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.h"
#include "rml/RadeonML_graph.h"
#include <inttypes.h>
#include <limits.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#define RML_CHECK(STATUS) \
do \
{ \
if (STATUS != RML_OK) \
{ \
printf("%s\n", rmlGetLastError());\
exit(EXIT_FAILURE); \
} \
} while (0)
#define CHECK(STATUS) \
do \
{ \
if (!(STATUS)) \
{ \
exit(EXIT_FAILURE); \
} \
} while (0)
#define NUM_INPUTS 4
#define NHWC_RANK 4
/*
* 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
* @param row_index - index in array of shape
* @return - created placeholder operation
*/
rml_op CreatePlaceholderOp(rml_graph graph, const char* name, const uint32_t* shape)
{
// Create placeholder operation
rml_op_desc input_desc = {
RML_OP_PLACEHOLDER, name, .placeholder = {RML_DTYPE_FLOAT32, RML_LAYOUT_NHWC}};
memcpy(input_desc.placeholder.tensor_info.shape, shape, NHWC_RANK * sizeof(int));
rml_op op_placeholder = NULL;
RML_CHECK(rmlCreateOperation(graph, &input_desc, &op_placeholder));
return op_placeholder;
}
/*
* Create operation that stores scalar int 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
*/
rml_op CreateScalarOp(rml_graph graph, const char* name, rml_dtype dtype, const void* value)
{
rml_op_desc op_desc = {RML_OP_CONST, name, .constant = {{dtype, RML_LAYOUT_SCALAR}, value}};
rml_op op_const = NULL;
RML_CHECK(rmlCreateOperation(graph, &op_desc, &op_const));
return op_const;
}
/*
* 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 char* name, rml_op_type op_type, rml_op input)
{
// Create unary operation
rml_op_desc op_desc = {op_type, name, .unary = {input}};
rml_op op_unary = NULL;
RML_CHECK(rmlCreateOperation(graph, &op_desc, &op_unary));
return op_unary;
}
/*
* 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 char* name,
rml_op_type op_type,
rml_op input1,
rml_op input2)
{
// Create binary operation
rml_op_desc op_desc = {op_type, name, .binary = {input1, input2}};
rml_op op_binary = NULL;
RML_CHECK(rmlCreateOperation(graph, &op_desc, &op_binary));
return op_binary;
}
/*
* 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 char* input_names[],
const rml_tensor_info* input_infos)
{
// Preprocessing graph includes exponential tone-mapping
// ldr_color = beta - exp(alpha * hdr_color)
// alpha = -1.0
// beta = 1.0
/* Create a context */
rml_graph preprocess_graph = NULL;
RML_CHECK(rmlCreateGraph(&preprocess_graph));
// Create placeholder per input
rml_op color_op = CreatePlaceholderOp(preprocess_graph, input_names[0], input_infos[0].shape);
rml_op albedo_op = CreatePlaceholderOp(preprocess_graph, input_names[1], input_infos[1].shape);
rml_op depth_op = CreatePlaceholderOp(preprocess_graph, input_names[2], input_infos[2].shape);
rml_op normal_op = CreatePlaceholderOp(preprocess_graph, input_names[3], input_infos[3].shape);
// Create alpha
float alpha_const = -1.0f;
rml_op alpha_op = CreateScalarOp(preprocess_graph, "alpha", RML_DTYPE_FLOAT32, &alpha_const);
// Create beta
float beta_const = 1.0f;
rml_op beta_op = CreateScalarOp(preprocess_graph, "beta", RML_DTYPE_FLOAT32, &beta_const);
// 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
int axis = -1;
rml_op axis_op = CreateScalarOp(preprocess_graph, "concat/axis", RML_DTYPE_INT32, &axis);
// Create inputs for concatenation
rml_op inputs[NUM_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 = {NUM_INPUTS, inputs, axis_op}};
rml_op op_concat = NULL;
RML_CHECK(rmlCreateOperation(preprocess_graph, &concat_desc, &op_concat));
// Get tail graph inputs
rml_strings tail_inputs;
RML_CHECK(rmlGetGraphInputNames(graph, &tail_inputs));
// Get head graph outputs
rml_strings head_outputs;
RML_CHECK(rmlGetGraphOutputNames(preprocess_graph, &head_outputs));
// Connect preprocessing graph with base graph
rml_graph connected_graph = NULL;
RML_CHECK(rmlConnectGraphs(preprocess_graph,
graph,
1,
&head_outputs.items[0],
&tail_inputs.items[0],
&connected_graph));
return connected_graph;
}
/*
* 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 char* input_name,
const uint32_t* input_shape)
{
// Postprocessing graph includes gamma-correction
// ldr_color = (clip(ldr_color, 0, 1)) ^ (gamma)
// gamma = 0.4
rml_graph postprocess_graph = NULL;
RML_CHECK(rmlCreateGraph(&postprocess_graph));
// 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 = {input_op, 0.f, 1.f}};
rml_op clip_op = NULL;
RML_CHECK(rmlCreateOperation(postprocess_graph, &clip_desc, &clip_op));
// Create gamma
float gamma_const = 0.4f;
rml_op gamma_op = CreateScalarOp(postprocess_graph, "gamma", RML_DTYPE_FLOAT32, &gamma_const);
// Create pow operation
rml_op pow_op = CreateBinaryOp(postprocess_graph, "pow", RML_OP_POW, clip_op, gamma_op);
// Get tail graph inputs
rml_strings tail_inputs;
RML_CHECK(rmlGetGraphInputNames(postprocess_graph, &tail_inputs));
// Get head graph outputs
rml_strings head_outputs;
RML_CHECK(rmlGetGraphOutputNames(graph, &head_outputs));
// Connect base graph with postprocessing graph
rml_graph connected_graph = NULL;
RML_CHECK(rmlConnectGraphs(graph,
postprocess_graph,
1,
&head_outputs.items[0],
&tail_inputs.items[0],
&connected_graph));
return connected_graph;
}
/*
* Read input from file
* Must be free memory after caller of this function
*
* @param input_file - name of input file
* @return - string content of file
*/
void* ReadInput(const char* input_file)
{
void* buffer;
FILE* file = fopen(input_file, "rb");
CHECK(file != NULL);
printf("Reading data from file: %s\n", input_file);
fseek(file, 0, SEEK_END);
long length = ftell(file);
fseek(file, 0, SEEK_SET);
buffer = malloc((length) * sizeof(char));
CHECK(buffer != NULL);
size_t num_read = fread(buffer, sizeof(char), length, file);
CHECK(num_read == length);
printf("Input data size: %zu\n", num_read);
fclose(file);
return buffer;
}
/*
* Write output to file
*
* @param output_file - name of the output file
* @param output - output data
* @param count - number of element in output
*/
void WriteOutput(const char* output_file, const void* output, const size_t count)
{
FILE* file = fopen(output_file, "wb");
CHECK(file != NULL);
printf("Writing result to file: %s\n", output_file);
size_t count_written = fwrite(output, sizeof(char), count, file);
CHECK(count_written == count);
printf("Output data size: %zu\n", count_written);
fclose(file);
}
/*
* This sample demonstrates how ldr-denosier could be converted to hdr-denoiser
* using tone-mapping as preprocessing and gamma-correction as postprocessing
*/
int main()
{
/* Set model parameters */
#if defined(_WIN32)
const rml_char* model_path = L"path/model";
#else
const rml_char* model_path = "path/model";
#endif
// Set input files
const char* input_files[] = {
"path/color",
"path/albedo",
"path/depth",
"path/normal",
};
// Set output file
const char* output_file = "path/output";
// Set input names
const char* input_names[] = {"hdr-color", "albedo", "depth", "normal"};
// Set input shapes
const rml_tensor_info input_infos[NUM_INPUTS] = {
{RML_DTYPE_FLOAT32, RML_LAYOUT_NHWC, {1, 600, 800, 3}},
{RML_DTYPE_FLOAT32, RML_LAYOUT_NHWC, {1, 600, 800, 3}},
{RML_DTYPE_FLOAT32, RML_LAYOUT_NHWC, {1, 600, 800, 1}},
{RML_DTYPE_FLOAT32, RML_LAYOUT_NHWC, {1, 600, 800, 2}}};
// Create a context
rml_context context = NULL;
RML_CHECK(rmlCreateDefaultContext(NULL, &context));
// Load model as a mutable graph
// model input - 9-channel 800x600 image (3-channel ldr-color,
// 3-channel albedo,
// 1-channel depth,
// 2-channel normal)
// model output - 3-channel 800x600 ldr image
rml_graph graph = NULL;
RML_CHECK(rmlLoadGraphFromFile(model_path, &graph));
// 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
rml_graph graph_with_preprocess = ConnectPreprocessingGraph(graph, input_names, input_infos);
rmlReleaseGraph(graph);
// Add postprocessing of base model outputs
// We should also apply gamma-correction for denoised image
rml_graph full_graph =
ConnectPostprocessingGraph(graph_with_preprocess, "input", input_infos[0].shape);
rmlReleaseGraph(graph_with_preprocess);
// Create immutable model from connected graphs
rml_model model = NULL;
RML_CHECK(rmlCreateModelFromGraph(context, full_graph, &model));
/* Release the graph */
rmlReleaseGraph(full_graph);
// Set up input info
size_t i;
for (i = 0; i < NUM_INPUTS; i++)
{
RML_CHECK(rmlSetModelInputInfo(model, input_names[i], &input_infos[i]));
}
// Check memory info
rml_memory_info memory_info;
RML_CHECK(rmlGetModelMemoryInfo(model, &memory_info));
// Create and fill the input tensors
rml_tensor inputs[NUM_INPUTS];
for (i = 0; i < NUM_INPUTS; i++)
{
rml_tensor input;
RML_CHECK(rmlCreateTensor(context, &input_infos[i], RML_ACCESS_MODE_WRITE_ONLY, &input));
size_t data_size = 0;
void* data = NULL;
RML_CHECK(rmlMapTensor(input, &data, &data_size));
void* file_data = ReadInput(input_files[i]);
memcpy(data, file_data, data_size);
free(file_data);
RML_CHECK(rmlUnmapTensor(input, data));
inputs[i] = input;
}
// Set model inputs
for (i = 0; i < NUM_INPUTS; i++)
{
RML_CHECK(rmlSetModelInput(model, input_names[i], inputs[i]));
}
// Get output tensor information
rml_tensor_info output_info;
RML_CHECK(rmlGetModelOutputInfo(model, NULL, &output_info));
// Create the output tensor
rml_tensor output_tensor = NULL;
RML_CHECK(rmlCreateTensor(context, &output_info, RML_ACCESS_MODE_READ_ONLY, &output_tensor));
// Set model output
RML_CHECK(rmlSetModelOutput(model, NULL, output_tensor));
// Run the inference
RML_CHECK(rmlInfer(model));
// Get data from output tensor
size_t output_size;
void* output_data = NULL;
RML_CHECK(rmlMapTensor(output_tensor, &output_data, &output_size));
// Unmap output data
RML_CHECK(rmlUnmapTensor(output_tensor, &output_data));
// Write the output
WriteOutput(output_file, output_data, output_size);
/* Release the input and output tensors */
rmlReleaseTensor(output_tensor);
for (i = 0; i < NUM_INPUTS; i++)
{
rmlReleaseTensor(inputs[i]);
}
/* Release the model */
rmlReleaseModel(model);
/* Release the context */
rmlReleaseContext(context);
return 0;
}