[bug] Fix zero division error

* add edge case handling in cosine_similarity function: in case of zero-valued tensor

Signed-off-by: skykongkong8 <ss.kong@samsung.com>

diff --git a/nntrainer/layers/#concat_layer.cpp# b/nntrainer/layers/#concat_layer.cpp#
deleted file mode 100644 (file)
index 3288448..0000000
--- a/nntrainer/layers/#concat_layer.cpp#
+++ /dev/null
@@ -1,199 +0,0 @@
-meson// SPDX-License-Identifier: Apache-2.0
-/**
- * Copyright (C) 2020 Jijoong Moon <jijoong.moon@samsung.com>
- *
- * @file   concat_layer.cpp
- * @date   27 Oct 2020
- * @see    https://github.com/nnstreamer/nntrainer
- * @author Jijoong Moon <jijoong.moon@samsung.com>
- * @bug    No known bugs except for NYI items
- * @brief  This is Concat Layer Class for Neural Network
- *
- * @todo merge concat and split layer to a common implementation
- */
-
-#include <concat_layer.h>
-#include <cstring>
-#include <layer_context.h>
-#include <nntrainer_error.h>
-#include <nntrainer_log.h>
-#include <node_exporter.h>
-#include <tensor_dim.h>
-#include <util_func.h>
-#include <iostream>
-
-namespace nntrainer {
-ConcatLayer::ConcatLayer() : Layer(), leading_helper_dim(1) {}
-
-static constexpr size_t SINGLE_INOUT_IDX = 0;
-
-void ConcatLayer::finalize(InitLayerContext &context) {
-  auto &concat_dimension_prop = std::get<props::ConcatDimension>(concat_props);
-  /** for backward compatibility, default concat dimension will be channel */
-  /// @todo this is hacky way to force concat dimension to width if channel
-  /// dimension is taken, this is because recurrent realizer, return sequence
-  /// exploits concat layer but have no control over where to stack/axis
-  unsigned int concat_dimension =
-    context.getInputDimensions().front().channel() > 1 ? 3 : 1;
-  if (!concat_dimension_prop.empty())
-    concat_dimension = concat_dimension_prop.get();
-
-  /**
-   * The concat is only done along the axis dimension.
-   * For example, consider 2 inputs a, b with dimensions [b,c,h,w] each
-   * 1. concat_dimension = 1, output_dim = [b,c_a+c_b,h,w]
-   * 2. concat_dimension = 2, output_dim = [b,c,h_a+h_b,w]
-   * 3. concat_dimension = 3, output_dim = [b,c,h,w_a+w_b]
-   */
-  auto const &input_dims = context.getInputDimensions();
-  const TensorDim &input_dim_0 = input_dims[SINGLE_INOUT_IDX];
-  unsigned int concat_dim_val = input_dim_0.getTensorDim(concat_dimension);
-
-  for (unsigned int idx = 1; idx < input_dims.size(); ++idx) {
-    const TensorDim &dim = input_dims[idx];
-
-    for (unsigned int i = 0; i < ml::train::TensorDim::getNumDim(); ++i) {
-      if (i == concat_dimension)
-        continue;
-      NNTR_THROW_IF(input_dim_0[i] != dim[i], std::runtime_error)
-        << "Error: concat layer requires same shape from all input layers "
-           "along non-concat dimension";
-    }
-    concat_dim_val += dim[concat_dimension];
-  }
-
-  TensorDim output_dim = input_dim_0;
-  output_dim.setTensorDim(concat_dimension, concat_dim_val);
-
-  context.setOutputDimensions({output_dim});
-
-  /**
-   * Setup output_reshape_helper to which output will be reshaped in forwarding
-   * to facilitate easier processing.
-   *
-   * The helper shape consolidates all the dimensions before the axis
-   * together and all the dimensions after the axis to faciliate
-   * easier splitting of the data.
-   */
-  leading_helper_dim = 1;
-  output_reshape_helper.channel(1);
-  output_reshape_helper.height(1);
-  output_reshape_helper.width(1);
-  for (unsigned int idx = 1; idx < concat_dimension; ++idx) {
-    leading_helper_dim *= output_dim.getTensorDim(idx);
-  }
-
-  output_reshape_helper.height(output_dim.getTensorDim(concat_dimension));
-
-  for (unsigned int idx = concat_dimension + 1;
-       idx < ml::train::TensorDim::getNumDim(); ++idx) {
-    output_reshape_helper.width(output_reshape_helper.width() *
-                                output_dim.getTensorDim(idx));
-  }
-
-  /**
-   * Setup input_reshape_helper to which inputs will be reshaped in forwarding
-   * to facilitate easier processing.
-   */
-  input_reshape_helper.resize(input_dims.size());
-  for (unsigned int idx = 0; idx < input_reshape_helper.size(); idx++) {
-    input_reshape_helper[idx] = output_reshape_helper;
-    input_reshape_helper[idx].height(
-      input_dims[idx].getTensorDim(concat_dimension));
-  }
-
-  setBatch(input_dims[SINGLE_INOUT_IDX].batch());
-}
-
-void ConcatLayer::forwarding(RunLayerContext &context, bool training) {
-  /**
-   * @todo avoid copy by creating input here as a shared_tensor of the output
-   * here and then this layer can be in_place as well
-   */
-  Tensor &output = context.getOutput(SINGLE_INOUT_IDX);
-
-  const TensorDim out_dim = output.getDim();
-  output.reshape(output_reshape_helper);
-  unsigned int output_height_offset = 0;
-  unsigned int data_copy_size = output_reshape_helper.width();
-
-  for (unsigned int idx = 0; idx < context.getNumInputs(); idx++) {
-    Tensor &input = context.getInput(idx);
-    const TensorDim in_dim = input.getDim();
-    auto const &irh = input_reshape_helper[idx];
-    input.reshape(irh);
-
-    /** loop over the dimensions before the concat dimension */
-    for (unsigned int batch = 0; batch < output.batch(); batch++) {
-      /** loop over the concat dimension itself */
-      for (unsigned int count = 0; count < irh.height(); count++) {
-        Tensor dest_tensor = Tensor::Map(
-          output.getAddress(batch, 0, output_height_offset + count, 0),
-          data_copy_size * sizeof(float), {1, 1, 1, data_copy_size});
-        const Tensor source_tensor = Tensor::Map(
-          input.getAddress(batch, 0, count, 0), data_copy_size * sizeof(float),
-          {1, 1, 1, data_copy_size});
-        dest_tensor.copy(source_tensor);
-      }
-    }
-
-    input.reshape(in_dim);
-    output_height_offset += irh.height();
-  }
-
-  output.reshape(out_dim);
-}
-
-void ConcatLayer::calcDerivative(RunLayerContext &context) {
-  /**
-   * @todo avoid copy by creating input here as a shared_tensor of the output
-   * here and then this layer can be in_place as well
-   */
-  Tensor output = context.getIncomingDerivative(SINGLE_INOUT_IDX);
-
-  output.reshape(output_reshape_helper);
-  unsigned int output_height_offset = 0;
-  unsigned int data_copy_size = output_reshape_helper.width();
-
-  for (unsigned int idx = 0; idx < context.getNumInputs(); idx++) {
-    Tensor &input = context.getOutgoingDerivative(idx);
-    const TensorDim in_dim = input.getDim();
-    auto const &irh = input_reshape_helper[idx];
-    input.reshape(irh);
-
-    /** loop over the dimensions before the concat dimension */
-    for (unsigned int batch = 0; batch < output.batch(); batch++) {
-      /** loop over the concat dimension itself */
-      for (unsigned int count = 0; count < irh.height(); count++) {
-        const Tensor source_tensor = Tensor::Map(
-          output.getAddress(batch, 0, output_height_offset + count, 0),
-          data_copy_size * sizeof(float), {1, 1, 1, data_copy_size});
-        Tensor dest_tensor = Tensor::Map(input.getAddress(batch, 0, count, 0),
-                                         data_copy_size * sizeof(float),
-                                         {1, 1, 1, data_copy_size});
-        dest_tensor.copy(source_tensor);
-      }
-    }
-
-    input.reshape(in_dim);
-    output_height_offset += irh.height();
-  }
-}
-
-void ConcatLayer::setProperty(const std::vector<std::string> &values) {
-  for(auto s: values)
-    std::cout << s<< std::endl;
-  auto remain_props = loadProperties(values, concat_props);
-
-  NNTR_THROW_IF(!remain_props.empty(), std::invalid_argument)
-    << "[ConcatLayer] Unknown Layer Properties count " +
-         std::to_string(values.size());
-}
-
-void ConcatLayer::exportTo(Exporter &exporter,
-                           const ml::train::ExportMethods &method) const {
-  Layer::exportTo(exporter, method);
-  exporter.saveResult(concat_props, method, this);
-}
-
-} /* namespace nntrainer */

diff --git a/nntrainer/layers/fc_layer.cpp b/nntrainer/layers/fc_layer.cpp
index 61ba0e4..9a87e04 100644 (file)
--- a/nntrainer/layers/fc_layer.cpp
+++ b/nntrainer/layers/fc_layer.cpp
@@ -66,7 +66,7 @@ void FullyConnectedLayer::finalize(InitLayerContext &context) {
   context.setEffDimFlagInputDimension(0, 0b1001);
   context.setDynDimFlagInputDimension(0, 0b1000);
 
-  bool is_nchw = (getTensorType() == Tformat::NCHW);
+  bool is_nchw = (context.getFormat() == Tformat::NCHW);
   /** set output dimensions */
   auto const &in_dim = context.getInputDimensions()[0];
   output_dims[0] = in_dim;

diff --git a/test/include/nntrainer_test_util.h b/test/include/nntrainer_test_util.h
index d32d978..9ecef18 100644 (file)
--- a/test/include/nntrainer_test_util.h
+++ b/test/include/nntrainer_test_util.h
@@ -270,6 +270,9 @@ double cosine_similarity(Ta *A /*Predict*/, Tb *B /*Reference */,
     denom_b += ref * ref;
   }
 
+  if (sqrt(denom_a) == 0 && sqrt(denom_b) == 0)
+    return 1;
+
   double cosine_sim = dot / (sqrt(denom_a) * sqrt(denom_b));
   return cosine_sim;
 }
@@ -279,7 +282,7 @@ float mse(Ta *A /* Predicted */, Tb *B /* Reference */, uint32_t size) {
   Ta pred;
   Tb ref;
   float mse_error = 0;
-  for (int i = 0; i < size; i++) {
+  for (uint32_t i = 0; i < size; i++) {
     pred = A[i];
     ref = B[i];
     float diff = pred - ref;