2481069ed4
* chore: adding numpy backend * creview comments * review comments * chore: adding math * chore: adding random module * chore: adding ranndom in init * review comments * chore: adding numpy and nn for numpy backend * chore: adding generic pool, max, and average pool * chore: adding the conv ops * chore: reformat code and using jax for conv and pool * chore: added self value * chore: activation tests pass * chore: adding post build method * chore: adding necessaity methods to the numpy trainer * chore: fixing utils test * chore: fixing losses test suite * chore: fix backend tests * chore: fixing initializers test * chore: fixing accuracy metrics test * chore: fixing ops test * chore: review comments * chore: init with image and fixing random tests * chore: skipping random seed set for numpy backend * chore: adding single resize image method * chore: skipping tests for applications and layers * chore: skipping tests for models * chore: skipping testsor saving * chore: skipping tests for trainers * chore:ixing one hot * chore: fixing vmap in numpy and metrics test * chore: adding a wrapper to numpy sum, started fixing layer tests * fix: is_tensor now accepts numpy scalars * chore: adding draw seed * fix: warn message for numpy masking * fix: checking whether kernel are tensors * chore: adding rnn * chore: adding dynamic backend for numpy * fix: axis cannot be None for normalize * chore: adding jax resize for numpy image * chore: adding rnn implementation in numpy * chore: using pytest fixtures * change: numpy import string * chore: review comments * chore: adding numpy to backend list of github actions * chore: remove debug print statements
29 lines
812 B
Python
29 lines
812 B
Python
try:
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# When using torch and tensorflow, torch needs to be imported first,
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# otherwise it will segfault upon import. This should force the torch
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# import to happen first for all tests.
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import torch # noqa: F401
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except ImportError:
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pass
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import pytest
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from keras_core.backend import backend
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def pytest_configure(config):
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config.addinivalue_line(
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"markers",
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"requires_trainable_backend: mark test for trainable backend only",
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)
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def pytest_collection_modifyitems(config, items):
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requires_trainable_backend = pytest.mark.skipif(
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backend() == "numpy",
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reason="Trainer not implemented for NumPy backend.",
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)
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for item in items:
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if "requires_trainable_backend" in item.keywords:
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item.add_marker(requires_trainable_backend)
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