[onert] Add unit tests for training Reshape op

This commit adds some tests to validate training of Reshape op.

ONE-DCO-1.0-Signed-off-by: ragmani <[email protected]>

tests/nnfw_api/src/GenModelTests/nontrainable_op_trains/Reshape.test.cc

@@ -0,0 +1,106 @@
+/*
+ * Copyright (c) 2024 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 "GenModelTrain.h"
+
+#include <memory>
+
+TEST_F(GenModelTrain, NonTrainableOps_FC_Reshape)
+{
+  CirclePlusGen cgen;
+
+  uint32_t weight_buf = cgen.addBuffer(std::vector<float>(2 * 3 * 3, 0.f));
+  uint32_t bias_buf = cgen.addBuffer(std::vector<float>(2, 0.f));
+  const auto new_shape = CircleGen::Shape{1, 18};
+  uint32_t shape_buf = cgen.addBuffer(std::vector<int32_t>(new_shape));
+  int input = cgen.addTensor({{1, 5, 5, 1}, circle::TensorType::TensorType_FLOAT32});
+  int weight = cgen.addTensor({{2, 3, 3, 1}, circle::TensorType::TensorType_FLOAT32, weight_buf});
+  int bias = cgen.addTensor({{2}, circle::TensorType::TensorType_FLOAT32, bias_buf});
+  int conv_output = cgen.addTensor({{1, 3, 3, 2}, circle::TensorType::TensorType_FLOAT32});
+  int shape = cgen.addTensor({{2}, circle::TensorType::TensorType_INT32, shape_buf});
+  int output = cgen.addTensor({{1, 18}, circle::TensorType::TensorType_FLOAT32});
+  cgen.addOperatorConv2D({{input, weight, bias}, {conv_output}}, circle::Padding_VALID, 1, 1,
+                         circle::ActivationFunctionType_NONE, 1, 1);
+  cgen.addOperatorReshape({{conv_output, shape}, {output}}, &new_shape);
+  cgen.setInputsAndOutputs({input}, {output});
+
+  float learning_rate = 0.01f;
+  int32_t batch_size = 1;
+  cgen.addTrainInfo({circle::Optimizer::Optimizer_SGD, learning_rate,
+                     circle::LossFn::LossFn_MEAN_SQUARED_ERROR,
+                     circle::LossReductionType::LossReductionType_SumOverBatchSize, batch_size});
+
+  _context = std::make_unique<GenModelTrainContext>(cgen.finish());
+  _context->addTrainCase(
+    uniformTCD<float>({{{4, 0,  -5, 1, 0,  4, -1, 1, -1, -3, 3,  -2, -4,
+                         1, -2, 2,  4, -4, 2, 2,  0, 4,  -1, -2, 4}}}, // input dataset
+                      {{{47, -4, -25, 9, 10, 10, -13, 11, -14, -26, -12, 26, 20, 40, 1, 3, 11,
+                         4}}},    // expected dataset
+                      {226.5260f} // last losses
+                      ));
+
+  _context->setBackends({"train"});
+  // To apply backward to loss, epoch should be >= 2
+  _context->setEpoch(4);
+
+  SUCCEED();
+}
+
+TEST_F(GenModelTrain, neg_NonTrainableOps_Reshape_InvalidShape)
+{
+  CirclePlusGen cgen;
+
+  uint32_t shape_buf = cgen.addBuffer(std::vector<float>{2, 3});
+  int input = cgen.addTensor({{1, 4}, circle::TensorType::TensorType_FLOAT32});
+  int shape = cgen.addTensor({{2}, circle::TensorType::TensorType_INT32, shape_buf});
+  // Invalid shape: The number of output elements should be equal to input
+  int output = cgen.addTensor({{2, 3}, circle::TensorType::TensorType_FLOAT32});
+  cgen.addOperatorReshape({{input, shape}, {output}});
+  cgen.setInputsAndOutputs({input}, {output});
+
+  float learning_rate = 0.01f;
+  int32_t batch_size = 1;
+  cgen.addTrainInfo({circle::Optimizer::Optimizer_SGD, learning_rate,
+                     circle::LossFn::LossFn_MEAN_SQUARED_ERROR,
+                     circle::LossReductionType::LossReductionType_SumOverBatchSize, batch_size});
+
+  _context = std::make_unique<GenModelTrainContext>(cgen.finish());
+  _context->setBackends({"train"});
+  _context->expectFailCompile();
+}
+
+TEST_F(GenModelTrain, neg_NonTrainableOps_Reshape_InvalidType)
+{
+  CirclePlusGen cgen;
+
+  uint32_t shape_buf = cgen.addBuffer(std::vector<float>{2, 2});
+  // Invalid type: input tensor type should be FLOAT32
+  int input = cgen.addTensor({{1, 4}, circle::TensorType::TensorType_INT32});
+  int shape = cgen.addTensor({{2}, circle::TensorType::TensorType_INT32, shape_buf});
+  int output = cgen.addTensor({{2, 2}, circle::TensorType::TensorType_FLOAT32});
+  cgen.addOperatorReshape({{input, shape}, {output}});
+  cgen.setInputsAndOutputs({input}, {output});
+
+  float learning_rate = 0.01f;
+  int32_t batch_size = 1;
+  cgen.addTrainInfo({circle::Optimizer::Optimizer_SGD, learning_rate,
+                     circle::LossFn::LossFn_MEAN_SQUARED_ERROR,
+                     circle::LossReductionType::LossReductionType_SumOverBatchSize, batch_size});
+
+  _context = std::make_unique<GenModelTrainContext>(cgen.finish());
+  _context->setBackends({"train"});
+  _context->expectFailCompile();
+}