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Checkpoint
was expecting detection_head to be a trackable object (an object derived from Trackable
)
#19425
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Checkpoint
was expecting detection_head to be a trackable object (an object derived from Trackable
), got [<DetectionHead name=detection_head_33, built=False>]. If you believe this object should be trackable (i.e. it is part of the TensorFlow Python API and manages state), please open an issue.Checkpoint
was expecting detection_head to be a trackable object (an object derived from Trackable
)
Similar issue with the |
Please find the attached gist for reference |
Could you try rerunning with |
The fix in #1932 is for optimizer, which is already got fixed and present in recent release if I am not wrong. The issue above is with checkpointing other extra modules. The errors seems to persist even after nightly installation. Here is the gist. Thanks |
The error message says:
What is the type of Could this be an issue with the Model Garden package? |
From code base I can see that Mask-RCNN model expecting detection head as
I have checked same with Keras2(tf-keras) version it seems to be working in that version: gist So to overcome this issue, is there way that I can change from Model Garden side to make them trackable. Thanks |
You could fork the Model Garden repo and open a PR? |
I apologize for any confusion in my previous message. I'm currently refactoring the code within the model garden itself to be compatible with Keras3. My question is: when checkpointing a Keras model, is there a way to keep track of list of Keras layers in Keras3 without going into above error? As the same works in Keras 2 List of heads(layers) are getting used for cascade r-cnn model. Hope this clairfies. Thanks |
Model Garden checkpoints decoders, detection and mask head as part of the checkpoint. I have seen the same issue with
optimizer
and it seems it has been resolved in release. Is there way that we can include these in tf.train.Checkpoint.Error:
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