diff --git a/.github/workflows/install.yml b/.github/workflows/install.yml index a3465e1..a09cf22 100755 --- a/.github/workflows/install.yml +++ b/.github/workflows/install.yml @@ -12,7 +12,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.6, 3.7] + python-version: [ 3.6, 3.7 ] steps: - name: Set up Python ${{ matrix.python-version }} diff --git a/.gitignore b/.gitignore index 16cf7a5..d0adda0 100644 --- a/.gitignore +++ b/.gitignore @@ -9,3 +9,4 @@ siamese_nets_classifier .coverage *egg-info logs +.python-version diff --git a/keras_fsl/losses/gram_matrix_losses.py b/keras_fsl/losses/gram_matrix_losses.py index f301c5d..e7362c3 100644 --- a/keras_fsl/losses/gram_matrix_losses.py +++ b/keras_fsl/losses/gram_matrix_losses.py @@ -12,8 +12,8 @@ """ import tensorflow as tf import tensorflow.keras.backend as K -from tensorflow.keras.losses import Loss import tensorflow_probability as tfp +from tensorflow.keras.losses import Loss class MeanScoreClassificationLoss(Loss): @@ -70,8 +70,11 @@ def __init__(self, lower=0.0, upper=1.0, **kwargs): def call(self, y_true, y_pred): loss = super().call(y_true, y_pred) - clip_mask = tf.math.logical_and(-tf.math.log(1 - self.lower) < loss, loss < -tf.math.log(1 - self.upper)) - return tf.cast(clip_mask, dtype=y_pred.dtype) * loss + clip_mask = tf.math.logical_and( + -tf.math.log(1 - tf.cast(self.lower, dtype=loss.dtype)) < loss, + loss < -tf.math.log(1 - tf.cast(self.upper, dtype=loss.dtype)), + ) + return tf.cast(clip_mask, dtype=loss.dtype) * loss # TODO: use reduction kwarg of loss when it becomes possible to give custom reduction to includes all other reductions below in diff --git a/keras_fsl/losses/tests/gram_matrix_losses_test.py b/keras_fsl/losses/tests/gram_matrix_losses_test.py index 9229110..2971a5c 100644 --- a/keras_fsl/losses/tests/gram_matrix_losses_test.py +++ b/keras_fsl/losses/tests/gram_matrix_losses_test.py @@ -140,6 +140,15 @@ def test_clipped_loss_should_equal_literal_calculation(self, y_true, adjacency_m ) np.testing.assert_almost_equal(tf_loss, np_loss, decimal=5) + @staticmethod + @pytest.mark.parametrize("dtype_policy", (tf.float16, tf.bfloat16, tf.float32, tf.float64)) + def test_clipped_loss_computes_in_all_float_dtypes(dtype_policy, y_true, y_pred): + y_true_tensor = tf.convert_to_tensor(y_true) + y_pred_tensor = tf.convert_to_tensor(y_pred) + ClippedBinaryCrossentropy(lower=0.05, upper=0.95)( + tf.cast(y_true_tensor, dtype=dtype_policy), tf.cast(y_pred_tensor, dtype=dtype_policy) + ) + def test_max_loss_should_equal_literal_calculation(self, y_true, adjacency_matrix, y_pred): np_loss = np.max(-(adjacency_matrix * np.log(y_pred) + (1 - adjacency_matrix) * np.log(1 - y_pred))) tf_loss = MaxBinaryCrossentropy()( diff --git a/keras_fsl/models/head_models/learnt_norms.py b/keras_fsl/models/head_models/learnt_norms.py index a6f37b8..ca5c234 100644 --- a/keras_fsl/models/head_models/learnt_norms.py +++ b/keras_fsl/models/head_models/learnt_norms.py @@ -1,4 +1,5 @@ import numpy as np +import tensorflow as tf from tensorflow.keras import activations from tensorflow.keras.layers import ( Concatenate, @@ -8,7 +9,9 @@ Input, Reshape, ) +from tensorflow.keras.mixed_precision.experimental import global_policy from tensorflow.keras.models import Model +from tensorflow.python.keras.layers import Activation def LearntNorms(input_shape, use_bias=True, activation="sigmoid"): @@ -31,6 +34,11 @@ def LearntNorms(input_shape, use_bias=True, activation="sigmoid"): ) output = Conv2D(filters=1, kernel_size=(1, 1), activation="linear", name="norms_average", use_bias=use_bias)(output) output = Flatten()(output) + output = Dense(1, name="raw_output", use_bias=use_bias)(output) - output = Dense(1, activation=activations.get(activation), name="output", use_bias=use_bias)(output) + global_dtype_policy = global_policy().name + if global_dtype_policy in ["mixed_float16", "mixed_bfloat16"]: + output = Activation(activations.get(activation), dtype=tf.float32, name="predictions")(output) + else: + output = Activation(activations.get(activation), name="predictions")(output) return Model(inputs=inputs, outputs=output) diff --git a/keras_fsl/models/head_models/tests/learnt_norms_test.py b/keras_fsl/models/head_models/tests/learnt_norms_test.py index 478c999..5224b1c 100644 --- a/keras_fsl/models/head_models/tests/learnt_norms_test.py +++ b/keras_fsl/models/head_models/tests/learnt_norms_test.py @@ -27,6 +27,24 @@ def test_should_fit(self, input_shape): learnt_norms.fit(dataset, epochs=1, steps_per_epoch=2, verbose=1) + @parameterized.named_parameters( + ("mixed_float16", "mixed_float16", "float32"), + ("mixed_bfloat16", "mixed_bfloat16", "float32"), + ("float32", "float32", "float32"), + ("float64", "float64", "float64"), + ) + def test_last_activation_fp32_in_mixed_precision(self, mixed_precision_policy, expected_last_layer_dtype_policy): + policy = tf.keras.mixed_precision.experimental.Policy(mixed_precision_policy) + tf.keras.mixed_precision.experimental.set_policy(policy) + learnt_norms = LearntNorms(input_shape=(10,)) + + # Check dtype policy of internal non-input layers + for layer in learnt_norms.layers[2:-1]: + assert layer._dtype_policy.name == mixed_precision_policy + + # Check dtype policy of last layer always at least FP32 + assert learnt_norms.layers[-1]._dtype_policy.name == expected_last_layer_dtype_policy + if __name__ == "__main__": tf.test.main() diff --git a/poetry.lock b/poetry.lock index 9092a6d..b6fc323 100644 --- a/poetry.lock +++ b/poetry.lock @@ -311,7 +311,7 @@ python-versions = ">=3.6" gpustat = ">=0.6.0" [package.extras] -publish = ["bumpversion (>=0.5.3)", "twine (>=1.13.0)"] +publish = ["twine (>=1.13.0)", "bumpversion (>=0.5.3)"] [[package]] name = "gpustat" @@ -743,17 +743,17 @@ python-versions = ">=3.5" decorator = ">=4.3.0" [package.extras] -all = ["numpy", "scipy", "pandas", "matplotlib", "pygraphviz", "pydot", "pyyaml", "gdal", "lxml", "pytest"] -gdal = ["gdal"] 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