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Transforming LDR denoiser to HDR (C++)¶
This example demonstrates how to transform LDR denosier to HDR denoiser using tone-mapping as preprocessing and gamma-correction as postprocessing.
#include "rml/RadeonML.hpp"
#include "rml/RadeonML_utils.hpp"
#include <cstring>
#include <fstream>
#include <iostream>
#include <sstream>
#include <stdexcept>
#include <string>
#include <vector>
/*
* Create operation that holds information aboud input data
*
* @param graph - graph where operation is created
* @param name - unique operation name
* @param shape - shape of input tensor
* @return - created placeholder operation
*/
rml_op CreatePlaceholderOp(rml::Graph& graph,
const std::string& name,
const std::vector<uint32_t>& shape)
{
// Create placeholder operation
rml_op_desc input_desc = {RML_OP_PLACEHOLDER, name.c_str()};
input_desc.placeholder = {
RML_DTYPE_FLOAT32, RML_LAYOUT_NHWC, {shape[0], shape[1], shape[2], shape[3]}};
return graph.CreateOperation(input_desc);
}
/*
* Create operation that stores scalar data
*
* @param graph - graph where operation is created
* @param name - unique operation name
* @param dtype - tensor data type
* @param value - scalar value
* @return - created scalar operation
*/
template<typename T>
rml_op CreateScalarOp(rml::Graph& graph, const std::string& name, rml_dtype dtype, T value)
{
// Create constant operation
rml_op_desc op_desc = {RML_OP_CONST, name.c_str()};
op_desc.constant = {{dtype, RML_LAYOUT_SCALAR}, &value};
return graph.CreateOperation(op_desc);
}
/*
* Create specilalized unary operation
*
* @param graph - graph where operation is created
* @param name - unique operation name
* @param op_type - type of unary operation
* @param input - input operation
* @return - created unary operation
*/
rml_op CreateUnaryOp(rml::Graph& graph, const std::string& name, rml_op_type op_type, rml_op input)
{
// Create unary operation
rml_op_desc op_desc = {op_type, name.c_str()};
op_desc.unary = {input};
return graph.CreateOperation(op_desc);
}
/*
* Create specilalized binary operation
*
* @param graph - graph where operation is created
* @param name - unique operation name
* @param op_type - type of binary operation
* @param input1 - input1 operation
* @param input2 - input2 operation
* @return - created binary operation
*/
rml_op CreateBinaryOp(rml::Graph& graph,
const std::string& name,
rml_op_type op_type,
rml_op input1,
rml_op input2)
{
// Create binary operation
rml_op_desc op_desc = {op_type, name.c_str()};
op_desc.binary = {input1, input2};
return graph.CreateOperation(op_desc);
}
/*
* Create preprocessing graph and connect it with base graph
*
* @param graph - base graph to be connected with preprocesssing graph
* @param input_names - list of unique input names of preprocessing graph
* @param input_shapes - list of input shapes of preprocessing graph
* @return - connected graph
*/
rml::Graph ConnectPreprocessingGraph(const rml::Graph& graph,
const std::vector<std::string>& input_names,
const std::vector<std::vector<uint32_t>>& input_shapes)
{
// Preprocessing graph includes exponential tone-mapping
// ldr_color = beta - exp(alpha * hdr_color)
// alpha = -1.0
// beta = 1.0
auto preprocess_graph = rml::CreateGraph();
// Create placeholder per input
rml_op color_op = CreatePlaceholderOp(preprocess_graph, input_names[0], input_shapes[0]);
rml_op albedo_op = CreatePlaceholderOp(preprocess_graph, input_names[1], input_shapes[1]);
rml_op depth_op = CreatePlaceholderOp(preprocess_graph, input_names[2], input_shapes[2]);
rml_op normal_op = CreatePlaceholderOp(preprocess_graph, input_names[3], input_shapes[3]);
// Create alpha
rml_op alpha_op = CreateScalarOp<float>(preprocess_graph, "alpha", RML_DTYPE_FLOAT32, -1.0f);
// Create beta
rml_op beta_op = CreateScalarOp<float>(preprocess_graph, "beta", RML_DTYPE_FLOAT32, 1.0f);
// Create mul, exp and sub opearions
rml_op mul_op = CreateBinaryOp(preprocess_graph, "mul", RML_OP_MUL, alpha_op, color_op);
rml_op exp_op = CreateUnaryOp(preprocess_graph, "exp", RML_OP_EXP, mul_op);
rml_op sub_op = CreateBinaryOp(preprocess_graph, "sub", RML_OP_SUB, beta_op, exp_op);
// Create axis for concatenation
rml_op axis_op = CreateScalarOp<int32_t>(preprocess_graph, "concat/axis", RML_DTYPE_INT32, -1);
// Create inputs for concatenation
std::vector<rml_op> inputs = {sub_op, albedo_op, depth_op, normal_op};
// Concatenate tone-mapped color with albedo, depth and normal
rml_op_desc concat_desc = {RML_OP_CONCAT, "concat"};
concat_desc.concat = {inputs.size(), inputs.data(), axis_op};
preprocess_graph.CreateOperation(concat_desc);
// Get tail graph inputs
std::vector<const char*> tail_inputs = graph.GetInputNames();
// Get head graph outputs
std::vector<const char*> head_outputs = preprocess_graph.GetOutputNames();
// Connect preprocessing graph with base graph
return rml::ConnectGraphs(preprocess_graph, graph, 1, &head_outputs[0], &tail_inputs[0]);
}
/*
* Create postprocessing graph and connect it with base graph
*
* @param graph - base graph to be connected with postprocessing graph
* @param input_name - unique input name of postprocessing graph
* @param input_shape - input shape of postprocessing graph
* @return connected graph
*/
rml::Graph ConnectPostprocessingGraph(rml::Graph& graph,
const std::string& input_name,
const std::vector<uint32_t>& input_shape)
{
// Postprocessing graph includes gamma-correction
// ldr_color = (clip(ldr_color, 0, 1)) ^ (gamma)
// gamma = 0.4
auto postprocess_graph = rml::CreateGraph();
// Create placeholder for color
rml_op input_op = CreatePlaceholderOp(postprocess_graph, input_name, input_shape);
// Clip color
rml_op_desc clip_desc = {RML_OP_CLIP, "clip"};
clip_desc.clip = {input_op, 0.f, 1.f};
rml_op clip_op = postprocess_graph.CreateOperation(clip_desc);
// Create gamma
rml_op gamma_op = CreateScalarOp<float>(postprocess_graph, "gamma", RML_DTYPE_FLOAT32, 0.4f);
// Create pow operation
CreateBinaryOp(postprocess_graph, "pow", RML_OP_POW, clip_op, gamma_op);
// Get tail graph inputs
std::vector<const char*> tail_inputs = postprocess_graph.GetInputNames();
// Get head graph outputs
std::vector<const char*> head_outputs = graph.GetOutputNames();
// Connect base graph with postprocessing graph
return rml::ConnectGraphs(graph, postprocess_graph, 1, &head_outputs[0], &tail_inputs[0]);
}
/*
* Read input from file
*
* @param input_file - name of input file
* @return - string content of file
*/
std::string ReadInput(const std::string& input_file)
{
std::istream* input_stream;
std::ifstream input_file_stream;
if (input_file.empty())
{
freopen(nullptr, "rb", stdin);
input_stream = &std::cin;
std::cout << "Reading data from stdin...\n";
}
else
{
input_file_stream.open(input_file, std::ios_base::binary);
if (input_file_stream.fail())
{
throw std::runtime_error(std::string("Error reading ") + input_file);
}
input_stream = &input_file_stream;
std::cout << "Reading data from file: " << input_file << "\n";
}
std::ostringstream stream;
stream << input_stream->rdbuf();
auto input = stream.str();
std::cout << "Input data size: " << input.size() << " bytes\n";
return input;
}
/*
* Write output to file
*
* @param output_file - name of output file
* @param output - output data
*/
void WriteOutput(const std::string& output_file, const std::string& output)
{
std::cout << "Output data size: " << output.size() << " bytes\n";
std::ostream* output_stream;
std::ofstream output_file_stream;
if (output_file.empty())
{
freopen(nullptr, "wb", stdout);
output_stream = &std::cout;
std::cout << "Writing result to stdout\n";
}
else
{
output_file_stream.open(output_file, std::ios_base::binary);
if (output_file_stream.fail())
{
throw std::runtime_error(std::string("Error writing ") + output_file);
}
output_stream = &output_file_stream;
std::cout << "Writing result to file: " << output_file << "\n";
}
output_stream->write(output.data(), output.size());
}
/*
* This sample demonstrates how ldr-denosier could be converted to hdr-denoiser
* using tone-mapping as preprocessing and gamma-correction as postprocessing
*/
int main() try
{
// Set model path
#if defined(_WIN32)
std::wstring model_path(L"path/model");
#else
std::string model_path("path/model");
#endif
// Set input files
const std::vector<std::string> input_files = {
"path/color",
"path/albedo",
"path/depth",
"path/normal",
};
// Set output file
const std::string output_file = "path/output";
// Set input names
const std::vector<std::string> input_names = {"hdr-color", "albedo", "depth", "normal"};
// Set input shapes
const std::vector<std::vector<uint32_t>> input_shapes = {
{1, 600, 800, 3},
{1, 600, 800, 3},
{1, 600, 800, 1},
{1, 600, 800, 2},
};
// Create a context
// The handles are released automatically upon scope exit
rml::Context context = rml::CreateDefaultContext();
// Load model as a mutable graph
// model input - 9-channel 800x600 image (3-channel hdr-color,
// 3-channel albedo,
// 1-channel depth,
// 2-channel normal)
// model output - 3-channel 800x600 ldr image
// The handles are released automatically upon scope exit
rml::Graph graph =
rml::LoadGraphFromFile(std::basic_string<rml_char>(model_path.begin(), model_path.end()));
// Add preprocessing of base model inputs
// Before we can use ldr-denoiser for hdr-data, we should adjust hdr-color
// using tone-mapping and concatenate it with albedo, depth and normal
graph = ConnectPreprocessingGraph(graph, input_names, input_shapes);
// Add postprocessing of base model outputs
// We should also apply gamma-correction for denoised image
graph = ConnectPostprocessingGraph(graph, "input", input_shapes[0]);
// Create immutable model from connected graphs
// The handles are released automatically upon scope exit
rml::Model model = context.CreateModel(graph);
// Set up input info
std::vector<rml_tensor_info> input_infos;
for (size_t i = 0; i < input_shapes.size(); i++)
{
rml_tensor_info input_info = {RML_DTYPE_FLOAT32, RML_LAYOUT_NHWC};
std::memcpy(input_info.shape,
input_shapes[i].data(),
std::min(input_shapes[i].size(), RML_TENSOR_MAX_RANK) * sizeof(uint32_t));
input_infos.push_back(input_info);
std::cout << "Input" << i << ": " << input_info << std::endl;
model.SetInputInfo(input_names[i], input_info);
}
// Check memory info
rml_memory_info memory_info = model.GetMemoryInfo();
std::cout << "Memory allocated: " << memory_info.gpu_total << std::endl;
// Create and fill the input tensors
std::vector<rml::Tensor> inputs;
// The handles are released automatically upon scope exit
for (size_t i = 0; i < input_shapes.size(); i++)
{
rml::Tensor input;
input = context.CreateTensor(input_infos[i], RML_ACCESS_MODE_WRITE_ONLY);
input.Write(ReadInput(input_files[i]));
inputs.push_back(std::move(input));
}
// Set model inputs
for (size_t i = 0; i < inputs.size(); i++)
{
model.SetInput(input_names[i], inputs[i]);
}
// Get output tensor information
auto output_info = model.GetOutputInfo();
std::cout << "Output: " << output_info << std::endl;
// Create the output tensor
// The handles are released automatically upon scope exit
auto output_tensor = context.CreateTensor(output_info, RML_ACCESS_MODE_READ_ONLY);
// Set model output
model.SetOutput(output_tensor);
// Run the inference
model.Infer();
// Get data from output tensor
size_t output_size;
void* output_data = output_tensor.Map(&output_size);
// Unmap output data
const std::string output(static_cast<char*>(output_data), output_size);
output_tensor.Unmap(output_data);
// Write the output
WriteOutput(output_file, output);
}
catch (const std::exception& e)
{
std::cerr << e.what() << std::endl;
return 1;
}