add leaky_relu activation layer (#2033)

* add leaky_relu activation layer

* add inplace case for leaky_relu and test_layer

* make clear that alpha is not learned by leaky_relu

* remove branch from cuda kernel

dlib/cuda/cpu_dlib.cpp

@@ -1607,6 +1607,57 @@ namespace dlib
             params_grad.host()[0] = pgrad;
         }
 
+    // ------------------------------------------------------------------------------------
+
+        void leaky_relu (
+            tensor& dest,
+            const tensor& src,
+            const float alpha
+        )
+        {
+            const float* s = src.host();
+            float* d = dest.host();
+            for (size_t i = 0; i < dest.size(); ++i)
+            {
+                if (s[i] > 0)
+                    d[i] = s[i];
+                else
+                    d[i] = alpha * s[i];
+            }
+        }
+
+        void leaky_relu_gradient (
+            tensor& grad,
+            const tensor& dest,
+            const tensor& gradient_input,
+            const float alpha
+        )
+        {
+            const float* gi = gradient_input.host();
+            const float* in = dest.host();
+            float* out = grad.host();
+            if (is_same_object(grad, gradient_input))
+            {
+                for (size_t i = 0; i < dest.size(); ++i)
+                {
+                    if (in[i] > 0)
+                        out[i] = gi[i];
+                    else
+                        out[i] = alpha * gi[i];
+                }
+            }
+            else
+            {
+                for (size_t i = 0; i < dest.size(); ++i)
+                {
+                    if (in[i] > 0)
+                        out[i] += gi[i];
+                    else
+                        out[i] += alpha * gi[i];
+                }
+            }
+        }
+
     // ------------------------------------------------------------------------------------
 
         void tanh (

dlib/cuda/cpu_dlib.h

@@ -323,6 +323,21 @@ namespace dlib
             tensor& params_grad 
         );
 
+    // ------------------------------------------------------------------------------------
+
+        void leaky_relu (
+            tensor& dest,
+            const tensor& src,
+            const float alpha
+        );
+
+        void leaky_relu_gradient (
+            tensor& grad,
+            const tensor& dest,
+            const tensor& gradient_input,
+            const float alpha
+        );
+
     // ------------------------------------------------------------------------------------
 
         void tanh (

dlib/cuda/cuda_dlib.cu

@@ -1350,6 +1350,73 @@ namespace dlib
                 grad.device(), src.device(), gradient_input.device(), grad.size(),
                 param.device(), params_grad.device());
         }
+    // ----------------------------------------------------------------------------------------
+
+        __global__ void _cuda_leaky_relu(const float* s, float* d, size_t n, const float alpha)
+        {
+            for (auto i : grid_stride_range(0, n))
+            {
+                if (s[i] > 0)
+                    d[i] = s[i];
+                else
+                    d[i] = alpha * s[i];
+            }
+        }
+
+        void leaky_relu(
+            tensor& dest,
+            const tensor &src,
+            const float alpha
+        )
+        {
+            launch_kernel(_cuda_leaky_relu, max_jobs(dest.size()),
+                src.device(), dest.device(), src.size(), alpha);
+        }
+
+    // ----------------------------------------------------------------------------------------
+
+        __global__ void _cuda_leaky_relu_gradient_inplace(float* out, const float* s, const float* gi, size_t n, const float alpha)
+        {
+            for (auto i : grid_stride_range(0, n))
+            {
+                if (s[i] > 0)
+                    out[i] = gi[i];
+                else
+                    out[i] = alpha * gi[i];
+            }
+        }
+
+        __global__ void _cuda_leaky_relu_gradient(float* out, const float* s, const float* gi, size_t n, const float alpha)
+        {
+            for (auto i : grid_stride_range(0, n))
+            {
+                if (s[i] > 0)
+                    out[i] += gi[i];
+                else
+                    out[i] += alpha * gi[i];
+            }
+        }
+
+        void leaky_relu_gradient (
+            tensor& grad,
+            const tensor& src,
+            const tensor& gradient_input,
+            const float alpha
+        )
+        {
+            float* out = grad.device();
+            const float *gi = gradient_input.device();
+            if (out == gi)
+            {
+                launch_kernel(_cuda_leaky_relu_gradient_inplace, max_jobs(grad.size()),
+                    out, src.device(), gi, grad.size(), alpha);
+            }
+            else
+            {
+                launch_kernel(_cuda_leaky_relu_gradient, max_jobs(grad.size()),
+                    out, src.device(), gi, grad.size(), alpha);
+            }
+        }
 
     // ----------------------------------------------------------------------------------------
 
@@ -1408,6 +1475,7 @@ namespace dlib
         {
             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 +367,21 @@ namespace dlib
             tensor& params_grad 
         );
 
+    // ----------------------------------------------------------------------------------------
+
+        void leaky_relu (
+            tensor& dest,
+            const tensor& src,
+            const float alpha
+        );
+
+        void leaky_relu_gradient (
+            tensor& grad,
+            const tensor& src,
+            const tensor& gradient_input,
+            const float alpha
+        );
+
     // ----------------------------------------------------------------------------------------
 
         void mish (

dlib/cuda/tensor_tools.cpp

@@ -909,6 +909,35 @@ namespace dlib { namespace tt
 #endif
     }
 
+// ----------------------------------------------------------------------------------------
+
+    void leaky_relu (
+        tensor& dest,
+        const tensor& src,
+        const float alpha
+    )
+    {
+#ifdef DLIB_USE_CUDA
+        cuda::leaky_relu(dest, src, alpha);
+#else
+        cpu::leaky_relu(dest, src, alpha);
+#endif
+    }
+
+    void leaky_relu_gradient (
+        tensor& grad,
+        const tensor& dest,
+        const tensor& gradient_input,
+        const float alpha
+    )
+    {
+#ifdef DLIB_USE_CUDA
+        cuda::leaky_relu_gradient(grad, dest, gradient_input, alpha);
+#else
+        cpu::leaky_relu_gradient(grad, dest, gradient_input, alpha);
+#endif
+    }
+
 // ----------------------------------------------------------------------------------------
 
     void tanh (

dlib/cuda/tensor_tools.h

@@ -1443,6 +1443,45 @@ namespace dlib { namespace tt
               adds the gradient with respect to src to #grad.
     !*/
 
+// ----------------------------------------------------------------------------------------
+
+    void leaky_relu (
+        tensor& dest,
+        const tensor& src,
+        const float alpha
+    );
+    /*!
+        requires
+            - have_same_dimensions(dest, src) == true
+        ensures
+            - for all valid i:
+                - if (src.host()[i] > 0) then
+                    - #dest.host()[i] == src.host()[i]
+                - else
+                    - #dest.host()[i] == src.host()[i] * alpha
+    !*/
+
+    void leaky_relu_gradient (
+        tensor& grad,
+        const tensor& dest,
+        const tensor& gradient_input,
+        const float alpha
+    );
+    /*!
+        requires
+            - have_same_dimensions(dest,gradient_input) == true
+            - have_same_dimensions(dest,grad) == true
+        ensures
+            - Recalling that dest is the output of leaky_relu(dest,SRC) for some SRC tensor,
+              let f(SRC) == dot(gradient_input,dest).  Then 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 tanh (

dlib/dnn/layers.h

@@ -2761,6 +2761,84 @@ namespace dlib
     template <typename SUBNET>
     using prelu = add_layer<prelu_, SUBNET>;
 
+// ----------------------------------------------------------------------------------------
+    class leaky_relu_
+    {
+    public:
+        explicit leaky_relu_(
+            float alpha_ = 0.01f
+        ) : alpha(alpha_)
+        {
+        }
+
+        float get_alpha(
+        ) const {
+            return alpha;
+        }
+
+        template <typename SUBNET>
+        void setup(const SUBNET& /*sub*/)
+        {
+        }
+
+        void forward_inplace(const tensor& input, tensor& output)
+        {
+            tt::leaky_relu(output, input, alpha);
+        }
+
+        void backward_inplace(
+            const tensor& computed_output,
+            const tensor& gradient_input,
+            tensor& data_grad,
+            tensor&
+        )
+        {
+            tt::leaky_relu_gradient(data_grad, computed_output, gradient_input, alpha);
+        }
+
+        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 leaky_relu_& item, std::ostream& out)
+        {
+            serialize("leaky_relu_", out);
+            serialize(item.alpha, out);
+        }
+
+        friend void deserialize(leaky_relu_& item, std::istream& in)
+        {
+            std::string version;
+            deserialize(version, in);
+            if (version != "leaky_relu_")
+                throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::leaky_relu_.");
+            deserialize(item.alpha, in);
+        }
+
+        friend std::ostream& operator<<(std::ostream& out, const leaky_relu_& item)
+        {
+            out << "leaky_relu\t("
+                << "alpha=" << item.alpha
+                << ")";
+            return out;
+        }
+
+        friend void to_xml(const leaky_relu_& item, std::ostream& out)
+        {
+            out << "<leaky_relu alpha='"<< item.alpha << "'>\n";
+            out << "<leaky_relu/>\n";
+        }
+
+    private:
+        resizable_tensor params;
+        float alpha;
+    };
+
+    template <typename SUBNET>
+    using leaky_relu = add_layer<leaky_relu_, SUBNET>;
+
 // ----------------------------------------------------------------------------------------
 
     class sig_

dlib/dnn/layers_abstract.h

@@ -2090,6 +2090,55 @@ namespace dlib
     template <typename SUBNET>
     using prelu = add_layer<prelu_, SUBNET>;
 
+// ----------------------------------------------------------------------------------------
+
+    class leaky_relu_
+    {
+        /*!
+            WHAT THIS OBJECT REPRESENTS
+                This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface
+                defined above.  In particular, it defines a leaky rectified linear
+                layer.  Therefore, it passes its inputs through the function
+                    f(x) = x>0 ? x : alpha*x
+                where f() is applied pointwise across the input tensor and alpha is a
+                non-learned scalar.
+
+                This is the layer type introduced in the paper:
+                    A. L. Maas, A. Y. Hannun, and A. Y. Ng. "Rectifier nonlinearities improve
+                    neural network acoustic models". In ICML, 2013.
+        !*/
+
+    public:
+        explicit leaky_relu_(
+            float alpha = 0.01f
+        );
+        /*!
+            ensures
+                - the alpha parameter will be initialized with the alpha value
+        !*/
+
+        float get_alpha(
+        ) const;
+        /*!
+            ensures
+                - returns the alpha parameter of the leaky_relu
+        !*/
+
+        template <typename SUBNET> void setup(const SUBNET& sub);
+        void forward_inplace(const tensor& input, tensor& output);
+        void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad);
+        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.
+        !*/
+    };
+
+    template <typename SUBNET>
+    using leaky_relu = add_layer<prelu_, SUBNET>;
+
 // ----------------------------------------------------------------------------------------
 
     class sig_

dlib/test/dnn.cpp

@@ -243,6 +243,32 @@ namespace
 #endif // DLIB_USE_CUDA
     }
 
+    void test_leaky_relu()
+    {
+#ifdef DLIB_USE_CUDA
+        using namespace dlib::tt;
+        print_spinner();
+        const long n = 5;
+        const long k = 5;
+        const long nr = 3;
+        const long nc = 3;
+        const float alpha = 0.01;
+        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::leaky_relu(dest1, src, alpha);
+        cpu::leaky_relu(dest2, src, alpha);
+
+        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;
@@ -1866,6 +1892,12 @@ namespace
             auto res = test_layer(l);
             DLIB_TEST_MSG(res, res);
         }
+        {
+            print_spinner();
+            leaky_relu_ l;
+            auto res = test_layer(l);
+            DLIB_TEST_MSG(res, res);
+        }
         {
             print_spinner();
             sig_ l;
@@ -3703,6 +3735,7 @@ namespace
             test_softmax_all();
             test_sigmoid();
             test_mish();
+            test_leaky_relu();
             test_batch_normalize();
             test_batch_normalize_conv();
             test_basic_tensor_ops();