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Support of TensorFlow 2.13 #76

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merged 4 commits into from
Aug 21, 2023
Merged

Support of TensorFlow 2.13 #76

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b01dfd0
chore: latest TF versions in tox environments
cofri Jul 24, 2023
ec6714b
fix: support TF 2.13 for conv_utils module
danibene Jul 20, 2023
e743035
fix(test): deserialization of h5 models w/ custom obj doesn't work in…
cofri Jul 24, 2023
1f91e7f
fix(test): random test was failing. Fix better seed.
cofri Jul 25, 2023
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5 changes: 3 additions & 2 deletions deel/lip/layers/convolutional.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,8 +43,9 @@

try:
from keras.utils import conv_utils # in Keras for TF >= 2.6
except ModuleNotFoundError:
from tensorflow.python.keras.utils import conv_utils # in TF.python for TF <= 2.5
except ImportError:
# conv_utils in tf.python for TF <= 2.5 and TF >= 2.13
from tensorflow.python.keras.utils import conv_utils


def _compute_conv_lip_factor(kernel_size, strides, input_shape, data_format):
Expand Down
9 changes: 8 additions & 1 deletion setup.cfg
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Expand Up @@ -8,7 +8,9 @@ per-file-ignores =
*/__init__.py: F401

[tox:tox]
envlist = py{37,38,39,310}-tf{22,23,24,25,26,27,28,29,latest},py{37,38,39,310}-lint
envlist =
py{37,38,39,310}-tf{22,23,24,25,26,27,28,29,210,211,212,213,latest}
py{37,38,39,310}-lint

[testenv]
deps =
Expand All @@ -22,6 +24,11 @@ deps =
tf27: tensorflow ~= 2.7.0
tf28: tensorflow ~= 2.8.0
tf29: tensorflow ~= 2.9.0
tf210: tensorflow ~= 2.10.0
tf211: tensorflow ~= 2.11.0
tf212: tensorflow ~= 2.12.0
tf213: tensorflow ~= 2.13.0

commands =
python -m unittest

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6 changes: 3 additions & 3 deletions tests/test_layers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -226,8 +226,8 @@ def train_k_lip_model(
)
empirical_lip_const = evaluate_lip_const(model=model, x=x, seed=42)
# save the model
model_checkpoint_path = os.path.join(logdir, "model.h5")
model.save(model_checkpoint_path, overwrite=True, save_format="h5")
model_checkpoint_path = os.path.join(logdir, "model.keras")
model.save(model_checkpoint_path, overwrite=True)
del model
K.clear_session()
model = load_model(model_checkpoint_path)
Expand Down Expand Up @@ -1050,7 +1050,7 @@ def test_vanilla_export(self):

# Test saving/loading model
with tempfile.TemporaryDirectory() as tmpdir:
model_path = os.path.join(tmpdir, "model.h5")
model_path = os.path.join(tmpdir, "model.keras")
model.save(model_path)
tf.keras.models.load_model(model_path)

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9 changes: 5 additions & 4 deletions tests/test_normalizers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@
)
from deel.lip.utils import _padding_circular

np.random.seed(42)
rng = np.random.default_rng(42)


class TestSpectralNorm(unittest.TestCase):
Expand All @@ -24,11 +24,11 @@ class TestSpectralNorm(unittest.TestCase):
def test_spectral_normalization(self):
# Dense kernel
kernel_shape = (15, 32)
kernel = np.random.normal(size=kernel_shape).astype("float32")
kernel = rng.normal(size=kernel_shape).astype("float32")
self._test_kernel(kernel)
# Dense kernel projection
kernel_shape = (32, 15)
kernel = np.random.normal(size=kernel_shape).astype("float32")
kernel = rng.normal(size=kernel_shape).astype("float32")
self._test_kernel(kernel)

def _test_kernel(self, kernel):
Expand All @@ -40,7 +40,8 @@ def _test_kernel(self, kernel):
).numpy()
SVmax = np.max(sigmas_svd)

W_bar, _u, sigma = spectral_normalization(kernel, u=None, eps=1e-6)
u = rng.normal(size=(1, kernel.shape[-1]))
W_bar, _u, sigma = spectral_normalization(kernel, u=u, eps=1e-6)
# Test sigma is close to the one computed with svd first run @ 1e-1
np.testing.assert_approx_equal(
sigma, SVmax, 1, "test failed with kernel_shape " + str(kernel.shape)
Expand Down