Custom RetinaNet save and load, added test case #1928
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| @@ -244,6 +244,49 @@ def test_saved_model(self, save_format, filename): | |||
| restored_output = restored_model(input_batch) | |||
| self.assertAllClose(model_output, restored_output) | |||
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| @pytest.mark.large | |||
| def test_custom_saved_model(self, save_format, filename): | |||
| class CustomPredictionHead( | |||
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Could we make sense simple convolutions or something with real variables?
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Also, how does the forward pass work if these classes are ill-defined?
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I ran the test and it worked, because the custom classes are inheriting from the classes that are used as default in the RetinaNet class, they just have different names. If you think we should also test for custom architecture, e.g. creating a totally new and independent class, I'll do it, but I don't think it would change anything.
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We could also test for saving/loading with custom Label Encoder, Label Decoder, Anchor Generator, Backbone, and so on.
| restored_model = keras.models.load_model( | ||
| save_path, | ||
| { | ||
| "RetinaNet": keras_cv.models.RetinaNet, |
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Do we need to provide this as a custom object? If so, why isn't it needed in all tests?
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This, my friend, I still don't understand, but from what I remember that's the only way to make the whole thing work using the different formats of saving/loading. I'll look into it and let you know why I did it that way.
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The problem is with the "h5" saving/loading format. Without the "RetinaNet": keras_cv.models.RetinaNet "h5" loading doesn't work, it gives you:
Traceback (most recent call last):
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/home/gianlucasama/.vscode/extensions/ms-python.python-2023.12.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module>
cli.main()
File "/home/gianlucasama/.vscode/extensions/ms-python.python-2023.12.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main
run()
File "/home/gianlucasama/.vscode/extensions/ms-python.python-2023.12.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file
runpy.run_path(target, run_name="__main__")
File "/home/gianlucasama/.vscode/extensions/ms-python.python-2023.12.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path
return _run_module_code(code, init_globals, run_name,
File "/home/gianlucasama/.vscode/extensions/ms-python.python-2023.12.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/home/gianlucasama/.vscode/extensions/ms-python.python-2023.12.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code
exec(code, run_globals)
File "/home/gianlucasama/Github/keras-cv/keras_cv/models/object_detection/retinanet/retinanet_test.py", line 374, in <module>
t.test_custom_saved_model("h5", "model")
File "/home/gianlucasama/Github/keras-cv/keras_cv/models/object_detection/retinanet/retinanet_test.py", line 274, in test_custom_saved_model
restored_model = keras.models.load_model(
File "/home/gianlucasama/.local/lib/python3.10/site-packages/keras/saving/saving_api.py", line 212, in load_model
return legacy_sm_saving_lib.load_model(
File "/home/gianlucasama/.local/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 61, in error_handler
return fn(*args, **kwargs)
File "/home/gianlucasama/.local/lib/python3.10/site-packages/keras/saving/legacy/save.py", line 245, in load_model
return hdf5_format.load_model_from_hdf5(
File "/home/gianlucasama/.local/lib/python3.10/site-packages/keras/saving/legacy/hdf5_format.py", line 192, in load_model_from_hdf5
model = model_config_lib.model_from_config(
File "/home/gianlucasama/.local/lib/python3.10/site-packages/keras/saving/legacy/model_config.py", line 55, in model_from_config
return deserialize(config, custom_objects=custom_objects)
File ["/home/gianlucasama/.local/lib/python3.10/site-packages/keras/layers/serialization.py",] line 265, in deserialize
return legacy_serialization.deserialize_keras_object(
File "/home/gianlucasama/.local/lib/python3.10/site-packages/keras/saving/legacy/serialization.py", line 486, in deserialize_keras_object
(cls, cls_config) = class_and_config_for_serialized_keras_object(
File "/home/gianlucasama/.local/lib/python3.10/site-packages/keras/saving/legacy/serialization.py", line 368, in class_and_config_for_serialized_keras_object
raise ValueError(
ValueError: Unknown layer: 'RetinaNet'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.
By the way, I noticed that in RetinaNetTest.test_saved_model, line 228, we are not testing the "h5" saving/loading format and that's probably why we are not adding the "RetinaNet": keras_cv.models.RetinaNet there.
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/gcbrun |
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You were right, the test would not run, because I forgot to commit the parametrization needed to run the test. |
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Thanks for the PR! Could you please merge the keras-team/master branch into your dev branch? There are some changes that were made on master that are required to fix our GCB tests. Thank you! 👍 |
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Sorry I messed up merging branches, it should be all fine now. |
Fixes #1885 lack of serialization and errors when saving and loading.