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 <stdint.h> |
17 | |
18 | #include "tensorflow/lite/c/common.h" |
19 | #include "tensorflow/lite/kernels/internal/reference/reference_ops.h" |
20 | #include "tensorflow/lite/kernels/internal/tensor.h" |
21 | #include "tensorflow/lite/kernels/internal/tensor_ctypes.h" |
22 | #include "tensorflow/lite/kernels/kernel_util.h" |
23 | #include "tensorflow/lite/string_util.h" |
24 | |
25 | namespace tflite { |
26 | namespace ops { |
27 | namespace builtin { |
28 | namespace fill { |
29 | |
30 | namespace { |
31 | |
32 | constexpr int kDimsTensor = 0; |
33 | constexpr int kValueTensor = 1; |
34 | constexpr int kOutputTensor = 0; |
35 | |
36 | template <typename T> |
37 | TfLiteStatus ResizeOutputImpl(TfLiteContext* context, const TfLiteTensor* dims, |
38 | TfLiteTensor* output) { |
39 | TfLiteIntArray* output_shape = TfLiteIntArrayCreate(dims->dims->data[0]); |
40 | for (int i = 0; i < output_shape->size; ++i) { |
41 | T data = GetTensorData<T>(dims)[i]; |
42 | if (data < 0) { |
43 | TfLiteIntArrayFree(output_shape); |
44 | TF_LITE_KERNEL_LOG(context, "Fill dimensions must be >= 0" , dims->type); |
45 | return kTfLiteError; |
46 | } |
47 | output_shape->data[i] = data; |
48 | } |
49 | return context->ResizeTensor(context, output, output_shape); |
50 | } |
51 | |
52 | TfLiteStatus ResizeOutput(TfLiteContext* context, const TfLiteTensor* dims, |
53 | TfLiteTensor* output) { |
54 | switch (dims->type) { |
55 | case kTfLiteInt32: |
56 | return ResizeOutputImpl<int32_t>(context, dims, output); |
57 | case kTfLiteInt64: |
58 | return ResizeOutputImpl<int64_t>(context, dims, output); |
59 | default: |
60 | TF_LITE_KERNEL_LOG( |
61 | context, |
62 | "Fill only currently supports int32, int64 for input 0, " |
63 | "got %d." , |
64 | dims->type); |
65 | return kTfLiteError; |
66 | } |
67 | } |
68 | |
69 | } // namespace |
70 | |
71 | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
72 | TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); |
73 | TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); |
74 | |
75 | const TfLiteTensor* dims; |
76 | TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kDimsTensor, &dims)); |
77 | const TfLiteTensor* value; |
78 | TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kValueTensor, &value)); |
79 | |
80 | // Make sure the 1st input tensor is 1-D. |
81 | TF_LITE_ENSURE_EQ(context, NumDimensions(dims), 1); |
82 | |
83 | // Make sure the 1st input tensor is int32 or int64. |
84 | const auto dtype = dims->type; |
85 | TF_LITE_ENSURE(context, dtype == kTfLiteInt32 || dtype == kTfLiteInt64); |
86 | |
87 | // Make sure the 2nd input tensor is a scalar. |
88 | TF_LITE_ENSURE_EQ(context, NumDimensions(value), 0); |
89 | |
90 | TfLiteTensor* output; |
91 | TF_LITE_ENSURE_OK(context, |
92 | GetOutputSafe(context, node, kOutputTensor, &output)); |
93 | output->type = value->type; |
94 | |
95 | TF_LITE_ENSURE_EQ(context, output->params.scale, value->params.scale); |
96 | TF_LITE_ENSURE_EQ(context, output->params.zero_point, |
97 | value->params.zero_point); |
98 | |
99 | if (value->type == kTfLiteInt16) { |
100 | TF_LITE_ENSURE_EQ(context, value->params.zero_point, 0); |
101 | } |
102 | |
103 | if (IsConstantTensor(dims)) { |
104 | TF_LITE_ENSURE_OK(context, ResizeOutput(context, dims, output)); |
105 | } else { |
106 | SetTensorToDynamic(output); |
107 | } |
108 | return kTfLiteOk; |
109 | } |
110 | |
111 | TfLiteStatus FillString(const TfLiteTensor* value, TfLiteTensor* output) { |
112 | DynamicBuffer buffer; |
113 | const auto string_ref = GetString(value, 0); |
114 | int n = 1; |
115 | for (int i = 0; i < output->dims->size; ++i) { |
116 | n *= output->dims->data[i]; |
117 | } |
118 | for (int i = 0; i < n; ++i) { |
119 | buffer.AddString(string_ref.str, string_ref.len); |
120 | } |
121 | buffer.WriteToTensor(output, /*new_shape=*/nullptr); |
122 | return kTfLiteOk; |
123 | } |
124 | |
125 | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { |
126 | const TfLiteTensor* value; |
127 | TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kValueTensor, &value)); |
128 | |
129 | TfLiteTensor* output; |
130 | TF_LITE_ENSURE_OK(context, |
131 | GetOutputSafe(context, node, kOutputTensor, &output)); |
132 | |
133 | if (IsDynamicTensor(output)) { |
134 | const TfLiteTensor* dims; |
135 | TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kDimsTensor, &dims)); |
136 | TF_LITE_ENSURE_OK(context, ResizeOutput(context, dims, output)); |
137 | } |
138 | #define TF_LITE_FILL(data_type) \ |
139 | reference_ops::Fill(GetTensorShape(value), GetTensorData<data_type>(value), \ |
140 | GetTensorShape(output), \ |
141 | GetTensorData<data_type>(output)) |
142 | switch (output->type) { |
143 | case kTfLiteInt8: |
144 | TF_LITE_FILL(int8_t); |
145 | break; |
146 | case kTfLiteInt16: |
147 | TF_LITE_FILL(int16_t); |
148 | break; |
149 | case kTfLiteInt32: |
150 | TF_LITE_FILL(int32_t); |
151 | break; |
152 | case kTfLiteInt64: |
153 | TF_LITE_FILL(int64_t); |
154 | break; |
155 | case kTfLiteFloat32: |
156 | TF_LITE_FILL(float); |
157 | break; |
158 | case kTfLiteBool: |
159 | TF_LITE_FILL(bool); |
160 | break; |
161 | case kTfLiteString: |
162 | FillString(value, output); |
163 | break; |
164 | default: |
165 | TF_LITE_KERNEL_LOG( |
166 | context, |
167 | "Fill only currently supports int8, int16, int32, int64, float32, " |
168 | "bool, string for input 1, got %d." , |
169 | value->type); |
170 | return kTfLiteError; |
171 | } |
172 | #undef TF_LITE_FILL |
173 | return kTfLiteOk; |
174 | } |
175 | |
176 | } // namespace fill |
177 | |
178 | TfLiteRegistration* Register_FILL() { |
179 | static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr, |
180 | fill::Prepare, fill::Eval}; |
181 | return &r; |
182 | } |
183 | |
184 | } // namespace builtin |
185 | } // namespace ops |
186 | } // namespace tflite |
187 | |