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Improve model summary

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This commit is contained in:
Matúš Námešný
2024-05-28 19:24:56 +02:00
parent 098fd65074
commit 046c1b0cc2
1 changed files with 87 additions and 38 deletions
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123
src/model/model.cpp
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@@ -2,6 +2,7 @@
#include <fstream>
#include <iostream>
#include <iomanip>
#include <string>
#include <unordered_map>
#include <vector>
@@ -11,13 +12,18 @@
using namespace CUDANet;
Model::Model(const shape2d inputSize, const int inputChannels, const int outputSize)
Model::Model(
const shape2d inputSize,
const int inputChannels,
const int outputSize
)
: inputSize(inputSize),
inputChannels(inputChannels),
outputSize(outputSize),
layers(std::vector<std::pair<std::string, Layers::SequentialLayer*>>()),
layerMap(std::unordered_map<std::string, Layers::SequentialLayer*>()) {
inputLayer = new Layers::Input(inputSize.first * inputSize.second * inputChannels);
inputLayer =
new Layers::Input(inputSize.first * inputSize.second * inputChannels);
outputLayer = new Layers::Output(outputSize);
};
@@ -50,11 +56,10 @@ float* Model::predict(const float* input) {
}
void Model::addLayer(const std::string& name, Layers::SequentialLayer* layer) {
Module* module = dynamic_cast<Module*>(layer);
if (module != nullptr) {
layers.push_back({ name, module });
layers.push_back({name, module});
for (const auto& moduleLayer : module->getLayers()) {
layerMap[moduleLayer.first] = moduleLayer.second;
}
@@ -62,7 +67,7 @@ void Model::addLayer(const std::string& name, Layers::SequentialLayer* layer) {
return;
}
layers.push_back({ name, layer });
layers.push_back({name, layer});
layerMap[name] = layer;
}
@@ -93,35 +98,33 @@ void Model::loadWeights(const std::string& path) {
file.read(&header[0], headerSize);
std::vector<TensorInfo> tensorInfos;
size_t pos = 0;
size_t pos = 0;
while (pos < header.size()) {
size_t nextPos = header.find('\n', pos);
if (nextPos == std::string::npos)
break;
if (nextPos == std::string::npos) break;
std::string line = header.substr(pos, nextPos - pos);
pos = nextPos + 1;
pos = nextPos + 1;
size_t commaPos = line.find(',');
if (commaPos == std::string::npos)
continue;
if (commaPos == std::string::npos) continue;
// Parse tensor name into name and type
std::string nameStr = line.substr(0, commaPos);
size_t dotPos = nameStr.find_last_of('.');
if (dotPos == std::string::npos)
continue;
size_t dotPos = nameStr.find_last_of('.');
if (dotPos == std::string::npos) continue;
std::string name = nameStr.substr(0, dotPos);
TensorType type = nameStr.substr(dotPos + 1) == "weight" ? TensorType::WEIGHT : TensorType::BIAS;
TensorType type = nameStr.substr(dotPos + 1) == "weight"
? TensorType::WEIGHT
: TensorType::BIAS;
line = line.substr(commaPos + 1);
commaPos = line.find(',');
if (commaPos == std::string::npos)
continue;
if (commaPos == std::string::npos) continue;
int size = std::stoi(line.substr(0, commaPos));
int size = std::stoi(line.substr(0, commaPos));
int offset = std::stoi(line.substr(commaPos + 1));
tensorInfos.push_back({name, type, size, offset});
@@ -130,39 +133,49 @@ void Model::loadWeights(const std::string& path) {
for (const auto& tensorInfo : tensorInfos) {
std::vector<float> values(tensorInfo.size);
file.seekg(sizeof(version) + sizeof(headerSize) + header.size() + tensorInfo.offset);
file.read(reinterpret_cast<char*>(values.data()), tensorInfo.size * sizeof(float));
file.seekg(
sizeof(version) + sizeof(headerSize) + header.size() +
tensorInfo.offset
);
file.read(
reinterpret_cast<char*>(values.data()),
tensorInfo.size * sizeof(float)
);
if (layerMap.find(tensorInfo.name) != layerMap.end()) {
Layers::WeightedLayer* wLayer = dynamic_cast<Layers::WeightedLayer*>(layerMap[tensorInfo.name]);
Layers::WeightedLayer* wLayer =
dynamic_cast<Layers::WeightedLayer*>(layerMap[tensorInfo.name]);
if (wLayer == nullptr) {
std::cerr << "Layer: " << tensorInfo.name << " does not have weights" << std::endl;
std::cerr << "Layer: " << tensorInfo.name
<< " does not have weights" << std::endl;
continue;
}
if (tensorInfo.type == TensorType::WEIGHT) {
if (wLayer->getWeights().size() != values.size()) {
std::cerr << "Layer: " << tensorInfo.name << " has incorrect number of weights, expected "
<< wLayer->getWeights().size() << " but got " << values.size() << ", skipping" << std::endl;
std::cerr << "Layer: " << tensorInfo.name
<< " has incorrect number of weights, expected "
<< wLayer->getWeights().size() << " but got "
<< values.size() << ", skipping" << std::endl;
continue;
}
wLayer->setWeights(values.data());
} else if (tensorInfo.type == TensorType::BIAS) {
if (wLayer->getBiases().size() != values.size()) {
std::cerr << "Layer: " << tensorInfo.name << " has incorrect number of biases, expected "
<< wLayer->getBiases().size() << " but got " << values.size() << ", skipping" << std::endl;
std::cerr << "Layer: " << tensorInfo.name
<< " has incorrect number of biases, expected "
<< wLayer->getBiases().size() << " but got "
<< values.size() << ", skipping" << std::endl;
continue;
}
wLayer->setBiases(values.data());
}
} else {
std::cerr << "Layer: " << tensorInfo.name << " does not exist, skipping" << std::endl;
std::cerr << "Layer: " << tensorInfo.name
<< " does not exist, skipping" << std::endl;
}
}
@@ -170,15 +183,16 @@ void Model::loadWeights(const std::string& path) {
}
bool Model::validate() {
bool valid = true;
int size = inputLayer->getInputSize();
int size = inputLayer->getInputSize();
for (const auto& layer : layers) {
if (layer.second->getInputSize() != size) {
valid = false;
std::cerr << "Layer: " << layer.first << " has incorrect input size, expected " << size << " but got "
<< layer.second->getInputSize() << std::endl;
std::cerr << "Layer: " << layer.first
<< " has incorrect input size, expected " << size
<< " but got " << layer.second->getInputSize()
<< std::endl;
break;
}
@@ -189,9 +203,44 @@ bool Model::validate() {
}
void Model::printSummary() {
std::cout << "Model summary:" << std::endl;
struct layer_info {
std::string name;
std::string inputSize;
std::string outputSize;
};
std::vector<layer_info> layerInfos;
int maxNameLength = 0;
int maxInputLength = 0;
int maxOutputLength = 0;
for (const auto& layer : layers) {
std::cout << "Layer: " << layer.first << ", input size: " << layer.second->getInputSize() << ", output size: "
<< layer.second->getOutputSize() << std::endl;
layer_info layerInfo = {
layer.first, std::to_string(layer.second->getInputSize()),
std::to_string(layer.second->getOutputSize())
};
layerInfos.push_back(layerInfo);
maxNameLength = std::max(maxNameLength, (int)layerInfo.name.size());
maxInputLength =
std::max(maxInputLength, (int)layerInfo.inputSize.size());
maxOutputLength =
std::max(maxOutputLength, (int)layerInfo.outputSize.size());
}
int rowLength = maxNameLength + maxInputLength + maxOutputLength + 6;
std::cout << "Model Summary:" << std::endl
<< std::string(rowLength, '-') << std::endl;
for (const auto& layerInfo : layerInfos) {
std::cout << std::left
<< std::setw(maxNameLength) << layerInfo.name
<< " | " << std::right
<< std::setw(maxInputLength) << layerInfo.inputSize
<< " | "
<< std::setw(maxOutputLength) << layerInfo.outputSize
<< std::endl;
}
}