1/* Copyright 2015 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#include <string>
17#include <unordered_set>
18#include <utility>
19
20#include "tensorflow/core/framework/op_kernel.h"
21#include "tensorflow/core/framework/register_types.h"
22#include "tensorflow/core/framework/tensor.h"
23#include "tensorflow/core/framework/tensor_shape.h"
24#include "tensorflow/core/lib/core/status.h"
25
26namespace tensorflow {
27template <typename T, typename Tidx>
28class ListDiffOp : public OpKernel {
29 public:
30 explicit ListDiffOp(OpKernelConstruction* context) : OpKernel(context) {
31 const DataType dt = DataTypeToEnum<T>::v();
32 const DataType dtidx = DataTypeToEnum<Tidx>::v();
33 OP_REQUIRES_OK(context, context->MatchSignature({dt, dt}, {dt, dtidx}));
34 }
35
36 void Compute(OpKernelContext* context) override {
37 const Tensor& x = context->input(0);
38 const Tensor& y = context->input(1);
39
40 OP_REQUIRES(context, TensorShapeUtils::IsVector(x.shape()),
41 errors::InvalidArgument("x should be a 1D vector."));
42
43 OP_REQUIRES(context, TensorShapeUtils::IsVector(y.shape()),
44 errors::InvalidArgument("y should be a 1D vector."));
45
46 const auto Tx = x.vec<T>();
47 const size_t x_size = Tx.size();
48 const auto Ty = y.vec<T>();
49 const size_t y_size = Ty.size();
50
51 OP_REQUIRES(context, x_size < std::numeric_limits<int32>::max(),
52 errors::InvalidArgument("x too large for int32 indexing"));
53
54 std::unordered_set<T> y_set;
55 y_set.reserve(y_size);
56 for (size_t i = 0; i < y_size; ++i) {
57 y_set.insert(Ty(i));
58 }
59
60 // Compute the size of the output.
61
62 int64_t out_size = 0;
63 for (size_t i = 0; i < x_size; ++i) {
64 if (y_set.count(Tx(i)) == 0) {
65 ++out_size;
66 }
67 }
68
69 // Allocate and populate outputs.
70 Tensor* out = nullptr;
71 OP_REQUIRES_OK(context, context->allocate_output(0, {out_size}, &out));
72 auto Tout = out->vec<T>();
73
74 Tensor* indices = nullptr;
75 OP_REQUIRES_OK(context, context->allocate_output(1, {out_size}, &indices));
76 auto Tindices = indices->vec<Tidx>();
77
78 for (Tidx i = 0, p = 0; i < static_cast<Tidx>(x_size); ++i) {
79 if (y_set.count(Tx(i)) == 0) {
80 OP_REQUIRES(context, p < out_size,
81 errors::InvalidArgument(
82 "Tried to set output index ", p,
83 " when output Tensor only had ", out_size,
84 " elements. Check that your "
85 "input tensors are not being concurrently mutated."));
86 Tout(p) = Tx(i);
87 Tindices(p) = i;
88 p++;
89 }
90 }
91 }
92};
93
94#define REGISTER_LISTDIFF(type) \
95 REGISTER_KERNEL_BUILDER(Name("ListDiff") \
96 .Device(DEVICE_CPU) \
97 .TypeConstraint<type>("T") \
98 .TypeConstraint<int32>("out_idx"), \
99 ListDiffOp<type, int32>) \
100 REGISTER_KERNEL_BUILDER(Name("ListDiff") \
101 .Device(DEVICE_CPU) \
102 .TypeConstraint<type>("T") \
103 .TypeConstraint<int64_t>("out_idx"), \
104 ListDiffOp<type, int64>)
105
106TF_CALL_REAL_NUMBER_TYPES(REGISTER_LISTDIFF);
107REGISTER_LISTDIFF(tstring);
108#undef REGISTER_LISTDIFF
109
110} // namespace tensorflow
111