1 /* Copyright 2018 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 #include "tensorflow/lite/c/builtin_op_data.h"
17 #include "tensorflow/lite/c/c_api_internal.h"
18 #include "tensorflow/lite/kernels/internal/reference/reference_ops.h"
19 #include "tensorflow/lite/kernels/internal/tensor.h"
20 #include "tensorflow/lite/kernels/kernel_util.h"
21
22 namespace tflite {
23 namespace ops {
24 namespace builtin {
25 namespace unpack {
26 namespace {
27
28 constexpr int kInputTensor = 0;
29
30 // Op data for unpack op.
31 struct OpData {
32 int num;
33 int axis;
34 };
35
Init(TfLiteContext * context,const char * buffer,size_t length)36 void* Init(TfLiteContext* context, const char* buffer, size_t length) {
37 auto* data = new OpData;
38 data->axis = 0;
39 return data;
40 }
41
Free(TfLiteContext * context,void * buffer)42 void Free(TfLiteContext* context, void* buffer) {
43 delete reinterpret_cast<OpData*>(buffer);
44 }
45
Prepare(TfLiteContext * context,TfLiteNode * node)46 TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
47 const OpData* data = reinterpret_cast<OpData*>(node->builtin_data);
48
49 TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
50 TF_LITE_ENSURE_EQ(context, NumOutputs(node), data->num);
51
52 const TfLiteTensor* input = GetInput(context, node, kInputTensor);
53 TF_LITE_ENSURE(context, NumDimensions(input) <= 4);
54 TF_LITE_ENSURE(context, NumDimensions(input) > 1);
55 int axis = data->axis;
56 if (axis < 0) {
57 axis += NumDimensions(input);
58 }
59 TF_LITE_ENSURE(context, 0 <= axis && axis < NumDimensions(input));
60 if (input->type != kTfLiteInt32 && input->type != kTfLiteFloat32) {
61 context->ReportError(context,
62 "Currently pack only supports int32 and float32.");
63 return kTfLiteError;
64 }
65
66 const TfLiteIntArray* input_shape = input->dims;
67 // Num should be equal to the shape[axis].
68 // Resize outputs. rank will be R - 1.
69 TfLiteIntArray* output_shape = TfLiteIntArrayCreate(NumDimensions(input) - 1);
70 int o = 0;
71 for (int index = 0; index < NumDimensions(input); ++index) {
72 if (index != axis) {
73 output_shape->data[o++] = input_shape->data[index];
74 }
75 }
76
77 TF_LITE_ENSURE_EQ(context, data->num, input_shape->data[axis]);
78 for (int i = 0; i < data->num; ++i) {
79 TfLiteIntArray* copied_output_shape = TfLiteIntArrayCopy(output_shape);
80 TfLiteTensor* output = GetOutput(context, node, i);
81 TF_LITE_ENSURE_EQ(context, output->type, input->type);
82 TF_LITE_ENSURE_OK(
83 context, context->ResizeTensor(context, output, copied_output_shape));
84 }
85
86 TfLiteIntArrayFree(output_shape);
87 return kTfLiteOk;
88 }
89
90 template <typename T>
UnpackImpl(TfLiteContext * context,TfLiteNode * node,const TfLiteTensor * input,int output_count,int axis)91 void UnpackImpl(TfLiteContext* context, TfLiteNode* node,
92 const TfLiteTensor* input, int output_count, int axis) {
93 tflite::UnpackParams op_params;
94 op_params.axis = axis;
95 op_params.num_split = output_count;
96 VectorOfTensors<T> all_outputs(*context, *node->outputs);
97 reference_ops::Unpack<T>(op_params, GetTensorShape(input),
98 GetTensorData<T>(input), **all_outputs.shapes(),
99 all_outputs.data());
100 }
101
Eval(TfLiteContext * context,TfLiteNode * node)102 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
103 const OpData* data = reinterpret_cast<OpData*>(node->builtin_data);
104
105 const TfLiteTensor* input = GetInput(context, node, kInputTensor);
106 switch (input->type) {
107 case kTfLiteFloat32: {
108 UnpackImpl<float>(context, node, input, data->num, data->axis);
109 break;
110 }
111 case kTfLiteInt32: {
112 UnpackImpl<int32_t>(context, node, input, data->num, data->axis);
113 break;
114 }
115 default: {
116 context->ReportError(context,
117 "Currently pack only supports int32 and float32.");
118 return kTfLiteError;
119 }
120 }
121
122 return kTfLiteOk;
123 }
124 } // namespace
125 } // namespace unpack
126
Register_UNPACK()127 TfLiteRegistration* Register_UNPACK() {
128 static TfLiteRegistration r = {unpack::Init, unpack::Free, unpack::Prepare,
129 unpack::Eval};
130 return &r;
131 }
132
133 } // namespace builtin
134 } // namespace ops
135 } // namespace tflite
136