Fix issue with big endian handling

Signed-off-by: Stefan Weil <[email protected]>

Author: stweil

src/lstm/input.cpp

@@ -42,14 +42,12 @@ Input::~Input() {
 
 // Writes to the given file. Returns false in case of error.
 bool Input::Serialize(TFile* fp) const {
-  if (!Network::Serialize(fp)) return false;
-  if (fp->FWrite(&shape_, sizeof(shape_), 1) != 1) return false;
-  return true;
+  return Network::Serialize(fp) && shape_.Serialize(fp);
 }
 
 // Reads from the given file. Returns false in case of error.
 bool Input::DeSerialize(TFile* fp) {
-  return fp->FReadEndian(&shape_, sizeof(shape_), 1) == 1;
+  return shape_.DeSerialize(fp);
 }
 
 // Returns an integer reduction factor that the network applies to the

src/lstm/static_shape.h

@@ -59,18 +59,40 @@ class StaticShape {
             height_, width_, depth_, loss_type_);
   }
 
+  bool DeSerialize(TFile *fp) {
+    int32_t tmp;
+    bool result =
+      fp->FReadEndian(&batch_, sizeof(batch_), 1) == 1 &&
+      fp->FReadEndian(&height_, sizeof(height_), 1) == 1 &&
+      fp->FReadEndian(&width_, sizeof(width_), 1) == 1 &&
+      fp->FReadEndian(&depth_, sizeof(depth_), 1) == 1 &&
+      fp->FReadEndian(&tmp, sizeof(tmp), 1) == 1;
+    loss_type_ = static_cast<LossType>(tmp);
+    return result;
+  }
+
+  bool Serialize(TFile *fp) const {
+    int32_t tmp = loss_type_;
+    return
+      fp->FWrite(&batch_, sizeof(batch_), 1) == 1 &&
+      fp->FWrite(&height_, sizeof(height_), 1) == 1 &&
+      fp->FWrite(&width_, sizeof(width_), 1) == 1 &&
+      fp->FWrite(&depth_, sizeof(depth_), 1) == 1 &&
+      fp->FWrite(&tmp, sizeof(tmp), 1) == 1;
+  }
+
  private:
   // Size of the 4-D tensor input/output to a network. A value of zero is
   // allowed for all except depth_ and means to be determined at runtime, and
   // regarded as variable.
   // Number of elements in a batch, or number of frames in a video stream.
-  int batch_;
+  int32_t batch_;
   // Height of the image.
-  int height_;
+  int32_t height_;
   // Width of the image.
-  int width_;
+  int32_t width_;
   // Depth of the image. (Number of "nodes").
-  int depth_;
+  int32_t depth_;
   // How to train/interpret the output.
   LossType loss_type_;
 };