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169 changes: 169 additions & 0 deletions
169
tensorflow_addons/custom_ops/image/cc/kernels/adjust_hsv_in_yiq_op.cc
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| /* Copyright 2019 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 | ||
| #endif // GOOGLE_CUDA | ||
|
|
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| #include <memory> | ||
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| #include "tensorflow/core/framework/register_types.h" | ||
| #include "tensorflow/core/framework/tensor.h" | ||
| #include "tensorflow/core/framework/tensor_shape.h" | ||
| #include "tensorflow/core/lib/core/status.h" | ||
| #include "tensorflow/core/platform/logging.h" | ||
| #include "tensorflow/core/util/work_sharder.h" | ||
| #include "tensorflow_addons/custom_ops/image/cc/kernels/adjust_hsv_in_yiq_op.h" | ||
|
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| namespace tensorflow { | ||
|
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| typedef Eigen::ThreadPoolDevice CPUDevice; | ||
| typedef Eigen::GpuDevice GPUDevice; | ||
|
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| class AdjustHsvInYiqOpBase : public OpKernel { | ||
| protected: | ||
| explicit AdjustHsvInYiqOpBase(OpKernelConstruction* context) | ||
| : OpKernel(context) {} | ||
|
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| struct ComputeOptions { | ||
| const Tensor* input = nullptr; | ||
| Tensor* output = nullptr; | ||
| const Tensor* delta_h = nullptr; | ||
| const Tensor* scale_s = nullptr; | ||
| const Tensor* scale_v = nullptr; | ||
| int64 channel_count = 0; | ||
| }; | ||
|
|
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| virtual void DoCompute(OpKernelContext* context, | ||
| const ComputeOptions& options) = 0; | ||
|
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| void Compute(OpKernelContext* context) override { | ||
| const Tensor& input = context->input(0); | ||
| const Tensor& delta_h = context->input(1); | ||
| const Tensor& scale_s = context->input(2); | ||
| const Tensor& scale_v = context->input(3); | ||
| OP_REQUIRES(context, input.dims() >= 3, | ||
| errors::InvalidArgument("input must be at least 3-D, got shape", | ||
| input.shape().DebugString())); | ||
| OP_REQUIRES(context, TensorShapeUtils::IsScalar(delta_h.shape()), | ||
| errors::InvalidArgument("delta_h must be scalar: ", | ||
| delta_h.shape().DebugString())); | ||
| OP_REQUIRES(context, TensorShapeUtils::IsScalar(scale_s.shape()), | ||
| errors::InvalidArgument("scale_s must be scalar: ", | ||
| scale_s.shape().DebugString())); | ||
| OP_REQUIRES(context, TensorShapeUtils::IsScalar(scale_v.shape()), | ||
| errors::InvalidArgument("scale_v must be scalar: ", | ||
| scale_v.shape().DebugString())); | ||
| auto channels = input.dim_size(input.dims() - 1); | ||
| OP_REQUIRES( | ||
| context, channels == kChannelSize, | ||
| errors::InvalidArgument("input must have 3 channels but instead has ", | ||
| channels, " channels.")); | ||
|
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| Tensor* output = nullptr; | ||
| OP_REQUIRES_OK(context, | ||
| context->allocate_output(0, input.shape(), &output)); | ||
|
|
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| if (input.NumElements() > 0) { | ||
| const int64 channel_count = input.NumElements() / channels; | ||
| ComputeOptions options; | ||
| options.input = &input; | ||
| options.delta_h = &delta_h; | ||
| options.scale_s = &scale_s; | ||
| options.scale_v = &scale_v; | ||
| options.output = output; | ||
| options.channel_count = channel_count; | ||
| DoCompute(context, options); | ||
| } | ||
| } | ||
| }; | ||
|
|
||
| template <class Device> | ||
| class AdjustHsvInYiqOp; | ||
|
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| template <> | ||
| class AdjustHsvInYiqOp<CPUDevice> : public AdjustHsvInYiqOpBase { | ||
| public: | ||
| explicit AdjustHsvInYiqOp(OpKernelConstruction* context) | ||
| : AdjustHsvInYiqOpBase(context) {} | ||
|
|
||
| void DoCompute(OpKernelContext* context, | ||
| const ComputeOptions& options) override { | ||
| const Tensor* input = options.input; | ||
| Tensor* output = options.output; | ||
| const int64 channel_count = options.channel_count; | ||
| auto input_data = input->shaped<float, 2>({channel_count, kChannelSize}); | ||
| const float delta_h = options.delta_h->scalar<float>()(); | ||
| const float scale_s = options.scale_s->scalar<float>()(); | ||
| const float scale_v = options.scale_v->scalar<float>()(); | ||
| auto output_data = output->shaped<float, 2>({channel_count, kChannelSize}); | ||
| float tranformation_matrix[kChannelSize * kChannelSize] = {0}; | ||
| internal::compute_tranformation_matrix<kChannelSize * kChannelSize>( | ||
| delta_h, scale_s, scale_v, tranformation_matrix); | ||
| const int kCostPerChannel = 10; | ||
| const DeviceBase::CpuWorkerThreads& worker_threads = | ||
| *context->device()->tensorflow_cpu_worker_threads(); | ||
| Shard(worker_threads.num_threads, worker_threads.workers, channel_count, | ||
| kCostPerChannel, [&input_data, &output_data, &tranformation_matrix]( | ||
| int64 start_channel, int64 end_channel) { | ||
| // Applying projection matrix to input RGB vectors. | ||
| const float* p = input_data.data() + start_channel * kChannelSize; | ||
| float* q = output_data.data() + start_channel * kChannelSize; | ||
| for (int i = start_channel; i < end_channel; i++) { | ||
| for (int q_index = 0; q_index < kChannelSize; q_index++) { | ||
| q[q_index] = 0; | ||
| for (int p_index = 0; p_index < kChannelSize; p_index++) { | ||
| q[q_index] += | ||
| p[p_index] * | ||
| tranformation_matrix[q_index + kChannelSize * p_index]; | ||
| } | ||
| } | ||
| p += kChannelSize; | ||
| q += kChannelSize; | ||
| } | ||
| }); | ||
| } | ||
| }; | ||
|
|
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| REGISTER_KERNEL_BUILDER( | ||
| Name("AdjustHsvInYiq").Device(DEVICE_CPU).TypeConstraint<float>("T"), | ||
| AdjustHsvInYiqOp<CPUDevice>); | ||
|
|
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| #if GOOGLE_CUDA | ||
| template <> | ||
| class AdjustHsvInYiqOp<GPUDevice> : public AdjustHsvInYiqOpBase { | ||
| public: | ||
| explicit AdjustHsvInYiqOp(OpKernelConstruction* context) | ||
| : AdjustHsvInYiqOpBase(context) {} | ||
|
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| void DoCompute(OpKernelContext* ctx, const ComputeOptions& options) override { | ||
| const int64 number_of_elements = options.input->NumElements(); | ||
| if (number_of_elements <= 0) { | ||
| return; | ||
| } | ||
| const float* delta_h = options.delta_h->flat<float>().data(); | ||
| const float* scale_s = options.scale_s->flat<float>().data(); | ||
| const float* scale_v = options.scale_v->flat<float>().data(); | ||
| functor::AdjustHsvInYiqGPU()(ctx, options.channel_count, options.input, | ||
| delta_h, scale_s, scale_v, options.output); | ||
| } | ||
| }; | ||
|
|
||
| REGISTER_KERNEL_BUILDER( | ||
| Name("AdjustHsvInYiq").Device(DEVICE_GPU).TypeConstraint<float>("T"), | ||
| AdjustHsvInYiqOp<GPUDevice>); | ||
| #endif | ||
|
|
||
| } // namespace tensorflow |
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