1 /* Copyright 2017 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 #include <stdint.h>
16
17 #include "tensorflow/lite/c/builtin_op_data.h"
18 #include "tensorflow/lite/c/common.h"
19 #include "tensorflow/lite/kernels/internal/compatibility.h"
20 #include "tensorflow/lite/kernels/internal/optimized/neon_check.h"
21 #include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
22 // clang-format off: Clang-format thinks this header is paired.
23 #include "tensorflow/lite/kernels/internal/optimized/resize_bilinear.h"
24 // clang-format on
25 #include "tensorflow/lite/kernels/internal/reference/reference_ops.h"
26 #include "tensorflow/lite/kernels/internal/tensor.h"
27 #include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
28 #include "tensorflow/lite/kernels/internal/types.h"
29 #include "tensorflow/lite/kernels/kernel_util.h"
30
31 namespace tflite {
32 namespace ops {
33 namespace builtin {
34 namespace resize_bilinear {
35
36 // This file has three implementation of RESIZE_BILINEAR.
37 enum KernelType {
38 kReference,
39 kOptimized,
40 };
41
42 constexpr int kInputTensor = 0;
43 constexpr int kSizeTensor = 1;
44 constexpr int kOutputTensor = 0;
45
ResizeOutputTensor(TfLiteContext * context,const TfLiteTensor * input,const TfLiteTensor * size,TfLiteTensor * output)46 TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
47 const TfLiteTensor* input,
48 const TfLiteTensor* size,
49 TfLiteTensor* output) {
50 const int32* size_data = GetTensorData<int32>(size);
51 // Sanity check, the up/down sampling size should always be positive.
52 TF_LITE_ENSURE(context, size_data[0] > 0);
53 TF_LITE_ENSURE(context, size_data[1] > 0);
54 TfLiteIntArray* output_size = TfLiteIntArrayCreate(4);
55 output_size->data[0] = input->dims->data[0];
56 output_size->data[1] = size_data[0];
57 output_size->data[2] = size_data[1];
58 output_size->data[3] = input->dims->data[3];
59 return context->ResizeTensor(context, output, output_size);
60 }
61
Prepare(TfLiteContext * context,TfLiteNode * node)62 TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
63 TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
64 TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
65
66 const TfLiteTensor* input;
67 TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input));
68 const TfLiteTensor* size;
69 TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kSizeTensor, &size));
70 TfLiteTensor* output;
71 TF_LITE_ENSURE_OK(context,
72 GetOutputSafe(context, node, kOutputTensor, &output));
73
74 // TODO(ahentz): Our current implementations rely on the inputs being 4D.
75 TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
76 TF_LITE_ENSURE_EQ(context, NumDimensions(size), 1);
77
78 TF_LITE_ENSURE_EQ(context, size->type, kTfLiteInt32);
79 // ResizeBilinear creates a float tensor even when the input is made of
80 // integers.
81 output->type = input->type;
82
83 if (!IsConstantTensor(size)) {
84 SetTensorToDynamic(output);
85 return kTfLiteOk;
86 }
87
88 // Ensure params are valid.
89 auto* params =
90 reinterpret_cast<TfLiteResizeBilinearParams*>(node->builtin_data);
91 if (params->half_pixel_centers && params->align_corners) {
92 TF_LITE_KERNEL_LOG(
93 context, "If half_pixel_centers is True, align_corners must be False.");
94 return kTfLiteError;
95 }
96
97 return ResizeOutputTensor(context, input, size, output);
98 }
99
100 template <KernelType kernel_type>
Eval(TfLiteContext * context,TfLiteNode * node)101 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
102 auto* params =
103 reinterpret_cast<TfLiteResizeBilinearParams*>(node->builtin_data);
104
105 const TfLiteTensor* input;
106 TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input));
107 TfLiteTensor* output;
108 TF_LITE_ENSURE_OK(context,
109 GetOutputSafe(context, node, kOutputTensor, &output));
110 const TfLiteTensor* size;
111 TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kSizeTensor, &size));
112
113 if (IsDynamicTensor(output)) {
114 TF_LITE_ENSURE_OK(context,
115 ResizeOutputTensor(context, input, size, output));
116 }
117
118 if (output->type == kTfLiteFloat32) {
119 #define TF_LITE_RESIZE_BILINEAR(type, opname, datatype) \
120 tflite::ResizeBilinearParams op_params; \
121 op_params.align_corners = params->align_corners; \
122 op_params.half_pixel_centers = params->half_pixel_centers; \
123 type::opname(op_params, GetTensorShape(input), \
124 GetTensorData<datatype>(input), GetTensorShape(size), \
125 GetTensorData<int32>(size), GetTensorShape(output), \
126 GetTensorData<datatype>(output))
127
128 if (kernel_type == kReference) {
129 TF_LITE_RESIZE_BILINEAR(reference_ops, ResizeBilinear, float);
130 } else if (kernel_type == kOptimized) {
131 TF_LITE_RESIZE_BILINEAR(optimized_ops, ResizeBilinear, float);
132 }
133 } else if (output->type == kTfLiteUInt8) {
134 if (kernel_type == kReference) {
135 TF_LITE_RESIZE_BILINEAR(reference_ops, ResizeBilinear, uint8_t);
136 } else if (kernel_type == kOptimized) {
137 TF_LITE_RESIZE_BILINEAR(optimized_ops, ResizeBilinear, uint8_t);
138 }
139 } else if (output->type == kTfLiteInt8) {
140 if (kernel_type == kReference) {
141 TF_LITE_RESIZE_BILINEAR(reference_ops, ResizeBilinearInteger, int8_t);
142 } else if (kernel_type == kOptimized) {
143 TF_LITE_RESIZE_BILINEAR(optimized_ops, ResizeBilinear, int8_t);
144 }
145 } else if (output->type == kTfLiteInt16) {
146 TF_LITE_RESIZE_BILINEAR(reference_ops, ResizeBilinearInteger, int16_t);
147 #undef TF_LITE_RESIZE_BILINEAR
148 } else {
149 TF_LITE_KERNEL_LOG(context, "Output type is %d, requires float.",
150 output->type);
151 return kTfLiteError;
152 }
153
154 return kTfLiteOk;
155 }
156
157 } // namespace resize_bilinear
158
Register_RESIZE_BILINEAR_REF()159 TfLiteRegistration* Register_RESIZE_BILINEAR_REF() {
160 static TfLiteRegistration r = {
161 nullptr, nullptr, resize_bilinear::Prepare,
162 resize_bilinear::Eval<resize_bilinear::kReference>};
163 return &r;
164 }
165
Register_RESIZE_BILINEAR()166 TfLiteRegistration* Register_RESIZE_BILINEAR() {
167 static TfLiteRegistration r = {
168 nullptr, nullptr, resize_bilinear::Prepare,
169 resize_bilinear::Eval<resize_bilinear::kOptimized>};
170 return &r;
171 }
172
173 } // namespace builtin
174 } // namespace ops
175 } // namespace tflite
176