Started implementing functional::normalize()

Author: NiklasGustafsson

src/Native/LibTorchSharp/THSNormalization.cpp

@@ -607,6 +607,15 @@ Tensor THSNN_batch_norm(const Tensor input, Tensor running_mean, const Tensor ru
     CATCH_TENSOR(torch::nn::functional::batch_norm(*input, *running_mean, *running_var, opts));
 }
 
+Tensor THSNN_normalize(const Tensor input, const double p, const int64_t dim, const double eps)
+{
+    auto opts = torch::nn::functional::NormalizeFuncOptions()
+        .p(p)
+        .dim(dim)
+        .eps(eps);
+    CATCH_TENSOR(torch::nn::functional::normalize(*input, opts));
+}
+
 Tensor THSNN_group_norm(const Tensor input, const int64_t num_groups, const Tensor weight, const Tensor bias, const double eps)
 {
     auto opts = torch::nn::functional::GroupNormFuncOptions(num_groups)

src/TorchSharp/NN/Normalization/Functional.cs

@@ -10,6 +10,18 @@ public static partial class nn
         {
             public static partial class functional
             {
+                /// <summary>
+                /// Perform normalization of inputs over specified dimension.
+                /// </summary>
+                /// <param name="input">Input tensor of any shape.</param>
+                /// <param name="p">the exponent value in the norm formulation</param>
+                /// <param name="dim">the dimension to reduce</param>
+                /// <param name="eps">small value to avoid division by zero</param>
+                public static Tensor normalize(Tensor input, double p = 2.0, long dim = 1L, double eps = 1e-12)
+                {
+                    return null;
+                }
+
                 /// <summary>
                 /// Applies Batch Normalization for each channel across a batch of data.
                 /// </summary>