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Fix dir of test case

This commit is contained in:
luoyang 2020-12-18 21:19:43 +08:00
parent ad1766492d
commit b40af48366
1 changed files with 25 additions and 66 deletions
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91
tests/cxx_st/dataset/test_de.cc
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@ -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(img->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);
}
seq++;
ASSERT_TRUE(ret == SUCCESS);
ASSERT_EQ(img->Shape()[1], 1);
ASSERT_EQ(img->Shape()[2], 1);
}
}