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tensorflow/tensorflow/core/kernels/image/image_ops.cc
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| /* Copyright 2020 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. | |
| ==============================================================================*/ | |
| #define EIGEN_USE_THREADS | |
| #if GOOGLE_CUDA | |
| #define EIGEN_USE_GPU | |
| #endif // GOOGLE_CUDA | |
| #include "tensorflow/core/kernels/image/image_ops.h" | |
| #include "tensorflow/core/framework/op_kernel.h" | |
| #include "tensorflow/core/framework/register_types.h" | |
| #include "tensorflow/core/framework/types.h" | |
| #include "tensorflow/core/platform/types.h" | |
| namespace tensorflow { | |
| namespace functor { | |
| // Explicit instantiation of the CPU functor. | |
| typedef Eigen::ThreadPoolDevice CPUDevice; | |
| template struct FillProjectiveTransform<CPUDevice, uint8>; | |
| template struct FillProjectiveTransform<CPUDevice, int32>; | |
| template struct FillProjectiveTransform<CPUDevice, int64_t>; | |
| template struct FillProjectiveTransform<CPUDevice, Eigen::half>; | |
| template struct FillProjectiveTransform<CPUDevice, float>; | |
| template struct FillProjectiveTransform<CPUDevice, double>; | |
| } // end namespace functor | |
| typedef Eigen::ThreadPoolDevice CPUDevice; | |
| using functor::FillProjectiveTransform; | |
| using generator::Interpolation; | |
| using generator::Mode; | |
| template <typename Device, typename T> | |
| void DoImageProjectiveTransformOp(OpKernelContext* ctx, | |
| const Interpolation& interpolation, | |
| const Mode& fill_mode) { | |
| const Tensor& images_t = ctx->input(0); | |
| const Tensor& transform_t = ctx->input(1); | |
| OP_REQUIRES(ctx, images_t.shape().dims() == 4, | |
| errors::InvalidArgument("Input images must have rank 4")); | |
| OP_REQUIRES(ctx, | |
| (TensorShapeUtils::IsMatrix(transform_t.shape()) && | |
| (transform_t.dim_size(0) == images_t.dim_size(0) || | |
| transform_t.dim_size(0) == 1) && | |
| transform_t.dim_size(1) == 8), | |
| errors::InvalidArgument( | |
| "Input transform should be num_images x 8 or 1 x 8")); | |
| int32_t out_height, out_width; | |
| // Kernel is shared by legacy "ImageProjectiveTransform" op with 2 args. | |
| if (ctx->num_inputs() >= 3) { | |
| const Tensor& shape_t = ctx->input(2); | |
| OP_REQUIRES(ctx, shape_t.dims() == 1, | |
| errors::InvalidArgument("output shape must be 1-dimensional", | |
| shape_t.shape().DebugString())); | |
| OP_REQUIRES(ctx, shape_t.NumElements() == 2, | |
| errors::InvalidArgument("output shape must have two elements", | |
| shape_t.shape().DebugString())); | |
| auto shape_vec = shape_t.vec<int32>(); | |
| out_height = shape_vec(0); | |
| out_width = shape_vec(1); | |
| OP_REQUIRES(ctx, out_height > 0 && out_width > 0, | |
| errors::InvalidArgument("output dimensions must be positive")); | |
| } else { | |
| // Shape is N (batch size), H (height), W (width), C (channels). | |
| out_height = images_t.shape().dim_size(1); | |
| out_width = images_t.shape().dim_size(2); | |
| } | |
| T fill_value(0); | |
| // Kernel is shared by "ImageProjectiveTransformV2" with 3 args. | |
| if (ctx->num_inputs() >= 4) { | |
| const Tensor& fill_value_t = ctx->input(3); | |
| OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(fill_value_t.shape()), | |
| errors::InvalidArgument("fill_value must be a scalar", | |
| fill_value_t.shape().DebugString())); | |
| fill_value = static_cast<T>(*(fill_value_t.scalar<float>().data())); | |
| } | |
| Tensor* output_t; | |
| TensorShape output_shape; | |
| OP_REQUIRES_OK( | |
| ctx, TensorShape::BuildTensorShape({images_t.dim_size(0), out_height, | |
| out_width, images_t.dim_size(3)}, | |
| &output_shape)); | |
| OP_REQUIRES_OK(ctx, ctx->allocate_output(0, output_shape, &output_t)); | |
| auto output = output_t->tensor<T, 4>(); | |
| auto images = images_t.tensor<T, 4>(); | |
| auto transform = transform_t.matrix<float>(); | |
| (FillProjectiveTransform<Device, T>(interpolation))( | |
| ctx->eigen_device<Device>(), &output, images, transform, fill_mode, | |
| fill_value); | |
| } | |
| template <typename Device, typename T> | |
| class ImageProjectiveTransformV2 : public OpKernel { | |
| private: | |
| Interpolation interpolation_; | |
| Mode fill_mode_; | |
| public: | |
| explicit ImageProjectiveTransformV2(OpKernelConstruction* ctx) | |
| : OpKernel(ctx) { | |
| string interpolation_str; | |
| OP_REQUIRES_OK(ctx, ctx->GetAttr("interpolation", &interpolation_str)); | |
| if (interpolation_str == "NEAREST") { | |
| interpolation_ = Interpolation::NEAREST; | |
| } else if (interpolation_str == "BILINEAR") { | |
| interpolation_ = Interpolation::BILINEAR; | |
| } else { | |
| LOG(ERROR) << "Invalid interpolation " << interpolation_str | |
| << ". Supported types: NEAREST, BILINEAR"; | |
| } | |
| string mode_str; | |
| OP_REQUIRES_OK(ctx, ctx->GetAttr("fill_mode", &mode_str)); | |
| if (mode_str == "REFLECT") { | |
| fill_mode_ = Mode::FILL_REFLECT; | |
| } else if (mode_str == "WRAP") { | |
| fill_mode_ = Mode::FILL_WRAP; | |
| } else if (mode_str == "CONSTANT") { | |
| fill_mode_ = Mode::FILL_CONSTANT; | |
| } else if (mode_str == "NEAREST") { | |
| fill_mode_ = Mode::FILL_NEAREST; | |
| } else { | |
| LOG(ERROR) << "Invalid mode " << mode_str | |
| << ". Supported types: REFLECT, WRAP, CONSTANT, NEAREST"; | |
| } | |
| } | |
| void Compute(OpKernelContext* ctx) override { | |
| DoImageProjectiveTransformOp<Device, T>(ctx, interpolation_, fill_mode_); | |
| } | |
| }; | |
| #define REGISTER(TYPE) \ | |
| REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransformV2") \ | |
| .Device(DEVICE_CPU) \ | |
| .TypeConstraint<TYPE>("dtype"), \ | |
| ImageProjectiveTransformV2<CPUDevice, TYPE>) | |
| TF_CALL_uint8(REGISTER); | |
| TF_CALL_int32(REGISTER); | |
| TF_CALL_int64(REGISTER); | |
| TF_CALL_half(REGISTER); | |
| TF_CALL_float(REGISTER); | |
| TF_CALL_double(REGISTER); | |
| #undef REGISTER | |
| template <typename Device, typename T> | |
| class ImageProjectiveTransformV3 | |
| : public ImageProjectiveTransformV2<Device, T> { | |
| public: | |
| explicit ImageProjectiveTransformV3(OpKernelConstruction* ctx) | |
| : ImageProjectiveTransformV2<Device, T>(ctx) {} | |
| }; | |
| #define REGISTER(TYPE) \ | |
| REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransformV3") \ | |
| .Device(DEVICE_CPU) \ | |
| .TypeConstraint<TYPE>("dtype"), \ | |
| ImageProjectiveTransformV3<CPUDevice, TYPE>) | |
| TF_CALL_uint8(REGISTER); | |
| TF_CALL_int32(REGISTER); | |
| TF_CALL_int64(REGISTER); | |
| TF_CALL_half(REGISTER); | |
| TF_CALL_float(REGISTER); | |
| TF_CALL_double(REGISTER); | |
| #undef REGISTER | |
| #if GOOGLE_CUDA | |
| typedef Eigen::GpuDevice GPUDevice; | |
| typedef generator::Mode Mode; | |
| namespace functor { | |
| // NOTE(ringwalt): We get an undefined symbol error if we don't explicitly | |
| // instantiate the operator() in GCC'd code. | |
| #define DECLARE_PROJECT_FUNCTOR(TYPE) \ | |
| template <> \ | |
| void FillProjectiveTransform<GPUDevice, TYPE>::operator()( \ | |
| const GPUDevice& device, OutputType* output, const InputType& images, \ | |
| const TransformsType& transform, const Mode fill_mode, \ | |
| const TYPE fill_value) const; \ | |
| extern template struct FillProjectiveTransform<GPUDevice, TYPE> | |
| TF_CALL_uint8(DECLARE_PROJECT_FUNCTOR); | |
| TF_CALL_int32(DECLARE_PROJECT_FUNCTOR); | |
| TF_CALL_int64(DECLARE_PROJECT_FUNCTOR); | |
| TF_CALL_half(DECLARE_PROJECT_FUNCTOR); | |
| TF_CALL_float(DECLARE_PROJECT_FUNCTOR); | |
| TF_CALL_double(DECLARE_PROJECT_FUNCTOR); | |
| } // end namespace functor | |
| namespace generator { | |
| #define DECLARE_MAP_FUNCTOR(Mode) \ | |
| template <> \ | |
| float MapCoordinate<GPUDevice, Mode>::operator()(const float out_coord, \ | |
| const DenseIndex len); \ | |
| extern template struct MapCoordinate<GPUDevice, Mode> | |
| DECLARE_MAP_FUNCTOR(Mode::FILL_REFLECT); | |
| DECLARE_MAP_FUNCTOR(Mode::FILL_WRAP); | |
| DECLARE_MAP_FUNCTOR(Mode::FILL_CONSTANT); | |
| DECLARE_MAP_FUNCTOR(Mode::FILL_NEAREST); | |
| } // end namespace generator | |
| #define REGISTER(TYPE) \ | |
| REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransformV2") \ | |
| .Device(DEVICE_GPU) \ | |
| .TypeConstraint<TYPE>("dtype") \ | |
| .HostMemory("output_shape"), \ | |
| ImageProjectiveTransformV2<GPUDevice, TYPE>) | |
| TF_CALL_uint8(REGISTER); | |
| TF_CALL_int32(REGISTER); | |
| TF_CALL_int64(REGISTER); | |
| TF_CALL_half(REGISTER); | |
| TF_CALL_float(REGISTER); | |
| TF_CALL_double(REGISTER); | |
| #undef REGISTER | |
| #define REGISTER(TYPE) \ | |
| REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransformV3") \ | |
| .Device(DEVICE_GPU) \ | |
| .TypeConstraint<TYPE>("dtype") \ | |
| .HostMemory("output_shape") \ | |
| .HostMemory("fill_value"), \ | |
| ImageProjectiveTransformV3<GPUDevice, TYPE>) | |
| TF_CALL_uint8(REGISTER); | |
| TF_CALL_int32(REGISTER); | |
| TF_CALL_int64(REGISTER); | |
| TF_CALL_half(REGISTER); | |
| TF_CALL_float(REGISTER); | |
| TF_CALL_double(REGISTER); | |
| #undef REGISTER | |
| #endif // GOOGLE_CUDA | |
| } // end namespace tensorflow |