forked from mindspore-Ecosystem/mindspore
!7688 clear warning of compiler
Merge pull request !7688 from zhanghaibo/master
This commit is contained in:
commit
5fa1047163
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@ -227,17 +227,17 @@ void DeConvWgMerge(const float *src, float *dst, size_t src_stride, size_t dst_s
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size_t src_step = src_stride * sizeof(float);
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size_t dst_step = dst_stride * sizeof(float);
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asm volatile(
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"mov r7, %[src_ptr]\n"
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"mov r11, %[src_ptr]\n"
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"mov r8, %[dst_ptr]\n"
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"mov r10, r8\n"
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"vld1.32 {q0}, [r7], %[src_step]\n"
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"vld1.32 {q0}, [r11], %[src_step]\n"
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"vld1.32 {q1}, [r8], %[dst_step]\n"
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"vld1.32 {q2}, [r7], %[src_step]\n"
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"vld1.32 {q2}, [r11], %[src_step]\n"
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"vld1.32 {q3}, [r8], %[dst_step]\n"
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"vadd.f32 q0, q0, q1\n"
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"vld1.32 {q8}, [r7], %[src_step]\n"
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"vld1.32 {q8}, [r11], %[src_step]\n"
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"vadd.f32 q2, q2, q3\n"
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"vst1.32 {q0}, [r10], %[dst_step]\n"
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@ -245,19 +245,19 @@ void DeConvWgMerge(const float *src, float *dst, size_t src_stride, size_t dst_s
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"vld1.32 {q9}, [r8], %[dst_step]\n"
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"vld1.32 {q10}, [r7], %[src_step]\n"
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"vld1.32 {q10}, [r11], %[src_step]\n"
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"vadd.f32 q8, q8, q9\n"
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"vld1.32 {q11}, [r8], %[dst_step]\n"
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"vadd.f32 q10, q10, q11\n"
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"vld1.32 {q0}, [r7], %[src_step]\n"
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"vld1.32 {q0}, [r11], %[src_step]\n"
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"vst1.32 {q8}, [r10], %[dst_step]\n"
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"vst1.32 {q10}, [r10], %[dst_step]\n"
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"vld1.32 {q1}, [r8], %[dst_step]\n"
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"vld1.32 {q2}, [r7], %[src_step]\n"
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"vld1.32 {q2}, [r11], %[src_step]\n"
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"vld1.32 {q3}, [r8], %[dst_step]\n"
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"vadd.f32 q0, q0, q1\n"
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@ -266,10 +266,10 @@ void DeConvWgMerge(const float *src, float *dst, size_t src_stride, size_t dst_s
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"vst1.32 {q0}, [r10], %[dst_step]\n"
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"vst1.32 {q2}, [r10], %[dst_step]\n"
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"vld1.32 {q8}, [r7], %[src_step]\n"
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"vld1.32 {q8}, [r11], %[src_step]\n"
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"vld1.32 {q9}, [r8], %[dst_step]\n"
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"vld1.32 {q10}, [r7], %[src_step]\n"
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"vld1.32 {q10}, [r11], %[src_step]\n"
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"vld1.32 {q11}, [r8], %[dst_step]\n"
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"vadd.f32 q8, q8, q9\n"
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@ -280,7 +280,7 @@ void DeConvWgMerge(const float *src, float *dst, size_t src_stride, size_t dst_s
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:
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: [ src_ptr ] "r"(src_ptr), [ dst_ptr ] "r"(dst_ptr), [ src_step ] "r"(src_step), [ dst_step ] "r"(dst_step)
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: "r7", "r8", "r10", "q0", "q1", "q2", "q3", "q8", "q9", "q10", "q11");
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: "r8", "r10", "r11", "q0", "q1", "q2", "q3", "q8", "q9", "q10", "q11");
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#else
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for (int j = 0; j < 8; j++) {
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const float *s = src_ptr + j * src_stride;
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@ -28,7 +28,9 @@ class SoftmaxOpenCLKernel : public OpenCLKernel {
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public:
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SoftmaxOpenCLKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
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const std::vector<lite::Tensor *> &outputs)
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: OpenCLKernel(parameter, inputs, outputs), parameter_(reinterpret_cast<SoftmaxParameter *>(parameter)) {}
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: OpenCLKernel(parameter, inputs, outputs) {
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parameter_ = reinterpret_cast<SoftmaxParameter *>(parameter);
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}
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~SoftmaxOpenCLKernel() override = default;
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int Init() override;
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@ -6,8 +6,6 @@ include_directories(${TOP_DIR})
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include_directories(${TEST_DIR})
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include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/dependency_gtest.cmake)
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string(REPLACE " -Werror " " " CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
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string(REPLACE " -Werror " " " CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
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STRING(REPLACE " -fvisibility=hidden " " -fvisibility=default " CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
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STRING(REPLACE " -fvisibility=hidden " " -fvisibility=default " CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
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@ -50,7 +50,7 @@ class CommonTest : public testing::Test {
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template <typename T>
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static void CompareOutputData(T *output_data, T *correct_data, int size, float err_bound) {
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for (size_t i = 0; i < size; i++) {
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for (int i = 0; i < size; i++) {
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T abs = fabs(output_data[i] - correct_data[i]);
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ASSERT_LE(abs, err_bound);
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}
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@ -6,8 +6,6 @@ include_directories(${TOP_DIR})
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include_directories(${TEST_DIR})
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add_compile_definitions(ENABLE_NNACL_INFER_SHAPE)
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string(REPLACE " -Werror " " " CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
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string(REPLACE " -Werror " " " CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
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STRING(REPLACE " -fvisibility=hidden " " -fvisibility=default " CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
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STRING(REPLACE " -fvisibility=hidden " " -fvisibility=default " CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
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@ -149,7 +149,7 @@ TEST_F(InferTest, TestConvNode) {
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ASSERT_NE(nullptr, output_data);
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//===================================================
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ASSERT_EQ(output_size, outTensor->Size());
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for (size_t i = 0; i < outTensor->ElementsNum(); i++) {
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for (int i = 0; i < outTensor->ElementsNum(); i++) {
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ASSERT_LE((output_data[i] - outData[i]), 0.001);
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}
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MS_LOG(INFO) << "Passed";
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@ -184,7 +184,7 @@ TEST_F(TestPack, PackWeightUint8) {
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std::string weight_path = "./test_data/conv/convuint8_weight_32_3_3_3.bin";
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auto weight_data = reinterpret_cast<uint8_t *>(mindspore::lite::ReadFile(weight_path.c_str(), &weight_size));
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auto int8_weight = reinterpret_cast<int8_t *>(malloc(weight_size));
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for (int i = 0; i < weight_size; i++) {
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for (unsigned int i = 0; i < weight_size; i++) {
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int8_weight[i] = (int8_t)(weight_data[i] - 128);
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}
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int32_t filter_zp = 20;
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@ -117,7 +117,7 @@ TEST_F(TestStridedSlice, StridedSliceInt8) {
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EXPECT_EQ(0, ret);
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int8_t expect[4] = {-6, -5, 7, 8};
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for (int i = 0; i < sizeof(expect); ++i) {
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for (unsigned int i = 0; i < sizeof(expect); ++i) {
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EXPECT_EQ(output_data[i], expect[i]);
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}
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@ -33,7 +33,7 @@ int ConstantOfShapeTestInit(std::vector<lite::Tensor *> *inputs_, std::vector<li
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inputs_->push_back(in_t);
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std::vector<int> c_shape(in_t->ElementsNum());
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for (int i = 0; i < c_shape.size(); ++i) {
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for (unsigned int i = 0; i < c_shape.size(); ++i) {
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c_shape[i] = a_ptr[i];
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}
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auto out_t = new lite::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR);
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@ -138,10 +138,10 @@ TEST_F(TestConvolutionDwFp32, ConvDwFp32Accuracy) {
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CompareOutputData(output_ptr, correct_data, outputs[0]->ElementsNum(), 0.0001);
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delete conv_param;
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for (int i = 0; i < inputs.size(); i++) {
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for (unsigned int i = 0; i < inputs.size(); i++) {
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delete inputs[i];
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}
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for (int i = 0; i < outputs.size(); i++) {
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for (unsigned int i = 0; i < outputs.size(); i++) {
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delete outputs[i];
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}
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delete kernel;
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@ -189,10 +189,10 @@ TEST_F(TestConvolutionDwFp32, ConvDwFp32Performance) {
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printf("Convolution_depthwise fp32 average time : %f ms\n", time_avg / 1000.0f);
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delete conv_param;
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for (int i = 0; i < inputs.size(); i++) {
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for (unsigned int i = 0; i < inputs.size(); i++) {
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delete inputs[i];
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}
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for (int i = 0; i < outputs.size(); i++) {
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for (unsigned int i = 0; i < outputs.size(); i++) {
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delete outputs[i];
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}
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delete kernel;
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@ -60,7 +60,7 @@ TEST_F(TestEluFp32, EluTest) {
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elu->Run();
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std::cout << "output shape:" << std::endl;
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for (int i = 0; i < outputs_.front()->shape().size(); ++i) {
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for (unsigned int i = 0; i < outputs_.front()->shape().size(); ++i) {
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std::cout << outputs_.front()->shape()[i] << ' ';
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}
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std::cout << std::endl;
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@ -75,7 +75,7 @@ TEST_F(TestEmbeddingLookupFp32, ElTest) {
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el->Run();
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std::cout << "output shape:" << std::endl;
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for (int i = 0; i < outputs_.front()->shape().size(); ++i) {
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for (unsigned int i = 0; i < outputs_.front()->shape().size(); ++i) {
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std::cout << outputs_.front()->shape()[i] << ' ';
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}
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std::cout << std::endl;
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@ -146,8 +146,8 @@ TEST_F(TestFcFp32, FcTest2) {
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CompareOutputData(reinterpret_cast<float *>(outputs_[0]->MutableData()), correct, total_size, 0.0001);
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}
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int FcTestInit3(std::vector<lite::Tensor *> *inputs_, std::vector<lite::Tensor *> *outputs_,
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MatMulParameter *matmal_param, float **correct) {
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void FcTestInit3(std::vector<lite::Tensor *> *inputs_, std::vector<lite::Tensor *> *outputs_,
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MatMulParameter *matmal_param, float **correct) {
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Tensor *in_t = new Tensor(kNumberTypeFloat, {1, 1, 1, 20}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR);
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in_t->MallocData();
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float in[] = {1, 0, 3, 0, 4, 5, 2, 5, 2, 5, 1, 5, 0, 1, 2, 0, 2, 1, 0, 5};
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@ -177,7 +177,6 @@ int FcTestInit3(std::vector<lite::Tensor *> *inputs_, std::vector<lite::Tensor *
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matmal_param->a_transpose_ = false;
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matmal_param->has_bias_ = false;
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matmal_param->act_type_ = ActType_No;
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return out_t->ElementsNum();
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}
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TEST_F(TestFcFp32, FcTest3) {
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@ -185,7 +184,7 @@ TEST_F(TestFcFp32, FcTest3) {
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std::vector<lite::Tensor *> outputs_;
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auto matmul_param = new MatMulParameter();
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float *correct;
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int total_size = FcTestInit3(&inputs_, &outputs_, matmul_param, &correct);
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FcTestInit3(&inputs_, &outputs_, matmul_param, &correct);
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lite::InnerContext *ctx = new lite::InnerContext;
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ctx->thread_num_ = 1;
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ASSERT_EQ(lite::RET_OK, ctx->Init());
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@ -171,10 +171,10 @@ TEST_F(LstmFp32, LstmForwardFp32Accuracy) {
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CompareOutput(outputs[2], output2_data);
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delete lstm_param;
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for (int i = 0; i < inputs.size() - 1; i++) {
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for (unsigned int i = 0; i < inputs.size() - 1; i++) {
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delete inputs[i];
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}
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for (int i = 0; i < outputs.size(); i++) {
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for (unsigned int i = 0; i < outputs.size(); i++) {
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delete outputs[i];
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}
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delete kernel;
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@ -321,10 +321,10 @@ TEST_F(LstmFp32, LstmBackwardFp32Accuracy) {
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CompareOutput(outputs[2], output2_data);
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delete lstm_param;
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for (int i = 0; i < inputs.size() - 1; i++) {
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for (unsigned int i = 0; i < inputs.size() - 1; i++) {
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delete inputs[i];
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}
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for (int i = 0; i < outputs.size(); i++) {
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for (unsigned int i = 0; i < outputs.size(); i++) {
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delete outputs[i];
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}
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delete kernel;
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@ -79,7 +79,6 @@ TEST_F(TestPowerFp32, Simple) {
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op->Init();
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op->Run();
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float correct[] = {1, 64, 2187, 65536};
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float *output = reinterpret_cast<float *>(outputs_[0]->MutableData());
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CompareOutputData(reinterpret_cast<float *>(outputs_[0]->MutableData()), correct, total_size, 0.0001);
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delete op;
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for (auto t : inputs_) delete t;
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@ -67,7 +67,7 @@ TEST_F(TestSkipGramFp32, ElTest) {
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el->Run();
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std::vector<StringPack> output = mindspore::lite::ParseTensorBuffer(outputs_[0]);
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for (int i = 0; i < output.size(); i++) {
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for (unsigned int i = 0; i < output.size(); i++) {
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for (int j = 0; j < output[i].len; j++) {
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printf("%c", output[i].data[j]);
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}
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@ -39,7 +39,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest4) {
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param.block_sizes_[0] = 2;
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param.block_sizes_[1] = 1;
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DoSpaceToBatchNHWC(input.data(), out, param.block_sizes_, in_shape.data(), out_shape.data());
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for (int i = 0; i < kOutSize; ++i) {
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for (unsigned int i = 0; i < kOutSize; ++i) {
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std::cout << out[i] << " ";
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}
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std::cout << "\n";
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@ -57,7 +57,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest5) {
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param.block_sizes_[0] = 1;
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param.block_sizes_[1] = 2;
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DoSpaceToBatchNHWC(input.data(), out, param.block_sizes_, in_shape.data(), out_shape.data());
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for (int i = 0; i < kOutSize; ++i) {
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for (unsigned int i = 0; i < kOutSize; ++i) {
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std::cout << out[i] << " ";
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}
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std::cout << "\n";
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@ -75,7 +75,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest6) {
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param.block_sizes_[0] = 2;
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param.block_sizes_[1] = 2;
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DoSpaceToBatchNHWC(input.data(), out, param.block_sizes_, in_shape.data(), out_shape.data());
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for (int i = 0; i < kOutSize; ++i) {
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for (unsigned int i = 0; i < kOutSize; ++i) {
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std::cout << out[i] << " ";
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}
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std::cout << "\n";
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@ -97,7 +97,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest7) {
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param.block_sizes_[0] = 2;
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param.block_sizes_[1] = 2;
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DoSpaceToBatchNHWC(input.data(), out, param.block_sizes_, in_shape.data(), out_shape.data());
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for (int i = 0; i < kOutSize; ++i) {
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for (unsigned int i = 0; i < kOutSize; ++i) {
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std::cout << out[i] << " ";
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}
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std::cout << "\n";
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@ -116,7 +116,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest8) {
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std::vector<int> out_shape = {1, 5, 5, 2};
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std::vector<int> padding = {0, 1, 0, 1};
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DoSpaceToBatchPaddingNHWC(input.data(), out, in_shape.data(), padding.data(), out_shape.data());
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for (int i = 0; i < kOutSize; ++i) {
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for (unsigned int i = 0; i < kOutSize; ++i) {
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std::cout << out[i] << " ";
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}
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std::cout << "\n";
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@ -136,7 +136,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest9) {
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std::vector<int> out_shape = {1, 6, 6, 2};
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std::vector<int> padding = {1, 1, 1, 1};
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DoSpaceToBatchPaddingNHWC(input.data(), out, in_shape.data(), padding.data(), out_shape.data());
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for (int i = 0; i < kOutSize; ++i) {
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for (unsigned int i = 0; i < kOutSize; ++i) {
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std::cout << out[i] << " ";
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}
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std::cout << "\n";
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@ -162,7 +162,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest10) {
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param.block_sizes_[0] = 2;
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param.block_sizes_[1] = 2;
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DoSpaceToBatchNHWC(pedding_out, out, param.block_sizes_, pedding_out_shape.data(), out_shape.data());
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for (int i = 0; i < kOutSize; ++i) {
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for (unsigned int i = 0; i < kOutSize; ++i) {
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std::cout << out[i] << " ";
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}
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std::cout << "\n";
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@ -276,10 +276,7 @@ TEST_F(TestPoolingGradFp32, AvgPoolGradStride2Fp32) {
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kernel->Init();
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auto time_start = mindspore::lite::GetTimeUs();
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kernel->Run();
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auto time_end = mindspore::lite::GetTimeUs();
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printf("single thread running time : %lu ms\n", time_end - time_start);
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std::string output_path = "./test_data/pooling/avgpoolgradfp32_s2_dx_3_28_28_3.bin";
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auto res = lite::CompareRelativeOutput(out_data, output_path);
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@ -340,10 +337,7 @@ TEST_F(TestPoolingGradFp32, AvgPoolGradStride3Fp32) {
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kernel->Init();
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auto time_start = mindspore::lite::GetTimeUs();
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kernel->Run();
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auto time_end = mindspore::lite::GetTimeUs();
|
||||
printf("single thread running time : %lu ms\n", time_end - time_start);
|
||||
|
||||
std::string output_path = "./test_data/pooling/avgpoolgradfp32_s3_dx_3_28_28_3.bin";
|
||||
auto res = lite::CompareRelativeOutput(out_data, output_path);
|
||||
|
@ -461,10 +455,7 @@ TEST_F(TestPoolingGradFp32, MaxPoolGradBatchFp32) {
|
|||
|
||||
kernel->Init();
|
||||
|
||||
auto time_start = mindspore::lite::GetTimeUs();
|
||||
kernel->Run();
|
||||
auto time_end = mindspore::lite::GetTimeUs();
|
||||
printf("single thread running time : %lu ms\n", time_end - time_start);
|
||||
|
||||
std::string output_path = "./test_data/pooling/maxpoolgradfp32_1_xgrad_3_28_28_3.bin";
|
||||
auto res = lite::CompareRelativeOutput(out_data, output_path);
|
||||
|
@ -535,10 +526,7 @@ TEST_F(TestPoolingGradFp32, MaxPoolGradStride2Fp32) {
|
|||
|
||||
kernel->Init();
|
||||
|
||||
auto time_start = mindspore::lite::GetTimeUs();
|
||||
kernel->Run();
|
||||
auto time_end = mindspore::lite::GetTimeUs();
|
||||
printf("single thread running time : %lu ms\n", time_end - time_start);
|
||||
|
||||
std::string output_path = "./test_data/pooling/maxpoolgradfp32_s2_xgrad_3_28_28_3.bin";
|
||||
auto res = lite::CompareRelativeOutput(out_data, output_path);
|
||||
|
@ -609,10 +597,7 @@ TEST_F(TestPoolingGradFp32, MaxPoolGradStride3Fp32) {
|
|||
|
||||
kernel->Init();
|
||||
|
||||
auto time_start = mindspore::lite::GetTimeUs();
|
||||
kernel->Run();
|
||||
auto time_end = mindspore::lite::GetTimeUs();
|
||||
printf("single thread running time : %lu ms\n", time_end - time_start);
|
||||
|
||||
std::string output_path = "./test_data/pooling/maxpoolgradfp32_s3_xgrad_3_28_28_3.bin";
|
||||
auto res = lite::CompareRelativeOutput(out_data, output_path);
|
||||
|
|
|
@ -108,7 +108,7 @@ TEST_F(TestReluXInt8, Relu6) {
|
|||
|
||||
// 0.0f, 0.0f, 1.25f, 3.0f, 4.5f, 6.0f, 6.0f, 6.0f
|
||||
int8_t expect[8] = {-128, -128, -96, -52, -14, 25, 25, 25};
|
||||
for (int i = 0; i < sizeof(expect); ++i) {
|
||||
for (unsigned int i = 0; i < sizeof(expect); ++i) {
|
||||
EXPECT_EQ(output_data[i], expect[i]);
|
||||
}
|
||||
|
||||
|
|
|
@ -68,12 +68,12 @@ TEST_F(TestNormalize, TestSentence) {
|
|||
kernel_ = creator_(inputs_, outputs_, ¶meter_, &ctx_, desc_, nullptr);
|
||||
ASSERT_NE(kernel_, nullptr);
|
||||
auto ret = kernel_->Init();
|
||||
MS_ASSERT(ret == 0);
|
||||
ASSERT_EQ(ret, 0);
|
||||
ret = kernel_->Run();
|
||||
MS_ASSERT(ret == 0);
|
||||
ASSERT_EQ(ret, 0);
|
||||
|
||||
std::vector<StringPack> output = mindspore::lite::ParseTensorBuffer(outputs_[0]);
|
||||
for (int i = 0; i < output.size(); i++) {
|
||||
for (unsigned int i = 0; i < output.size(); i++) {
|
||||
for (int j = 0; j < output[i].len; j++) {
|
||||
printf("%c", output[i].data[j]);
|
||||
}
|
||||
|
|
|
@ -91,7 +91,6 @@ TEST_F(TestActivationOpenCL, ReluFp_dim4) {
|
|||
MS_LOG(INFO) << "Init tensors.";
|
||||
std::vector<int> input_shape = {1, 9};
|
||||
schema::Format format = schema::Format_NC;
|
||||
schema::Format op_format = schema::Format_NC4;
|
||||
auto tensor_type = lite::Tensor::CONST_TENSOR;
|
||||
auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
|
||||
if (input_tensor == nullptr) {
|
||||
|
@ -198,7 +197,6 @@ TEST_F(TestActivationOpenCL, Relu6Fp_dim4) {
|
|||
MS_LOG(INFO) << "Init tensors.";
|
||||
std::vector<int> input_shape = {1, 9};
|
||||
schema::Format format = schema::Format_NC;
|
||||
schema::Format op_format = schema::Format_NC4;
|
||||
auto tensor_type = lite::Tensor::CONST_TENSOR;
|
||||
auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
|
||||
if (input_tensor == nullptr) {
|
||||
|
@ -308,7 +306,6 @@ TEST_F(TestActivationOpenCL, SigmoidFp_dim4) {
|
|||
MS_LOG(INFO) << "Init tensors.";
|
||||
std::vector<int> input_shape = {1, 9};
|
||||
schema::Format format = schema::Format_NC;
|
||||
schema::Format op_format = schema::Format_NC4;
|
||||
auto tensor_type = lite::Tensor::CONST_TENSOR;
|
||||
auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
|
||||
if (input_tensor == nullptr) {
|
||||
|
@ -411,15 +408,14 @@ TEST_F(TestActivationOpenCL, LeakyReluFp_dim4) {
|
|||
MS_LOG(INFO) << "Leaky relu Begin test!";
|
||||
auto ocl_runtime = lite::opencl::OpenCLRuntimeWrapper().GetInstance();
|
||||
ocl_runtime->Init();
|
||||
auto data_type = kNumberTypeFloat16; // need modify
|
||||
auto data_type = kNumberTypeFloat16;
|
||||
ocl_runtime->SetFp16Enable(data_type == kNumberTypeFloat16);
|
||||
bool enable_fp16 = ocl_runtime->GetFp16Enable();
|
||||
|
||||
MS_LOG(INFO) << "Init tensors.";
|
||||
std::vector<int> input_shape = {1, 9}; // need modify
|
||||
std::vector<int> input_shape = {1, 9};
|
||||
auto tensor_type = lite::Tensor::CONST_TENSOR;
|
||||
schema::Format format = schema::Format_NC; // need modify
|
||||
schema::Format op_format = schema::Format_NHWC4; // need modify
|
||||
schema::Format format = schema::Format_NC;
|
||||
auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
|
||||
if (input_tensor == nullptr) {
|
||||
MS_LOG(ERROR) << "new input tensor error!";
|
||||
|
@ -527,7 +523,6 @@ TEST_F(TestActivationOpenCLTanh, TanhFp_dim4) {
|
|||
MS_LOG(INFO) << "Init tensors.";
|
||||
std::vector<int> input_shape = {1, 2, 3, 9};
|
||||
schema::Format format = schema::Format_NHWC;
|
||||
schema::Format op_format = schema::Format_NC4HW4;
|
||||
auto tensor_type = lite::Tensor::CONST_TENSOR;
|
||||
auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
|
||||
if (input_tensor == nullptr) {
|
||||
|
|
|
@ -77,13 +77,12 @@ TEST_F(TestBiasAddOpenCL, BiasAddFp32_dim4) {
|
|||
MS_LOG(INFO) << "BiasAdd Begin test:";
|
||||
auto ocl_runtime = lite::opencl::OpenCLRuntimeWrapper().GetInstance();
|
||||
ocl_runtime->Init();
|
||||
auto data_type = kNumberTypeFloat16; // need modify
|
||||
auto data_type = kNumberTypeFloat16;
|
||||
ocl_runtime->SetFp16Enable(data_type == kNumberTypeFloat16);
|
||||
std::vector<int> input_shape = {1, 9}; // need modify
|
||||
std::vector<int> output_shape = {1, 9}; // need modify
|
||||
std::vector<int> input_shape = {1, 9};
|
||||
std::vector<int> output_shape = {1, 9};
|
||||
auto tensor_type = lite::Tensor::CONST_TENSOR;
|
||||
schema::Format type = schema::Format_NC; // need modify
|
||||
schema::Format op_format = schema::Format_NC4; // need modify
|
||||
schema::Format type = schema::Format_NC;
|
||||
int weight_shape = 0;
|
||||
if (input_shape.size() == 4) {
|
||||
weight_shape = input_shape[3];
|
||||
|
|
|
@ -86,7 +86,6 @@ TEST_F(TestPReluOpenCL, PReluFp32_dim4) {
|
|||
auto data_type = kNumberTypeFloat16;
|
||||
ocl_runtime->SetFp16Enable(data_type == kNumberTypeFloat16);
|
||||
schema::Format format = schema::Format_NHWC;
|
||||
schema::Format op_format = schema::Format_NC4HW4;
|
||||
auto tensor_type = lite::Tensor::CONST_TENSOR;
|
||||
auto input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
|
||||
if (input_tensor == nullptr) {
|
||||
|
|
Loading…
Reference in New Issue