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Fix broken test for models with batchnorm #17841

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Merged
Rocketknight1 merged 3 commits into from
Jun 23, 2022
Merged

Fix broken test for models with batchnorm #17841

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a66ef99
Fix tests that broke when models used batchnorm
Rocketknight1 Jun 23, 2022
d598141
Initializing the model twice does not actually...
Rocketknight1 Jun 23, 2022
56bb4c9
Fix speed regression
Rocketknight1 Jun 23, 2022
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15 changes: 14 additions & 1 deletion tests/test_modeling_tf_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1383,6 +1383,10 @@ def test_keras_fit(self):
else:
metrics = []

model(model.dummy_inputs) # Build the model so we can get some constant weights
model_weights = model.get_weights()

# Run eagerly to save some expensive compilation times
model.compile(optimizer=tf.keras.optimizers.SGD(0.0), run_eagerly=True, metrics=metrics)
# Make sure the model fits without crashing regardless of where we pass the labels
history1 = model.fit(
Expand All @@ -1394,6 +1398,11 @@ def test_keras_fit(self):
)
val_loss1 = history1.history["val_loss"][0]
accuracy1 = {key: val[0] for key, val in history1.history.items() if key.endswith("accuracy")}

# We reinitialize the model here even though our learning rate was zero
# because BatchNorm updates weights by means other than gradient descent.
model.set_weights(model_weights)

history2 = model.fit(
inputs_minus_labels,
labels,
Expand All @@ -1403,7 +1412,7 @@ def test_keras_fit(self):
shuffle=False,
)
val_loss2 = history2.history["val_loss"][0]
accuracy2 = {key: val[0] for key, val in history1.history.items() if key.endswith("accuracy")}
accuracy2 = {key: val[0] for key, val in history2.history.items() if key.endswith("accuracy")}
self.assertTrue(np.allclose(val_loss1, val_loss2, atol=1e-2, rtol=1e-3))
self.assertEqual(history1.history.keys(), history2.history.keys())
for key in history1.history.keys():
Expand All @@ -1416,6 +1425,10 @@ def test_keras_fit(self):
dataset = tf.data.Dataset.from_tensor_slices(prepared_for_class)
# Pass in all samples as a batch to match other `fit` calls
dataset = dataset.batch(len(dataset))

# Reinitialize to fix batchnorm again
model.set_weights(model_weights)

history3 = model.fit(
dataset,
validation_data=dataset,
Expand Down