1 files changed, 99 insertions, 0 deletions
diff --git a/compute/cker/include/cker/operation/InstanceNorm.h b/compute/cker/include/cker/operation/InstanceNorm.h
new file mode 100644
index 000000000..794dcebc8
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+++ b/compute/cker/include/cker/operation/InstanceNorm.h
@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __NNFW_CKER_INSTANCE_NORM_H__
+#define __NNFW_CKER_INSTANCE_NORM_H__
+
+#include "cker/Shape.h"
+#include "cker/Types.h"
+#include "cker/Utils.h"
+
+#include <cmath>
+
+namespace nnfw
+{
+namespace cker
+{
+
+struct InstanceNormParams
+{
+  float epsilon;
+  float float_activation_min;
+  float float_activation_max;
+};
+
+inline void InstanceNorm(const InstanceNormParams &params, const Shape &input_shape,
+                         const float *input_data, const Shape &gamma_shape, const float *gamma_data,
+                         const Shape &beta_shape, const float *beta_data, const Shape &output_shape,
+                         float *output_data)
+{
+  const int32_t batches = MatchingDim(input_shape, 0, output_shape, 0);
+  const int32_t heights = MatchingDim(input_shape, 1, output_shape, 1);
+  const int32_t widths = MatchingDim(input_shape, 2, output_shape, 2);
+  const int32_t channels = MatchingDim(input_shape, 3, output_shape, 3);
+  const float output_activation_min = params.float_activation_min;
+  const float output_activation_max = params.float_activation_max;
+
+  UNUSED_RELEASE(gamma_shape);
+  UNUSED_RELEASE(beta_shape);
+  assert(output_activation_min <= output_activation_max);
+
+  for (int32_t batch = 0; batch < batches; batch++)
+  {
+    for (int32_t channel = 0; channel < channels; channel++)
+    {
+      double sum = 0.0f;
+      double square_sum = 0.0f;
+      int32_t size = heights * widths;
+
+      for (int32_t height = 0; height < heights; height++)
+      {
+        for (int32_t width = 0; width < widths; width++)
+        {
+          double input_val = input_data[Offset(input_shape, batch, height, width, channel)];
+          sum += input_val;
+          square_sum += (input_val * input_val);
+        }
+      }
+
+      double mean = sum / size;
+      double var = square_sum / size - mean * mean;
+
+      double gamma = gamma_data[channel];
+      double beta = beta_data[channel];
+
+      double a = gamma / (std::sqrt(var + params.epsilon));
+      double b = -mean * a + beta;
+
+      for (int32_t height = 0; height < heights; height++)
+      {
+        for (int32_t width = 0; width < widths; width++)
+        {
+          double input_value = input_data[Offset(output_shape, batch, height, width, channel)];
+          double output_value = input_value * a + b;
+          output_data[Offset(output_shape, batch, height, width, channel)] =
+              ActivationFunctionWithMinMax((float)output_value, output_activation_min,
+                                           output_activation_max);
+        }
+      }
+    }
+  }
+}
+
+} // namespace cker
+} // namespace nnfw
+
+#endif // __NNFW_CKER_INSTANCE_NORM_H__