add mish kernel

tensorflow_addons/activations/BUILD

@@ -9,6 +9,7 @@ py_library(
         "gelu.py",
         "hardshrink.py",
         "lisht.py",
+        "mish.py",
         "rrelu.py",
         "softshrink.py",
         "sparsemax.py",
@@ -86,6 +87,19 @@ py_test(
     ],
 )
 
+py_test(
+    name = "mish_test",
+    size = "small",
+    srcs = [
+        "mish_test.py",
+    ],
+    main = "mish_test.py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":activations",
+    ],
+)
+
 py_test(
     name = "softshrink_test",
     size = "small",

tensorflow_addons/activations/README.md

@@ -6,6 +6,7 @@
 | gelu      | @AakashKumarNain @WindQAQ | [email protected] [email protected] |
 | hardshrink| @WindQAQ                  | [email protected]                        |
 | lisht     | @WindQAQ                  | [email protected]                        |
+| mish      | @digantamisra98 @WindQAQ  | [email protected], [email protected] |
 | softshrink| @WindQAQ                  | [email protected]                        |
 | sparsemax | @AndreasMadsen            | [email protected]                |
 | tanhshrink| @fsx950223                | [email protected]                      |
@@ -17,12 +18,12 @@
 | gelu      | gelu       | https://arxiv.org/abs/1606.08415 |
 | hardshrink| hardshrink |                                  |
 | lisht     | lisht      | https://arxiv.org/abs/1901.05894 | 
+| mish      | mish       | https://arxiv.org/abs/1908.08681 |
 | softshrink| softshrink |                                  |
 | sparsemax | sparsemax  | https://arxiv.org/abs/1602.02068 |
 | tanhshrink| tanhshrink |                                  |
 | rrelu     | rrelu      | https://arxiv.org/abs/1505.00853 |
 
-
 ## Contribution Guidelines
 #### Standard API
 In order to conform with the current API standard, all activations

tensorflow_addons/activations/__init__.py

@@ -21,6 +21,7 @@
 from tensorflow_addons.activations.gelu import gelu
 from tensorflow_addons.activations.hardshrink import hardshrink
 from tensorflow_addons.activations.lisht import lisht
+from tensorflow_addons.activations.mish import mish
 from tensorflow_addons.activations.softshrink import softshrink
 from tensorflow_addons.activations.rrelu import rrelu
 from tensorflow_addons.activations.sparsemax import sparsemax

tensorflow_addons/activations/activations_test.py

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

tensorflow_addons/activations/mish.py

@@ -0,0 +1,49 @@
+# 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 mish(x):
+    """Mish: A Self Regularized Non-Monotonic Neural Activation Function.
+
+    Computes mish activation: x * tanh(softplus(x))
+
+    See [Mish: A Self Regularized Non-Monotonic Neural Activation Function](https://arxiv.org/abs/1908.08681).
+
+    Args:
+        x: A `Tensor`. Must be one of the following types:
+            `float16`, `float32`, `float64`.
+    Returns:
+        A `Tensor`. Has the same type as `x`.
+    """
+    x = tf.convert_to_tensor(x)
+    return _activation_ops_so.addons_mish(x)
+
+
+@tf.RegisterGradient("Addons>Mish")
+def _mish_grad(op, grad):
+    return _activation_ops_so.addons_mish_grad(grad, op.inputs[0])

tensorflow_addons/activations/mish_test.py

@@ -0,0 +1,59 @@
+# 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 mish
+from tensorflow_addons.utils import test_utils
+
+
+@test_utils.run_all_in_graph_and_eager_modes
+class MishTest(tf.test.TestCase, parameterized.TestCase):
+    @parameterized.named_parameters(("float16", np.float16),
+                                    ("float32", np.float32),
+                                    ("float64", np.float64))
+    def test_mish(self, dtype):
+        x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=dtype)
+        expected_result = tf.constant(
+            [-0.2525015, -0.30340144, 0.0, 0.86509836, 1.943959], dtype=dtype)
+        self.assertAllCloseAccordingToType(mish(x), expected_result)
+
+    @parameterized.named_parameters(("float32", np.float32),
+                                    ("float64", np.float64))
+    def test_theoretical_gradients(self, dtype):
+        # Only test theoretical gradients for float32 and float64
+        # because of the instability of float16 while computing jacobian
+        x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=dtype)
+
+        theoretical, numerical = tf.test.compute_gradient(mish, [x])
+        self.assertAllCloseAccordingToType(theoretical, numerical, atol=1e-4)
+
+    def test_unknown_shape(self):
+        fn = mish.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), mish(x))
+
+
+if __name__ == "__main__":
+    tf.test.main()

tensorflow_addons/custom_ops/activations/BUILD

@@ -13,6 +13,8 @@ custom_op_library(
         "cc/kernels/hardshrink_op.h",
         "cc/kernels/lisht_op.cc",
         "cc/kernels/lisht_op.h",
+        "cc/kernels/mish_op.cc",
+        "cc/kernels/mish_op.h",
         "cc/kernels/rrelu_op.cc",
         "cc/kernels/rrelu_op.h",
         "cc/kernels/softshrink_op.cc",
@@ -22,6 +24,7 @@ custom_op_library(
         "cc/ops/gelu_op.cc",
         "cc/ops/hardshrink_op.cc",
         "cc/ops/lisht_op.cc",
+        "cc/ops/mish_op.cc",
         "cc/ops/rrelu_op.cc",
         "cc/ops/softshrink_op.cc",
         "cc/ops/tanhshrink_op.cc",
@@ -33,6 +36,8 @@ custom_op_library(
         "cc/kernels/hardshrink_op_gpu.cu.cc",
         "cc/kernels/lisht_op.h",
         "cc/kernels/lisht_op_gpu.cu.cc",
+        "cc/kernels/mish_op.h",
+        "cc/kernels/mish_op_gpu.cu.cc",
         "cc/kernels/rrelu_op.h",
         "cc/kernels/rrelu_op_gpu.cu.cc",
         "cc/kernels/softshrink_op.h",

tensorflow_addons/custom_ops/activations/cc/kernels/mish_op.cc

@@ -0,0 +1,79 @@
+/* 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/mish_op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+
+namespace tensorflow {
+namespace addons {
+
+using CPUDevice = Eigen::ThreadPoolDevice;
+
+#define REGISTER_MISH_KERNELS(type)                                         \
+  REGISTER_KERNEL_BUILDER(                                                  \
+      Name("Addons>Mish").Device(DEVICE_CPU).TypeConstraint<type>("T"),     \
+      MishOp<CPUDevice, type>);                                             \
+  REGISTER_KERNEL_BUILDER(                                                  \
+      Name("Addons>MishGrad").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
+      MishGradOp<CPUDevice, type>);
+
+// Mish only makes sense with floating points.
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_MISH_KERNELS);
+#undef REGISTER_MISH_KERNELS
+
+#if GOOGLE_CUDA
+
+using GPUDevice = Eigen::GpuDevice;
+
+// Forward declarations of the functor specializations for GPU.
+namespace functor {
+#define DECLARE_GPU_SPEC(T)                                          \
+  template <>                                                        \
+  void Mish<GPUDevice, T>::operator()(                               \
+      const GPUDevice& d, typename TTypes<T>::ConstTensor features,  \
+      typename TTypes<T>::Tensor activations);                       \
+  extern template struct Mish<GPUDevice, T>;                         \
+                                                                     \
+  template <>                                                        \
+  void MishGrad<GPUDevice, T>::operator()(                           \
+      const GPUDevice& d, typename TTypes<T>::ConstTensor gradients, \
+      typename TTypes<T>::ConstTensor features,                      \
+      typename TTypes<T>::Tensor backprops);                         \
+  extern template struct MishGrad<GPUDevice, T>;
+
+TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC);
+#undef DECLARE_GPU_SPEC
+}  // namespace functor
+
+// Registration of the GPU implementations.
+#define REGISTER_MISH_GPU_KERNELS(type)                                     \
+  REGISTER_KERNEL_BUILDER(                                                  \
+      Name("Addons>Mish").Device(DEVICE_GPU).TypeConstraint<type>("T"),     \
+      MishOp<GPUDevice, type>);                                             \
+  REGISTER_KERNEL_BUILDER(                                                  \
+      Name("Addons>MishGrad").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
+      MishGradOp<GPUDevice, type>);
+
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_MISH_GPU_KERNELS);
+#undef REGISTER_MISH_GPU_KERNELS
+
+#endif  // GOOGLE_CUDA
+
+}  // namespace addons
+}  // namespace tensorflow