implement instancenorm

src/CMakeLists.txt

@@ -141,6 +141,7 @@ ncnn_add_layer(DetectionOutput)
 ncnn_add_layer(Interp)
 ncnn_add_layer(DeconvolutionDepthWise)
 ncnn_add_layer(ShuffleChannel)
+ncnn_add_layer(InstanceNorm)
 
 add_library(ncnn STATIC ${ncnn_SRCS})
 

src/layer/instancenorm.cpp

@@ -0,0 +1,88 @@
+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
+//
+// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+// in compliance with the License. You may obtain a copy of the License at
+//
+// https://opensource.org/licenses/BSD-3-Clause
+//
+// 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.
+
+#include "instancenorm.h"
+#include <math.h>
+
+namespace ncnn {
+
+DEFINE_LAYER_CREATOR(InstanceNorm)
+
+InstanceNorm::InstanceNorm()
+{
+    one_blob_only = true;
+    support_inplace = true;
+}
+
+int InstanceNorm::load_param(const ParamDict& pd)
+{
+    channels = pd.get(0, 0);
+    eps = pd.get(1, 0.001f);
+
+    return 0;
+}
+
+int InstanceNorm::load_model(const ModelBin& mb)
+{
+    gamma_data = mb.load(channels, 1);
+    if (gamma_data.empty())
+        return -100;
+
+    beta_data = mb.load(channels, 1);
+    if (beta_data.empty())
+        return -100;
+
+    return 0;
+}
+
+int InstanceNorm::forward_inplace(Mat& bottom_top_blob) const
+{
+    // x = (x - mean) / (sqrt(var) + eps) * gamma + beta
+
+    int w = bottom_top_blob.w;
+    int h = bottom_top_blob.h;
+    int size = w * h;
+
+    #pragma omp parallel for
+    for (int q=0; q<channels; q++)
+    {
+        float* ptr = bottom_top_blob.channel(q);
+
+        // mean and var
+        float sum = 0.f;
+        float sqsum = 0.f;
+        for (int i=0; i<size; i++)
+        {
+            sum += ptr[i];
+            sqsum += ptr[i] * ptr[i];
+        }
+        float mean = sum / size;
+        float var = sqsum / size - mean * mean;
+
+        float gamma = gamma_data[q];
+        float beta = beta_data[q];
+
+        float a = gamma / (sqrt(var) + eps);
+        float b = - mean * a + beta;
+
+        for (int i=0; i<size; i++)
+        {
+            ptr[i] = ptr[i] * a + b;
+        }
+    }
+
+    return 0;
+}
+
+} // namespace ncnn

src/layer/instancenorm.h

@@ -0,0 +1,45 @@
+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
+//
+// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+// in compliance with the License. You may obtain a copy of the License at
+//
+// https://opensource.org/licenses/BSD-3-Clause
+//
+// 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 LAYER_INSTANCENORM_H
+#define LAYER_INSTANCENORM_H
+
+#include "layer.h"
+
+namespace ncnn {
+
+class InstanceNorm : public Layer
+{
+public:
+    InstanceNorm();
+
+    virtual int load_param(const ParamDict& pd);
+
+    virtual int load_model(const ModelBin& mb);
+
+    virtual int forward_inplace(Mat& bottom_top_blob) const;
+
+public:
+    // param
+    int channels;
+    float eps;
+
+    // model
+    Mat gamma_data;
+    Mat beta_data;
+};
+
+} // namespace ncnn
+
+#endif // LAYER_INSTANCENORM_H