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esrgan.hpp
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esrgan.hpp
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#ifndef __ESRGAN_HPP__
#define __ESRGAN_HPP__
#include "ggml_extend.hpp"
#include "model.h"
/*
=================================== ESRGAN ===================================
References:
https://github.com/xinntao/Real-ESRGAN/blob/master/inference_realesrgan.py
https://github.com/XPixelGroup/BasicSR/blob/v1.4.2/basicsr/archs/rrdbnet_arch.py
*/
class ResidualDenseBlock : public GGMLBlock {
protected:
int num_feat;
int num_grow_ch;
public:
ResidualDenseBlock(int num_feat = 64, int num_grow_ch = 32)
: num_feat(num_feat), num_grow_ch(num_grow_ch) {
blocks["conv1"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_grow_ch, {3, 3}, {1, 1}, {1, 1}));
blocks["conv2"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat + num_grow_ch, num_grow_ch, {3, 3}, {1, 1}, {1, 1}));
blocks["conv3"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, {3, 3}, {1, 1}, {1, 1}));
blocks["conv4"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, {3, 3}, {1, 1}, {1, 1}));
blocks["conv5"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat + 4 * num_grow_ch, num_feat, {3, 3}, {1, 1}, {1, 1}));
}
struct ggml_tensor* lrelu(struct ggml_context* ctx, struct ggml_tensor* x) {
return ggml_leaky_relu(ctx, x, 0.2f, true);
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
// x: [n, num_feat, h, w]
// return: [n, num_feat, h, w]
auto conv1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv1"]);
auto conv2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv2"]);
auto conv3 = std::dynamic_pointer_cast<Conv2d>(blocks["conv3"]);
auto conv4 = std::dynamic_pointer_cast<Conv2d>(blocks["conv4"]);
auto conv5 = std::dynamic_pointer_cast<Conv2d>(blocks["conv5"]);
auto x1 = lrelu(ctx, conv1->forward(ctx, x));
auto x_cat = ggml_concat(ctx, x, x1, 2);
auto x2 = lrelu(ctx, conv2->forward(ctx, x_cat));
x_cat = ggml_concat(ctx, x_cat, x2, 2);
auto x3 = lrelu(ctx, conv3->forward(ctx, x_cat));
x_cat = ggml_concat(ctx, x_cat, x3, 2);
auto x4 = lrelu(ctx, conv4->forward(ctx, x_cat));
x_cat = ggml_concat(ctx, x_cat, x4, 2);
auto x5 = conv5->forward(ctx, x_cat);
x5 = ggml_add(ctx, ggml_scale(ctx, x5, 0.2f), x);
return x5;
}
};
class RRDB : public GGMLBlock {
public:
RRDB(int num_feat, int num_grow_ch = 32) {
blocks["rdb1"] = std::shared_ptr<GGMLBlock>(new ResidualDenseBlock(num_feat, num_grow_ch));
blocks["rdb2"] = std::shared_ptr<GGMLBlock>(new ResidualDenseBlock(num_feat, num_grow_ch));
blocks["rdb3"] = std::shared_ptr<GGMLBlock>(new ResidualDenseBlock(num_feat, num_grow_ch));
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
// x: [n, num_feat, h, w]
// return: [n, num_feat, h, w]
auto rdb1 = std::dynamic_pointer_cast<ResidualDenseBlock>(blocks["rdb1"]);
auto rdb2 = std::dynamic_pointer_cast<ResidualDenseBlock>(blocks["rdb2"]);
auto rdb3 = std::dynamic_pointer_cast<ResidualDenseBlock>(blocks["rdb3"]);
auto out = rdb1->forward(ctx, x);
out = rdb2->forward(ctx, out);
out = rdb3->forward(ctx, out);
out = ggml_add(ctx, ggml_scale(ctx, out, 0.2f), x);
return out;
}
};
class RRDBNet : public GGMLBlock {
protected:
int scale = 4; // default RealESRGAN_x4plus_anime_6B
int num_block = 6; // default RealESRGAN_x4plus_anime_6B
int num_in_ch = 3;
int num_out_ch = 3;
int num_feat = 64; // default RealESRGAN_x4plus_anime_6B
int num_grow_ch = 32; // default RealESRGAN_x4plus_anime_6B
public:
RRDBNet() {
blocks["conv_first"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_in_ch, num_feat, {3, 3}, {1, 1}, {1, 1}));
for (int i = 0; i < num_block; i++) {
std::string name = "body." + std::to_string(i);
blocks[name] = std::shared_ptr<GGMLBlock>(new RRDB(num_feat, num_grow_ch));
}
blocks["conv_body"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
// upsample
blocks["conv_up1"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
blocks["conv_up2"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
blocks["conv_hr"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
blocks["conv_last"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_out_ch, {3, 3}, {1, 1}, {1, 1}));
}
struct ggml_tensor* lrelu(struct ggml_context* ctx, struct ggml_tensor* x) {
return ggml_leaky_relu(ctx, x, 0.2f, true);
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
// x: [n, num_in_ch, h, w]
// return: [n, num_out_ch, h*4, w*4]
auto conv_first = std::dynamic_pointer_cast<Conv2d>(blocks["conv_first"]);
auto conv_body = std::dynamic_pointer_cast<Conv2d>(blocks["conv_body"]);
auto conv_up1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up1"]);
auto conv_up2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up2"]);
auto conv_hr = std::dynamic_pointer_cast<Conv2d>(blocks["conv_hr"]);
auto conv_last = std::dynamic_pointer_cast<Conv2d>(blocks["conv_last"]);
auto feat = conv_first->forward(ctx, x);
auto body_feat = feat;
for (int i = 0; i < num_block; i++) {
std::string name = "body." + std::to_string(i);
auto block = std::dynamic_pointer_cast<RRDB>(blocks[name]);
body_feat = block->forward(ctx, body_feat);
}
body_feat = conv_body->forward(ctx, body_feat);
feat = ggml_add(ctx, feat, body_feat);
// upsample
feat = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx, feat, 2)));
feat = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx, feat, 2)));
auto out = conv_last->forward(ctx, lrelu(ctx, conv_hr->forward(ctx, feat)));
return out;
}
};
struct ESRGAN : public GGMLModule {
RRDBNet rrdb_net;
int scale = 4;
int tile_size = 128; // avoid cuda OOM for 4gb VRAM
ESRGAN(ggml_backend_t backend,
ggml_type wtype)
: GGMLModule(backend, wtype) {
rrdb_net.init(params_ctx, wtype);
}
std::string get_desc() {
return "esrgan";
}
bool load_from_file(const std::string& file_path) {
LOG_INFO("loading esrgan from '%s'", file_path.c_str());
alloc_params_buffer();
std::map<std::string, ggml_tensor*> esrgan_tensors;
rrdb_net.get_param_tensors(esrgan_tensors);
ModelLoader model_loader;
if (!model_loader.init_from_file(file_path)) {
LOG_ERROR("init esrgan model loader from file failed: '%s'", file_path.c_str());
return false;
}
bool success = model_loader.load_tensors(esrgan_tensors, backend);
if (!success) {
LOG_ERROR("load esrgan tensors from model loader failed");
return false;
}
LOG_INFO("esrgan model loaded");
return success;
}
struct ggml_cgraph* build_graph(struct ggml_tensor* x) {
struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
x = to_backend(x);
struct ggml_tensor* out = rrdb_net.forward(compute_ctx, x);
ggml_build_forward_expand(gf, out);
return gf;
}
void compute(const int n_threads,
struct ggml_tensor* x,
ggml_tensor** output,
ggml_context* output_ctx = NULL) {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(x);
};
GGMLModule::compute(get_graph, n_threads, false, output, output_ctx);
}
};
#endif // __ESRGAN_HPP__