Halide::Func convolution_layer(Halide::Func input, Halide::Func weights,
Halide::Func bias, int filter_size, int input_layers, int pool_size) {
// Convolution
Halide::Func convolution;
Halide::Var x, y, z, w;
Halide::RDom r(0, filter_size, 0, filter_size, 0, input_layers);
convolution(x, y, z, w) = 0.0f;
convolution(x, y, z, w) += weights(r.x, r.y, r.z, z) *
input(x + r.x, y + r.y, r.z, w);
// Max pool
Halide::Func subsample;
Halide::RDom s(0, pool_size, 0, pool_size);
subsample(x, y, z, w) = 0.0f;
subsample(x, y, z, w) = Halide::max(convolution(pool_size * x + s.x,
pool_size * y + s.y, z, w), subsample(x, y, z, w));
// Non-linear bias
Halide::Func biased;
biased(x, y, z, w) = tanh(subsample(x, y, z, w) + bias(z, 0));
Halide::Var x_inner, x_outer, y_inner, y_outer;
biased.parallel(w);
biased.tile(x, y, x_outer, y_outer, x_inner, y_inner, VECTORS, 2);
biased.vectorize(x_inner);
biased.unroll(y_inner);
return biased;
}
Halide::Func fully_connected_layer(Halide::Func input, Halide::Func weights,
Halide::Func bias, int size) {
Halide::Func product;
Halide::Var x, y, z;
Halide::RDom r(0, size);
// Only y = 0 should be used
product(x, y, z) = 0.0f;
product(x, y, z) += weights(r.x, x) * input(r.x, y, z);
product(x, y, z) = tanh(product(x, y, z) + bias(x, 0));
product.vectorize(x, VECTORS);
return product;
}
void NamedWindow::showImage2D(Halide::Image<uint8_t> im)
{
static Halide::Func convert("convertToMat2D");
static Halide::ImageParam ip(Halide::UInt(8), 2);
static Halide::Var x, y;
if (!convert.defined())
{
convert(x, y) = ip(x, y);
convert.vectorize(x, 4).parallel(y, 4);
}
ip.set(im);
cv::Mat mat(im.height(), im.width(), CV_8UC1, cv::Scalar(0));
convert.realize(Halide::Buffer(Halide::UInt(8), im.width(), im.height(), 0, 0, mat.data));
cv::imshow(name, mat);
}
void NamedWindow::showImage3D(Halide::Image<float> im)
{
static Halide::Func convert("convertToMat3D");
static Halide::ImageParam ip(Halide::Float(32), 3);
static Halide::Var x, y, c;
if (!convert.defined())
{
convert(c, x, y) = Halide::cast<uint8_t>(ip(x, y, 2 - c) * 255);
convert.vectorize(x, 4).parallel(y, 4);
}
ip.set(im);
cv::Mat mat(im.height(), im.width(), CV_8UC3, cv::Scalar(0));
convert.realize(Halide::Buffer(Halide::UInt(8), im.channels(), im.width(), im.height(), 0, mat.data));
cv::imshow(name, mat);
}
