1 /*
2 * Copyright (c) 2022 Huawei Device Co., Ltd.
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 "pooling_builder.h"
17
18 #include "transform.h"
19 #include "validation.h"
20 #include "ops_validation.h"
21
22 namespace OHOS {
23 namespace NeuralNetworkRuntime {
24 namespace Ops {
25 static const int INPUT_NUM = 1;
26 static const int OUTPUT_NUM = 1;
27 static const int PARAM_MAX_NUM = 7;
28 static const int SCALAR_LENGTH = 1;
29 static const int NUM_ELEMENT_PAD_MODE = 1;
30 static const int NUM_ELEMENT_PAD_LIST = 4;
31 static const int ACTIVATION_LENGTH = 1;
32 static const std::unordered_map<int, mindspore::lite::RoundMode> roundList = {{0, mindspore::lite::ROUND_MODE_FLOOR},
33 {1, mindspore::lite::ROUND_MODE_CEIL}};
34
PoolingBuild(const std::vector<uint32_t> & paramsIndex,const std::vector<uint32_t> & inputsIndex,const std::vector<uint32_t> & outputsIndex,const std::vector<std::shared_ptr<NNTensor>> & allTensors)35 OH_NN_ReturnCode PoolingBuilder::PoolingBuild(const std::vector<uint32_t>& paramsIndex,
36 const std::vector<uint32_t>& inputsIndex,
37 const std::vector<uint32_t>& outputsIndex,
38 const std::vector<std::shared_ptr<NNTensor>>& allTensors)
39 {
40 if (m_isBuild) {
41 LOGE("[PoolingBuilder] PoolingBuild failed, operation has been build, cannot build again.");
42 return OH_NN_OPERATION_FORBIDDEN;
43 }
44
45 // Set input and output
46 OH_NN_ReturnCode returnCode = SetInputAndOutput(inputsIndex, outputsIndex, allTensors);
47 if (returnCode != OH_NN_SUCCESS) {
48 LOGE("[PoolingBuilder] PoolingBuild failed, the SetInputAndOutput failed.");
49 return returnCode;
50 }
51
52 returnCode = CheckParamIndex(paramsIndex, allTensors, PARAM_MAX_NUM);
53 if (returnCode != OH_NN_SUCCESS) {
54 LOGE("[PoolingBuilder] PoolingBuild failed, passed invalid param index of Onehot operation index.");
55 return returnCode;
56 }
57
58 for (int i : paramsIndex) {
59 std::shared_ptr<NNTensor> tensor = allTensors[i];
60 if (m_paramMap.find(tensor->GetType()) != m_paramMap.end()) {
61 returnCode = (this->*(m_paramMap[tensor->GetType()]))(tensor);
62 } else {
63 LOGE("[PoolingBuilder] Build failed, param invalid, type=%d", tensor->GetType());
64 return OH_NN_INVALID_PARAMETER;
65 }
66
67 if (returnCode != OH_NN_SUCCESS) {
68 LOGE("[PoolingBuilder] PoolingBuild failed, passed invalid param.");
69 return returnCode;
70 }
71 }
72
73 // The quantization type of the first output determinies that of the operator.
74 SetQuantType(outputsIndex, allTensors);
75
76 return OH_NN_SUCCESS;
77 }
78
SetInputAndOutput(const std::vector<uint32_t> & inputsIndex,const std::vector<uint32_t> & outputsIndex,const std::vector<std::shared_ptr<NNTensor>> & allTensors)79 OH_NN_ReturnCode PoolingBuilder::SetInputAndOutput(const std::vector<uint32_t>& inputsIndex,
80 const std::vector<uint32_t>& outputsIndex,
81 const std::vector<std::shared_ptr<NNTensor>>& allTensors)
82 {
83 OH_NN_ReturnCode returnCode = CheckIOIndex(inputsIndex, outputsIndex, allTensors, INPUT_NUM, OUTPUT_NUM);
84 if (returnCode != OH_NN_SUCCESS) {
85 LOGE("[PoolingBuilder] SetInputAndOutput failed, passed invalid input or output index.");
86 return returnCode;
87 }
88
89 m_inputsIndex = inputsIndex;
90 m_outputsIndex = outputsIndex;
91
92 return OH_NN_SUCCESS;
93 }
94
SetKernel(const std::shared_ptr<NNTensor> & tensor)95 OH_NN_ReturnCode PoolingBuilder::SetKernel(const std::shared_ptr<NNTensor>& tensor)
96 {
97 tensor->IdentifyOpParameter();
98 // Set kernelSize
99 if (tensor->GetDataType() != OH_NN_INT64) {
100 LOGE("[PoolingBuilder] SetKernel failed, the KernelSize should be type OH_NN_INT64.");
101 return OH_NN_INVALID_PARAMETER;
102 }
103
104 void* buffer = tensor->GetBuffer();
105 if (buffer == nullptr) {
106 LOGE("[PoolingBuilder] SetKernel GetBuffer return nullptr");
107 return OH_NN_INVALID_PARAMETER;
108 }
109
110 const int64_t* pKernelSize = reinterpret_cast<const int64_t*>(buffer);
111 uint32_t kernelSize = tensor->GetElementCount();
112 m_kernelSize.assign(pKernelSize, pKernelSize + kernelSize);
113
114 return OH_NN_SUCCESS;
115 }
116
SetStrides(const std::shared_ptr<NNTensor> & tensor)117 OH_NN_ReturnCode PoolingBuilder::SetStrides(const std::shared_ptr<NNTensor>& tensor)
118 {
119 tensor->IdentifyOpParameter();
120 // Set Strides
121 if (tensor->GetDataType() != OH_NN_INT64) {
122 LOGE("[PoolingBuilder] SetStrides failed, the Strides should be type OH_NN_INT64.");
123 return OH_NN_INVALID_PARAMETER;
124 }
125
126 void* buffer = tensor->GetBuffer();
127 if (buffer == nullptr) {
128 LOGE("[PoolingBuilder] SetStrides GetBuffer return nullptr");
129 return OH_NN_INVALID_PARAMETER;
130 }
131
132 const int64_t* pStrides = reinterpret_cast<const int64_t*>(buffer);
133 uint32_t strideslSize = tensor->GetElementCount();
134 m_strides.assign(pStrides, pStrides + strideslSize);
135
136 return OH_NN_SUCCESS;
137 }
138
SetPadModeOrPaddings(const std::shared_ptr<NNTensor> & tensor)139 OH_NN_ReturnCode PoolingBuilder::SetPadModeOrPaddings(const std::shared_ptr<NNTensor>& tensor)
140 {
141 tensor->IdentifyOpParameter();
142
143 void* buffer = tensor->GetBuffer();
144 if (buffer == nullptr) {
145 LOGE("[PoolingBuilder] SetPadModeOrPaddings GetBuffer return nullptr");
146 return OH_NN_INVALID_PARAMETER;
147 }
148 size_t tensorElementCount = tensor->GetElementCount();
149 // Set PadMode or PadList
150 if (tensorElementCount == NUM_ELEMENT_PAD_MODE) {
151 // PadMode
152 if (tensor->GetDataType() != OH_NN_INT8) {
153 LOGE("[PoolingBuilder] SetPadModeOrPaddings failed, the type of padMode should be OH_NN_INT8.");
154 return OH_NN_INVALID_PARAMETER;
155 }
156
157 int8_t* pPadMode = static_cast<int8_t*>(buffer);
158 if (!OHOS::NeuralNetworkRuntime::Validation::ValidatePadMode(*pPadMode)) {
159 LOGE("[PoolingBuilder] SetPadModeOrPaddings failed, invalid pad mode.");
160 return OH_NN_INVALID_PARAMETER;
161 }
162 m_padMode = NNToMS::TransformPadModeValue(*pPadMode);
163 } else if (tensorElementCount == NUM_ELEMENT_PAD_LIST) {
164 if (tensor->GetDataType() != OH_NN_INT64) {
165 LOGE("[PoolingBuilder] SetPadModeOrPaddings failed, the type of padList should be OH_NN_INT64.");
166 return OH_NN_INVALID_PARAMETER;
167 }
168
169 int64_t* pPad = static_cast<int64_t*>(buffer);
170 // PadList
171 m_pad.clear();
172 for (int i = 0; i < NUM_ELEMENT_PAD_LIST; i++) {
173 m_pad.emplace_back(static_cast<int64_t>(pPad[i]));
174 }
175 } else {
176 LOGE("[PoolingBuilder] SetPadModeOrPaddings failed, invalid element size of padMode or padList,"
177 "padMode should be single value, and padList should be 4.");
178 return OH_NN_INVALID_PARAMETER;
179 }
180 return OH_NN_SUCCESS;
181 }
182
SetRoundMode(const std::shared_ptr<NNTensor> & tensor)183 OH_NN_ReturnCode PoolingBuilder::SetRoundMode(const std::shared_ptr<NNTensor>& tensor)
184 {
185 tensor->IdentifyOpParameter();
186
187 if (tensor->GetElementCount() != ACTIVATION_LENGTH) {
188 LOGE("[PoolingBuilder] SetRoundMode failed, the roundMode shoule be a scalar");
189 return OH_NN_INVALID_PARAMETER;
190 }
191
192 if (tensor->GetDataType() != OH_NN_INT32) {
193 LOGE("[PoolingBuilder] SetRoundMode failed, the roundMode should be type OH_NN_INT32.");
194 return OH_NN_INVALID_PARAMETER;
195 }
196
197 void* buffer = tensor->GetBuffer();
198 if (buffer == nullptr) {
199 LOGE("[PoolingBuilder] SetRoundMode GetBuffer return nullptr");
200 return OH_NN_INVALID_PARAMETER;
201 }
202
203 int roundModeKey = *(static_cast<int*>(buffer));
204 auto it = roundList.find(roundModeKey);
205 if (it != roundList.end()) {
206 m_roundMode = it->second;
207 } else {
208 LOGE("[PoolingBuilder] The roundMode value should between [0, 1], but get %d.", roundModeKey);
209 LOGE("[PoolingBuilder] roundMode: 0-OH_NN_ROUND_FLOOR, 1-OH_NN_ROUND_CEIL");
210 return OH_NN_INVALID_PARAMETER;
211 }
212
213 return OH_NN_SUCCESS;
214 }
215
SetActivation(const std::shared_ptr<NNTensor> & tensor)216 OH_NN_ReturnCode PoolingBuilder::SetActivation(const std::shared_ptr<NNTensor>& tensor)
217 {
218 tensor->IdentifyOpParameter();
219
220 if (tensor->GetElementCount() != ACTIVATION_LENGTH) {
221 LOGE("[PoolingBuilder] SetActivation failed, the Activation shoule be a scalar");
222 return OH_NN_INVALID_PARAMETER;
223 }
224
225 if (tensor->GetDataType() != OH_NN_INT8) {
226 LOGE("[PoolingBuilder] SetActivation failed, the ActivationType should be type OH_NN_INT8.");
227 return OH_NN_INVALID_PARAMETER;
228 }
229
230 void* buffer = tensor->GetBuffer();
231 if (buffer == nullptr) {
232 LOGE("[PoolingBuilder] SetActivation GetBuffer return nullptr");
233 return OH_NN_INVALID_PARAMETER;
234 }
235
236 int8_t* pFuseData = static_cast<int8_t*>(buffer);
237 if (!OHOS::NeuralNetworkRuntime::Validation::ValidateFuseType(static_cast<OH_NN_FuseType>(*pFuseData))) {
238 LOGE("[PoolingBuilder] SetActivation failed, activation input is invalid.");
239 return OH_NN_INVALID_PARAMETER;
240 }
241 auto fuseType = (OH_NN_FuseType)(*pFuseData);
242 m_activationType = NNToMS::TransfromFusionType(fuseType);
243
244 return OH_NN_SUCCESS;
245 }
246
SetGlobal(const std::shared_ptr<NNTensor> & tensor)247 OH_NN_ReturnCode PoolingBuilder::SetGlobal(const std::shared_ptr<NNTensor>& tensor)
248 {
249 if (tensor->GetDataType() != OH_NN_BOOL) {
250 LOGE("[PoolingBuilder] The global should be type OH_NN_BOOL.");
251 return OH_NN_INVALID_PARAMETER;
252 }
253
254 if (tensor->GetElementCount() != SCALAR_LENGTH) {
255 LOGE("[PoolingBuilder] The global should be scalar.");
256 return OH_NN_INVALID_PARAMETER;
257 }
258
259 void* buffer = tensor->GetBuffer();
260 if (buffer == nullptr) {
261 LOGE("[PoolingBuilder] Tensor buffer is nullptr.");
262 return OH_NN_INVALID_PARAMETER;
263 }
264 m_global = *(static_cast<bool*>(buffer));
265
266 return OH_NN_SUCCESS;
267 }
268 } // namespace Ops
269 } // namespace NeuralNetworkRuntime
270 } // namespace OHOS
271