:github_url: https://github.com/pytorch/pytorch


.. _program_listing_file_torch_csrc_api_include_torch_nn_modules_instancenorm.h:

Program Listing for File instancenorm.h
=======================================

|exhale_lsh| :ref:`Return to documentation for file <file_torch_csrc_api_include_torch_nn_modules_instancenorm.h>` (``torch/csrc/api/include/torch/nn/modules/instancenorm.h``)

.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS

.. code-block:: cpp

   #pragma once
   
   #include <torch/nn/functional/instancenorm.h>
   #include <torch/nn/modules/batchnorm.h>
   #include <torch/nn/options/instancenorm.h>
   
   namespace torch::nn {
   
   template <size_t D, typename Derived>
   // NOLINTNEXTLINE(bugprone-crtp-constructor-accessibility)
   class InstanceNormImpl
       : public torch::nn::NormImplBase<D, Derived, InstanceNormOptions> {
    private:
     inline Tensor apply_instance_norm(const Tensor& input) {
       return torch::nn::functional::detail::instance_norm(
           input,
           this->running_mean,
           this->running_var,
           this->weight,
           this->bias,
           this->is_training() || !this->options.track_running_stats(),
           this->options.momentum(),
           this->options.eps());
     }
   
     inline Tensor handle_no_batch_input(const Tensor& input) {
       return this->apply_instance_norm(input.unsqueeze(0)).squeeze(0);
     }
   
    public:
     using torch::nn::NormImplBase<D, Derived, InstanceNormOptions>::NormImplBase;
   
     Tensor forward(const Tensor& input) {
       this->_check_input_dim(input);
   
       // For InstanceNorm1D, 2D is unbatched and 3D is batched
       // For InstanceNorm2D, 3D is unbatched and 4D is batched
       // For InstanceNorm3D, 4D is unbatched and 5D is batched
       // check if input does not have a batch-dim
       if (input.dim() == D + 1) {
         return this->handle_no_batch_input(input);
       }
   
       return this->apply_instance_norm(input);
     }
   
     void pretty_print(std::ostream& stream) const override {
       stream << std::boolalpha << "torch::nn::InstanceNorm" << D << "d("
              << this->options.num_features() << ", "
              << "eps=" << this->options.eps() << ", "
              << "momentum=" << this->options.momentum() << ", "
              << "affine=" << this->options.affine() << ", "
              << "track_running_stats=" << this->options.track_running_stats()
              << ')';
     }
   };
   
   // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ InstanceNorm1d
   // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
   
   class TORCH_API InstanceNorm1dImpl
       : public InstanceNormImpl<1, InstanceNorm1dImpl> {
    protected:
     void _check_input_dim(const Tensor& input) override;
   
    public:
     using InstanceNormImpl<1, InstanceNorm1dImpl>::InstanceNormImpl;
   };
   
   TORCH_MODULE(InstanceNorm1d);
   
   // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ InstanceNorm2d
   // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
   
   class TORCH_API InstanceNorm2dImpl
       : public InstanceNormImpl<2, InstanceNorm2dImpl> {
    protected:
     void _check_input_dim(const Tensor& input) override;
   
    public:
     using InstanceNormImpl<2, InstanceNorm2dImpl>::InstanceNormImpl;
   };
   
   TORCH_MODULE(InstanceNorm2d);
   
   // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ InstanceNorm3d
   // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
   
   class TORCH_API InstanceNorm3dImpl
       : public InstanceNormImpl<3, InstanceNorm3dImpl> {
    protected:
     void _check_input_dim(const Tensor& input) override;
   
    public:
     using InstanceNormImpl<3, InstanceNorm3dImpl>::InstanceNormImpl;
   };
   
   TORCH_MODULE(InstanceNorm3d);
   
   } // namespace torch::nn