| /packages/modules/NeuralNetworks/common/types/operations/src/ |
| D | Softmax.cpp | 46 const auto inputRank = getNumberOfDimensions(context->getInputShape(kInputTensor)); in validate() local
47 if (inputRank != 0) { in validate()
48 NN_RET_CHECK_LE(inputRank, 4u); in validate()
54 if (inputRank != 2 && inputRank != 4 && inputRank != 0) { in validate()
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| D | Concatenation.cpp | 54 const uint32_t inputRank = getNumberOfDimensions(context->getInputShape(i)); in validate() local
55 if (inputRank != 0) { in validate()
56 NN_RET_CHECK_LE(inputRank, 4u); in validate()
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| D | Pack.cpp | 63 if (const size_t inputRank = inputShape.dimensions.size()) { in validate() local
65 NN_RET_CHECK_EQ(requiredInputRank, inputRank) in validate()
68 requiredInputRank = inputRank; in validate()
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| D | Reshape.cpp | 148 const auto inputRank = context->getInputShape(0).dimensions.size(); in validatePad() local
149 NN_RET_CHECK_LE(inputRank, 4u) in validatePad()
195 const auto inputRank = context->getInputShape(0).dimensions.size(); in validatePadV2() local
196 NN_RET_CHECK_LE(inputRank, 4u) in validatePadV2()
340 const auto inputRank = context->getInputShape(0).dimensions.size(); in validateReshape() local
341 NN_RET_CHECK_LE(inputRank, 4u) in validateReshape()
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| D | Conv2D.cpp | 31 const auto inputRank = getNumberOfDimensions(context->getInputShape(kInputTensor)); in validate() local
33 if (inputRank != 0) { in validate()
34 NN_RET_CHECK_EQ(inputRank, 4u); in validate()
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| D | SimpleMath.cpp | 29 const auto inputRank = context->getInputShape(0).dimensions.size(); in validate() local
30 NN_RET_CHECK_LE(inputRank, 4u) in validate()
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| /packages/modules/NeuralNetworks/common/cpu_operations/ |
| D | Reduce.cpp | 49 const uint32_t inputRank = getNumberOfDimensions(inputShape); in compute() local
55 reinterpret_cast<const int32_t*>(inputShape.dimensions.data()), inputRank, in compute()
67 const uint32_t inputRank = getNumberOfDimensions(inputShape); in prepare() local
68 NN_RET_CHECK_LE(inputRank, 4u); in prepare()
70 std::vector<bool> shouldReduce(inputRank); in prepare()
77 NN_RET_CHECK(handleNegativeAxis(inputRank, &axis)); in prepare()
85 for (uint32_t axis = 0; axis < inputRank; ++axis) { in prepare()
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| D | LSTM.cpp | 429 const uint32_t inputRank = getNumberOfDimensions(input_shape); in LSTMEvalFloat32() local
430 NN_CHECK(inputRank == 2 || inputRank == 3); in LSTMEvalFloat32()
433 (inputRank == 3) ? getSizeOfDimension(input_shape, timeMajor ? 0 : 1) : 1; in LSTMEvalFloat32()
434 const uint32_t batchSize = (inputRank == 3) ? getSizeOfDimension(input_shape, timeMajor ? 1 : 0) in LSTMEvalFloat32()
436 const uint32_t inputSize = getSizeOfDimension(input_shape, inputRank - 1); in LSTMEvalFloat32()
549 const uint32_t inputRank = getNumberOfDimensions(input_shape); in LSTMEvalFloat16() local
550 NN_CHECK(inputRank == 2 || inputRank == 3); in LSTMEvalFloat16()
553 (inputRank == 3) ? getSizeOfDimension(input_shape, timeMajor ? 0 : 1) : 1; in LSTMEvalFloat16()
554 const uint32_t batchSize = (inputRank == 3) ? getSizeOfDimension(input_shape, timeMajor ? 1 : 0) in LSTMEvalFloat16()
556 const uint32_t inputSize = getSizeOfDimension(input_shape, inputRank - 1); in LSTMEvalFloat16()
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| D | MirrorPad.cpp | 42 const auto inputRank = getNumberOfDimensions(inputShape); in prepare() local
43 NN_RET_CHECK_GT(inputRank, 0U); in prepare()
58 for (uint32_t i = 0; i < inputRank; ++i) { in prepare()
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| D | UnidirectionalSequenceLSTM.cpp | 91 const uint32_t inputRank = getNumberOfDimensions(inputShape); in prepare() local
92 NN_RET_CHECK_EQ(inputRank, 3u) << "Invalid input tensor rank: " << inputRank; in prepare()
97 const uint32_t inputSize = getSizeOfDimension(inputShape, inputRank - 1); in prepare()
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| D | QLSTM.cpp | 64 const uint32_t inputRank = getNumberOfDimensions(inputShape); in prepare() local
65 NN_RET_CHECK_EQ(inputRank, 2u) << "Invalid input tensor rank: " << inputRank; in prepare()
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| /packages/modules/NeuralNetworks/common/ |
| D | LegacyUtils.cpp | 766 const auto inputRank = operands[inputIndexes[0]].dimensions.size(); in validateOperation() local
767 if (inputRank > 4) { in validateOperation()
1297 const auto inputRank = operands[inputIndexes[0]].dimensions.size(); in validateOperation() local
1298 if (inputRank > 4) { in validateOperation()
1347 const auto inputRank = operands[inputIndexes[0]].dimensions.size(); in validateOperation() local
1348 if (inputRank > 4) { in validateOperation()
1412 const auto inputRank = operands[inputIndexes[0]].dimensions.size(); in validateOperation() local
1413 if (inputRank > 4) { in validateOperation()
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| /packages/modules/NeuralNetworks/runtime/test/ |
| D | TestValidateOperations.cpp | 4725 constexpr size_t inputRank = sizeof(inputDimensions) / sizeof(inputDimensions[0]); in packTest() local
4727 getOpType(operandCode, inputRank, inputDimensions); in packTest()
4729 constexpr uint32_t outputRank = inputRank + 1; in packTest()
4778 constexpr size_t inputRank = sizeof(inputDimensions) / sizeof(inputDimensions[0]); in packTestBadQuantization() local
4780 getOpType(operandCode, inputRank, inputDimensions); in packTestBadQuantization()
4783 constexpr uint32_t outputRank = inputRank + 1; in packTestBadQuantization()
4786 outputDimensions[inputRank] = inputTensorCount; in packTestBadQuantization()
4839 constexpr size_t inputRank = sizeof(inputDimensions) / sizeof(inputDimensions[0]); in packTestBadRank() local
4841 getOpType(operandCode, inputRank, inputDimensions); in packTestBadRank()
4844 constexpr uint32_t outputRank = inputRank + 1; in packTestBadRank()
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