Imported Upstream version 1.11.0upstream/1.11.0

1 files changed, 241 insertions, 0 deletions

diff --git a/tests/tools/nnpackage_run/src/h5formatter.cc b/tests/tools/nnpackage_run/src/h5formatter.cc
new file mode 100644
index 000000000..3929c8d90
--- /dev/null
+++ b/tests/tools/nnpackage_run/src/h5formatter.cc
@@ -0,0 +1,241 @@
+/*
+ * Copyright (c) 2019 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.
+ */
+
+#include "h5formatter.h"
+#include "nnfw.h"
+#include "nnfw_util.h"
+
+#include <iostream>
+#include <stdexcept>
+#include <H5Cpp.h>
+
+namespace
+{
+nnpkg_run::TensorShape getShape(H5::DataSet &data_set)
+{
+  std::vector<hsize_t> h5_shape; // hsize_t is unsigned long long
+  H5::DataSpace data_space = data_set.getSpace();
+  int rank = data_space.getSimpleExtentNdims();
+  h5_shape.resize(rank);
+
+  // read shape info from H5 file
+  data_space.getSimpleExtentDims(h5_shape.data(), NULL);
+
+  nnpkg_run::TensorShape shape;
+  for (auto dim : h5_shape)
+    shape.emplace_back(static_cast<int>(dim));
+
+  return shape;
+}
+} // namespace
+
+namespace nnpkg_run
+{
+static const char *h5_value_grpname = "value";
+
+std::vector<TensorShape> H5Formatter::readTensorShapes(const std::string &filename)
+{
+  uint32_t num_inputs;
+  NNPR_ENSURE_STATUS(nnfw_input_size(session_, &num_inputs));
+  std::vector<TensorShape> tensor_shapes;
+
+  try
+  {
+    H5::Exception::dontPrint();
+
+    H5::H5File file(filename, H5F_ACC_RDONLY);
+    H5::Group value_group = file.openGroup(h5_value_grpname);
+
+    // Constraints: if there are n data set names, they should be unique and
+    //              one of [ "0", "1", .. , "n-1" ]
+    for (uint32_t i = 0; i < num_inputs; ++i)
+    {
+      H5::DataSet data_set = value_group.openDataSet(std::to_string(i));
+      H5::DataType type = data_set.getDataType();
+      auto shape = getShape(data_set);
+
+      tensor_shapes.emplace_back(shape);
+    }
+
+    return tensor_shapes;
+  }
+  catch (const H5::Exception &e)
+  {
+    H5::Exception::printErrorStack();
+    std::exit(-1);
+  }
+  catch (const std::exception &e)
+  {
+    std::cerr << e.what() << std::endl;
+    std::exit(-1);
+  }
+}
+
+void H5Formatter::loadInputs(const std::string &filename, std::vector<Allocation> &inputs)
+{
+  uint32_t num_inputs;
+  NNPR_ENSURE_STATUS(nnfw_input_size(session_, &num_inputs));
+  try
+  {
+    // Turn off the automatic error printing.
+    H5::Exception::dontPrint();
+
+    H5::H5File file(filename, H5F_ACC_RDONLY);
+    H5::Group value_group = file.openGroup(h5_value_grpname);
+    for (uint32_t i = 0; i < num_inputs; ++i)
+    {
+      nnfw_tensorinfo ti;
+      NNPR_ENSURE_STATUS(nnfw_input_tensorinfo(session_, i, &ti));
+
+      // TODO Add Assert(nnfw shape, h5 file shape size)
+
+      // allocate memory for data
+      auto bufsz = bufsize_for(&ti);
+      inputs[i].alloc(bufsz);
+
+      H5::DataSet data_set = value_group.openDataSet(std::to_string(i));
+      H5::DataType type = data_set.getDataType();
+      switch (ti.dtype)
+      {
+        case NNFW_TYPE_TENSOR_FLOAT32:
+          if (type == H5::PredType::IEEE_F32BE || type == H5::PredType::IEEE_F32LE)
+            data_set.read(inputs[i].data(), H5::PredType::NATIVE_FLOAT);
+          else
+            throw std::runtime_error("model input type is f32. But h5 data type is different.");
+          break;
+        case NNFW_TYPE_TENSOR_INT32:
+          if (type == H5::PredType::STD_I32BE || type == H5::PredType::STD_I32LE)
+            data_set.read(inputs[i].data(), H5::PredType::NATIVE_INT32);
+          else
+            throw std::runtime_error("model input type is i32. But h5 data type is different.");
+          break;
+        case NNFW_TYPE_TENSOR_INT64:
+          if (type == H5::PredType::STD_I64BE || type == H5::PredType::STD_I64LE)
+            data_set.read(inputs[i].data(), H5::PredType::NATIVE_INT64);
+          else
+            throw std::runtime_error("model input type is i64. But h5 data type is different.");
+          break;
+        case NNFW_TYPE_TENSOR_QUANT8_ASYMM:
+        case NNFW_TYPE_TENSOR_BOOL:
+        case NNFW_TYPE_TENSOR_UINT8:
+          if (type == H5::PredType::STD_U8BE || type == H5::PredType::STD_U8LE)
+            data_set.read(inputs[i].data(), H5::PredType::NATIVE_UINT8);
+          else
+            throw std::runtime_error(
+                "model input type is qasymm8, bool or uint8. But h5 data type is different.");
+          break;
+        default:
+          throw std::runtime_error("nnpkg_run can load f32, i32, qasymm8, bool and uint8.");
+      }
+      NNPR_ENSURE_STATUS(nnfw_set_input(session_, i, ti.dtype, inputs[i].data(), bufsz));
+      NNPR_ENSURE_STATUS(nnfw_set_input_layout(session_, i, NNFW_LAYOUT_CHANNELS_LAST));
+    }
+  }
+  catch (const H5::Exception &e)
+  {
+    H5::Exception::printErrorStack();
+    std::exit(-1);
+  }
+  catch (const std::exception &e)
+  {
+    std::cerr << e.what() << std::endl;
+    std::exit(-1);
+  }
+};
+
+void H5Formatter::dumpOutputs(const std::string &filename, std::vector<Allocation> &outputs)
+{
+  uint32_t num_outputs;
+  NNPR_ENSURE_STATUS(nnfw_output_size(session_, &num_outputs));
+  try
+  {
+    // Turn off the automatic error printing.
+    H5::Exception::dontPrint();
+
+    H5::H5File file(filename, H5F_ACC_TRUNC);
+    H5::Group value_group = file.createGroup(h5_value_grpname);
+    for (uint32_t i = 0; i < num_outputs; i++)
+    {
+      nnfw_tensorinfo ti;
+      NNPR_ENSURE_STATUS(nnfw_output_tensorinfo(session_, i, &ti));
+      std::vector<hsize_t> dims(ti.rank);
+      for (uint32_t j = 0; j < ti.rank; ++j)
+      {
+        if (ti.dims[j] >= 0)
+          dims[j] = static_cast<hsize_t>(ti.dims[j]);
+        else
+        {
+          std::cerr << "Negative dimension in output tensor" << std::endl;
+          exit(-1);
+        }
+      }
+      H5::DataSpace data_space(ti.rank, dims.data());
+      switch (ti.dtype)
+      {
+        case NNFW_TYPE_TENSOR_FLOAT32:
+        {
+          H5::DataSet data_set =
+              value_group.createDataSet(std::to_string(i), H5::PredType::IEEE_F32BE, data_space);
+          data_set.write(outputs[i].data(), H5::PredType::NATIVE_FLOAT);
+          break;
+        }
+        case NNFW_TYPE_TENSOR_INT32:
+        {
+          H5::DataSet data_set =
+              value_group.createDataSet(std::to_string(i), H5::PredType::STD_I32LE, data_space);
+          data_set.write(outputs[i].data(), H5::PredType::NATIVE_INT32);
+          break;
+        }
+        case NNFW_TYPE_TENSOR_INT64:
+        {
+          H5::DataSet data_set =
+              value_group.createDataSet(std::to_string(i), H5::PredType::STD_I64LE, data_space);
+          data_set.write(outputs[i].data(), H5::PredType::NATIVE_INT64);
+          break;
+        }
+        case NNFW_TYPE_TENSOR_UINT8:
+        case NNFW_TYPE_TENSOR_QUANT8_ASYMM:
+        {
+          H5::DataSet data_set =
+              value_group.createDataSet(std::to_string(i), H5::PredType::STD_U8BE, data_space);
+          data_set.write(outputs[i].data(), H5::PredType::NATIVE_UINT8);
+          break;
+        }
+        case NNFW_TYPE_TENSOR_BOOL:
+        {
+          H5::DataSet data_set =
+              value_group.createDataSet(std::to_string(i), H5::PredType::STD_U8LE, data_space);
+          data_set.write(outputs[i].data(), H5::PredType::NATIVE_INT8);
+          break;
+        }
+        default:
+          throw std::runtime_error("nnpkg_run can dump f32, i32, qasymm8, bool and uint8.");
+      }
+    }
+  }
+  catch (const H5::Exception &e)
+  {
+    H5::Exception::printErrorStack();
+    std::exit(-1);
+  }
+  catch (const std::runtime_error &e)
+  {
+    std::cerr << "Error during dumpOutputs on nnpackage_run : " << e.what() << std::endl;
+    std::exit(-1);
+  }
+};
+
+} // end of namespace nnpkg_run