Migrate conv2d layer

include/layers/batch_norm.hpp

@@ -0,0 +1,170 @@
+#ifndef CUDANET_BATCH_NORM_H
+#define CUDANET_BATCH_NORM_H
+
+#include <vector>
+
+#include "activation.hpp"
+#include "layer.hpp"
+
+namespace CUDANet::Layers {
+
+class BatchNorm2d : public WeightedLayer, public TwoDLayer {
+  public:
+    BatchNorm2d(
+        shape2d        inputSize,
+        int            inputChannels,
+        float          epsilon,
+        ActivationType activationType
+    );
+
+    ~BatchNorm2d();
+
+    /**
+     * @brief Compute the forward pass of the batchnorm layer
+     *
+     * @param d_input Device pointer to the input
+     * @return float* Device pointer to the output
+     */
+    float* forward(const float* d_input);
+
+    /**
+     * @brief Set the weights of the batchnorm layer
+     *
+     * @param weights_input Pointer to the weights
+     */
+    void setWeights(const float* weights_input);
+
+    /**
+     * @brief Get the weights of the batchnorm layer
+     *
+     * @return std::vector<float>
+     */
+    std::vector<float> getWeights();
+
+    /**
+     * @brief Set the biases of the batchnorm layer
+     *
+     * @param biases_input Pointer to the biases
+     */
+    void setBiases(const float* biases_input);
+
+    /**
+     * @brief Get the biases of the batchnorm layer
+     *
+     * @return std::vector<float>
+     */
+    std::vector<float> getBiases();
+
+    /**
+     * @brief Set the Running Mean
+     *
+     * @param running_mean_input
+     */
+    void setRunningMean(const float* running_mean_input);
+
+    /**
+     * @brief Get the Running Mean
+     *
+     */
+    std::vector<float> getRunningMean();
+
+    /**
+     * @brief Set the Running Var
+     *
+     * @param running_mean_input
+     */
+    void setRunningVar(const float* running_mean_input);
+
+    /**
+     * @brief Get the Running Var
+     *
+     */
+    std::vector<float> getRunningVar();
+
+    /**
+     * @brief Get output size
+     *
+     * @return int output size
+     */
+    int getOutputSize();
+
+    /**
+     * @brief Get input size
+     *
+     * @return int input size
+     */
+    int getInputSize();
+
+    shape2d getOutputDims();
+
+  private:
+    shape2d inputSize;
+    int     inputChannels;
+    float   epsilon;
+
+    int gridSize;
+
+#ifdef USE_CUDA
+
+    float* d_output;
+
+    float* d_running_mean;
+    float* d_running_var;
+
+    float* d_length;
+    float* d_epsilon;
+
+    float* d_weights;
+    float* d_biases;
+
+    void initCUDA();
+    void delCUDA();
+
+    /**
+     * @brief Copy weights and biases to the device
+     *
+     */
+    void toCuda();
+
+    float* forwardCUDA(const float* d_input);
+
+#endif
+
+    std::vector<float> weights;
+    std::vector<float> biases;
+
+    std::vector<float> running_mean;
+    std::vector<float> running_var;
+
+    Activation* activation;
+
+    float* forwardCPU(const float* input);
+
+    /**
+     * @brief Initialize weights of the batchnorm layer with zeros
+     *
+     */
+    void initializeWeights();
+
+    /**
+     * @brief Initialize biases of the batchnorm layer with zeros
+     *
+     */
+    void initializeBiases();
+
+    /**
+     * @brief Initialize mean of the batchnorm layer with zeros
+     *
+     */
+    void initializeRunningMean();
+
+    /**
+     * @brief Initialize sqrt of variance of the batchnorm layer with ones
+     *
+     */
+    void initializeRunningVar();
+};
+
+}  // namespace CUDANet::Layers
+
+#endif  // CUDANET_BATCH_NORM_H