cwenzi/neuroflow-cpp

1#include <iostream>2#include <exception>3#include "../include/neuroflow/model.hpp"4#include "../include/neuroflow/memory.hpp"5#include "../include/neuroflow/networks.hpp"6 7using namespace neuroflow;8 9int main() {10    try {11        std::cout << "Step by step forward test..." << std::endl;12        13        NeuroFlowModel::Config cfg;14        cfg.input_dim = 64;15        cfg.hidden_dim = 32;16        cfg.output_dim = 5;17        cfg.memory_slots = 8;18        cfg.memory_dim = 16;19        cfg.num_layers = 1;20        cfg.num_associations = 2;21        cfg.use_mla = false;22        23        NeuroFlowModel model(cfg);24        size_t batch = 2;25        26        Tensor x({batch, cfg.input_dim});27        for (size_t i = 0; i < x.numel(); ++i) x.as_fp32()[i] = 0.1f * i;28        29        std::cout << "1. Input projection..." << std::endl;30        Tensor h = model.input_proj_linear->forward(x);31        std::cout << "  h shape: [" << h.shape_[0] << ", " << h.shape_[1] << "]" << std::endl;32        h = model.input_proj_norm->forward(h);33        h = model.input_proj_gelu->forward(h);34        std::cout << "  after norm/gelu: [" << h.shape_[0] << ", " << h.shape_[1] << "]" << std::endl;35        36        std::cout << "2. SN forward..." << std::endl;37        auto sn_out = model.sn->forward(h);38        std::cout << "  saliency: [" << sn_out.saliency.shape_[0] << ", " << sn_out.saliency.shape_[1] << "]" << std::endl;39        std::cout << "  gates: [" << sn_out.gates.shape_[0] << ", " << sn_out.gates.shape_[1] << "]" << std::endl;40        41        std::cout << "3. ECN forward..." << std::endl;42        auto ecn_out = model.ecn->forward(h);43        std::cout << "  decision: [" << ecn_out.decision.shape_[0] << ", " << ecn_out.decision.shape_[1] << "]" << std::endl;44        std::cout << "  value: [" << ecn_out.value.shape_[0] << ", " << ecn_out.value.shape_[1] << "]" << std::endl;45        46        std::cout << "4. Memory encode..." << std::endl;47        Tensor mem_seed = model.memory->encode(h);48        std::cout << "  mem_seed: [" << mem_seed.shape_[0] << ", " << mem_seed.shape_[1] << "]" << std::endl;49        50        std::cout << "5. DMN forward..." << std::endl;51        auto dmn_out = model.dmn->forward(mem_seed);52        std::cout << "  vision shape size: " << dmn_out.vision.shape_.size() << std::endl;53        for (size_t i = 0; i < dmn_out.vision.shape_.size(); ++i) 54            std::cout << "    dim " << i << ": " << dmn_out.vision.shape_[i] << std::endl;55        56        std::cout << "6. Memory retrieve..." << std::endl;57        auto mem_out = model.memory->forward(h);58        std::cout << "  retrieved: [" << mem_out.retrieved.shape_[0] << ", " << mem_out.retrieved.shape_[1] << "]" << std::endl;59        60        std::cout << "7. Reshaping dmn_out.vision..." << std::endl;61        std::cout << "  vision numel: " << dmn_out.vision.numel() << std::endl;62        std::cout << "  trying reshape to [" << batch << ", " << dmn_out.vision.shape_[1] << "]" << std::endl;63        std::cout << "  expected numel: " << (batch * dmn_out.vision.shape_[1]) << std::endl;64        65        if (dmn_out.vision.numel() != batch * dmn_out.vision.shape_[1]) {66            std::cout << "  MISMATCH! vision actual shape may be different" << std::endl;67        }68        69        Tensor dmn_weighted = dmn_out.vision.reshape({batch, dmn_out.vision.shape_[1]});70        std::cout << "  reshape success" << std::endl;71        72        std::cout << "All steps passed!" << std::endl;73        return 0;74    } catch (const std::exception& e) {75        std::cout << "Error: " << e.what() << std::endl;76        return 1;77    }78}79