1 | /* Copyright 2019 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_IN_TOPK_OP_H_ |
17 | #define TENSORFLOW_CORE_KERNELS_IN_TOPK_OP_H_ |
18 | |
19 | #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM |
20 | #define EIGEN_USE_GPU |
21 | #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM |
22 | |
23 | #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" |
24 | #include "tensorflow/core/framework/bounds_check.h" |
25 | #include "tensorflow/core/framework/op_kernel.h" |
26 | #include "tensorflow/core/framework/tensor.h" |
27 | #include "tensorflow/core/framework/tensor_types.h" |
28 | |
29 | namespace tensorflow { |
30 | namespace functor { |
31 | |
32 | typedef Eigen::ThreadPoolDevice CPUDevice; |
33 | typedef Eigen::GpuDevice GPUDevice; |
34 | |
35 | // InTopK argument can be passed either via mode attribute (InTopK op), or as an |
36 | // input tensor (InTopKV2 op). |
37 | struct TopKArg { |
38 | int64_t k_value = -1; |
39 | const Tensor* k_tensor = nullptr; |
40 | }; |
41 | |
42 | template <typename Device, typename T, typename TargetT> |
43 | struct InTopKFunctor { |
44 | template <int ndims> |
45 | using Dims = Eigen::DSizes<Eigen::Index, ndims>; |
46 | |
47 | void operator()(OpKernelContext* context, |
48 | typename TTypes<T, 2>::ConstTensor predictions, |
49 | typename TTypes<TargetT>::ConstVec targets, const TopKArg k, |
50 | typename TTypes<bool>::Vec output) {} |
51 | }; |
52 | |
53 | template <typename T, typename TargetT> |
54 | struct InTopKFunctor<CPUDevice, T, TargetT> { |
55 | void operator()(OpKernelContext* context, |
56 | typename TTypes<T, 2>::ConstTensor predictions, |
57 | typename TTypes<TargetT>::ConstVec targets, const TopKArg k, |
58 | typename TTypes<bool>::Vec output) { |
59 | const Eigen::Index num_targets = predictions.dimension(0); |
60 | const Eigen::Index num_classes = predictions.dimension(1); |
61 | |
62 | int64_t k_val = k.k_value; |
63 | if (k.k_tensor != nullptr) { |
64 | if (k.k_tensor->dtype() == DT_INT32) { |
65 | k_val = k.k_tensor->scalar<int32>()(); |
66 | } else { |
67 | k_val = k.k_tensor->scalar<int64_t>()(); |
68 | } |
69 | } |
70 | |
71 | for (int batch_idx = 0; batch_idx < num_targets; batch_idx++) { |
72 | auto target = internal::SubtleMustCopy(targets(batch_idx)); |
73 | |
74 | bool cannot_say = !FastBoundsCheck(target, num_classes) || |
75 | !std::isfinite(predictions(batch_idx, target)); |
76 | |
77 | int more_probable_classes = 0; |
78 | if (!cannot_say) { |
79 | const T target_prediction = predictions(batch_idx, target); |
80 | |
81 | for (int class_idx = 0; class_idx < num_classes; ++class_idx) { |
82 | T pred = predictions(batch_idx, class_idx); |
83 | if (!std::isfinite(pred)) { |
84 | cannot_say = true; |
85 | break; |
86 | } else if (pred > target_prediction) { |
87 | ++more_probable_classes; |
88 | if (more_probable_classes > k_val) break; |
89 | } |
90 | } |
91 | } |
92 | output(batch_idx) = cannot_say ? false : (more_probable_classes < k_val); |
93 | } |
94 | } |
95 | }; |
96 | |
97 | } // namespace functor |
98 | } // namespace tensorflow |
99 | |
100 | #endif // TENSORFLOW_CORE_KERNELS_IN_TOPK_OP_H_ |
101 | |