Imported Upstream version 1.8.0

[platform/core/ml/nnfw.git] / compiler / luci-value-test / tester / src / EvalTester.cpp

diff --git a/compiler/luci-value-test/tester/src/EvalTester.cpp b/compiler/luci-value-test/tester/src/EvalTester.cpp
index 58f62f5..09eef22 100644 (file)
--- a/compiler/luci-value-test/tester/src/EvalTester.cpp
+++ b/compiler/luci-value-test/tester/src/EvalTester.cpp
@@ -129,7 +129,7 @@ int entry(int argc, char **argv)
   assert(num_inputs == input_nodes.size());
   for (int32_t i = 0; i < num_inputs; i++)
   {
-    const auto *input_node = dynamic_cast<const luci::CircleInput *>(input_nodes[i]);
+    const auto *input_node = loco::must_cast<const luci::CircleInput *>(input_nodes[i]);
     std::vector<char> input_data(getTensorSize(input_node));
     readDataFromFile(std::string(input_prefix) + std::to_string(i), input_data.data(),
                      input_data.size());
@@ -141,24 +141,35 @@ int entry(int argc, char **argv)
 
   // Get output.
   const auto output_nodes = loco::output_nodes(module->graph());
-  // TODO: Support multiple outputs
-  assert(output_nodes.size() == 1);
-  const auto *output_node = dynamic_cast<const luci::CircleOutput *>(output_nodes[0]);
-  std::vector<char> output_data(getTensorSize(output_node));
-  interpreter.readOutputTensor(output_node, output_data.data(), output_data.size());
-
-  // Output data is written in ${output_file}
-  // (ex: Add.circle.output)
-  // Output shape is written in ${output_file}.shape
-  // (ex: Add.circle.output.shape)
-  // TODO: Use HDF5 file format
-  writeDataToFile(output_file, output_data.data(), output_data.size());
-  auto shape_str = std::to_string(output_node->dim(0).value());
-  for (int i = 1; i < output_node->rank(); i++)
+  for (int i = 0; i < module->graph()->outputs()->size(); i++)
   {
-    shape_str += ",";
-    shape_str += std::to_string(output_node->dim(i).value());
+    const auto *output_node = loco::must_cast<const luci::CircleOutput *>(output_nodes[i]);
+    std::vector<char> output_data(getTensorSize(output_node));
+    interpreter.readOutputTensor(output_node, output_data.data(), output_data.size());
+
+    // Output data is written in ${output_file}
+    // (ex: Add.circle.output0)
+    // Output shape is written in ${output_file}.shape
+    // (ex: Add.circle.output0.shape)
+    writeDataToFile(std::string(output_file) + std::to_string(i), output_data.data(),
+                    output_data.size());
+    // In case of Tensor output is Scalar value.
+    // The output tensor with rank 0 is treated as a scalar with shape (1)
+    if (output_node->rank() == 0)
+    {
+      writeDataToFile(std::string(output_file) + std::to_string(i) + ".shape", "1", 1);
+    }
+    else
+    {
+      auto shape_str = std::to_string(output_node->dim(0).value());
+      for (int j = 1; j < output_node->rank(); j++)
+      {
+        shape_str += ",";
+        shape_str += std::to_string(output_node->dim(j).value());
+      }
+      writeDataToFile(std::string(output_file) + std::to_string(i) + ".shape", shape_str.c_str(),
+                      shape_str.size());
+    }
   }
-  writeDataToFile(std::string(output_file) + ".shape", shape_str.c_str(), shape_str.size());
   return EXIT_SUCCESS;
 }