add rrelu kernel

tensorflow_addons/activations/BUILD

@@ -9,6 +9,7 @@ py_library(
         "gelu.py",
         "hardshrink.py",
         "lisht.py",
+        "rrelu.py",
         "softshrink.py",
         "sparsemax.py",
         "tanhshrink.py",
@@ -110,3 +111,16 @@ py_test(
         ":activations",
     ],
 )
+
+py_test(
+    name = "rrelu_test",
+    size = "small",
+    srcs = [
+        "rrelu_test.py",
+    ],
+    main = "rrelu_test.py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":activations",
+    ],
+)

tensorflow_addons/activations/README.md

@@ -9,6 +9,7 @@
 | softshrink| @WindQAQ                  | [email protected]                        |
 | sparsemax | @AndreasMadsen            | [email protected]                |
 | tanhshrink| @fsx950223                | [email protected]                      |
+| rrelu     | @fsx950223                | [email protected]                      |
 
 ## Contents
 | Submodule | Activation | Reference                        |
@@ -19,6 +20,7 @@
 | softshrink| softshrink |                                  |
 | sparsemax | sparsemax  | https://arxiv.org/abs/1602.02068 |
 | tanhshrink| tanhshrink |                                  |
+| rrelu     | rrelu      | https://arxiv.org/abs/1505.00853 |
 
 
 ## Contribution Guidelines

tensorflow_addons/activations/__init__.py

@@ -22,5 +22,6 @@
 from tensorflow_addons.activations.hardshrink import hardshrink
 from tensorflow_addons.activations.lisht import lisht
 from tensorflow_addons.activations.softshrink import softshrink
+from tensorflow_addons.activations.rrelu import rrelu
 from tensorflow_addons.activations.sparsemax import sparsemax
 from tensorflow_addons.activations.tanhshrink import tanhshrink

tensorflow_addons/activations/activations_test.py

@@ -26,7 +26,8 @@
 class ActivationsTest(tf.test.TestCase):
 
     ALL_ACTIVATIONS = [
-        "gelu", "hardshrink", "lisht", "softshrink", "sparsemax", "tanhshrink"
+        "gelu", "hardshrink", "lisht", "softshrink", "sparsemax", "rrelu",
+        "tanhshrink"
     ]
 
     def test_serialization(self):

tensorflow_addons/activations/rrelu.py

@@ -0,0 +1,63 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from tensorflow_addons.utils import keras_utils
+from tensorflow_addons.utils.resource_loader import get_path_to_datafile
+
+_activation_ops_so = tf.load_op_library(
+    get_path_to_datafile("custom_ops/activations/_activation_ops.so"))
+
+
+@keras_utils.register_keras_custom_object
+@tf.function
+def rrelu(x, lower=0.125, upper=0.3333333333333333, training=None, seed=None):
+    """rrelu function.
+
+    Computes rrelu function:
+    `x if x > 0 else random(lower,upper) * x` or
+    `x if x > 0 else x * (lower + upper) / 2`
+    depending on whether training is enabled.
+
+    See [Empirical Evaluation of Rectified Activations in Convolutional Network](https://arxiv.org/abs/1505.00853).
+
+    Args:
+        x: A `Tensor`. Must be one of the following types:
+            `float16`, `float32`, `float64`.
+        lower: `float`, lower bound for random alpha.
+        upper: `float`, upper bound for random alpha.
+        training: `bool`, indicating whether the `call`
+        is meant for training or inference.
+    Returns:
+        result: A `Tensor`. Has the same type as `x`.
+    """
+    x = tf.convert_to_tensor(x)
+    if training is None:
+        training = tf.keras.backend.learning_phase()
+        training = bool(tf.keras.backend.get_value(training))
+    seed1, seed2 = tf.compat.v1.random.get_seed(seed)
+    result, _ = _activation_ops_so.addons_rrelu(x, lower, upper, training,
+                                                seed1, seed2)
+    return result
+
+
+@tf.RegisterGradient("Addons>Rrelu")
+def _rrelu_grad(op, *grad):
+    return _activation_ops_so.addons_rrelu_grad(grad[0], op.inputs[0],
+                                                op.outputs[1])

tensorflow_addons/activations/rrelu_test.py

@@ -0,0 +1,77 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from absl.testing import parameterized
+
+import numpy as np
+import tensorflow as tf
+from tensorflow_addons.activations import rrelu
+from tensorflow_addons.utils import test_utils
+
+
+def _ref_rrelu(x, lower, upper):
+    return tf.where(x >= 0, x, (lower + upper) * x / 2)
+
+
+@test_utils.run_all_in_graph_and_eager_modes
+class RreluTest(tf.test.TestCase, parameterized.TestCase):
+    @parameterized.named_parameters(("float16", np.float16),
+                                    ("float32", np.float32),
+                                    ("float64", np.float64))
+    @tf.function
+    def test_rrelu(self, dtype):
+        x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=dtype)
+        lower = 0.1
+        upper = 0.2
+        result = rrelu(x, lower, upper, training=False)
+        expect_result = _ref_rrelu(x, lower, upper)
+        self.assertAllCloseAccordingToType(result, expect_result)
+
+    @parameterized.named_parameters(("float32", np.float32),
+                                    ("float64", np.float64))
+    def test_theoretical_gradients(self, dtype):
+        x = tf.constant([-2.0, -1.0, -0.1, 0.1, 1.0, 2.0], dtype=dtype)
+        lower = 0.1
+        upper = 0.2
+        for training in [True, False]:
+            with self.subTest(training=training):
+                theoretical, numerical = tf.test.compute_gradient(
+                    lambda x: rrelu(
+                        x, lower, upper, training=training, seed=111111), [x])
+                if training is True and tf.test.is_gpu_available() is False:
+                    numerical = [[[0.134971, 0., 0., 0., 0., 0.],
+                                  [0., 0.15648358, 0., 0., 0., 0.],
+                                  [0., 0., 0.18776372, 0., 0., 0.],
+                                  [0., 0., 0., 1., 0., 0.],
+                                  [0., 0., 0., 0., 1., 0.],
+                                  [0., 0., 0., 0., 0., 1.]]]
+                self.assertAllCloseAccordingToType(
+                    theoretical, numerical, rtol=5e-4, atol=5e-4)
+
+    def test_unknown_shape(self):
+        fn = rrelu.get_concrete_function(
+            tf.TensorSpec(shape=None, dtype=tf.float32))
+
+        for shape in [(1,), (1, 2), (1, 2, 3), (1, 2, 3, 4)]:
+            x = tf.ones(shape=shape, dtype=tf.float32)
+            self.assertAllClose(fn(x), rrelu(x))
+
+
+if __name__ == "__main__":
+    tf.test.main()

tensorflow_addons/custom_ops/activations/BUILD

@@ -13,13 +13,16 @@ custom_op_library(
         "cc/kernels/hardshrink_op.h",
         "cc/kernels/lisht_op.cc",
         "cc/kernels/lisht_op.h",
+        "cc/kernels/rrelu_op.cc",
+        "cc/kernels/rrelu_op.h",
         "cc/kernels/softshrink_op.cc",
         "cc/kernels/softshrink_op.h",
         "cc/kernels/tanhshrink_op.cc",
         "cc/kernels/tanhshrink_op.h",
         "cc/ops/gelu_op.cc",
         "cc/ops/hardshrink_op.cc",
         "cc/ops/lisht_op.cc",
+        "cc/ops/rrelu_op.cc",
         "cc/ops/softshrink_op.cc",
         "cc/ops/tanhshrink_op.cc",
     ],
@@ -30,6 +33,8 @@ custom_op_library(
         "cc/kernels/hardshrink_op_gpu.cu.cc",
         "cc/kernels/lisht_op.h",
         "cc/kernels/lisht_op_gpu.cu.cc",
+        "cc/kernels/rrelu_op.h",
+        "cc/kernels/rrelu_op_gpu.cu.cc",
         "cc/kernels/softshrink_op.h",
         "cc/kernels/softshrink_op_gpu.cu.cc",
         "cc/kernels/tanhshrink_op.h",

tensorflow_addons/custom_ops/activations/cc/kernels/rrelu_op.cc

@@ -0,0 +1,78 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#define EIGEN_USE_THREADS
+
+#include "tensorflow_addons/custom_ops/activations/cc/kernels/rrelu_op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+
+namespace tensorflow {
+namespace addons {
+
+using CPUDevice = Eigen::ThreadPoolDevice;
+
+#define REGISTER_RRELU_KERNELS(T)                                         \
+  REGISTER_KERNEL_BUILDER(                                                \
+      Name("Addons>Rrelu").Device(DEVICE_CPU).TypeConstraint<T>("T"),     \
+      RreluOp<CPUDevice, T>);                                             \
+  REGISTER_KERNEL_BUILDER(                                                \
+      Name("Addons>RreluGrad").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      RreluGradOp<CPUDevice, T>);
+
+// Rrelu only makes sense with floating points.
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_RRELU_KERNELS);
+#undef REGISTER_RRELU_KERNELS
+
+#if GOOGLE_CUDA
+
+using GPUDevice = Eigen::GpuDevice;
+
+namespace functor {
+#define DECLARE_GPU_SPEC(T)                                                  \
+  template <>                                                                \
+  void Rrelu<GPUDevice, T>::operator()(                                      \
+      const GPUDevice& d, typename TTypes<T>::ConstTensor features, T lower, \
+      T upper, bool training, typename TTypes<T>::Tensor activations,        \
+      typename TTypes<T>::Tensor alpha,                                      \
+      typename random::SimplePhilox& random);                                \
+  extern template struct Rrelu<GPUDevice, T>;                                \
+                                                                             \
+  template <>                                                                \
+  void RreluGrad<GPUDevice, T>::operator()(                                  \
+      const GPUDevice& d, typename TTypes<T>::ConstTensor gradients,         \
+      typename TTypes<T>::ConstTensor features,                              \
+      typename TTypes<T>::ConstTensor alpha,                                 \
+      typename TTypes<T>::Tensor backprops);                                 \
+  extern template struct RreluGrad<GPUDevice, T>;
+
+TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC);
+#undef DECLARE_GPU_SPEC
+}  // namespace functor
+
+#define REGISTER_RRELU_GPU_KERNELS(T)                                     \
+  REGISTER_KERNEL_BUILDER(                                                \
+      Name("Addons>Rrelu").Device(DEVICE_GPU).TypeConstraint<T>("T"),     \
+      RreluOp<GPUDevice, T>);                                             \
+  REGISTER_KERNEL_BUILDER(                                                \
+      Name("Addons>RreluGrad").Device(DEVICE_GPU).TypeConstraint<T>("T"), \
+      RreluGradOp<GPUDevice, T>);
+
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_RRELU_GPU_KERNELS);
+#undef REGISTER_RRELU_GPU_KERNELS
+
+#endif  // GOOGLE_CUDA
+}  // namespace addons
+}  // namespace tensorflow