Function SetRandomInputs

tools/pplnn.cc:549–595

}

static bool SetRandomInputs(const vector<vector<int64_t>>& input_shapes, Runtime* runtime, vector<string>* input_data) {
    for (uint32_t c = 0; c < runtime->GetInputCount(); ++c) {
        auto t = runtime->GetInputTensor(c);
        auto shape = t->GetShape();

        if (input_shapes.empty()) {
            auto dim_count = shape->GetRealDimCount();
            if (dim_count == 0) {
                continue;
            }

            auto dims = GenerateRandomDims(dim_count);

            if (shape->GetDim(0) == INVALID_DIM_VALUE) {
                shape->SetDim(0, 1);
            }
            for (uint32_t j = 1; j < dim_count; ++j) {
                if (shape->GetDim(j) == INVALID_DIM_VALUE) {
                    shape->SetDim(j, dims[j]);
                }
            }
        } else {
            shape->Reshape(input_shapes[c]);
        }

        auto nr_element = shape->CalcBytesIncludingPadding() / sizeof(float);
        vector<float> buffer(nr_element);

        std::default_random_engine eng;
        std::uniform_real_distribution<float> dis(-1.0f, 1.0f);
        for (uint32_t i = 0; i < nr_element; ++i) {
            buffer[i] = dis(eng);
        }

        ppl::nn::TensorShape src_desc = *t->GetShape();
        src_desc.SetDataFormat(DATAFORMAT_NDARRAY);
        auto status = t->ConvertFromHost(buffer.data(), src_desc);
        if (status != RC_SUCCESS) {
            LOG(ERROR) << "set tensor[" << t->GetName() << "] content failed: " << GetRetCodeStr(status);
            return false;
        }

        input_data->emplace_back(string((const char*)buffer.data(), buffer.size() * sizeof(float)));
    }

    return true;
}

static string GetBasename(const string& path) {
    string last_entry;