//
//  CropTest.cpp
//  MNNTests
//
//  Created by MNN on 2019/01/15.
//  Copyright © 2018, Alibaba Group Holding Limited
//

#include <MNN/expr/Expr.hpp>
#include <MNN/expr/ExprCreator.hpp>
#include "MNNTestSuite.h"
#include "TestUtils.h"
using namespace MNN::Express;
class CropTest : public MNNTestCase {
public:
    virtual ~CropTest() = default;
    virtual bool run(int precision) {
        {
            // Simple
            auto input = _Input({1, 1, 4, 4}, NCHW);
            input->setName("input_tensor");
            // set input data
            const float inpudata[] = {1.0, 2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,
                                      9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0};
            auto inputPtr          = input->writeMap<float>();
            memcpy(inputPtr, inpudata, 16 * sizeof(float));
            input->unMap();
            const float size_data[]                 = {0.0, 0.0, 0.0, 0.0};
            auto size                               = _Const(size_data, {1, 1, 2, 2}, NCHW);
            input                                   = _Convert(input, NC4HW4);
            auto output                             = _Crop(input, size, 2, {1, 1});
            output                                  = _Convert(output, NCHW);
            const std::vector<float> expectedOutput = {6.0, 7.0, 10.0, 11.0};
            auto gotOutput                          = output->readMap<float>();
            if (!checkVector<float>(gotOutput, expectedOutput.data(), 4, 0.01)) {
                MNN_ERROR("CropTest test failed!\n");
                return false;
            }
            const std::vector<int> expectedDim = {1, 1, 2, 2};
            auto gotDim                        = output->getInfo()->dim;
            if (!checkVector<int>(gotDim.data(), expectedDim.data(), 4, 0)) {
                MNN_ERROR("CropTest test failed!\n");
                return false;
            }
        }
        {
            auto input = _Input({1, 3, 640, 360}, NC4HW4);
            input->setName("input_tensor");
            // set input data
            auto inputPtr = input->writeMap<float>();
            input->unMap();
            const float size_data[] = {0.0, 0.0, 0.0, 0.0};
            auto size               = _Const(0.0f, {1, 3, 640, 352}, NCHW);
            auto output             = _Crop(input, size, 2, {4, 4});
            auto gotOutput          = output->readMap<float>();
        }
        return true;
    }
};
MNNTestSuiteRegister(CropTest, "op/crop");