module API 输出为空

[jtyan123]

module API 输出为空

[jxt1234]

1. 先用 testMNNFromOnnx.py 测试保证 mnn 模型没问题，然后用 GetMNNInfo 看下模型信息。
2. 输出为空时会打 log 的
3. 这个看上去是用的 uint8_t 输入，大概率是有问题的，一般都是float

[jtyan123]

1:testMNNFromOnnx.py成功了输出如下：

Dir exist
onnx/test.onnx
2024-04-12 17:26:12.595200 [W:onnxruntime:, graph.cc:3593 CleanUnusedInitializersAndNodeArgs] Removing initializer '370'. It is not used by any node and should be removed from the model.
tensor(float)
['keypoints', 'scores', 'descriptors']
inputs:
image
onnx/
outputs:
onnx/keypoints.txt (1, 564, 2)
onnx/
onnx/scores.txt (1, 564)
onnx/
onnx/descriptors.txt (1, 564, 256)
onnx/
hw.cpufamily: 458787763 , size = 4
The device support i8sdot:1, support fp16:1, support i8mm: 0
Start to Convert Other Model Format To MNN Model..., target version: 2.8
[17:26:13] /Users/yjt/Downloads/MNN-2.8.1/tools/converter/source/onnx/onnxConverter.cpp:46: ONNX Model ir version: 8
[17:26:13] /Users/yjt/Downloads/MNN-2.8.1/tools/converter/source/onnx/onnxConverter.cpp:47: ONNX Model opset version: 17
[17:26:13] /Users/yjt/Downloads/MNN-2.8.1/tools/converter/source/onnx/onnxConverter.cpp:133: Check it out ==> /Clip_output_0 has empty input, the index is 2
[17:26:13] /Users/yjt/Downloads/MNN-2.8.1/tools/converter/source/onnx/onnxConverter.cpp:133: Check it out ==> /Clip_1_output_0 has empty input, the index is 2
Start to Optimize the MNN Net...
inputTensors : [ image, ]
outputTensors: [ descriptors, keypoints, scores, ]
The model has subgraphs, please use MNN::Express::Module to run it
Converted Success!
Check convert result by onnx, thredhold is 0.01
image
output: keypoints
output: scores
output: descriptors
keypoints: (1, 564, 2, )
scores: (1, 564, )
descriptors: (1, 564, 256, )
TEST_SUCCESS

2:GetMNNInfo信息如下：

hw.cpufamily: 458787763 , size = 4
The device support i8sdot:1, support fp16:1, support i8mm: 0
Model default dimensionFormat is NCHW
Model Inputs:
[ image ]: dimensionFormat: NC4HW4, size: [ 1,1,320,640 ], type is float
Model Outputs:
[ descriptors ]
[ keypoints ]
[ scores ]
Model Version: 2.8.1 

3:相关代码也改成float了。但是输出还是为空。为什么呢？

std::vector<MNN::Express::VARP> getMNNInputs(std::string file_name) {
    int width = 640;
    int height = 320;
    int channels;
    unsigned char *data = stbi_load(file_name.c_str(), &width, &height, &channels, 3);
    if (data == nullptr) {
        std::cout << "Failed to load image: " << file_name << std::endl;
        return {};
    }
    MNN::CV::ImageProcess::Config config;
    config.filterType = MNN::CV::BILINEAR;
    float mean[3]     = {0.0f, 0.0f, 0.0f};
    float normals[3] = {1.0f / 255.0f, 1.0f / 255.0f, 1.0f / 255.0f};
    ::memcpy(config.mean, mean, sizeof(mean));
    ::memcpy(config.normal, normals, sizeof(normals));
    config.sourceFormat = RGB;
    config.destFormat = GRAY;
    std::shared_ptr<MNN::CV::ImageProcess> pretreat(MNN::CV::ImageProcess::create(config));
    auto img_tensor = MNN::Express::_Input({1, height, width, 1}, MNN::Express::NHWC, halide_type_of<float>());
    pretreat->convert(data, width, height, 0, img_tensor->writeMap<float>(), width, height, 0, 0, halide_type_of<float>());
    img_tensor = MNN::Express::_Convert(img_tensor, MNN::Express::NC4HW4);
    std::vector<MNN::Express::VARP> inputs;
    inputs.emplace_back(img_tensor);
    stbi_image_free(data);
    return inputs;
}

[jxt1234]

转模型时加个 --keepInputFormat=true ，

img_tensor = MNN::Express::_Convert(img_tensor, MNN::Express::NC4HW4);
改成
img_tensor = MNN::Express::_Convert(img_tensor, MNN::Express::NCHW);

试下，另外看下 log

[jxt1234]

我看你运行代码是 ios 上，先确认一下 ios 上的 mnn 版本和 pc 上一致

[jtyan123]

已解决。问题是输入写的不对fix如下：

std::vector<MNN::Express::VARP> getMNNInputs(std::string file_name) {
    int width = 640;
    int height = 320;
    int inputWidth = 0;
    int inputHeight = 0;
    int channels;
    unsigned char *data = stbi_load(file_name.c_str(), &inputWidth, &inputHeight, &channels, 3);
    if (data == nullptr) {
        std::cout << "Failed to load image: " << file_name << std::endl;
        return {};
    }
    MNN::CV::ImageProcess::Config config;
    config.filterType = MNN::CV::BILINEAR;
    float mean[3]     = {0.0f, 0.0f, 0.0f};
    float normals[3] = {1.0f / 255.0f, 1.0f / 255.0f, 1.0f / 255.0f};
    ::memcpy(config.mean, mean, sizeof(mean));
    ::memcpy(config.normal, normals, sizeof(normals));
    config.sourceFormat = RGB;
    config.destFormat = GRAY;
    Matrix trans;
    // Set transform, from dst scale to src, the ways below are both ok
    trans.setScale((float)(inputWidth-1) / (width-1), (float)(inputHeight-1) / (height-1));
    std::shared_ptr<MNN::CV::ImageProcess> pretreat(MNN::CV::ImageProcess::create(config));
    pretreat->setMatrix(trans);
    auto img_tensor = MNN::Express::_Input({1,1, height, width}, MNN::Express::NC4HW4, halide_type_of<float>());
    pretreat->convert(data, inputWidth, inputHeight, 0, img_tensor->writeMap<float>(), width, height, 0, 0, halide_type_of<float>());
    std::vector<MNN::Express::VARP> inputs;
    inputs.emplace_back(img_tensor);
    stbi_image_free(data);
    return inputs;
}