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#ifndef AUGMENTATION_LAYER_BASE_H_
#define AUGMENTATION_LAYER_BASE_H_
#include "tensorflow/core/framework/tensor_types.h"
#include <iostream>
#include <string>
#include <vector>
namespace tensorflow {
template<typename T>
class OptionalType {
public:
OptionalType(const T default_value) : default_value(default_value), has_value(false) {}
operator bool() const {
return has_value;
}
OptionalType& operator=(T val) {
has_value = true;
value = val;
return *this;
}
const T operator()() const {
return has_value ? value : default_value;
}
void clear() {
has_value = false;
}
const T get_default() {
return default_value;
}
private:
T value;
bool has_value;
const T default_value;
};
class AugmentationCoeff {
public:
// Spatial Types
OptionalType<float>dx;
OptionalType<float>dy;
OptionalType<float>angle;
OptionalType<float>zoom_x;
OptionalType<float>zoom_y;
// Chromatic Types
OptionalType<float>gamma;
OptionalType<float>brightness;
OptionalType<float>contrast;
OptionalType<float>color1;
OptionalType<float>color2;
OptionalType<float>color3;
AugmentationCoeff() : dx(0.0), dy(0.0), angle(0.0), zoom_x(1.0), zoom_y(1.0), gamma(1.0),
brightness(0.0), contrast(1.0), color1(1.0), color2(1.0), color3(1.0) {}
AugmentationCoeff(const AugmentationCoeff& coeff) : AugmentationCoeff() {
replace_with(coeff);
}
void clear();
void combine_with(const AugmentationCoeff& coeff);
void replace_with(const AugmentationCoeff& coeff);
};
typedef struct AugmentationParam {
std::string rand_type;
bool should_exp;
float mean;
float spread;
float prob;
} AugmentationParam;
class AugmentationParams {
public:
int crop_height;
int crop_width;
// Spatial options
OptionalType<struct AugmentationParam>translate;
OptionalType<struct AugmentationParam>rotate;
OptionalType<struct AugmentationParam>zoom;
OptionalType<struct AugmentationParam>squeeze;
// Chromatic options
OptionalType<struct AugmentationParam>gamma;
OptionalType<struct AugmentationParam>brightness;
OptionalType<struct AugmentationParam>contrast;
OptionalType<struct AugmentationParam>color;
inline AugmentationParams(int crop_height,
int crop_width,
std::vector<std::string>params_name,
std::vector<std::string>params_rand_type,
std::vector<bool> params_exp,
std::vector<float> params_mean,
std::vector<float> params_spread,
std::vector<float> params_prob) :
crop_height(crop_height),
crop_width(crop_width),
translate(AugmentationParam()),
rotate(AugmentationParam()),
zoom(AugmentationParam()),
squeeze(AugmentationParam()),
gamma(AugmentationParam()),
brightness(AugmentationParam()),
contrast(AugmentationParam()),
color(AugmentationParam()) {
for (int i = 0; i < params_name.size(); i++) {
const std::string name = params_name[i];
const std::string rand_type = params_rand_type[i];
const bool should_exp = params_exp[i];
const float mean = params_mean[i];
const float spread = params_spread[i];
const float prob = params_prob[i];
struct AugmentationParam param = { rand_type, should_exp, mean, spread, prob };
if (name == "translate") {
this->translate = param;
} else if (name == "rotate") {
this->rotate = param;
} else if (name == "zoom") {
this->zoom = param;
} else if (name == "squeeze") {
this->squeeze = param;
} else if (name == "noise") {
// NoOp: We handle noise on the Python side
} else if (name == "gamma") {
this->gamma = param;
} else if (name == "brightness") {
this->brightness = param;
} else if (name == "contrast") {
this->contrast = param;
} else if (name == "color") {
this->color = param;
} else {
std::cout << "Ignoring unknown augmentation parameter: " << name << std::endl;
}
}
}
bool should_do_spatial_transform() {
return this->translate || this->rotate || this->zoom || this->squeeze;
}
bool should_do_chromatic_transform() {
return this->gamma || this->brightness || this->contrast || this->color;
}
};
class AugmentationLayerBase {
public:
class TransMat {
/**
* Translation matrix class for spatial augmentation
* | 0 1 2 |
* | 3 4 5 |
*/
public:
float t0, t1, t2;
float t3, t4, t5;
void fromCoeff(AugmentationCoeff *coeff,
int out_width,
int out_height,
int src_width,
int src_height);
void fromTensor(const float *tensor_data);
TransMat inverse();
void leftMultiply(float u0,
float u1,
float u2,
float u3,
float u4,
float u5);
void toIdentity();
};
// TODO: Class ChromaticCoeffs
static float rng_generate(const AugmentationParam& param,
float discount_coeff,
const float default_value);
static void clear_spatial_coeffs(AugmentationCoeff& coeff);
static void generate_chromatic_coeffs(float discount_coeff,
const AugmentationParams& aug,
AugmentationCoeff & coeff);
static void generate_spatial_coeffs(float discount_coeff,
const AugmentationParams& aug,
AugmentationCoeff & coeff);
static void generate_valid_spatial_coeffs(float discount_coeff,
const AugmentationParams& aug,
AugmentationCoeff & coeff,
int src_width,
int src_height,
int out_width,
int out_height);
static void copy_chromatic_coeffs_to_tensor(const std::vector<AugmentationCoeff>& coeff_arr,
typename TTypes<float, 2>::Tensor& out);
static void copy_spatial_coeffs_to_tensor(const std::vector<AugmentationCoeff>& coeff_arr,
const int out_width,
const int out_height,
const int src_width,
const int src_height,
typename TTypes<float, 2>::Tensor& out,
const bool invert = false);
};
} // namespace tensorflow
#endif // AUGMENTATION_LAYER_BASE_H_
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