| /hardware/qcom/neuralnetworks/hvxservice/1.0/ |
| D | HexagonModel.cpp | 261 static bool verifyOperationInputs(const std::vector<hexagon_nn_input>& inputs) { in verifyOperationInputs() argument
262 for (const hexagon_nn_input& input : inputs) { in verifyOperationInputs()
280 const std::vector<hexagon_nn_input>& inputs, in addOperationInternal() argument
282 HEXAGON_SOFT_ASSERT(verifyOperationInputs(inputs), in addOperationInternal()
287 return hexagon::Controller::getInstance().append_node(mGraphId, node, op, pad, inputs.data(), in addOperationInternal()
288 inputs.size(), outputs.data(), in addOperationInternal()
323 const std::vector<hexagon_nn_input>& inputs, in addBasicOperation() argument
326 uint32_t node = addOperationInternal(op, pad, inputs, outs); in addBasicOperation()
354 const std::vector<hexagon_nn_input>& inputs, in addFloatOperationWithActivation() argument
359 uint32_t node = addOperationInternal(op, pad, inputs, outs); in addFloatOperationWithActivation()
[all …]
|
| D | HexagonModel.h | 114 const std::vector<hexagon_nn_input>& inputs,
118 const std::vector<hexagon_nn_input>& inputs,
122 const std::vector<hexagon_nn_input>& inputs,
126 const std::vector<hexagon_nn_input>& inputs,
130 const std::vector<hexagon_nn_input>& inputs,
140 const std::vector<hexagon_nn_input>& inputs,
|
| D | HexagonController.cpp | 127 hexagon_nn_padding_type padding, const hexagon_nn_input* inputs, in append_node() argument
130 CONTROLLER_CHECK(append_node, id, node_id, operation, padding, inputs, num_inputs, outputs, in append_node()
140 int Controller::execute_new(hexagon_nn_nn_id id, const hexagon_nn_tensordef* inputs, in execute_new() argument
142 CONTROLLER_CHECK(execute_new, id, inputs, n_inputs, outputs, n_outputs); in execute_new()
|
| D | HexagonController.h | 70 hexagon_nn_padding_type padding, const hexagon_nn_input* inputs,
76 int execute_new(hexagon_nn_nn_id id, const hexagon_nn_tensordef* inputs, uint32_t n_inputs,
|
| D | HexagonOperationsPrepare.cpp | 108 std::vector<hexagon_nn_input> inputs(numInputTensors + 1); in concatenation() local
110 inputs[i + 1] = model->getTensor(ins[i]); in concatenation()
116 inputs[0] = model->createScalar<int32_t>(axis + (4 - dims)); in concatenation()
119 return model->addBasicOperation(OP_Concat_f, NN_PAD_NA, inputs, outs); in concatenation()
565 std::vector<hexagon_nn_input> inputs(numInputTensors * 3 + 1); in concatenation() local
567 inputs[i + 1 + numInputTensors * 0] = model->getTensor(ins[i]); in concatenation()
568 inputs[i + 1 + numInputTensors * 1] = model->getQuantizationMin(ins[i]); in concatenation()
569 inputs[i + 1 + numInputTensors * 2] = model->getQuantizationMax(ins[i]); in concatenation()
575 inputs[0] = model->createScalar<int32_t>(axis + (4 - dims)); in concatenation()
578 return model->addBasicOperation(OP_QuantizedConcat_8, NN_PAD_NA, inputs, outs); in concatenation()
|
| /hardware/libhardware_legacy/audio/ |
| D | audio_policy.conf | 37 inputs {
55 inputs {
|
| /hardware/interfaces/neuralnetworks/1.1/vts/functional/ |
| D | ValidateRequest.cpp | 136 for (size_t input = 0; input < request.inputs.size(); ++input) { in removeInputTest()
139 [input](Request* request) { hidl_vec_removeAt(&request->inputs, input); }); in removeInputTest()
162 const MixedTyped& inputs = example.first; in createRequests() local
170 for_all(inputs, [&inputs_info, &inputSize](int index, auto, auto s) { in createRequests()
227 for_all(inputs, [&inputs_info, inputPtr](int index, auto p, auto s) { in createRequests()
235 requests.push_back({.inputs = inputs_info, .outputs = outputs_info, .pools = pools}); in createRequests()
|
| D | ValidateModel.cpp | 290 if (operation.type == OperationType::LSH_PROJECTION && operand == operation.inputs[1]) { in mutateOperationOperandTypeSkip()
348 for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) { in mutateOperationInputOperandIndexTest()
353 model->operations[operation].inputs[input] = invalidOperand; in mutateOperationInputOperandIndexTest()
393 removeValueAndDecrementGreaterValues(&operation.inputs, index); in removeOperand()
411 for (uint32_t operand : model->operations[index].inputs) { in removeOperation()
429 for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) { in removeOperationInputTest()
434 if (op.type == V1_1::OperationType::CONCATENATION && op.inputs.size() > 2 && in removeOperationInputTest()
435 input != op.inputs.size() - 1) { in removeOperationInputTest()
442 uint32_t operand = model->operations[operation].inputs[input]; in removeOperationInputTest()
444 hidl_vec_removeAt(&model->operations[operation].inputs, input); in removeOperationInputTest()
[all …]
|
| /hardware/interfaces/neuralnetworks/1.0/vts/functional/ |
| D | ValidateRequest.cpp | 135 for (size_t input = 0; input < request.inputs.size(); ++input) { in removeInputTest()
138 [input](Request* request) { hidl_vec_removeAt(&request->inputs, input); }); in removeInputTest()
161 const MixedTyped& inputs = example.first; in createRequests() local
169 for_all(inputs, [&inputs_info, &inputSize](int index, auto, auto s) { in createRequests()
226 for_all(inputs, [&inputs_info, inputPtr](int index, auto p, auto s) { in createRequests()
234 requests.push_back({.inputs = inputs_info, .outputs = outputs_info, .pools = pools}); in createRequests()
|
| D | ValidateModel.cpp | 274 if (operation.type == OperationType::LSH_PROJECTION && operand == operation.inputs[1]) { in mutateOperationOperandTypeSkip()
332 for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) { in mutateOperationInputOperandIndexTest()
337 model->operations[operation].inputs[input] = invalidOperand; in mutateOperationInputOperandIndexTest()
377 removeValueAndDecrementGreaterValues(&operation.inputs, index); in removeOperand()
395 for (uint32_t operand : model->operations[index].inputs) { in removeOperation()
413 for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) { in removeOperationInputTest()
418 if (op.type == V1_0::OperationType::CONCATENATION && op.inputs.size() > 2 && in removeOperationInputTest()
419 input != op.inputs.size() - 1) { in removeOperationInputTest()
426 uint32_t operand = model->operations[operation].inputs[input]; in removeOperationInputTest()
428 hidl_vec_removeAt(&model->operations[operation].inputs, input); in removeOperationInputTest()
[all …]
|
| D | GeneratedTestHarness.cpp | 78 const MixedTyped& inputs = example.first; in EvaluatePreparedModel() local
86 for_all(inputs, [&inputs_info, &inputSize](int index, auto, auto s) { in EvaluatePreparedModel()
146 for_all(inputs, [&inputs_info, inputPtr](int index, auto p, auto s) { in EvaluatePreparedModel()
160 {.inputs = inputs_info, .outputs = outputs_info, .pools = pools}, executionCallback); in EvaluatePreparedModel()
|
| /hardware/qcom/neuralnetworks/hvxservice/1.0/hexagon_nn_controller/ |
| D | hexagon_nn_controller.h | 88 const hexagon_nn_input* inputs, unsigned int num_inputs, const hexagon_nn_output* outputs,
98 const hexagon_nn_tensordef* inputs,
|
| /hardware/interfaces/neuralnetworks/1.1/ |
| D | IDevice.hal | 75 * prepareModel function must verify the inputs to the prepareModel function
78 * IPreparedModel, then return with the same ErrorStatus. If the inputs to
98 * inputs to the model. Note that the same prepared model object can be
99 * used with different shapes of inputs on different (possibly concurrent)
|
| D | types.hal | 352 * Describes the table that contains the indexes of the inputs of the
355 vec<uint32_t> inputs;
|
| /hardware/interfaces/neuralnetworks/1.0/ |
| D | IDevice.hal | 70 * prepareModel function must verify the inputs to the prepareModel function
73 * IPreparedModel, then return with the same ErrorStatus. If the inputs to
93 * inputs to the model. Note that the same prepared model object can be
94 * used with different shapes of inputs on different (possibly concurrent)
|
| D | IPreparedModel.hal | 30 * execute must verify the inputs to the function are correct. If there is
33 * the inputs to the function are valid and there is no error, execute must
|
| D | types.hal | 192 * [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of
492 * outputs = activation(inputs * weights’ + bias)
505 * number of inputs to the layer, matching the second dimension of
676 * inputs within depth_radius.
816 * The operation has the following independently optional inputs:
1177 * outputs = state = activation(inputs * input_weights +
1181 * * “input_weights” is a weight matrix that multiplies the inputs;
1310 * memory = push(conv1d(inputs, weights_feature, feature_dim,
1315 * * “weights_feature” is a weights matrix that processes the inputs (by
1616 * Describes the table that contains the indexes of the inputs of the
[all …]
|
| /hardware/libhardware/modules/usbcamera/ |
| D | Camera.cpp | 289 int inputs = 0; in isValidStreamSetLocked() local
300 inputs++; in isValidStreamSetLocked()
305 __func__, mId, outputs, inputs); in isValidStreamSetLocked()
310 if (inputs > 1) { in isValidStreamSetLocked()
|
| /hardware/libhardware/modules/camera/3_0/ |
| D | Camera.cpp | 239 int inputs = 0; in isValidStreamSet() local
254 inputs++; in isValidStreamSet()
259 __func__, mId, outputs, inputs); in isValidStreamSet()
264 if (inputs > 1) { in isValidStreamSet()
|
| /hardware/libhardware_legacy/include/hardware_legacy/ |
| D | AudioPolicyManagerBase.h | 428 SortedVector<audio_io_handle_t>& inputs,
|
| /hardware/interfaces/broadcastradio/2.0/ |
| D | ITunerSession.hal | 151 * return a status for a subset of the provided inputs, at its discretion.
|
| /hardware/libhardware/modules/camera/3_4/ |
| D | README.md | 83 which provides simpler inputs and outputs around the V4L2 ioctls
|
| /hardware/interfaces/keymaster/4.0/ |
| D | IKeymasterDevice.hal | 372 * secure in the sense that if any one of the mixing function inputs is provided with any data
|