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[onert] Add unit tests for training Reshape op #13196

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Jun 19, 2024
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[onert] Add unit tests for training Reshape op #13196

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[onert] Add unit tests for training Reshape op
ragmani Jun 14, 2024
4955eba
Add calling `cgen.markAllOpsAsTrainable()`
ragmani Jun 17, 2024
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109 changes: 109 additions & 0 deletions tests/nnfw_api/src/GenModelTests/nontrainable_op_trains/Reshape.test.cc
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/*
* 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});
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cgen.markAllOpsAsTrainable();

_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
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The result of executing the same model with the same data on tensorflow:

Epoch 1/5
1/1 [==============================] - 0s 163ms/step - loss: 403.3333 - categorical_accuracy: 1.0000
Epoch 2/5
1/1 [==============================] - 0s 2ms/step - loss: 324.0978 - categorical_accuracy: 0.0000e+00 
Epoch 3/5 
1/1 [==============================] - 0s 2ms/step - loss: 267.7882 - categorical_accuracy: 0.0000e+00 
Epoch 4/5 
1/1 [==============================] - 0s 2ms/step - loss: 226.5260 - categorical_accuracy: 0.0000e+00 
Epoch 5/5 
1/1 [==============================] - 0s 2ms/step - loss: 195.3313 - categorical_accuracy: 0.0000e+00

));

_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});
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cgen.markAllOpsAsTrainable();

_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});
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cgen.markAllOpsAsTrainable();

_context = std::make_unique<GenModelTrainContext>(cgen.finish());
_context->setBackends({"train"});
_context->expectFailCompile();
}
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