Fix dir of test case

tests/cxx_st/dataset/test_de.cc

@@ -28,42 +28,29 @@
 using namespace mindspore::api;
 using namespace mindspore::dataset::vision;
 
-static void SaveFile(int idx, Buffer buffer, int seq) {
-  std::string path = "mnt/disk1/yolo_dvpp_result/result_Files/output" + std::to_string(idx) +
-                     "_in_YoloV3-DarkNet_coco_bs_dvpp_" + std::to_string(seq) + ".bin";
-  FILE *output_file = fopen(path.c_str(), "wb");
-  if (output_file == nullptr) {
-    std::cout << "Write file" << path << "failed when fopen" << std::endl;
-    return;
-  }
-
-  size_t wsize = fwrite(buffer.Data(), buffer.DataSize(), sizeof(int8_t), output_file);
-  if (wsize == 0) {
-    std::cout << "Write file" << path << " failed when fwrite." << std::endl;
-    return;
-  }
-  fclose(output_file);
-  std::cout << "Save file " << path << "length" << buffer.DataSize() << " success." << std::endl;
-}
-
 class TestDE : public ST::Common {
  public:
   TestDE() {}
 };
 
-TEST_F(TestDE, ResNetPreprocess) {
+TEST_F(TestDE, TestResNetPreprocess) {
+  // Read images from target directory
   std::vector<std::shared_ptr<Tensor>> images;
   MindDataEager::LoadImageFromDir("/home/workspace/mindspore_dataset/imagenet/imagenet_original/val/n01440764",
                                   &images);
 
-  MindDataEager Compose({Decode(), Resize({224, 224}),
-                         Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}),
-                         HWC2CHW()});
+  // Define transform operations
+  MindDataEager Transform({Decode(), Resize({224, 224}),
+                           Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}),
+                           HWC2CHW()});
 
+  // Apply transform on images
   for (auto &img : images) {
-    img = Compose(img);
+    img = Transform(img);
   }
 
+  // Check shape of result
+  ASSERT_NE(images.size(), 0);
   ASSERT_EQ(images[0]->Shape().size(), 3);
   ASSERT_EQ(images[0]->Shape()[0], 3);
   ASSERT_EQ(images[0]->Shape()[1], 224);
@@ -71,55 +58,27 @@ TEST_F(TestDE, ResNetPreprocess) {
 }
 
 TEST_F(TestDE, TestDvpp) {
+  // Read images from target directory
   std::vector<std::shared_ptr<Tensor>> images;
-  MindDataEager::LoadImageFromDir("/root/Dvpp_Unit_Dev/val2014_test/", &images);
+  MindDataEager::LoadImageFromDir("/home/workspace/mindspore_dataset/imagenet/imagenet_original/val/n01440764",
+                                  &images);
+
+  // Define dvpp transform
   std::vector<uint32_t> crop_size = {224, 224};
   std::vector<uint32_t> resize_size = {256, 256};
-  MindDataEager Solo({DvppDecodeResizeCropJpeg(crop_size, resize_size)});
+  MindDataEager Transform({DvppDecodeResizeCropJpeg(crop_size, resize_size)});
+
+  // Apply transform on images
   for (auto &img : images) {
-    img = Solo(img);
-    ASSERT_EQ(images[0]->Shape().size(), 3);
+    img = Transform(img);
+    ASSERT_NE(img, nullptr);
+    ASSERT_EQ(img->Shape().size(), 3);
     if (crop_size.size() == 1) {
-      ASSERT_EQ(images[0]->Shape()[0], pow(crop_size[0], 2) 1.5);
+      ASSERT_EQ(img->Shape()[0], pow(crop_size[0], 2) * 1.5);
     } else {
-      ASSERT_EQ(images[0]->Shape()[0], crop_size[0] * crop_size[1] * 1.5);
-    }
-    ASSERT_EQ(images[0]->Shape()[1], 1);
-    ASSERT_EQ(images[0]->Shape()[2], 1);
-  }
-}
-
-TEST_F(TestDE, TestYoloV3_with_Dvpp) {
-  std::vector<std::shared_ptr<Tensor>> images;
-  MindDataEager::LoadImageFromDir("/home/lizhenglong/val2014", &images);
-  MindDataEager SingleOp({DvppDecodeResizeCropJpeg({416, 416}, {416, 416})});
-  constexpr auto yolo_mindir_file = "/home/zhoufeng/yolov3/yolov3_darknet53.mindir";
-  Context::Instance().SetDeviceTarget(kDeviceTypeAscend310).SetDeviceID(1);
-  auto graph = Serialization::LoadModel(yolo_mindir_file, ModelType::kMindIR);
-  Model yolov3((GraphCell(graph)));
-  Status ret = yolov3.Build({{kModelOptionInsertOpCfgPath, "/mnt/disk1/yolo_dvpp_result/aipp_resnet50.cfg"}});
-  ASSERT_TRUE(ret == SUCCESS);
-
-  std::vector<std::string> names;
-  std::vector<std::vector<int64_t>> shapes;
-  std::vector<DataType> data_types;
-  std::vector<size_t> mem_sizes;
-  yolov3.GetOutputsInfo(&names, &shapes, &data_types, &mem_sizes);
-  std::vector<Buffer> outputs;
-  std::vector<Buffer> inputs;
-
-  int64_t seq = 0;
-  for (auto &img : images) {
-    img = SingleOp(img);
-    std::vector<float> input_shape = {416, 416};
-    inputs.clear();
-    inputs.emplace_back(img->Data(), img->DataSize());
-    inputs.emplace_back(input_shape.data(), input_shape.size() * sizeof(float));
-    ret = yolov3.Predict(inputs, &outputs);
-    for (size_t i = 0; i < outputs.size(); ++i) {
-      SaveFile(i, outputs[i], seq);
+      ASSERT_EQ(img->Shape()[0], crop_size[0] * crop_size[1] * 1.5);
     }
-    seq++;
-    ASSERT_TRUE(ret == SUCCESS);
+    ASSERT_EQ(img->Shape()[1], 1);
+    ASSERT_EQ(img->Shape()[2], 1);
   }
 }