1 /* Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 3 Licensed under the Apache License, Version 2.0 (the "License"); 4 you may not use this file except in compliance with the License. 5 You may obtain a copy of the License at 6 7 http://www.apache.org/licenses/LICENSE-2.0 8 9 Unless required by applicable law or agreed to in writing, software 10 distributed under the License is distributed on an "AS IS" BASIS, 11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 See the License for the specific language governing permissions and 13 limitations under the License. 14 ==============================================================================*/ 15 16 #ifndef TENSORFLOW_CORE_KERNELS_DEEP_CONV2D_H_ 17 #define TENSORFLOW_CORE_KERNELS_DEEP_CONV2D_H_ 18 19 #include "tensorflow/core/framework/types.h" 20 21 namespace tensorflow { 22 23 class OpKernelContext; 24 25 // DeepConv2D is a Conv2D implementation specialized for deep (i.e. large 26 // in_depth * out_depth product) convolutions (see deep_conv2d.cc for details). 27 28 // DeepConv2DTransform is an interface for implementing transforms for 29 // DeepConv2D. Implementations must specify transform matrices and 30 // input/output/filter shapes. DeepConv2d computes: 31 // 32 // y = C[Ad * Bg] 33 // 34 // C: output transform matrix 35 // A: input data transform matrix 36 // B: filter transform matrix 37 // d: vectorized 2D data tile 38 // g: vectorized 2D filter tile 39 // y: vectorized 2D output tile 40 41 template <typename T> 42 class DeepConv2DTransform { 43 public: 44 virtual ~DeepConv2DTransform() {} 45 46 virtual void GetFilterTransformMatrix(const int64 rows, const int64 cols, 47 T* transform_matrix) const = 0; 48 49 virtual void GetInputTransformMatrix(const int64 rows, const int64 cols, 50 T* transform_matrix) const = 0; 51 52 virtual void GetOutputTransformMatrix(const int64 rows, const int64 cols, 53 T* transform_matrix) const = 0; 54 55 struct Shape { 56 Shape(int64 r, int64 c) : rows(r), cols(c) {} 57 int64 rows; 58 int64 cols; 59 }; 60 61 virtual const Shape& filter_shape() const = 0; 62 virtual const Shape& input_shape() const = 0; 63 virtual const Shape& output_shape() const = 0; 64 }; 65 66 // Conv2D arguments used by DeepConv2D implementation. 67 struct Conv2DArgs { 68 // Input layer dimensions 69 int batch; 70 int in_rows; 71 int in_cols; 72 int in_depth; 73 int filter_rows; 74 int filter_cols; 75 int pad_rows; 76 int pad_cols; 77 78 // Output layer dimensions 79 int out_rows; 80 int out_cols; 81 int out_depth; 82 83 Conv2DArgs() 84 : batch(0), 85 in_rows(0), 86 in_cols(0), 87 in_depth(0), 88 filter_rows(0), 89 filter_cols(0), 90 pad_rows(0), 91 pad_cols(0), 92 out_rows(0), 93 out_cols(0), 94 out_depth(0) {} 95 }; 96 97 // Returns true if convolution operation specified by function arguments 98 // can use DeepConv2D implementation, and false otherwise. 99 // May return false based on parameters, cost, or whether feature is disabled. 100 bool CanUseDeepConv2D(int stride_rows, int stride_cols, int filter_rows, 101 int filter_cols, int in_depth, int out_depth, 102 int out_rows, int out_cols); 103 104 namespace functor { 105 106 // Calls DeepConv2D implementation (see deep_conv2d.cc for details). 107 template <typename Device, typename T> 108 struct DeepConv2D { 109 void operator()(OpKernelContext* ctx, const Conv2DArgs& args, const T* input, 110 const T* filter, T* output); 111 }; 112 113 } // namespace functor 114 115 } // namespace tensorflow 116 117 #endif // TENSORFLOW_CORE_KERNELS_DEEP_CONV2D_H_ 118
