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/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#if GOOGLE_CUDA
#define EIGEN_USE_GPU
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/kernels/adjust_hue_op.h"
#include "tensorflow/core/util/cuda_kernel_helper.h"
namespace tensorflow {
namespace internal {
namespace {
typedef struct RgbTuple {
float r;
float g;
float b;
} RgbTuple;
typedef struct HsvTuple {
float h;
float s;
float v;
} HsvTuple;
} // anon namespace
__device__ HsvTuple rgb2hsv_cuda(const float r, const float g, const float b)
{
HsvTuple tuple;
const float M = fmaxf(r, fmaxf(g, b));
const float m = fminf(r, fminf(g, b));
const float chroma = M - m;
float h = 0.0f, s = 0.0f;
// hue
if (chroma > 0.0f) {
if (M == r) {
const float num = (g - b) / chroma;
const float sign = copysignf(1.0f, num);
h = ((sign < 0.0f) * 6.0f + sign * fmodf(sign * num, 6.0f)) / 6.0f;
} else if (M == g) {
h = ((b - r) / chroma + 2.0f) / 6.0f;
} else {
h = ((r - g) / chroma + 4.0f) / 6.0f;
}
} else {
h = 0.0f;
}
// saturation
if (M > 0.0) {
s = chroma / M;
} else {
s = 0.0f;
}
tuple.h = h;
tuple.s = s;
tuple.v = M;
return tuple;
}
__device__ RgbTuple hsv2rgb_cuda(const float h, const float s, const float v)
{
RgbTuple tuple;
const float new_h = h * 6.0f;
const float chroma = v * s;
const float x = chroma * (1.0f - fabsf(fmodf(new_h, 2.0f) - 1.0f));
const float new_m = v - chroma;
const bool between_0_and_1 = new_h >= 0.0f && new_h < 1.0f;
const bool between_1_and_2 = new_h >= 1.0f && new_h < 2.0f;
const bool between_2_and_3 = new_h >= 2.0f && new_h < 3.0f;
const bool between_3_and_4 = new_h >= 3.0f && new_h < 4.0f;
const bool between_4_and_5 = new_h >= 4.0f && new_h < 5.0f;
const bool between_5_and_6 = new_h >= 5.0f && new_h < 6.0f;
tuple.r = chroma * (between_0_and_1 || between_5_and_6) +
x * (between_1_and_2 || between_4_and_5) + new_m;
tuple.g = chroma * (between_1_and_2 || between_2_and_3) +
x * (between_0_and_1 || between_3_and_4) + new_m;
tuple.b = chroma * (between_3_and_4 || between_4_and_5) +
x * (between_2_and_3 || between_5_and_6) + new_m;
return tuple;
}
__global__ void adjust_hue_nhwc(const int64 number_elements,
const float * const __restrict__ input,
float * const output,
const float * const hue_delta)
{
// multiply by 3 since we're dealing with contiguous RGB bytes for each pixel (NHWC)
const int64 idx = (blockDim.x * blockIdx.x + threadIdx.x) * 3;
// bounds check
if (idx > number_elements - 1) {
return;
}
const float delta = hue_delta[0];
const HsvTuple hsv = rgb2hsv_cuda(input[idx], input[idx + 1], input[idx + 2]);
// hue adjustment
float new_h = fmodf(hsv.h + delta, 1.0f);
if (new_h < 0.0f) {
new_h = fmodf(1.0f + new_h, 1.0f);
}
const RgbTuple rgb = hsv2rgb_cuda(new_h, hsv.s, hsv.v);
output[idx] = rgb.r;
output[idx + 1] = rgb.g;
output[idx + 2] = rgb.b;
}
} // namespace internal
namespace functor {
void AdjustHueGPU::operator()(
GPUDevice* device,
const int64 number_of_elements,
const float* const input,
const float* const delta,
float* const output
) {
const auto stream = device->stream();
const CudaLaunchConfig config = GetCudaLaunchConfig(number_of_elements, *device);
const int threads_per_block = config.thread_per_block;
const int block_count = (number_of_elements + threads_per_block - 1) / threads_per_block;
internal::adjust_hue_nhwc<<<block_count, threads_per_block, 0, stream>>>(
number_of_elements, input, output, delta
);
}
} // namespace functor
} // namespace tensorflow
#endif // GOOGLE_CUDA
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