[CI] Enable CI for Huawei kirin NPU, Rockchip NPU and MediaTek APU (#4408)

lite/CMakeLists.txt

@@ -38,34 +38,31 @@ if (LITE_WITH_LIGHT_WEIGHT_FRAMEWORK AND NOT LITE_ON_TINY_PUBLISH)
 endif()
 
 if (WITH_TESTING)
+    set(LITE_URL_FOR_UNITTESTS "http://paddle-inference-dist.bj.bcebos.com/PaddleLite/models_and_data_for_unittests")
+    # models
     lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "lite_naive_model.tar.gz")
+    lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v1.tar.gz")
+    lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v2_relu.tar.gz")
+    lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "inception_v4_simple.tar.gz")
     if(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
-	    lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v1.tar.gz")
 	    lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v1_int16.tar.gz")
-        lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v2_relu.tar.gz")
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "resnet50.tar.gz")
-        lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "inception_v4_simple.tar.gz")
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "MobileNetV1_quant.tar.gz")
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "transformer_with_mask_fp32.tar.gz")
-    endif()
-    if(NOT LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
+        lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "mobilenet_v1_int8_for_mediatek_apu.tar.gz")
+        lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "mobilenet_v1_int8_for_rockchip_npu.tar.gz")
+    else()
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "GoogleNet_inference.tar.gz")
-        lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v1.tar.gz")
-        lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v2_relu.tar.gz")
-        lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "inception_v4_simple.tar.gz")
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "step_rnn.tar.gz")
-
-        set(LITE_URL_FOR_UNITTESTS "http://paddle-inference-dist.bj.bcebos.com/PaddleLite/models_and_data_for_unittests")
-        # models
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "resnet50.tar.gz")
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "bert.tar.gz")
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "ernie.tar.gz")
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "GoogLeNet.tar.gz")
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "VGG19.tar.gz")
-        # data
-        lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "ILSVRC2012_small.tar.gz")
-        lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "bert_data.tar.gz")
     endif()
+    # data
+    lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "ILSVRC2012_small.tar.gz")
+    lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "bert_data.tar.gz")
 endif()
 
 # ----------------------------- PUBLISH -----------------------------

lite/core/arena/CMakeLists.txt

@@ -6,5 +6,5 @@ endif()
 lite_cc_library(arena_framework SRCS framework.cc DEPS program gtest)
 
 if((NOT LITE_WITH_OPENCL) AND (LITE_WITH_X86 OR LITE_WITH_ARM))
-  lite_cc_test(test_arena_framework SRCS framework_test.cc DEPS arena_framework ${rknpu_kernels} ${mlu_kernels} ${bm_kernels} ${npu_kernels} ${huawei_ascend_npu_kernels} ${xpu_kernels} ${x86_kernels} ${cuda_kernels} ${fpga_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
+  lite_cc_test(test_arena_framework SRCS framework_test.cc DEPS arena_framework ${rknpu_kernels} ${mlu_kernels} ${bm_kernels} ${npu_kernels} ${apu_kernels} ${huawei_ascend_npu_kernels} ${xpu_kernels} ${x86_kernels} ${cuda_kernels} ${fpga_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
 endif()

lite/tests/api/CMakeLists.txt

-if(LITE_WITH_ARM)
-    lite_cc_test(test_transformer_with_mask_fp32_arm SRCS test_transformer_with_mask_fp32_arm.cc
+function(lite_cc_test_with_model_and_data TARGET)
+    if(NOT WITH_TESTING)
+        return()
+    endif()
+
+    set(options "")
+    set(oneValueArgs MODEL DATA CONFIG ARGS)
+    set(multiValueArgs "")
+    cmake_parse_arguments(args "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+    set(ARGS "")
+    if(DEFINED args_MODEL)
+        set(ARGS "${ARGS} --model_dir=${LITE_MODEL_DIR}/${args_MODEL}")
+    endif()
+    if(DEFINED args_DATA)
+        set(ARGS "${ARGS} --data_dir=${LITE_MODEL_DIR}/${args_DATA}")
+    endif()
+    if(DEFINED args_CONFIG)
+        set(ARGS "${ARGS} --config_dir=${LITE_MODEL_DIR}/${args_CONFIG}")
+    endif()
+    if(DEFINED args_ARGS)
+        set(ARGS "${ARGS} ${args_ARGS}")
+    endif()
+    lite_cc_test(${TARGET} SRCS ${TARGET}.cc
         DEPS ${lite_model_test_DEPS} paddle_api_full
         ARM_DEPS ${arm_kernels}
-        ARGS --model_dir=${LITE_MODEL_DIR}/transformer_with_mask_fp32 SERIAL)
-    if(WITH_TESTING)
-        add_dependencies(test_transformer_with_mask_fp32_arm extern_lite_download_transformer_with_mask_fp32_tar_gz)
+        X86_DEPS ${x86_kernels}
+        NPU_DEPS ${npu_kernels} ${npu_bridges}
+        HUAWEI_ASCEND_NPU_DEPS ${huawei_ascend_npu_kernels} ${huawei_ascend_npu_bridges}
+        XPU_DEPS ${xpu_kernels} ${xpu_bridges}
+        APU_DEPS ${apu_kernels} ${apu_bridges}
+        RKNPU_DEPS ${rknpu_kernels} ${rknpu_bridges}
+        BM_DEPS ${bm_kernels} ${bm_bridges}
+        MLU_DEPS ${mlu_kernels} ${mlu_bridges}
+        ARGS ${ARGS} SERIAL)
+    if(DEFINED args_MODEL)
+        add_dependencies(${TARGET} extern_lite_download_${args_MODEL}_tar_gz)
     endif()
-endif()
-
-function(xpu_x86_without_xtcl_test TARGET MODEL DATA)
-    if(${DATA} STREQUAL "")
-        lite_cc_test(${TARGET} SRCS ${TARGET}.cc
-            DEPS mir_passes lite_api_test_helper paddle_api_full paddle_api_light gflags utils
-            ${ops} ${host_kernels} ${x86_kernels} ${xpu_kernels}
-            ARGS --model_dir=${LITE_MODEL_DIR}/${MODEL})
-    else()
-        lite_cc_test(${TARGET} SRCS ${TARGET}.cc
-            DEPS mir_passes lite_api_test_helper paddle_api_full paddle_api_light gflags utils
-            ${ops} ${host_kernels} ${x86_kernels} ${xpu_kernels}
-            ARGS --model_dir=${LITE_MODEL_DIR}/${MODEL} --data_dir=${LITE_MODEL_DIR}/${DATA})
+    if(DEFINED args_DATA)
+        add_dependencies(${TARGET} extern_lite_download_${args_DATA}_tar_gz)
     endif()
-
-    if(WITH_TESTING)
-        add_dependencies(${TARGET} extern_lite_download_${MODEL}_tar_gz)
-        if(NOT ${DATA} STREQUAL "")
-            add_dependencies(${TARGET} extern_lite_download_${DATA}_tar_gz)
-        endif()
+    if(DEFINED args_CONFIG)
+        add_dependencies(${TARGET} extern_lite_download_${args_CONFIG}_tar_gz)
     endif()
 endfunction()
 
+if(LITE_WITH_ARM)
+    lite_cc_test_with_model_and_data(test_transformer_with_mask_fp32_arm MODEL transformer_with_mask_fp32 ARGS)
+endif()
+
+if(LITE_WITH_NPU)
+    lite_cc_test_with_model_and_data(test_mobilenetv1_fp32_huawei_kirin_npu MODEL mobilenet_v1 DATA ILSVRC2012_small)
+    lite_cc_test_with_model_and_data(test_mobilenetv2_fp32_huawei_kirin_npu MODEL mobilenet_v2_relu DATA ILSVRC2012_small)
+    lite_cc_test_with_model_and_data(test_resnet50_fp32_huawei_kirin_npu MODEL resnet50 DATA ILSVRC2012_small)
+endif()
+
 if(LITE_WITH_XPU AND NOT LITE_WITH_XTCL)
-    xpu_x86_without_xtcl_test(test_resnet50_fp32_xpu resnet50 ILSVRC2012_small)
-    xpu_x86_without_xtcl_test(test_googlenet_fp32_xpu GoogLeNet ILSVRC2012_small)
-    xpu_x86_without_xtcl_test(test_vgg19_fp32_xpu VGG19 ILSVRC2012_small)
-    xpu_x86_without_xtcl_test(test_ernie_fp32_xpu ernie bert_data)
-    xpu_x86_without_xtcl_test(test_bert_fp32_xpu bert bert_data)
+    lite_cc_test_with_model_and_data(test_resnet50_fp32_xpu MODEL resnet50 DATA ILSVRC2012_small)
+    lite_cc_test_with_model_and_data(test_googlenet_fp32_xpu MODEL GoogLeNet DATA ILSVRC2012_small)
+    lite_cc_test_with_model_and_data(test_vgg19_fp32_xpu MODEL VGG19 DATA ILSVRC2012_small)
+    lite_cc_test_with_model_and_data(test_ernie_fp32_xpu MODEL ernie DATA bert_data)
+    lite_cc_test_with_model_and_data(test_bert_fp32_xpu MODEL bert DATA bert_data)
 endif()
 
 if(LITE_WITH_RKNPU)
-    lite_cc_test(test_mobilenetv1_int8_rknpu SRCS test_mobilenetv1_int8_rknpu.cc
-        DEPS ${lite_model_test_DEPS} paddle_api_full
-        RKNPU_DEPS ${rknpu_kernels} ${rknpu_bridges}
-        ARGS --model_dir=${LITE_MODEL_DIR}/MobilenetV1_full_quant SERIAL)
+    lite_cc_test_with_model_and_data(test_mobilenetv1_int8_rockchip_npu MODEL mobilenet_v1_int8_for_rockchip_npu DATA ILSVRC2012_small)
 endif()
 
 if(LITE_WITH_APU)
-    lite_cc_test(test_mobilenetv1_int8_apu SRCS test_mobilenetv1_int8_apu.cc
-        DEPS ${lite_model_test_DEPS} paddle_api_full
-        APU_DEPS ${apu_kernels} ${apu_bridges}
-        ARGS --model_dir=${LITE_MODEL_DIR}/MobilenetV1_full_quant SERIAL)
+    lite_cc_test_with_model_and_data(test_mobilenetv1_int8_mediatek_apu MODEL mobilenet_v1_int8_for_mediatek_apu DATA ILSVRC2012_small)
 endif()

lite/tests/api/test_mobilenetv1_fp32_huawei_kirin_npu.cc

+// Copyright (c) 2019 PaddlePaddle Authors. 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 <gflags/gflags.h>
+#include <gtest/gtest.h>
+#include <vector>
+#include "lite/api/lite_api_test_helper.h"
+#include "lite/api/paddle_api.h"
+#include "lite/api/paddle_use_kernels.h"
+#include "lite/api/paddle_use_ops.h"
+#include "lite/api/paddle_use_passes.h"
+#include "lite/api/test_helper.h"
+#include "lite/tests/api/ILSVRC2012_utility.h"
+#include "lite/utils/cp_logging.h"
+
+DEFINE_string(data_dir, "", "data dir");
+DEFINE_int32(iteration, 100, "iteration times to run");
+DEFINE_int32(batch, 1, "batch of image");
+DEFINE_int32(channel, 3, "image channel");
+
+namespace paddle {
+namespace lite {
+
+TEST(MobileNetV1, test_mobilenetv1_fp32_huawei_kirin_npu) {
+  lite_api::CxxConfig config;
+  config.set_model_dir(FLAGS_model_dir);
+  config.set_valid_places({lite_api::Place{TARGET(kARM), PRECISION(kFloat)},
+                           lite_api::Place{TARGET(kNPU), PRECISION(kFloat)}});
+  auto predictor = lite_api::CreatePaddlePredictor(config);
+
+  std::string raw_data_dir = FLAGS_data_dir + std::string("/raw_data");
+  std::vector<int> input_shape{
+      FLAGS_batch, FLAGS_channel, FLAGS_im_width, FLAGS_im_height};
+  auto raw_data = ReadRawData(raw_data_dir, input_shape, FLAGS_iteration);
+
+  int input_size = 1;
+  for (auto i : input_shape) {
+    input_size *= i;
+  }
+
+  for (int i = 0; i < FLAGS_warmup; ++i) {
+    auto input_tensor = predictor->GetInput(0);
+    input_tensor->Resize(
+        std::vector<int64_t>(input_shape.begin(), input_shape.end()));
+    auto* data = input_tensor->mutable_data<float>();
+    for (int j = 0; j < input_size; j++) {
+      data[j] = 0.f;
+    }
+    predictor->Run();
+  }
+
+  std::vector<std::vector<float>> out_rets;
+  out_rets.resize(FLAGS_iteration);
+  double cost_time = 0;
+  for (size_t i = 0; i < raw_data.size(); ++i) {
+    auto input_tensor = predictor->GetInput(0);
+    input_tensor->Resize(
+        std::vector<int64_t>(input_shape.begin(), input_shape.end()));
+    auto* data = input_tensor->mutable_data<float>();
+    memcpy(data, raw_data[i].data(), sizeof(float) * input_size);
+
+    double start = GetCurrentUS();
+    predictor->Run();
+    cost_time += GetCurrentUS() - start;
+
+    auto output_tensor = predictor->GetOutput(0);
+    auto output_shape = output_tensor->shape();
+    auto output_data = output_tensor->data<float>();
+    ASSERT_EQ(output_shape.size(), 2UL);
+    ASSERT_EQ(output_shape[0], 1);
+    ASSERT_EQ(output_shape[1], 1000);
+
+    int output_size = output_shape[0] * output_shape[1];
+    out_rets[i].resize(output_size);
+    memcpy(&(out_rets[i].at(0)), output_data, sizeof(float) * output_size);
+  }
+
+  LOG(INFO) << "================== Speed Report ===================";
+  LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
+            << ", warmup: " << FLAGS_warmup << ", batch: " << FLAGS_batch
+            << ", iteration: " << FLAGS_iteration << ", spend "
+            << cost_time / FLAGS_iteration / 1000.0 << " ms in average.";
+
+  std::string labels_dir = FLAGS_data_dir + std::string("/labels.txt");
+  float out_accuracy = CalOutAccuracy(out_rets, labels_dir);
+  ASSERT_GE(out_accuracy, 0.57f);
+}
+
+}  // namespace lite
+}  // namespace paddle

lite/tests/api/test_mobilenetv1_int8_apu.cc

-// Copyright (c) 2019 PaddlePaddle Authors. 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 <fstream>
-#include <iostream>
-#include <numeric>
-#include <string>
-#include <vector>
-
-#include "lite/api/paddle_api.h"
-#include "lite/api/paddle_use_kernels.h"
-#include "lite/api/paddle_use_ops.h"
-#include "lite/api/paddle_use_passes.h"
-using namespace paddle::lite_api;  // NOLINT
-
-inline double GetCurrentUS() {
-  struct timeval time;
-  gettimeofday(&time, NULL);
-  return 1e+6 * time.tv_sec + time.tv_usec;
-}
-
-inline int64_t ShapeProduction(std::vector<int64_t> shape) {
-  int64_t s = 1;
-  for (int64_t dim : shape) {
-    s *= dim;
-  }
-  return s;
-}
-
-int main(int argc, char** argv) {
-  if (argc < 2) {
-    std::cerr << "[ERROR] usage: ./" << argv[0]
-              << " model_dir [thread_num] [warmup_times] [repeat_times] "
-                 "[input_data_path] [output_data_path]"
-              << std::endl;
-    return -1;
-  }
-  std::string model_dir = argv[1];
-  int thread_num = 1;
-  if (argc > 2) {
-    thread_num = atoi(argv[2]);
-  }
-  int warmup_times = 5;
-  if (argc > 3) {
-    warmup_times = atoi(argv[3]);
-  }
-  int repeat_times = 10;
-  if (argc > 4) {
-    repeat_times = atoi(argv[4]);
-  }
-  std::string input_data_path;
-  if (argc > 5) {
-    input_data_path = argv[5];
-  }
-  std::string output_data_path;
-  if (argc > 6) {
-    output_data_path = argv[6];
-  }
-  paddle::lite_api::CxxConfig config;
-  config.set_model_dir(model_dir);
-  config.set_threads(thread_num);
-  config.set_power_mode(paddle::lite_api::LITE_POWER_HIGH);
-  config.set_valid_places(
-      {paddle::lite_api::Place{
-           TARGET(kARM), PRECISION(kFloat), DATALAYOUT(kNCHW)},
-       paddle::lite_api::Place{
-           TARGET(kARM), PRECISION(kInt8), DATALAYOUT(kNCHW)},
-       paddle::lite_api::Place{
-           TARGET(kAPU), PRECISION(kInt8), DATALAYOUT(kNCHW)}});
-  auto predictor = paddle::lite_api::CreatePaddlePredictor(config);
-
-  std::unique_ptr<paddle::lite_api::Tensor> input_tensor(
-      std::move(predictor->GetInput(0)));
-  input_tensor->Resize({1, 3, 224, 224});
-  auto input_data = input_tensor->mutable_data<float>();
-  auto input_size = ShapeProduction(input_tensor->shape());
-
-  // test loop
-  int total_imgs = 500;
-  float test_num = 0;
-  float top1_num = 0;
-  float top5_num = 0;
-  int output_len = 1000;
-  std::vector<int> index(1000);
-  bool debug = true;  // false;
-  int show_step = 500;
-  for (int i = 0; i < total_imgs; i++) {
-    // set input
-    std::string filename = input_data_path + "/" + std::to_string(i);
-    std::ifstream fs(filename, std::ifstream::binary);
-    if (!fs.is_open()) {
-      std::cout << "open input file fail.";
-    }
-    auto input_data_tmp = input_data;
-    for (int i = 0; i < input_size; ++i) {
-      fs.read(reinterpret_cast<char*>(input_data_tmp), sizeof(*input_data_tmp));
-      input_data_tmp++;
-    }
-    int label = 0;
-    fs.read(reinterpret_cast<char*>(&label), sizeof(label));
-    fs.close();
-
-    if (debug && i % show_step == 0) {
-      std::cout << "input data:" << std::endl;
-      std::cout << input_data[0] << " " << input_data[10] << " "
-                << input_data[input_size - 1] << std::endl;
-      std::cout << "label:" << label << std::endl;
-    }
-
-    // run
-    predictor->Run();
-    auto output0 = predictor->GetOutput(0);
-    auto output0_data = output0->data<float>();
-
-    // get output
-    std::iota(index.begin(), index.end(), 0);
-    std::stable_sort(
-        index.begin(), index.end(), [output0_data](size_t i1, size_t i2) {
-          return output0_data[i1] > output0_data[i2];
-        });
-    test_num++;
-    if (label == index[0]) {
-      top1_num++;
-    }
-    for (int i = 0; i < 5; i++) {
-      if (label == index[i]) {
-        top5_num++;
-      }
-    }
-
-    if (debug && i % show_step == 0) {
-      std::cout << index[0] << " " << index[1] << " " << index[2] << " "
-                << index[3] << " " << index[4] << std::endl;
-      std::cout << output0_data[index[0]] << " " << output0_data[index[1]]
-                << " " << output0_data[index[2]] << " "
-                << output0_data[index[3]] << " " << output0_data[index[4]]
-                << std::endl;
-      std::cout << output0_data[630] << std::endl;
-    }
-    if (i % show_step == 0) {
-      std::cout << "step " << i << "; top1 acc:" << top1_num / test_num
-                << "; top5 acc:" << top5_num / test_num << std::endl;
-    }
-  }
-  std::cout << "final result:" << std::endl;
-  std::cout << "top1 acc:" << top1_num / test_num << std::endl;
-  std::cout << "top5 acc:" << top5_num / test_num << std::endl;
-  return 0;
-}

lite/tests/api/test_mobilenetv1_int8_mediatek_apu.cc

+// Copyright (c) 2019 PaddlePaddle Authors. 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 <gflags/gflags.h>
+#include <gtest/gtest.h>
+#include <vector>
+#include "lite/api/lite_api_test_helper.h"
+#include "lite/api/paddle_api.h"
+#include "lite/api/paddle_use_kernels.h"
+#include "lite/api/paddle_use_ops.h"
+#include "lite/api/paddle_use_passes.h"
+#include "lite/api/test_helper.h"
+#include "lite/tests/api/ILSVRC2012_utility.h"
+#include "lite/utils/cp_logging.h"
+
+DEFINE_string(data_dir, "", "data dir");
+DEFINE_int32(iteration, 100, "iteration times to run");
+DEFINE_int32(batch, 1, "batch of image");
+DEFINE_int32(channel, 3, "image channel");
+
+namespace paddle {
+namespace lite {
+
+TEST(MobileNetV1, test_mobilenetv1_int8_mediatek_apu) {
+  lite_api::CxxConfig config;
+  config.set_model_dir(FLAGS_model_dir);
+  config.set_valid_places({lite_api::Place{TARGET(kARM), PRECISION(kFloat)},
+                           lite_api::Place{TARGET(kARM), PRECISION(kInt8)},
+                           lite_api::Place{TARGET(kAPU), PRECISION(kInt8)}});
+  auto predictor = lite_api::CreatePaddlePredictor(config);
+
+  std::string raw_data_dir = FLAGS_data_dir + std::string("/raw_data");
+  std::vector<int> input_shape{
+      FLAGS_batch, FLAGS_channel, FLAGS_im_width, FLAGS_im_height};
+  auto raw_data = ReadRawData(raw_data_dir, input_shape, FLAGS_iteration);
+
+  int input_size = 1;
+  for (auto i : input_shape) {
+    input_size *= i;
+  }
+
+  for (int i = 0; i < FLAGS_warmup; ++i) {
+    auto input_tensor = predictor->GetInput(0);
+    input_tensor->Resize(
+        std::vector<int64_t>(input_shape.begin(), input_shape.end()));
+    auto* data = input_tensor->mutable_data<float>();
+    for (int j = 0; j < input_size; j++) {
+      data[j] = 0.f;
+    }
+    predictor->Run();
+  }
+
+  std::vector<std::vector<float>> out_rets;
+  out_rets.resize(FLAGS_iteration);
+  double cost_time = 0;
+  for (size_t i = 0; i < raw_data.size(); ++i) {
+    auto input_tensor = predictor->GetInput(0);
+    input_tensor->Resize(
+        std::vector<int64_t>(input_shape.begin(), input_shape.end()));
+    auto* data = input_tensor->mutable_data<float>();
+    memcpy(data, raw_data[i].data(), sizeof(float) * input_size);
+
+    double start = GetCurrentUS();
+    predictor->Run();
+    cost_time += GetCurrentUS() - start;
+
+    auto output_tensor = predictor->GetOutput(0);
+    auto output_shape = output_tensor->shape();
+    auto output_data = output_tensor->data<float>();
+    ASSERT_EQ(output_shape.size(), 2UL);
+    ASSERT_EQ(output_shape[0], 1);
+    ASSERT_EQ(output_shape[1], 1000);
+
+    int output_size = output_shape[0] * output_shape[1];
+    out_rets[i].resize(output_size);
+    memcpy(&(out_rets[i].at(0)), output_data, sizeof(float) * output_size);
+  }
+
+  LOG(INFO) << "================== Speed Report ===================";
+  LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
+            << ", warmup: " << FLAGS_warmup << ", batch: " << FLAGS_batch
+            << ", iteration: " << FLAGS_iteration << ", spend "
+            << cost_time / FLAGS_iteration / 1000.0 << " ms in average.";
+
+  std::string labels_dir = FLAGS_data_dir + std::string("/labels.txt");
+  float out_accuracy = CalOutAccuracy(out_rets, labels_dir);
+  ASSERT_GE(out_accuracy, 0.55f);
+}
+
+}  // namespace lite
+}  // namespace paddle

lite/tests/api/test_mobilenetv1_int8_rknpu.cc

-// Copyright (c) 2019 PaddlePaddle Authors. 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 <sys/time.h>
-#include <fstream>
-#include <iostream>
-#include <string>
-#include <vector>
-#include "lite/api/paddle_api.h"
-#include "lite/api/paddle_use_kernels.h"
-#include "lite/api/paddle_use_ops.h"
-#include "lite/api/paddle_use_passes.h"
-
-inline double GetCurrentUS() {
-  struct timeval time;
-  gettimeofday(&time, NULL);
-  return 1e+6 * time.tv_sec + time.tv_usec;
-}
-
-inline int64_t ShapeProduction(std::vector<int64_t> shape) {
-  int64_t s = 1;
-  for (int64_t dim : shape) {
-    s *= dim;
-  }
-  return s;
-}
-
-int main(int argc, char** argv) {
-  if (argc < 2) {
-    std::cerr << "[ERROR] usage: ./" << argv[0]
-              << " model_dir [thread_num] [warmup_times] [repeat_times] "
-                 "[input_data_path] [output_data_path]"
-              << std::endl;
-    return -1;
-  }
-  std::string model_dir = argv[1];
-  int thread_num = 1;
-  if (argc > 2) {
-    thread_num = atoi(argv[2]);
-  }
-  int warmup_times = 5;
-  if (argc > 3) {
-    warmup_times = atoi(argv[3]);
-  }
-  int repeat_times = 10;
-  if (argc > 4) {
-    repeat_times = atoi(argv[4]);
-  }
-  std::string input_data_path;
-  if (argc > 5) {
-    input_data_path = argv[5];
-  }
-  std::string output_data_path;
-  if (argc > 6) {
-    output_data_path = argv[6];
-  }
-  paddle::lite_api::CxxConfig config;
-  config.set_model_dir(model_dir);
-  config.set_threads(thread_num);
-  config.set_power_mode(paddle::lite_api::LITE_POWER_HIGH);
-  config.set_valid_places(
-      {paddle::lite_api::Place{
-           TARGET(kARM), PRECISION(kFloat), DATALAYOUT(kNCHW)},
-       paddle::lite_api::Place{
-           TARGET(kARM), PRECISION(kInt8), DATALAYOUT(kNCHW)},
-       paddle::lite_api::Place{
-           TARGET(kARM), PRECISION(kInt8), DATALAYOUT(kNCHW)},
-       paddle::lite_api::Place{
-           TARGET(kRKNPU), PRECISION(kInt8), DATALAYOUT(kNCHW)}});
-  auto predictor = paddle::lite_api::CreatePaddlePredictor(config);
-
-  std::unique_ptr<paddle::lite_api::Tensor> input_tensor(
-      std::move(predictor->GetInput(0)));
-  input_tensor->Resize({1, 3, 224, 224});
-  auto input_data = input_tensor->mutable_data<float>();
-  auto input_size = ShapeProduction(input_tensor->shape());
-  if (input_data_path.empty()) {
-    for (int i = 0; i < input_size; i++) {
-      input_data[i] = 1;
-    }
-  } else {
-    std::fstream fs(input_data_path, std::ios::in);
-    if (!fs.is_open()) {
-      std::cerr << "open input data file failed." << std::endl;
-      return -1;
-    }
-    for (int i = 0; i < input_size; i++) {
-      fs >> input_data[i];
-    }
-  }
-
-  for (int i = 0; i < warmup_times; ++i) {
-    predictor->Run();
-  }
-
-  auto start = GetCurrentUS();
-  for (int i = 0; i < repeat_times; ++i) {
-    predictor->Run();
-  }
-
-  std::cout << "Model: " << model_dir << ", threads num " << thread_num
-            << ", warmup times: " << warmup_times
-            << ", repeat times: " << repeat_times << ", spend "
-            << (GetCurrentUS() - start) / repeat_times / 1000.0
-            << " ms in average." << std::endl;
-
-  std::unique_ptr<const paddle::lite_api::Tensor> output_tensor(
-      std::move(predictor->GetOutput(0)));
-  auto output_data = output_tensor->data<float>();
-  auto output_size = ShapeProduction(output_tensor->shape());
-  std::cout << "output data:";
-  for (int i = 0; i < output_size; i += 100) {
-    std::cout << "[" << i << "] " << output_data[i] << std::endl;
-  }
-  return 0;
-}

lite/tests/api/test_mobilenetv1_int8_rockchip_npu.cc

+// Copyright (c) 2019 PaddlePaddle Authors. 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 <gflags/gflags.h>
+#include <gtest/gtest.h>
+#include <vector>
+#include "lite/api/lite_api_test_helper.h"
+#include "lite/api/paddle_api.h"
+#include "lite/api/paddle_use_kernels.h"
+#include "lite/api/paddle_use_ops.h"
+#include "lite/api/paddle_use_passes.h"
+#include "lite/api/test_helper.h"
+#include "lite/tests/api/ILSVRC2012_utility.h"
+#include "lite/utils/cp_logging.h"
+
+DEFINE_string(data_dir, "", "data dir");
+DEFINE_int32(iteration, 100, "iteration times to run");
+DEFINE_int32(batch, 1, "batch of image");
+DEFINE_int32(channel, 3, "image channel");
+
+namespace paddle {
+namespace lite {
+
+TEST(MobileNetV1, test_mobilenetv1_int8_rockchip_apu) {
+  lite_api::CxxConfig config;
+  config.set_model_dir(FLAGS_model_dir);
+  config.set_valid_places({lite_api::Place{TARGET(kARM), PRECISION(kFloat)},
+                           lite_api::Place{TARGET(kARM), PRECISION(kInt8)},
+                           lite_api::Place{TARGET(kRKNPU), PRECISION(kInt8)}});
+  auto predictor = lite_api::CreatePaddlePredictor(config);
+
+  std::string raw_data_dir = FLAGS_data_dir + std::string("/raw_data");
+  std::vector<int> input_shape{
+      FLAGS_batch, FLAGS_channel, FLAGS_im_width, FLAGS_im_height};
+  auto raw_data = ReadRawData(raw_data_dir, input_shape, FLAGS_iteration);
+
+  int input_size = 1;
+  for (auto i : input_shape) {
+    input_size *= i;
+  }
+
+  for (int i = 0; i < FLAGS_warmup; ++i) {
+    auto input_tensor = predictor->GetInput(0);
+    input_tensor->Resize(
+        std::vector<int64_t>(input_shape.begin(), input_shape.end()));
+    auto* data = input_tensor->mutable_data<float>();
+    for (int j = 0; j < input_size; j++) {
+      data[j] = 0.f;
+    }
+    predictor->Run();
+  }
+
+  std::vector<std::vector<float>> out_rets;
+  out_rets.resize(FLAGS_iteration);
+  double cost_time = 0;
+  for (size_t i = 0; i < raw_data.size(); ++i) {
+    auto input_tensor = predictor->GetInput(0);
+    input_tensor->Resize(
+        std::vector<int64_t>(input_shape.begin(), input_shape.end()));
+    auto* data = input_tensor->mutable_data<float>();
+    memcpy(data, raw_data[i].data(), sizeof(float) * input_size);
+
+    double start = GetCurrentUS();
+    predictor->Run();
+    cost_time += GetCurrentUS() - start;
+
+    auto output_tensor = predictor->GetOutput(0);
+    auto output_shape = output_tensor->shape();
+    auto output_data = output_tensor->data<float>();
+    ASSERT_EQ(output_shape.size(), 2UL);
+    ASSERT_EQ(output_shape[0], 1);
+    ASSERT_EQ(output_shape[1], 1000);
+
+    int output_size = output_shape[0] * output_shape[1];
+    out_rets[i].resize(output_size);
+    memcpy(&(out_rets[i].at(0)), output_data, sizeof(float) * output_size);
+  }
+
+  LOG(INFO) << "================== Speed Report ===================";
+  LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
+            << ", warmup: " << FLAGS_warmup << ", batch: " << FLAGS_batch
+            << ", iteration: " << FLAGS_iteration << ", spend "
+            << cost_time / FLAGS_iteration / 1000.0 << " ms in average.";
+
+  std::string labels_dir = FLAGS_data_dir + std::string("/labels.txt");
+  float out_accuracy = CalOutAccuracy(out_rets, labels_dir);
+  ASSERT_GE(out_accuracy, 0.52f);
+}
+
+}  // namespace lite
+}  // namespace paddle

lite/tests/api/test_mobilenetv2_fp32_huawei_kirin_npu.cc

+// Copyright (c) 2019 PaddlePaddle Authors. 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 <gflags/gflags.h>
+#include <gtest/gtest.h>
+#include <vector>
+#include "lite/api/lite_api_test_helper.h"
+#include "lite/api/paddle_api.h"
+#include "lite/api/paddle_use_kernels.h"
+#include "lite/api/paddle_use_ops.h"
+#include "lite/api/paddle_use_passes.h"
+#include "lite/api/test_helper.h"
+#include "lite/tests/api/ILSVRC2012_utility.h"
+#include "lite/utils/cp_logging.h"
+
+DEFINE_string(data_dir, "", "data dir");
+DEFINE_int32(iteration, 100, "iteration times to run");
+DEFINE_int32(batch, 1, "batch of image");
+DEFINE_int32(channel, 3, "image channel");
+
+namespace paddle {
+namespace lite {
+
+TEST(MobileNetV2, test_mobilenetv2_fp32_huawei_kirin_npu) {
+  lite_api::CxxConfig config;
+  config.set_model_dir(FLAGS_model_dir);
+  config.set_valid_places({lite_api::Place{TARGET(kARM), PRECISION(kFloat)},
+                           lite_api::Place{TARGET(kNPU), PRECISION(kFloat)}});
+  auto predictor = lite_api::CreatePaddlePredictor(config);
+
+  std::string raw_data_dir = FLAGS_data_dir + std::string("/raw_data");
+  std::vector<int> input_shape{
+      FLAGS_batch, FLAGS_channel, FLAGS_im_width, FLAGS_im_height};
+  auto raw_data = ReadRawData(raw_data_dir, input_shape, FLAGS_iteration);
+
+  int input_size = 1;
+  for (auto i : input_shape) {
+    input_size *= i;
+  }
+
+  for (int i = 0; i < FLAGS_warmup; ++i) {
+    auto input_tensor = predictor->GetInput(0);
+    input_tensor->Resize(
+        std::vector<int64_t>(input_shape.begin(), input_shape.end()));
+    auto* data = input_tensor->mutable_data<float>();
+    for (int j = 0; j < input_size; j++) {
+      data[j] = 0.f;
+    }
+    predictor->Run();
+  }
+
+  std::vector<std::vector<float>> out_rets;
+  out_rets.resize(FLAGS_iteration);
+  double cost_time = 0;
+  for (size_t i = 0; i < raw_data.size(); ++i) {
+    auto input_tensor = predictor->GetInput(0);
+    input_tensor->Resize(
+        std::vector<int64_t>(input_shape.begin(), input_shape.end()));
+    auto* data = input_tensor->mutable_data<float>();
+    memcpy(data, raw_data[i].data(), sizeof(float) * input_size);
+
+    double start = GetCurrentUS();
+    predictor->Run();
+    cost_time += GetCurrentUS() - start;
+
+    auto output_tensor = predictor->GetOutput(0);
+    auto output_shape = output_tensor->shape();
+    auto output_data = output_tensor->data<float>();
+    ASSERT_EQ(output_shape.size(), 2UL);
+    ASSERT_EQ(output_shape[0], 1);
+    ASSERT_EQ(output_shape[1], 1000);
+
+    int output_size = output_shape[0] * output_shape[1];
+    out_rets[i].resize(output_size);
+    memcpy(&(out_rets[i].at(0)), output_data, sizeof(float) * output_size);
+  }
+
+  LOG(INFO) << "================== Speed Report ===================";
+  LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
+            << ", warmup: " << FLAGS_warmup << ", batch: " << FLAGS_batch
+            << ", iteration: " << FLAGS_iteration << ", spend "
+            << cost_time / FLAGS_iteration / 1000.0 << " ms in average.";
+
+  std::string labels_dir = FLAGS_data_dir + std::string("/labels.txt");
+  float out_accuracy = CalOutAccuracy(out_rets, labels_dir);
+  ASSERT_GE(out_accuracy, 0.57f);
+}
+
+}  // namespace lite
+}  // namespace paddle

lite/tests/api/test_resnet50_fp32_huawei_kirin_npu.cc

+// Copyright (c) 2019 PaddlePaddle Authors. 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 <gflags/gflags.h>
+#include <gtest/gtest.h>
+#include <vector>
+#include "lite/api/lite_api_test_helper.h"
+#include "lite/api/paddle_api.h"
+#include "lite/api/paddle_use_kernels.h"
+#include "lite/api/paddle_use_ops.h"
+#include "lite/api/paddle_use_passes.h"
+#include "lite/api/test_helper.h"
+#include "lite/tests/api/ILSVRC2012_utility.h"
+#include "lite/utils/cp_logging.h"
+
+DEFINE_string(data_dir, "", "data dir");
+DEFINE_int32(iteration, 100, "iteration times to run");
+DEFINE_int32(batch, 1, "batch of image");
+DEFINE_int32(channel, 3, "image channel");
+
+namespace paddle {
+namespace lite {
+
+TEST(ResNet50, test_resnet50_fp32_huawei_kirin_npu) {
+  lite_api::CxxConfig config;
+  config.set_model_dir(FLAGS_model_dir);
+  config.set_valid_places({lite_api::Place{TARGET(kARM), PRECISION(kFloat)},
+                           lite_api::Place{TARGET(kNPU), PRECISION(kFloat)}});
+  auto predictor = lite_api::CreatePaddlePredictor(config);
+
+  std::string raw_data_dir = FLAGS_data_dir + std::string("/raw_data");
+  std::vector<int> input_shape{
+      FLAGS_batch, FLAGS_channel, FLAGS_im_width, FLAGS_im_height};
+  auto raw_data = ReadRawData(raw_data_dir, input_shape, FLAGS_iteration);
+
+  int input_size = 1;
+  for (auto i : input_shape) {
+    input_size *= i;
+  }
+
+  for (int i = 0; i < FLAGS_warmup; ++i) {
+    auto input_tensor = predictor->GetInput(0);
+    input_tensor->Resize(
+        std::vector<int64_t>(input_shape.begin(), input_shape.end()));
+    auto* data = input_tensor->mutable_data<float>();
+    for (int j = 0; j < input_size; j++) {
+      data[j] = 0.f;
+    }
+    predictor->Run();
+  }
+
+  std::vector<std::vector<float>> out_rets;
+  out_rets.resize(FLAGS_iteration);
+  double cost_time = 0;
+  for (size_t i = 0; i < raw_data.size(); ++i) {
+    auto input_tensor = predictor->GetInput(0);
+    input_tensor->Resize(
+        std::vector<int64_t>(input_shape.begin(), input_shape.end()));
+    auto* data = input_tensor->mutable_data<float>();
+    memcpy(data, raw_data[i].data(), sizeof(float) * input_size);
+
+    double start = GetCurrentUS();
+    predictor->Run();
+    cost_time += GetCurrentUS() - start;
+
+    auto output_tensor = predictor->GetOutput(0);
+    auto output_shape = output_tensor->shape();
+    auto output_data = output_tensor->data<float>();
+    ASSERT_EQ(output_shape.size(), 2UL);
+    ASSERT_EQ(output_shape[0], 1);
+    ASSERT_EQ(output_shape[1], 1000);
+
+    int output_size = output_shape[0] * output_shape[1];
+    out_rets[i].resize(output_size);
+    memcpy(&(out_rets[i].at(0)), output_data, sizeof(float) * output_size);
+  }
+
+  LOG(INFO) << "================== Speed Report ===================";
+  LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
+            << ", warmup: " << FLAGS_warmup << ", batch: " << FLAGS_batch
+            << ", iteration: " << FLAGS_iteration << ", spend "
+            << cost_time / FLAGS_iteration / 1000.0 << " ms in average.";
+
+  std::string labels_dir = FLAGS_data_dir + std::string("/labels.txt");
+  float out_accuracy = CalOutAccuracy(out_rets, labels_dir);
+  ASSERT_GE(out_accuracy, 0.64f);
+}
+
+}  // namespace lite
+}  // namespace paddle