1/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
2
3Licensed under the Apache License, Version 2.0 (the "License");
4you may not use this file except in compliance with the License.
5You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9Unless required by applicable law or agreed to in writing, software
10distributed under the License is distributed on an "AS IS" BASIS,
11WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12See the License for the specific language governing permissions and
13limitations 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
21namespace tensorflow {
22
23class 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
41template <typename T>
42class DeepConv2DTransform {
43 public:
44 virtual ~DeepConv2DTransform() {}
45
46 virtual void GetFilterTransformMatrix(const int64_t rows, const int64_t cols,
47 T* transform_matrix) const = 0;
48
49 virtual void GetInputTransformMatrix(const int64_t rows, const int64_t cols,
50 T* transform_matrix) const = 0;
51
52 virtual void GetOutputTransformMatrix(const int64_t rows, const int64_t cols,
53 T* transform_matrix) const = 0;
54
55 struct Shape {
56 Shape(int64_t r, int64_t c) : rows(r), cols(c) {}
57 int64_t rows;
58 int64_t 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.
67struct 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.
100bool 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
104namespace functor {
105
106// Calls DeepConv2D implementation (see deep_conv2d.cc for details).
107template <typename Device, typename T>
108struct 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