[Layer] Introduce upsample2d layer

Add `upsample2d` layer in nntrainer. This could be used in YOLO or other layers.

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Co-authored-by: Boseong Seo <[email protected]>
Co-authored-by: kimhan0515 <[email protected]>
Signed-off-by: heka1024 <[email protected]>

api/ccapi/include/layer.h

@@ -98,6 +98,7 @@ enum LayerType {
   LAYER_REDUCE_MEAN,              /**< Reduce mean Layer type */
   LAYER_LOSS_CONSTANT_DERIVATIVE, /**< Synthetic loss layer to feed constant
                                      derivative */
+  LAYER_UPSAMPLE2D,               /**< Upsample 2D Layer type */
   LAYER_UNKNOWN = ML_TRAIN_LAYER_TYPE_UNKNOWN /**< Unknown */
 };
 
@@ -535,6 +536,14 @@ Identity(const std::vector<std::string> &properties = {}) {
   return createLayer(LayerType::LAYER_IDENTITY, properties);
 }
 
+/**
+ * @brief Helper function to create Upsample2d layer
+ */
+inline std::unique_ptr<Layer>
+Upsample2D(const std::vector<std::string> &properties = {}) {
+  return createLayer(LayerType::LAYER_UPSAMPLE2D, properties);
+}
+
 /**
  * @brief Helper function to create activation layer
  */

nntrainer/app_context.cpp

@@ -76,6 +76,7 @@
 #include <zoneout_lstmcell.h>
 
 #ifdef ENABLE_TFLITE_BACKBONE
+#include "upsample2d_layer.h"
 #include <tflite_layer.h>
 #endif
 
@@ -306,6 +307,8 @@ static void add_default_object(AppContext &ac) {
                      LayerType::LAYER_POSITIONAL_ENCODING);
   ac.registerFactory(nntrainer::createLayer<IdentityLayer>, IdentityLayer::type,
                      LayerType::LAYER_IDENTITY);
+  ac.registerFactory(nntrainer::createLayer<Upsample2dLayer>,
+                     Upsample2dLayer::type, LayerType::LAYER_UPSAMPLE2D);
 
 #ifdef ENABLE_NNSTREAMER_BACKBONE
   ac.registerFactory(nntrainer::createLayer<NNStreamerLayer>,

nntrainer/layers/common_properties.h

@@ -869,10 +869,9 @@ struct ActivationTypeInfo {
     Enum::ACT_GELU,    Enum::ACT_QUICK_GELU, Enum::ACT_NONE,
     Enum::ACT_UNKNOWN};
 
-  static constexpr const char *EnumStr[] = {"tanh",    "sigmoid",    "relu",
-                                            "softmax", "leaky_relu", "swish",
-                                            "gelu",    "quick_gelu", "none",
-                                            "unknown"};
+  static constexpr const char *EnumStr[] = {
+    "tanh",  "sigmoid", "relu",       "softmax", "leaky_relu",
+    "swish", "gelu",    "quick_gelu", "none",    "unknown"};
 };
 
 /**
@@ -1068,6 +1067,31 @@ class WeightRegularizer final : public BasicRegularizer {
   static constexpr const char *key = "weight_regularizer";
 };
 
+/**
+ * @brief     Enumeration of upsample type
+ * @todo Support torch and keras supported modes like bicubic
+ */
+struct UpsampleModeInfo {
+  /**
+   * @brief   Upsampling operation type class
+   */
+  enum class Enum { nearest, bilinear };
+  static constexpr std::initializer_list<Enum> EnumList = {Enum::nearest,
+                                                           Enum::bilinear};
+
+  static constexpr const char *EnumStr[] = {"nearest", "bilinear"};
+};
+
+/**
+ * @brief Upsample Type Enumeration Information
+ *
+ */
+class UpsampleMode final : public EnumProperty<UpsampleModeInfo> {
+public:
+  using prop_tag = enum_class_prop_tag;
+  static constexpr const char *key = "upsample";
+};
+
 /**
  * @brief     Enumeration of pooling type
  */

nntrainer/layers/meson.build

@@ -42,7 +42,8 @@ layer_sources = [
   'reshape_layer.cpp',
   'reduce_mean_layer.cpp',
   'positional_encoding_layer.cpp',
-  'identity_layer.cpp'
+  'identity_layer.cpp',
+  'upsample2d_layer.cpp'
 ]
 
 layer_headers = [

nntrainer/layers/upsample2d_layer.cpp

@@ -0,0 +1,199 @@
+// SPDX-License-Identifier: Apache-2.0
+/**
+ * Copyright (C) 2024 heka1024 <[email protected]>
+ *
+ * @file   upsample2d_layer.h
+ * @date   15 June 2024
+ * @brief  It is a implementation of upsample layer for given size and
+ * interpolation method
+ * @see    https://github.com/nnstreamer/nntrainer
+ * @author heka1024 <[email protected]>
+ * @bug    No known bugs except for NYI items
+ */
+
+#include <layer_context.h>
+#include <node_exporter.h>
+#include <upsample2d_layer.h>
+
+namespace nntrainer {
+static constexpr size_t SINGLE_INOUT_IDX = 0;
+
+Upsample2dLayer::Upsample2dLayer() :
+  Layer(),
+  upsample2d_props(props::UpsampleMode(),
+                   std::array<props::KernelSize, UPSAMPLE2D_DIM>()) {}
+
+void Upsample2dLayer::finalize(nntrainer::InitLayerContext &context) {
+  std::vector<nntrainer::TensorDim> dim = context.getInputDimensions();
+
+  const auto &kernel_size =
+    std::get<std::array<props::KernelSize, UPSAMPLE2D_DIM>>(upsample2d_props);
+
+  for (unsigned int i = 0; i < dim.size(); ++i) {
+    if (dim[i].getDataLen() == 0) {
+      throw std::invalid_argument("Input dimension is not set");
+    } else {
+      dim[i].channel(dim[i].channel());
+      dim[i].height(dim[i].height() * kernel_size[0]);
+      dim[i].width(dim[i].width() * kernel_size[1]);
+    }
+  }
+
+  context.setOutputDimensions(dim);
+}
+
+void Upsample2dLayer::forwarding(nntrainer::RunLayerContext &context,
+                                 bool training) {
+  nntrainer::Tensor &in = context.getInput(SINGLE_INOUT_IDX);
+  nntrainer::Tensor &out = context.getOutput(SINGLE_INOUT_IDX);
+
+  const auto &upsampling_type =
+    std::get<props::UpsampleMode>(upsample2d_props).get();
+  const auto &kernel_size =
+    std::get<std::array<props::KernelSize, UPSAMPLE2D_DIM>>(upsample2d_props);
+
+  switch (upsampling_type) {
+  case props::UpsampleModeInfo::Enum::nearest:
+    for (int b = 0; b < (int)out.batch(); b++) {
+      for (int c = 0; c < (int)out.channel(); c++) {
+        for (int h = 0; h < (int)out.height(); h++) {
+          for (int w = 0; w < (int)out.width(); w++) {
+            out.setValue(
+              b, c, h, w,
+              in.getValue(b, c, h / kernel_size[0], w / kernel_size[1]));
+          }
+        }
+      }
+    }
+    break;
+  case props::UpsampleModeInfo::Enum::bilinear: {
+    float scale_h = kernel_size[0];
+    float scale_w = kernel_size[1];
+
+    for (int b = 0; b < (int)out.batch(); b++) {
+      for (int c = 0; c < (int)out.channel(); c++) {
+        for (int h = 0; h < (int)out.height(); h++) {
+          for (int w = 0; w < (int)out.width(); w++) {
+            float x_in = (w + 0.5f) / scale_w - 0.5f;
+            float y_in = (h + 0.5f) / scale_h - 0.5f;
+
+            if (x_in < 0) {
+              x_in = 0.0f;
+            }
+            if (y_in < 0) {
+              y_in = 0.0f;
+            }
+
+            int x0 = static_cast<int>(floor(x_in));
+            int y0 = static_cast<int>(floor(y_in));
+            int x1 = std::min(x0 + 1, (int)in.width() - 1);
+            int y1 = std::min(y0 + 1, (int)in.height() - 1);
+
+            float dx = x_in - x0;
+            float dy = y_in - y0;
+
+            float top = (1.0f - dx) * in.getValue(b, c, y1, x0) +
+                        dx * in.getValue(b, c, y1, x1);
+            float bottom = (1.0f - dx) * in.getValue(b, c, y0, x0) +
+                           dx * in.getValue(b, c, y0, x1);
+            float v = (1.0f - dy) * bottom + dy * top;
+            out.setValue(b, c, h, w, v);
+          }
+        }
+      }
+    }
+  } break;
+  default:
+    throw std::runtime_error("Error: Unknown Upsample Mode Type");
+  }
+}
+
+void Upsample2dLayer::calcDerivative(nntrainer::RunLayerContext &context) {
+  const nntrainer::Tensor &derivative_ =
+    context.getIncomingDerivative(SINGLE_INOUT_IDX);
+
+  nntrainer::Tensor &dx = context.getOutgoingDerivative(SINGLE_INOUT_IDX);
+
+  const auto &kernel_size =
+    std::get<std::array<props::KernelSize, UPSAMPLE2D_DIM>>(upsample2d_props);
+  const auto &upsampling_type =
+    std::get<props::UpsampleMode>(upsample2d_props).get();
+
+  switch (upsampling_type) {
+  case props::UpsampleModeInfo::Enum::nearest: {
+    float val = 0;
+    for (int b = 0; b < (int)derivative_.batch(); b++) {
+      for (int c = 0; c < (int)derivative_.channel(); c++) {
+        for (int h = 0; h < (int)derivative_.height(); h++) {
+          for (int w = 0; w < (int)derivative_.width(); w++) {
+            if (h % kernel_size[0] == 0 && w % kernel_size[1] == 0) {
+              dx.setValue(b, c, h / kernel_size[0], w / kernel_size[1], 0);
+            }
+
+            val = dx.getValue(b, c, h / kernel_size[0], w / kernel_size[1]) +
+                  derivative_.getValue(b, c, h, w);
+            dx.setValue(b, c, h / kernel_size[0], w / kernel_size[1], val);
+          }
+        }
+      }
+    }
+  } break;
+  case props::UpsampleModeInfo::Enum::bilinear: {
+    dx.setZero();
+
+    int input_height = dx.height();
+    int input_width = dx.width();
+
+    for (int b = 0; b < (int)derivative_.batch(); b++) {
+      for (int c = 0; c < (int)derivative_.channel(); c++) {
+        for (int h = 0; h < (int)derivative_.height(); h++) {
+          for (int w = 0; w < (int)derivative_.width(); w++) {
+            float in_h = (h + 0.5f) / kernel_size[0] - 0.5f;
+            float in_w = (w + 0.5f) / kernel_size[1] - 0.5f;
+
+            if (in_h < 0) {
+              in_h = 0.0f;
+            }
+            if (in_w < 0) {
+              in_w = 0.0f;
+            }
+
+            int y0 = static_cast<int>(floor(in_h));
+            int x0 = static_cast<int>(floor(in_w));
+            int y1 = std::min(y0 + 1, input_height - 1);
+            int x1 = std::min(x0 + 1, input_width - 1);
+
+            float dx_ = (in_w - x0); // Due to name conflict with dx
+            float dy_ = (in_h - y0);
+
+            float top_left_weight = (1.0 - dy_) * (1.0 - dx_);
+            float top_right_weight = (1.0 - dy_) * dx_;
+            float bottom_left_weight = dy_ * (1.0 - dx_);
+            float bottom_right_weight = dy_ * dx_;
+
+            float grad = derivative_.getValue(b, c, h, w);
+
+            dx.addValue(b, c, y0, x0, top_left_weight * grad, 1.0f);
+            dx.addValue(b, c, y0, x1, top_right_weight * grad, 1.0f);
+            dx.addValue(b, c, y1, x0, bottom_left_weight * grad, 1.0f);
+            dx.addValue(b, c, y1, x1, bottom_right_weight * grad, 1.0f);
+          }
+        }
+      }
+    }
+  } break;
+  default:
+    throw std::runtime_error("Error: Unknown Upsample Mode Type");
+  }
+}
+
+void Upsample2dLayer::setProperty(const std::vector<std::string> &values) {
+  auto remain_props = loadProperties(values, upsample2d_props);
+
+  if (!remain_props.empty()) {
+    std::string msg = "[Upsample2dLayer] Unknown properties set with count" +
+                      std::to_string(values.size());
+    throw exception::not_supported(msg);
+  }
+}
+} // namespace nntrainer

nntrainer/layers/upsample2d_layer.h

@@ -0,0 +1,90 @@
+// SPDX-License-Identifier: Apache-2.0
+/**
+ * Copyright (C) 2024 heka1024 <[email protected]>
+ *
+ * @file   upsample2d_layer.h
+ * @date   15 June 2024
+ * @brief  This is Upsample2d Layer Class of Neural Network
+ * @see    https://github.com/nnstreamer/nntrainer
+ * @author heka1024 <[email protected]>
+ * @bug    No known bugs except for NYI items
+ *
+ */
+
+#ifndef __UPSAMPLE2D_LAYER_H__
+#define __UPSAMPLE2D_LAYER_H__
+#ifdef __cplusplus
+
+#include <common_properties.h>
+#include <layer_impl.h>
+
+#include <node_exporter.h>
+
+namespace nntrainer {
+
+constexpr const unsigned int UPSAMPLE2D_DIM = 2;
+
+/**
+ * @class   Upsample2dLayer
+ * @brief   Upsamle 2d layer
+ */
+class Upsample2dLayer : public Layer {
+public:
+  /**
+   * @brief Construct a new Upsample layer object
+   *
+   */
+  Upsample2dLayer();
+
+  /**
+   * @brief Destroy the Upsample layer object
+   *
+   */
+  ~Upsample2dLayer() {}
+
+  /**
+   * @copydoc Layer::finalize(InitLayerContext &context)
+   */
+  void finalize(nntrainer::InitLayerContext &context) override;
+
+  /**
+   * @copydoc Layer::forwarding(RunLayerContext &context, bool training)
+   */
+  void forwarding(nntrainer::RunLayerContext &context, bool training) override;
+
+  /**
+   * @copydoc Layer::calcDerivative(RunLayerContext &context)
+   */
+  void calcDerivative(nntrainer::RunLayerContext &context) override;
+
+  /**
+   * @copydoc bool supportBackwarding() const
+   */
+  bool supportBackwarding() const override { return true; };
+
+  /**
+   * @copydoc Layer::exportTo(Exporter &exporter, ExportMethods method)
+   */
+  void exportTo(nntrainer::Exporter &exporter,
+                const ml::train::ExportMethods &method) const override{};
+
+  /**
+   * @copydoc Layer::getType()
+   */
+  const std::string getType() const override { return Upsample2dLayer::type; };
+
+  /**
+   * @copydoc Layer::setProperty(const std::vector<std::string> &values)
+   */
+  void setProperty(const std::vector<std::string> &values) override;
+
+  inline static const std::string type = "upsample2d";
+
+private:
+  std::tuple<props::UpsampleMode, std::array<props::KernelSize, UPSAMPLE2D_DIM>>
+    upsample2d_props; /* mode, size of kernel */
+};
+} // namespace nntrainer
+
+#endif /* __cplusplus */
+#endif /* __UPSAMPLE2D_LAYER_H__ */