021-05-21 23:24:58.278036: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0001:00:00.0 name: NVIDIA Tesla K80 computeCapability: 3.7
coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s
2021-05-21 23:24:58.278088: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-05-21 23:24:58.278132: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2021-05-21 23:24:58.278154: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2021-05-21 23:24:58.278175: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2021-05-21 23:24:58.278196: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2021-05-21 23:24:58.278254: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2021-05-21 23:24:58.278272: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2021-05-21 23:24:58.279654: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-05-21 23:24:58.279729: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-05-21 23:24:58.279744: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0
2021-05-21 23:24:58.279755: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N
2021-05-21 23:24:58.281188: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10626 MB memory) -> physical GPU (device: 0, name: NVIDIA Tesla K80, pci bus id: 0001:00:00.0, compute capability: 3.7)
2021-05-21 23:25:12.761873: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:816] Optimization results for grappler item: graph_to_optimize
2021-05-21 23:25:12.761928: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:818]   constant_folding: Graph size after: 2975 nodes (-2093), 3458 edges (-2093), time = 10111.0283ms.
2021-05-21 23:25:12.761935: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:818]   function_optimizer: function_optimizer did nothing. time = 7.178ms.
2021-05-21 23:25:12.761940: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:818]   constant_folding: Graph size after: 2975 nodes (0), 3458 edges (0), time = 2458.67505ms.
2021-05-21 23:25:12.761945: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:818]   function_optimizer: function_optimizer did nothing. time = 7.563ms.
2021-05-21 23:25:18,163 - INFO - Using tensorflow=2.3.0, onnx=1.9.0, tf2onnx=1.9.0/936cec
2021-05-21 23:25:18,163 - INFO - Using opset <onnx, 13>
2021-05-21 23:25:43,869 - INFO - Computed 0 values for constant folding
2021-05-21 23:27:02,967 - INFO - Optimizing ONNX model
2021-05-21 23:27:33,000 - INFO - After optimization: BatchNormalization -156 (163->7), Cast -115 (115->0), Concat -52 (55->3), Const -1343 (1933->590), GlobalAveragePool +55 (0->55), Identity -53 (53->0), ReduceMean -55 (55->0), Reshape -59 (115->56), Shape -55 (55->0), Slice -55 (55->0), Split +3 (0->3), Transpose -929 (931->2), Unsqueeze -220 (220->0)
2021-05-21 23:27:39,233 - INFO -
2021-05-21 23:27:39,234 - INFO - Successfully converted TensorFlow model ./saveModel/optimised to ONNX
2021-05-21 23:27:39,234 - INFO - Model inputs: ['efficientnetb7_input:0']
2021-05-21 23:27:39,234 - INFO - Model outputs: ['output_layer']
2021-05-21 23:27:39,234 - INFO - ONNX model is saved at ./saveModel/model.onnx

And when I tried to load the Onnx model for inference, it is predicting as expected using:
import numpy as np
import onnxruntime as ort

sess_ort = ort.InferenceSession("./saveModel/model.onnx")
input_name = sess_ort.get_inputs()[0].name
label_name = sess_ort.get_outputs()[0].name

img = cv2.imread('./predictImage/image.jpg')
X_test = img .reshape(1, 380, 380, 3)

sess_ort.run(output_names = [label_name], input_feed = {input_name: X_test .astype(np.float32)})[0]

Hope that helped


firmware: misconfigured plugin dependencies regarding other plugins

Important notices
Before you add a new report, we ask you kindly to acknowledge the following:

[x] I have read the contributing guide lines at https://github.com/opnsense/core/blob/master/CONTRIBUTING.md
[x] I am convinced that my issue is new after having checked both open and closed issues at https://github.com/opnsense/core/issues?q=is%3Aissue

Describe the bug
In rare case where plugin must depend on another plugin the firmware believes the plugin dependencies has been misconfigured, but that isn't the case.
To Reproduce
Install business edition os-OPNProxy and see misconfiguration issue for os-redis.
Expected behavior
Ideally, we shouldn't complain about this situation.
Describe alternatives you considered
Maybe we need to avoid scanning plugins that have the automatic flag set?
Screenshots
N/A
Relevant log files
N/A
Additional context
N/A
Environment
OPNsense 21.4.1 (amd64, OpenSSL)

Test on 21.4.1 went fine. No reason not to have it.


Under what conditions the "servers" event is triggered?

Please indicate in the readme that the "servers" event is triggered after successful execution of the "logOn" method. this information would have saved me time.

What were you doing that the specific timing of the 'servers' event made a difference?

I just needed a list of active steam server addresses. I expected this event to fire immediately after the "connected" event. In any case, I ended up with this list, only it was a bit non-obvious. Thanks :)

It's emitted when Steam sends you a server list. That seems to happen upon logging in, but it could change. Either way you shouldn't rely on this event to be emitted at all.


Any way to mutablly borrow the internal buffer directly ?

Hi there, thanks for the amazing crate.  mini_fb is awesome and  I really like the simple API and minimal dependencies.
Currently it seems we don't have a good way to avoid copy the buffer, since the API just allow developer provide a reference to an external buffer. And mini_fb will copy the external buffer to the internal one.
So I am wondering is there any way to directly borrow the internal buffer and manipulate it with external code ? Is there any chance to improve the performance by doing this ?
Thanks in advance!

Hi, Thanks for the appreciation :)
So there are some reasons why the API is like this but the main one is threading and life times.

Threading: A user may generate the buffer on a different thread than the main thread that minifb is running on. This would make things much more complicated to support as another thread would need to borrow something mutable from the main thread which is illegal in minifb.
How the backend API works for each platform varies. On macOS the buffer will be uploaded to the GPU at a point when a certain event comes to the application. This would mean there are potential problems if the user may changing the buffer while minifb needs to read from it.
A user may chose to only update a part of the buffer for minifb (a slice) if the display area is bigger (say scrolling up and down where you need extra memory on the "sides") if only using borrowing this would mean that the user would have to copy into the buffer by them self.
memcpy is usually implemented in an efficient way on most platforms as it's a common operation so I haven't seen that much issue with it in practice. That being said if it takes much time it's possible to leave the main thread only dealing with minifb and have another thread (as stated in 1) doing all the generation of the buffer.

I hope this explains the reasoning behind the current API.
Cheers!

Thanks for the explain.
I can understand why this is tricky, but it still seems doable to me. I am just curious if there's a plan for avoiding copy at least in some use cases. (I will explain the background later).
For the reason of copying data, i sort of agree and disagree with.

For threading, I know it's tricky to make a borrow. I would image an Arc based token do the trick to address the lifetime and if we add a wrapper which packs an Arc and some additional information (plus we can also make sure that the Arc isn't cloned if it's hidden behind our wrapper), when the render thread is done, just simply send the token back to the main thread would do the thing. At least I feel this is solvable problem.
For platform difference, I am sort of disagree, since even if on some platform it's impossible to avoid copy, but this is not a reason for copying data on a platform where this is possible (such as Linux).
For the partial update. Same to 2, it doesn't means we should disallow borrowing even for a full update.
Memcpy is efficient, but it's not free, just wondering if we can do better. Plus the cost is related to the size of the buffer (especially image buffer isn't a small one).

I maintains a plotting library called plotters, in my issue list, people mentioned the combination of mini_fb and plotters for real-time data visualization a few times that sounds really interesting to me.
In my issue list,  people who want to have realtime display do really care about the performance a lot. (Not only FPS but CPU load matters) That's why I am thinking about if we can avoid copy in the entire process and this sounds a good story (Plus incremental drawing with plotters).
So I am thinking about provide built-in support for mini_fb in some really efficient way.
Finally, I am really appreciate you detailed explain, but I am still imagine this feature would be cool for me.
Thanks!

Arc<> would not give mutability and you need something like RefCell or such as well. It will be kinda annoying when doing threading. Also Linux will soon have the same issue as macOS as I'm adding (optional) support for OpenGL as it's a lot faster.
I can put it on my list of things to look at but it's growing to see if it would work on most platforms without causing issues but I don't think I'm going to implement it for a while at least.
Cheers!

So another problem  has turned up. Some Window managers on Linux will force a certain widow size (even if you set the window to a fixed size) So the problem is then that the user will expect the buffer to be 256x256 it may end up being 643x245 because that is what the window manager has decided. I really don't want the user to have to deal with that but should be able to render in 256x256 and minifb deals with resizing/poisoning/etc
What I will try is to skip the extra memcpy when possible (so just add it to the GPU queue on such platforms) when the sizes matches up (which will be the majority of the cases) but to give access to the internal buffer won't work due to this.

Closing this one now in favour of trying to skip internal memcpys when possible

Just FYI I have removed the internal memcpy on all platforms on the dev branch (will be part of 0.15)

If I may ask whether solutions like double-buffering have been considered? IMO this would address most of the issues mentioned, including the issue of the buffer changing arbitrarily. As acquiring a mutable reference to the buffer is not the primary intended usage method, it could be stated clearly in the documentation that this should be considered. Since Rust enforces single-mutable-borrower patterns, if the internal structure of the buffer is sound, then this carries out to users. If the back buffer holds a lock when it is not in use, calls to get_buffer_mut() would lock it etc. Calling update* would automatically acquire a lock on the back buffer and swap them.
Perhaps my youthful naivité is blinding me here, but this at least sounds reasonable to me

I honestly don't think it's worth the added extra complexity to implement this.

That's disappointing but understandable as it is quite an involved upgrade. Perhaps if there were a way to delegate this to third-party crates, this might be feasible?

As this is an internal implementation detail for all the backends (windows, macos, linux, etc) I don't think so

understood. well regardless, it's a wonderful little crate which often gets me off the ground immensely quickly

Thanks! :)


[AKS] Improve messages for upgrade command

Inform users of specific behaviours when they try to upgrade a cluster
to the current version of that cluster.

Mirror PR of this https://github.com/Azure/azure-cli/pull/8625

This checklist is used to make sure that common guidelines for a pull request are followed.

For new extensions:

[ ] My extension description/summary conforms to the Extension Summary Guidelines.

If this PR is for a new extension or change to an existing extension, use the following to try out the changes in this PR:
docker run -it microsoft/azure-cli:latest
export EXT=<NAME>
pip install --upgrade --target ~/.azure/cliextensions/$EXT "git+https://github.com/lalo/azure-cli-extensions.git@edus-improve-upgrade-msg#subdirectory=src/$EXT&egg=$EXT"

@zqingqing1 can you review as codeowner?

@lalo , what about the cluster is at updating/Deleting status, should we message customer?

@zqingqing1 this PR is mainly to keep it in sync with the non-extension code (https://github.com/Azure/azure-cli/pull/8625). I don't think we need to notify the customer on those cases, what makes upgrading special is that there is certain logic that gets triggered only if a user upgrades a failed cluster (mainly a cluster that failed previously while upgrading) and nothing happens if its in succeeded state. In the other cases, everything should be handled correctly already.

Got you, thanks :)


Unable to pass Data to Modal Controller

Hi why i am unable to pass my data to ModalController. I have forked original JSFiddle example for angularModalService and made it a complex example.
http://jsfiddle.net/601kzvs6/1/
Thanks

I'm getting same issue? did you solved this issue?


Suggest: release a version

Dear GenoCAE maintainers,
I quote from the GitHub docs:

Releases are deployable software iterations you can package and make available for a wider audience to download and use.

I would love to have a named release version, e.g. v1.0 (as that is the version shown in the help) that I can use (over the commit hash) for the gcaer R package I am writing to install this cool tool.
It's easy to make one:

Sure, I volunteer to do this, but for that I need more access rights than you may want to give (which I'd understand :-) ).

Thanks for the suggestion, release v1.0 has just been published.


Ac uk localization

Hi @DannyCork - here's the mods I made for .ac.uk domains. Sorry to have been slow - just learning this git stuff!

excellent, thanks.
Will cut a new release any day now


Improving ip tagging filter

Title: Allow setting tags in a custom header
Description:
Now in ip tagging filter, all tags will be put into a fixed header. It would be better to add a custom header field in the proto then users can export tags to the custom header.
[optional Relevant Links:]
A draft pr: https://github.com/envoyproxy/envoy/pull/35628

cc @alyssawilk @JuniorHsu as codeowners


Require the epsilon value (tolerance) to be specified explicitly, and remove the default

As @Andlon explains:

I'm not sure if it's actually wise to provide a default epsilon/tolerance - this is the very problem that Bruce Dawson discusses in the randomascii blog I know we've both spent some time reading. The problem with absolute differences is that there is really no sensible default - it depends entirely on the numbers you are comparing.

Note that I was referring explicitly to the absolute difference test for equality in that code, by the way. A default tolerance makes more sense for relative/ulp-based comparison.

Right! Thanks for the clarification.

What do you think a good default epsilon would be for ulps and relative?

Which epsilon do you mean, exactly? The absolute difference for comparing near-zero numbers that Bruce Dawson advocates in his blog, or the tolerance used for comparison with relative error or ULPs respectively?
IIRC, I think the defaults you've already been using in this crate have been fairly sane.


Handle stopwords (MPL and other)

The issue is that
>>> plt.xlim([1, 2])
(2.0, 3.0)

and we really don't want to doctest it. We still need to make sure the code compiles.
refguide-check solves stores the _example on the Checker() instance and skips the check, which is a hack.
Need move the logic to a Finder/Parser and patch up the example so that got is empty?

The source has to be looked up on
finder.find(module, name).examples[0].source, where name can be empty --- then its a function / module name.

fixed by eb2efcd67486a1f030253e32d4882dfc8941baef


WIP - testing build failure description

I tried adding some basic handling of errors in the scripts. if the command being ran returns a non-zero return code to echo a big block of text in the step that failed. But failFast sends an interrupt to each stage which in turn causes each process that is interrupted to return a non-zero return code, so all stages end up showing the error message instead of just those that that actually failed. Thus, I'm closing this.


Add new pango attributes

Also it seems like the new_ prefix of all the constructors is a bit un-Rust-y and should probably be removed.

What do you mean? Most Rust constructors use new AFAICT :) The problem here is that we construct a variant of the type. If we'd used overline instead of new_overline that would potentially clash with an overline getter. I had the same problem with glib::Param while aligning getters with Rust styles in https://github.com/gtk-rs/gtk-rs/pull/211. See get_flags which was "rightfully" renamed as flags when there was already a flags constructor which lead me to rename all those with a new prefix, which I believe makes it clearer what the intent is.

@fengalin Complicated. For "variant constructors" I saw in many libraries that they only use the name of the variant without new_ as function name but that can indeed conflict with getters if the API is confusing enough like in paramspec (a flags variant and all pspecs also have flags... OTOH maybe there should be no flags constructor but it should be ParamSpecFlags::new() instead).

Yes, the Type::new would probably be the way to go, or maybe a builder pattern.

Yes, the Type::new would probably be the way to go, or maybe a builder pattern.

They're actually different types that are returned there, so builder doesn't make too much sense. The main problem is that basically all other API wants a ParamSpec and not a ParamSpecInt (etc) so user code would have to cast or the new function would have to return something else than Self. Anyway, unrelated to this PR!

I was talking about this PR too :) We could introduce an AttributeBuilder capable of building an Attribute with the expected variant. Ex. AttriubuteBuilder::overline (or longer AttriubuteBuilder::build_overline) wouldn't clash with an Attribute getter.

That doesn't seem like an improvement in usability though :)

Let's agree to disagree then :) I think we should come up with a general approach for this pattern so that it would be the same for all these situations in the organization though.

Let's worry about that in a separate issue, can you create one? This one can go in as-is apart from the above questions then.

Thanks!


[chore] Fixed the flying search result and opening a search result po…

…st opens in a new tab now.

Merged.


[Bug Report] There is a problem with the location of the holes in the drawing for S35 (top_plate_baseline.dxf)

There is a problem with the location of the drawing holes, resulting that the Control Board can not be installed.
https://github.com/nasa-jpl/open-source-rover/blob/master/mechanical/body_assembly/laser_cut_parts/top_plate_baseline.dxf

closing since no longer relevant with latest update


Set up authentication endpoints on server

We will be using supabase for our server needs.

[ ] Create an new user registration endpoint, which calls the superbase authorisation methods to authorise the new user
[ ] Create a login endpoint similarly

hey, rahul
i can do this work
can you assign this to me

@janmesh799 sure


Missing translation for FR and ES

Translation for FR and ES for order cancellation after HPP redirect is missing.

Fixed with #792


Now Including Support for Coze

OpenAI Conversion Proxy Update: Now Including Support for Coze
I am excited to announce the latest update to the OpenAI Conversion Proxy, which now includes support for Coze. This enhancement allows me to seamlessly integrate and utilize Coze's AI services through a familiar OpenAI-style interface, offering a more diversified AI service experience.
Configuration Update in wrangler.toml:
To accommodate this new integration, I have updated the wrangler.toml file as follows:
name = "openai-conversion-proxy"
main = "src/index.ts"
compatibility_date = "2024-03-04"

[vars]
COZE_API_KEY = "xxx"
COZE_BOT_IDS = [
    { "botId" = "7332758857208566670", "modelName" = "gpt4", "gpt4Default" = true, "gpt35Default" = true },
    { "botId" = "7337893161670399237", "modelName" = "WebDeveloper" },
    { "botId" = "7332788701796818950", "modelName" = "GithubExpert" },
]

Please make sure to replace "xxx" with your actual Coze API key.
Deploying the Update:
To deploy the update, simply run:
npm run deploy

Obtaining Coze API Key and botId:

The Coze API Key can be generated at Coze's API Page by clicking the "add new token" button.
The botId is derived from the second segment of numbers in your Coze bot URL, for example, 73428668***** from https://www.coze.com/space/73428668341****/bot/73428668*****. You can freely create your bot under the Personal page by clicking "create bot".

Advantages:
While the pricing aligns with the official OpenAI rates, Coze offers a more flexible payment option through PayPal. Additionally, Coze features a rich set of plugins that can significantly enhance the capabilities of your GPT models. The best part? These powerful plugins are available at no extra cost, effectively providing free enhancements to your AI capabilities.
By incorporating Coze's support into the OpenAI Conversion Proxy, I’m broadening the spectrum of AI services and functionalities available. This upgrade not only enhances the AI experience but also ensures access to a wide array of tools and capabilities to meet diverse needs. I’m excited for you to explore the new possibilities this update brings to your AI projects.

A bot that can be networked


chore: release 5.1.0

:robot: I have created a release *beep* *boop*
5.1.0 (2019-07-24)
Features

types: expose ProjectIdCallback interface (#753) (5577f0d)

This PR was generated with Release Please.

Release is at https://github.com/googleapis/google-auth-library-nodejs/releases/tag/v5.1.0

The release build has started, the log can be viewed here. :sunflower:

:egg: You hatched a release! The release build finished successfully! :purple_heart:


Plan to translate this site into other languages?

Thank you @koji ! This is definitely something we would love to do.
Do you have any pointers for good references or frameworks to help with this would be awesome.
This project by https://github.com/lukeed/rosetta @lukeed seems like a super interesting and javascript-y way to add internationalization. Other references would be great!

@joeyklee
This site is using Gatsby, so gatsby-plugin-i18n could be good.
To get non-engineers, Crowdin is a good option. Personally I translate some statements for Material-UI. Crowdin offers a free plan to open source.
https://crowdin.com/page/open-source-project-setup-request
I think rosetta is also good.
Recently, I saw an open-source product on Producthunt.
https://localizer.dev/?ref=producthunt
https://medium.com/@nikitakolmogorov/how-to-setup-localizer-for-you-product-360bd40e86cb

Yess! @koji you are an amazing treasure trove of knowledge. I will give some of these links a look.
There's definitely a lot of opportunities here and I wonder what suggestions you might have for approaching this.
On one hand there is the ml5 website that only really is comprised of:

landing page
community blog page
About page

On the other hand, our ml5 reference page actually is a different micro-site built on Docsify (at the moment) that lives in the ml5-library repo and is not actually part of the ml5-website repo.

@joeyklee

On the other hand, our ml5 reference page actually is a different micro-site built on Docsify (at the moment) that lives in the ml5-library repo and is not actually part of the ml5-website repo.

Lol, actually I thought why ref page is learn. ??? when I saw it.
Now, I got it.
I guess community blog page isn't really necessary to be translated since people can access demos and they could understand something.
I think the landing page might be good as a starting point. Probably most people will access it instead of About page lol.

This is a great point @koji. At least starting is certainly worth doing.
I hope at some point this summer we could do some major updates to ml5js.org page which include some great design contributions from our past work-study students as well as to forefront our community statement on the main page.

@joeyklee, Sounds awesome.
If I can do anything, please let me know.

Thanks @koji - I've added this to the website design project board https://github.com/ml5js/ml5-library/projects/6 in the ml5-library repo where many of the ongoing works are being planned for in a more organized way.
If you would like to make a PR to do a proof of concept, I'd highly welcome that but I know this is a big undertaking so no pressure!
Maybe @wenqili also has some thoughts about ways we might get started?

Joey Lee!!! 🙌

@lukeed !!!! I was so excited when I saw you post on Twitter and thought, "holy cow, that's Luke!" Rosetta is such a cool library, I'm looking forward to checking it out soon!

I just cracked up while making the same discovery here. Small world? 😅
Looking forward to checking out what you're doing here. Only a quick glance so far and it looks awesome 💪

@lukeed - Super small, but such a nice internet moment. You can see some of the projects people have made with ml5.js here https://ml5js.org/community

Hey guys, sorry to be late for the party lool.
There going to be a newly designed site soon right? Maybe we can incorporate i18n in the roadmap? @joeyklee


await does not chain well - alternate syntax needed

await does not chain well. Consider:
result = await (await (await f1()).f2()).f3();

I've been using an async/await substitute (steamline.js) for several years and we have a lot of code that chains async calls. With streamline we write the above as:
result = f1(_).f2(_).f3(_);

The streamline.js syntax is a hack (for practical reasons it was easier to reserve a token than to extend the grammar) but why not use the sugar proposed in the concurrency strawman?(http://wiki.ecmascript.org/doku.php?id=strawman:concurrency):
result = f1!().f2!().f3!();

Closing this issue out for now.
The design point that async/await as a proposal takes is very much the one @zenparsing has spoken to here.
Importantly, with await you can (and very often do) also await a value, not just the result of a function call.  This necessitates syntax that fits cleanly into the expression grammar.
If I consider real world async code, I don't think it's clear that the syntax suggestion here is a net win.


Add missing info to package.json

closed in https://github.com/declandewet/vue-meta/commit/017fde49e88f0f5b0da555e974241c8a9b9b992e