private static void test_cifar_csvtrain(string filename, string weightfile)
{
Network net = Parser.parse_network_cfg(filename);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Data.Data test = Data.Data.load_all_cifar10();
Matrix pred = Network.network_predict_data(net, test);
int i;
for (i = 0; i < test.X.Rows; ++i)
{
Image im = new Image(32, 32, 3, test.X.Vals[i]);
LoadArgs.flip_image(im);
}
Matrix pred2 = Network.network_predict_data(net, test);
pred.scale_matrix(.5f);
pred2.scale_matrix(.5f);
Matrix.matrix_add_matrix(pred2, pred);
pred.to_csv();
Console.Error.Write($"Accuracy: %f\n", Matrix.matrix_topk_accuracy(test.Y, pred, 1));
}
private static void optimize_picture(Network net, Image orig, int maxLayer, float scale, float rate, float thresh, bool norm)
{
net.N = maxLayer + 1;
int dx = Utils.Rand.Next() % 16 - 8;
int dy = Utils.Rand.Next() % 16 - 8;
bool flip = Utils.Rand.Next() % 2 != 0;
Image crop = LoadArgs.crop_image(orig, dx, dy, orig.W, orig.H);
Image im = LoadArgs.resize_image(crop, (int)(orig.W * scale), (int)(orig.H * scale));
if (flip)
{
LoadArgs.flip_image(im);
}
Network.resize_network(net, im.W, im.H);
Layer last = net.Layers[net.N - 1];
Image delta = new Image(im.W, im.H, im.C);
NetworkState state = new NetworkState();
state.Input = (float[])im.Data.Clone();
state.Delta = (float[])im.Data.Clone();
Network.forward_network_gpu(net, state);
Blas.copy_ongpu(last.Outputs, last.OutputGpu, last.DeltaGpu);
Array.Copy(last.DeltaGpu, last.Delta, last.Outputs);
calculate_loss(last.Delta, last.Delta, last.Outputs, thresh);
Array.Copy(last.Delta, last.DeltaGpu, last.Outputs);
Network.backward_network_gpu(net, state);
Array.Copy(state.Delta, delta.Data, im.W * im.H * im.C);
if (flip)
{
LoadArgs.flip_image(delta);
}
Image resized = LoadArgs.resize_image(delta, orig.W, orig.H);
Image outi = LoadArgs.crop_image(resized, -dx, -dy, orig.W, orig.H);
if (norm)
{
Utils.normalize_array(outi.Data, outi.W * outi.H * outi.C);
}
Blas.Axpy_cpu(orig.W * orig.H * orig.C, rate, outi.Data, orig.Data);
LoadArgs.constrain_image(orig);
}
private static void test_cifar_multi(string filename, string weightfile)
{
Network net = Parser.parse_network_cfg(filename);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Network.set_batch_network(net, 1);
float avgAcc = 0;
Data.Data test = Data.Data.load_cifar10_data("Data.Data/cifar/cifar-10-batches-bin/test_batch.bin");
int i;
for (i = 0; i < test.X.Rows; ++i)
{
Image im = new Image(32, 32, 3, test.X.Vals[i]);
float[] pred = new float[10];
float[] p = Network.network_predict(net, im.Data);
Blas.Axpy_cpu(10, 1, p, pred);
LoadArgs.flip_image(im);
p = Network.network_predict(net, im.Data);
Blas.Axpy_cpu(10, 1, p, pred);
int index = Utils.max_index(pred, 10);
int sclass = Utils.max_index(test.Y.Vals[i], 10);
if (index == sclass)
{
avgAcc += 1;
}
Console.Write($"%4d: %.2f%%\n", i, 100f * avgAcc / (i + 1));
}
}
private static void validate_classifier_multi(string datacfg, string filename, string weightfile)
{
int i, j;
Network net = Parser.parse_network_cfg(filename);
Network.set_batch_network(net, 1);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
var options = OptionList.read_data_cfg(datacfg);
string labelList = OptionList.option_find_str(options, "labels", "Data.Data/labels.list");
string validList = OptionList.option_find_str(options, "valid", "Data.Data/train.list");
int classes = OptionList.option_find_int(options, "classes", 2);
int topk = OptionList.option_find_int(options, "top", 1);
string[] labels = Data.Data.get_labels(labelList);
int[] scales = { 224, 288, 320, 352, 384 };
int nscales = scales.Length;
string[] paths = Data.Data.GetPaths(validList);
int m = paths.Length;
float avgAcc = 0;
float avgTopk = 0;
int[] indexes = new int[topk];
for (i = 0; i < m; ++i)
{
int class2 = -1;
string path = paths[i];
for (j = 0; j < classes; ++j)
{
if (path.Contains(labels[j]))
{
class2 = j;
break;
}
}
float[] pred = new float[classes];
Image im = LoadArgs.load_image_color(paths[i], 0, 0);
for (j = 0; j < nscales; ++j)
{
Image r = LoadArgs.resize_min(im, scales[j]);
Network.resize_network(net, r.W, r.H);
float[] p = Network.network_predict(net, r.Data);
if (net.Hierarchy != null)
{
net.Hierarchy.Hierarchy_predictions(p, 0, net.Outputs, true);
}
Blas.Axpy_cpu(classes, 1, p, pred);
LoadArgs.flip_image(r);
p = Network.network_predict(net, r.Data);
Blas.Axpy_cpu(classes, 1, p, pred);
}
Utils.top_k(pred, classes, topk, indexes);
if (indexes[0] == class2)
{
avgAcc += 1;
}
for (j = 0; j < topk; ++j)
{
if (indexes[j] == class2)
{
avgTopk += 1;
}
}
Console.Write($"%d: top 1: %f, top %d: %f\n", i, avgAcc / (i + 1), topk, avgTopk / (i + 1));
}
}
private static void validate_classifier_10(string datacfg, string filename, string weightfile)
{
int i, j;
Network net = Parser.parse_network_cfg(filename);
Network.set_batch_network(net, 1);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
var options = OptionList.read_data_cfg(datacfg);
string labelList = OptionList.option_find_str(options, "labels", "Data.Data/labels.list");
string validList = OptionList.option_find_str(options, "valid", "Data.Data/train.list");
int classes = OptionList.option_find_int(options, "classes", 2);
int topk = OptionList.option_find_int(options, "top", 1);
string[] labels = Data.Data.get_labels(labelList);
string[] paths = Data.Data.GetPaths(validList);
int m = paths.Length;
float avgAcc = 0;
float avgTopk = 0;
int[] indexes = new int[topk];
for (i = 0; i < m; ++i)
{
int class2 = -1;
string path = paths[i];
for (j = 0; j < classes; ++j)
{
if (path.Contains(labels[j]))
{
class2 = j;
break;
}
}
int w = net.W;
int h = net.H;
int shift = 32;
Image im = LoadArgs.load_image_color(paths[i], w + shift, h + shift);
Image[] images = new Image[10];
images[0] = LoadArgs.crop_image(im, -shift, -shift, w, h);
images[1] = LoadArgs.crop_image(im, shift, -shift, w, h);
images[2] = LoadArgs.crop_image(im, 0, 0, w, h);
images[3] = LoadArgs.crop_image(im, -shift, shift, w, h);
images[4] = LoadArgs.crop_image(im, shift, shift, w, h);
LoadArgs.flip_image(im);
images[5] = LoadArgs.crop_image(im, -shift, -shift, w, h);
images[6] = LoadArgs.crop_image(im, shift, -shift, w, h);
images[7] = LoadArgs.crop_image(im, 0, 0, w, h);
images[8] = LoadArgs.crop_image(im, -shift, shift, w, h);
images[9] = LoadArgs.crop_image(im, shift, shift, w, h);
float[] pred = new float[classes];
for (j = 0; j < 10; ++j)
{
float[] p = Network.network_predict(net, images[j].Data);
if (net.Hierarchy != null)
{
net.Hierarchy.Hierarchy_predictions(p, 0, net.Outputs, true);
}
Blas.Axpy_cpu(classes, 1, p, pred);
}
Utils.top_k(pred, classes, topk, indexes);
if (indexes[0] == class2)
{
avgAcc += 1;
}
for (j = 0; j < topk; ++j)
{
if (indexes[j] == class2)
{
avgTopk += 1;
}
}
Console.Write($"%d: top 1: %f, top %d: %f\n", i, avgAcc / (i + 1), topk, avgTopk / (i + 1));
}
}