| 21 | virtual ~ScaleGradTest() = default; |
| 22 | |
| 23 | virtual bool run(int precision) { |
| 24 | const int len = 4; |
| 25 | auto input = _Input({1, len, 1, 1}, NCHW); |
| 26 | const float inpudata[] = {-1.0, -2.0, 0.0, 4.0}; |
| 27 | auto inputPtr = input->writeMap<float>(); |
| 28 | memcpy(inputPtr, inpudata, len * sizeof(float)); |
| 29 | |
| 30 | std::vector<float> scale = {0.1, 0.2, 0.3, 0.4}; |
| 31 | std::vector<float> bias = {1, 2, 3, 4}; |
| 32 | auto output = _Scale(input, len, std::move(scale), std::move(bias)); |
| 33 | auto opExpr = output->expr().first; |
| 34 | |
| 35 | auto grad = OpGrad::get(opExpr->get()->type()); |
| 36 | float outputDiff[len] = {0.1, -0.2, -0.3, 0.4}; |
| 37 | auto inputGrad = grad->onGrad(opExpr, {_Const(outputDiff, {1, len, 1, 1})}); |
| 38 | |
| 39 | const std::vector<float> expectedOutput = {0.01, -0.04, -0.09, 0.16}; |
| 40 | auto gotOutput = inputGrad[0]->readMap<float>(); |
| 41 | |
| 42 | for (int i = 0; i < len; ++i) { |
| 43 | auto diff = ::fabsf(gotOutput[i] - expectedOutput[i]); |
| 44 | if (diff > 0.0001) { |
| 45 | MNN_ERROR("%s grad test failed, expected: %f, but got: %f!\n", name, expectedOutput[i], gotOutput[i]); |
| 46 | return false; |
| 47 | } |
| 48 | } |
| 49 | |
| 50 | return true; |
| 51 | } |
| 52 | }; |
| 53 | |
| 54 | MNNTestSuiteRegister(ScaleGradTest, "grad/scale"); |