[GitHub] sandeep-krishnamurthy commented on a change in pull request #13614: Make to_tensor and normalize to accept 3D or 4D tensor inputs

[GitBox]

sandeep-krishnamurthy commented on a change in pull request #13614: Make 
to_tensor and normalize to accept 3D or 4D tensor inputs
URL: https://github.com/apache/incubator-mxnet/pull/13614#discussion_r240819963
 
 

 ##########
 File path: src/operator/image/image_random-inl.h
 ##########
 @@ -123,28 +159,50 @@ inline bool NormalizeShape(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
+void NormalizeImpl(const std::vector<TBlob> &inputs,
+                          const std::vector<TBlob> &outputs,
+                          const NormalizeParam &param,
+                          const int length,
+                          const int channel,
+                          const int step = 0) {
+    MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      DType* input = inputs[0].dptr<DType>();
+      DType* output = outputs[0].dptr<DType>();
+
+      for (int i = 0; i < channel; ++i) {
+        DType mean = param.mean[param.mean.ndim() > 1 ? i : 0];
+        DType std_dev = param.std[param.std.ndim() > 1 ? i : 0];
+        for (int j = 0; j < length; ++j) {
+          output[step + i*length + j] = (input[step + i*length + j] - mean) / 
std_dev;
+        }
+      }
+    });
+}
+
 void Normalize(const nnvm::NodeAttrs &attrs,
                       const OpContext &ctx,
                       const std::vector<TBlob> &inputs,
                       const std::vector<OpReqType> &req,
                       const std::vector<TBlob> &outputs) {
   const NormalizeParam &param = nnvm::get<NormalizeParam>(attrs.parsed);
 
-  int nchannels = inputs[0].shape_[0];
-  int length = inputs[0].shape_[1] * inputs[0].shape_[2];
-
-  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
-    DType* input = inputs[0].dptr<DType>();
-    DType* output = outputs[0].dptr<DType>();
-
-    for (int i = 0; i < nchannels; ++i) {
-      DType mean = param.mean[param.mean.ndim() > 1 ? i : 0];
-      DType std = param.std[param.std.ndim() > 1 ? i : 0];
-      for (int j = 0; j < length; ++j) {
-        output[i*length + j] = (input[i*length + j] - mean) / std;
-      }
+  // 3D input (c, h, w)
+  if (inputs[0].ndim() == 3) {
+    const int length = inputs[0].shape_[1] * inputs[0].shape_[2];
+    const int channel = inputs[0].shape_[0];
+    NormalizeImpl(inputs, outputs, param, length, channel);
+  } else if (inputs[0].ndim() == 4) {
+    // 4D input (n, c, h, w)
+    const int batch_size = inputs[0].shape_[0];
+    const int length = inputs[0].shape_[2] * inputs[0].shape_[3];
+    const int channel = inputs[0].shape_[1];
+    const int step = channel*length;
 
 Review comment:
   Done

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