Adding Mish activation function

dlib/cuda/cpu_dlib.cpp

@@ -1467,6 +1467,58 @@ namespace dlib
             }
         }
 
+    // ------------------------------------------------------------------------------------
+
+        void mish (
+            tensor& dest,
+            const tensor& src
+        )
+        {
+            const auto d = dest.host_write_only();
+            const auto s = src.host();
+            for (size_t i = 0; i < src.size(); ++i)
+            {
+                const auto e = std::exp(s[i]);
+                const auto delta = 2*e + e*e + 2;
+                d[i] = s[i] - 2*s[i]/delta;
+            }
+        }
+
+        void mish_gradient(
+            tensor& grad,
+            const tensor& src,
+            const tensor& gradient_input
+        )
+        {
+            const auto g = grad.host();
+            const auto s = src.host();
+            const auto in = gradient_input.host();
+
+            const auto calculate_gradient = [](float x)
+            {
+                if (x >= 8)
+                    return 1.f;
+                if (x <= -8)
+                    return 0.f;
+
+                const auto e = std::exp(x);
+                const auto delta = 2*e + e*e + 2;
+                const auto omega = 4*(x + 1) + 4*e*e + e*e*e + e*(4*x + 6);
+                return e*omega/(delta*delta);
+            };
+
+            if (is_same_object(gradient_input, grad))
+            {
+                for (size_t i = 0; i < src.size(); ++i)
+                    g[i] = in[i]*calculate_gradient(s[i]);
+            }
+            else
+            {
+                for (size_t i = 0; i < src.size(); ++i)
+                    g[i] += in[i]*calculate_gradient(s[i]);
+            }
+        }
+
     // ------------------------------------------------------------------------------------
 
         void relu (

dlib/cuda/cpu_dlib.h

@@ -281,6 +281,19 @@ namespace dlib
             const tensor& gradient_input
         );
 
+    // ------------------------------------------------------------------------------------
+
+        void mish (
+            tensor& dest,
+            const tensor& src
+        );
+
+        void mish_gradient (
+            tensor& grad,
+            const tensor& src,
+            const tensor& gradient_input
+        );
+
     // ------------------------------------------------------------------------------------
 
         void relu (

dlib/cuda/cuda_dlib.cu

@@ -1351,6 +1351,63 @@ namespace dlib
                 param.device(), params_grad.device());
         }
 
+    // ----------------------------------------------------------------------------------------
+
+        __global__ void _cuda_mish(const float* s, float* d, size_t n)
+        {
+            for (auto i : grid_stride_range(0, n))
+            {
+                const auto e = std::exp(s[i]);
+                const auto delta = 2*e + e*e + 2;
+                d[i] = s[i] - 2*s[i]/delta;
+            }
+        }
+
+        void mish (
+            tensor& dest,
+            const tensor& src
+        )
+        {
+            launch_kernel(_cuda_mish, max_jobs(dest.size()), src.device(), dest.device(), src.size());
+        }
+
+    // ----------------------------------------------------------------------------------------
+
+        __global__ void _cuda_mish_gradient(float* out, const float* s, const float* gi, size_t n)
+        {
+            const auto calculate_gradient = [](float x)
+            {
+                if (x >= 8)
+                    return 1.f;
+                if (x <= -8)
+                    return 0.f;
+
+                const auto e = std::exp(x);
+                const auto delta = 2*e + e*e + 2;
+                const auto omega = 4*(x + 1) + 4*e*e + e*e*e + e*(4*x + 6);
+                return e*omega/(delta*delta);
+            };
+
+            if (out == gi)
+            {
+                for (auto i : grid_stride_range(0, n))
+                    out[i] = gi[i]*calculate_gradient(s[i]);
+            }
+            else
+            {
+                for (auto i : grid_stride_range(0, n))
+                    out[i] += gi[i]*calculate_gradient(s[i]);
+            }
+        }
+
+        void mish_gradient (
+            tensor& grad,
+            const tensor& src,
+            const tensor& gradient_input
+        )
+        {
+            launch_kernel(_cuda_mish_gradient, max_jobs(grad.size()), grad.device(), src.device(), gradient_input.device(), grad.size());
+        }
     // ----------------------------------------------------------------------------------------
 
         __global__ void _cuda_resize_bilinear(size_t dsize, size_t dchan_size, size_t dnc, float* d, 

dlib/cuda/cuda_dlib.h

@@ -367,6 +367,18 @@ namespace dlib
             tensor& params_grad 
         );
 
+    // ----------------------------------------------------------------------------------------
+
+        void mish (
+            tensor& dest,
+            const tensor& src
+        );
+
+        void mish_gradient (
+            tensor& grad,
+            const tensor& src,
+            const tensor& gradient_input
+        );
 
     // ----------------------------------------------------------------------------------------
 

dlib/cuda/tensor_tools.cpp

@@ -826,6 +826,33 @@ namespace dlib { namespace tt
 #endif
     }
 
+// ----------------------------------------------------------------------------------------
+
+    void mish (
+        tensor& dest,
+        const tensor& src
+    )
+    {
+#ifdef DLIB_USE_CUDA
+        cuda::mish(dest,src);
+#else
+        cpu::mish(dest,src);
+#endif
+    }
+
+    void mish_gradient (
+        tensor& grad,
+        const tensor& src,
+        const tensor& gradient_input
+    )
+    {
+#ifdef DLIB_USE_CUDA
+        cuda::mish_gradient(grad, src, gradient_input);
+#else
+        cpu::mish_gradient(grad, src, gradient_input);
+#endif
+    }
+
 // ----------------------------------------------------------------------------------------
 
     void relu (

dlib/cuda/tensor_tools.h

@@ -1330,6 +1330,40 @@ namespace dlib { namespace tt
               is_same_object(grad, gradient_input)==true
     !*/
 
+// ----------------------------------------------------------------------------------------
+
+    void mish (
+        tensor& dest,
+        const tensor& src
+    );
+    /*!
+        requires
+            - have_same_dimensions(dest, src) == true
+        ensures
+            - for all valid i:
+                - #dest.host()[i] == src.host()[i]*std::tanh(std::log(1+std::exp(src.host()[i])))
+            - This function supports in-place operation, i.e. having
+              is_same_object(dest, src)==true
+    !*/
+
+    void mish_gradient (
+        tensor& grad,
+        const tensor& dest,
+        const tensor& gradient_input
+    );
+    /*!
+        requires
+            - have_same_dimensions(dest,gradient_input) == true
+            - have_same_dimensions(dest,grad) == true
+        ensures
+            - This function computes the gradient of f() with respect to SRC and stores
+              it to grad.  Moreover, if is_same_object(grad,gradient_input)==true then
+              the output is assigned to grad, replacing its previous contents.
+              Otherwise the output is added to grad.
+            - This function supports in-place operation, i.e. having
+              is_same_object(grad, gradient_input)==true
+    !*/
+
 // ----------------------------------------------------------------------------------------
 
     void relu (

dlib/dnn/layers.h

@@ -2829,6 +2829,79 @@ namespace dlib
     template <typename SUBNET>
     using sig = add_layer<sig_, SUBNET>;
 
+// ----------------------------------------------------------------------------------------
+
+    class mish_
+    {
+    public:
+        mish_()
+        {
+        }
+
+        template <typename SUBNET>
+        void setup (const SUBNET& /*sub*/)
+        {
+        }
+
+        template <typename SUBNET>
+        void forward(
+            const SUBNET& sub,
+            resizable_tensor& data_output
+        )
+        {
+            data_output.copy_size(sub.get_output());
+            tt::mish(data_output, sub.get_output());
+        }
+
+        template <typename SUBNET>
+        void backward(
+            const tensor& gradient_input,
+            SUBNET& sub,
+            tensor&
+        )
+        {
+            tt::mish_gradient(sub.get_gradient_input(), sub.get_output(), gradient_input);
+        }
+
+        inline dpoint map_input_to_output (const dpoint& p) const { return p; }
+        inline dpoint map_output_to_input (const dpoint& p) const { return p; }
+
+        const tensor& get_layer_params() const { return params; }
+        tensor& get_layer_params() { return params; }
+
+        friend void serialize(const mish_& , std::ostream& out)
+        {
+            serialize("mish_", out);
+        }
+
+        friend void deserialize(mish_& , std::istream& in)
+        {
+            std::string version;
+            deserialize(version, in);
+            if (version != "mish_")
+                throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::mish_.");
+        }
+
+        friend std::ostream& operator<<(std::ostream& out, const mish_& )
+        {
+            out << "mish";
+            return out;
+        }
+
+        friend void to_xml(const mish_& /*item*/, std::ostream& out)
+        {
+            out << "<mish/>\n";
+        }
+
+
+    private:
+        resizable_tensor params;
+    };
+
+
+    template <typename SUBNET>
+    using mish = add_layer<mish_, SUBNET>;
+
 // ----------------------------------------------------------------------------------------
 
     class htan_

dlib/dnn/layers_abstract.h

@@ -2125,6 +2125,41 @@ namespace dlib
     template <typename SUBNET>
     using sig = add_layer<sig_, SUBNET>;
 
+// ----------------------------------------------------------------------------------------
+
+    class mish_
+    {
+        /*!
+            WHAT THIS OBJECT REPRESENTS
+                This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface
+                defined above.  In particular, it defines a mish layer.  Therefore, it
+                passes its inputs through the function
+                    f(x)= x*tanh(log(1+exp(x)))
+                where f() is applied pointwise across the input tensor.
+        !*/
+
+    public:
+
+        mish_(
+        );
+
+        template <typename SUBNET> void setup (const SUBNET& sub);
+        template <typename SUBNET> void forward(const SUBNET& sub, resizable_tensor& data_output);
+        template <typename SUBNET> void backward(const tensor& gradient_input, SUBNET& sub, tensor&);
+        dpoint map_input_to_output(dpoint p) const;
+        dpoint map_output_to_input(dpoint p) const;
+        const tensor& get_layer_params() const;
+        tensor& get_layer_params();
+        /*!
+            These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_
+            interface.  Note that this layer doesn't have any parameters, so the tensor
+            returned by get_layer_params() is always empty.
+        !*/
+    };
+
+    template <typename SUBNET>
+    using mish = add_layer<mish_, SUBNET>;
+
 // ----------------------------------------------------------------------------------------
 
     class htan_

dlib/test/dnn.cpp

@@ -172,7 +172,7 @@ namespace
         print_spinner();
         const long nr = 3;
         const long nc = 3;
-        resizable_tensor src(5,5,nr,nr), dest(5,5,nr,nc), gradient_input(5,5,nr,nc);
+        resizable_tensor src(5,5,nr,nc), dest(5,5,nr,nc), gradient_input(5,5,nr,nc);
         tt::tensor_rand rnd;
         rnd.fill_uniform(src);
         rnd.fill_uniform(dest);
@@ -217,6 +217,32 @@ namespace
 #endif
     }
 
+    void test_mish()
+    {
+#ifdef DLIB_USE_CUDA
+        // make sure that cuda::mish and cpu::mish return the same results
+        using namespace dlib::tt;
+        print_spinner();
+        const long n = 5;
+        const long k = 5;
+        const long nr = 3;
+        const long nc = 3;
+        resizable_tensor src(n,k,nr,nc);
+        tt::tensor_rand rnd;
+        rnd.fill_uniform(src);
+
+        resizable_tensor dest1, dest2;
+        dest1.copy_size(src);
+        dest2.copy_size(src);
+        // initialize to different values in order to make sure the output is actually changed
+        dest1 = 1;
+        dest2 = 2;
+        cuda::mish(dest1, src);
+        cpu::mish(dest2, src);
+        DLIB_TEST_MSG(max(abs(mat(dest1) - mat(dest2))) < 1e-7, max(abs(mat(dest1) - mat(dest2))));
+#endif // DLIB_USE_CUDA
+    }
+
     void test_batch_normalize()
     {
         using namespace dlib::tt;
@@ -1832,6 +1858,12 @@ namespace
             auto res = test_layer(l);
             DLIB_TEST_MSG(res, res);
         }
+        {
+            print_spinner();
+            mish_ l;
+            auto res = test_layer(l);
+            DLIB_TEST_MSG(res, res);
+        }
         {
             print_spinner();
             htan_ l;
@@ -3382,6 +3414,7 @@ namespace
             test_softmax();
             test_softmax_all();
             test_sigmoid();
+            test_mish();
             test_batch_normalize();
             test_batch_normalize_conv();
             test_basic_tensor_ops();