| /frameworks/base/cmds/statsd/src/metrics/ |
| D | MetricProducer.cpp | 123 std::shared_ptr<Activation> activation = in addActivation() local
125 mEventActivationMap.emplace(activationTrackerIndex, activation); in addActivation()
128 deactivationList.push_back(activation); in addActivation()
137 auto& activation = it->second; in activateLocked() local
138 if (ACTIVATE_ON_BOOT == activation->activationType) { in activateLocked()
139 if (ActivationState::kNotActive == activation->state) { in activateLocked()
140 activation->state = ActivationState::kActiveOnBoot; in activateLocked()
145 activation->start_ns = elapsedTimestampNs; in activateLocked()
146 activation->state = ActivationState::kActive; in activateLocked()
170 const auto& activeEventActivation = activeMetric.activation(i); in loadActiveMetricLocked()
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| /frameworks/ml/nn/common/operations/ |
| D | Broadcast.cpp | 46 switch (activation) { \
66 int32_t activation, float* out, const Shape& shapeOut)>;
69 const Shape& shape2, int32_t activation, _Float16* out, in binaryOperationFloat16() argument
77 operationFloat32(in1_float32.data(), shape1, in2_float32.data(), shape2, activation, in binaryOperationFloat16()
85 int32_t activation, float* out, const Shape& shapeOut) { in addFloat32() argument
90 #define ANDROID_NN_BROADCAST_ADD(activation) \ in addFloat32() argument
91 tflite::optimized_ops::BroadcastAdd<tflite::FusedActivationFunctionType::activation>( \ in addFloat32()
99 #define ANDROID_NN_ADD(activation) \ in addFloat32() argument
100 tflite::optimized_ops::Add<tflite::FusedActivationFunctionType::activation>( \ in addFloat32()
112 int32_t activation, _Float16* out, const Shape& shapeOut) { in addFloat16() argument
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| D | Activation.cpp | 29 namespace activation { namespace
111 #define ANDROID_NN_RELUX_QUANT8(activation) \ argument
116 CalculateActivationRangeUint8(activation, inputShape, &output_activation_min, \
377 NN_REGISTER_OPERATION(RELU, "RELU", std::bind(activation::validate, OperationType::RELU, _1),
378 std::bind(activation::prepare, OperationType::RELU, _1),
379 activation::executeRelu, .allowZeroSizedInput = true);
380 NN_REGISTER_OPERATION(RELU1, "RELU1", std::bind(activation::validate, OperationType::RELU1, _1),
381 std::bind(activation::prepare, OperationType::RELU1, _1),
382 activation::executeRelu1, .allowZeroSizedInput = true);
383 NN_REGISTER_OPERATION(RELU6, "RELU6", std::bind(activation::validate, OperationType::RELU6, _1),
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| D | Conv2D.cpp | 55 int32_t activation; member
66 activation = context->getInputValue<int32_t>(6); in initialize()
82 activation = context->getInputValue<int32_t>(9); in initialize()
114 NN_RET_CHECK_GE(activation, 0); in initialize()
168 int32_t dilation_width_factor, int32_t dilation_height_factor, int32_t activation, in convNhwc() argument
175 CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max); in convNhwc()
193 int32_t dilation_width_factor, int32_t dilation_height_factor, int32_t activation, in convNhwc() argument
214 CalculateActivationRangeUint8(activation, outputShape, &output_activation_min, in convNhwc()
240 int32_t dilation_width_factor, int32_t dilation_height_factor, int32_t activation, in convNhwc() argument
256 dilation_height_factor, activation, outputData_float32.data(), outputShape); in convNhwc()
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| D | DepthwiseConv2D.cpp | 33 int32_t dilationHeightFactor, int32_t depthMultiplier, int32_t activation, in depthwiseConvFloat16() argument
47 dilationHeightFactor, depthMultiplier, activation, in depthwiseConvFloat16()
70 int32_t depthMultiplier, int32_t activation, float* outputData, in depthwiseConvFloat32() argument
77 CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max); in depthwiseConvFloat32()
103 int32_t dilationHeightFactor, int32_t depthMultiplier, int32_t activation, in depthwiseConvQuant8() argument
120 CalculateActivationRangeUint8(activation, outputShape, &output_activation_min, in depthwiseConvQuant8()
154 int32_t depthMultiplier, int32_t activation, uint8_t* outputData, in depthwiseConvQuant8PerChannel() argument
192 CalculateActivationRangeUint8(activation, outputShape, &output_activation_min, in depthwiseConvQuant8PerChannel()
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| D | GroupedConv2D.cpp | 46 int32_t numGroups, int32_t activation, float* outputData, in groupedConvFloat32() argument
52 CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max); in groupedConvFloat32()
103 int32_t numGroups, int32_t activation, uint8_t* outputData, in groupedConvQuant8() argument
122 CalculateActivationRangeUint8(activation, outputShape, &output_activation_min, in groupedConvQuant8()
182 int32_t activation, uint8_t* outputData, in groupedConvQuant8PerChannel() argument
208 CalculateActivationRangeUint8(activation, outputShape, &output_activation_min, in groupedConvQuant8PerChannel()
267 int32_t activation, _Float16* outputData, const Shape& outputShape) { in groupedConvFloat16() argument
281 padding_bottom, stride_width, stride_height, numGroups, activation, in groupedConvFloat16()
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| D | FullyConnected.cpp | 49 const float* biasData, const Shape& biasShape, int32_t activation, in fullyConnectedFloat32() argument
53 CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max); in fullyConnectedFloat32()
79 const _Float16* biasData, const Shape& biasShape, int32_t activation, in fullyConnectedFloat16() argument
91 weightsShape, biasDataFloat32.data(), biasShape, activation, in fullyConnectedFloat16()
100 const int32_t* biasData, const Shape& biasShape, int32_t activation, in fullyConnectedQuant8() argument
118 CalculateActivationRangeUint8(activation, outputShape, &outputActivationMin, in fullyConnectedQuant8()
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| /frameworks/ml/nn/runtime/test/specs/V1_2/ |
| D | sub_v1_2_broadcast.mod.py | 20 activation = Int32Scalar("act", 0) variable
23 model = Model().Operation("SUB", input0, input1, activation).To(output0)
35 }).AddVariations("float16").AddAllActivations(output0, activation)
41 activation = 0 variable
44 model = Model("quant8").Operation("SUB", input0, input1, activation).To(output0)
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| D | unidirectional_sequence_rnn.mod.py | 20 activation, time_major, output, input_data, weights_data, argument
22 activation = Int32Scalar("activation", activation)
25 recurrent_weights, bias, hidden_state, activation,
152 activation=1,
174 activation=1,
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| D | sub_v1_2.mod.py | 24 activation = Int32Scalar("act", 0) variable
27 model = Model().Operation("SUB", input0, input1, activation).To(output0)
33 }).AddVariations("float16").AddAllActivations(output0, activation)
40 activation = 0 variable
43 model = Model("quant8").Operation("SUB", input0, input1, activation).To(output0)
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| D | bidirectional_sequence_rnn.mod.py | 40 bw_hidden_state, aux_input, fw_aux_weights, bw_aux_weights, activation, argument
47 activation = Int32Scalar("activation", activation)
53 bw_hidden_state, aux_input, fw_aux_weights, bw_aux_weights, activation,
238 activation=1,
288 activation=1,
339 activation=1,
395 activation=1,
449 activation=1,
511 activation=1,
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| /frameworks/ml/nn/runtime/test/ |
| D | TestTrivialModel.cpp | 63 int32_t activation(ANEURALNETWORKS_FUSED_NONE); in CreateAddTwoTensorModel() local
68 model->setOperandValue(d, &activation, sizeof(activation)); in CreateAddTwoTensorModel()
80 int32_t activation(ANEURALNETWORKS_FUSED_NONE); in CreateAddThreeTensorModel() local
88 model->setOperandValue(f, &activation, sizeof(activation)); in CreateAddThreeTensorModel()
162 auto activation = modelBroadcastAdd2.addOperand(&scalarType); in TEST_F() local
163 modelBroadcastAdd2.setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in TEST_F()
171 modelBroadcastAdd2.addOperation(ANEURALNETWORKS_ADD, {a, b, activation}, {c}); in TEST_F()
194 auto activation = modelBroadcastMul2.addOperand(&scalarType); in TEST_F() local
195 modelBroadcastMul2.setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in TEST_F()
203 modelBroadcastMul2.addOperation(ANEURALNETWORKS_MUL, {a, b, activation}, {c}); in TEST_F()
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| D | TestMemory.cpp | 62 int32_t activation(0); in TEST_F() local
72 model.setOperandValue(f, &activation, sizeof(activation)); in TEST_F()
120 int32_t activation(0); in TEST_F() local
130 model.setOperandValue(f, &activation, sizeof(activation)); in TEST_F()
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| /frameworks/ml/nn/runtime/test/generated/models/ |
| D | max_pool_quant8_2.model.cpp | 12 auto activation = model->addOperand(&type1); in CreateModel() local
22 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel()
23 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel()
45 auto activation = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
55 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
56 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel_dynamic_output_shape()
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| D | avg_pool_quant8_2.model.cpp | 12 auto activation = model->addOperand(&type1); in CreateModel() local
22 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel()
23 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel()
45 auto activation = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
55 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
56 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel_dynamic_output_shape()
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| D | max_pool_float_2.model.cpp | 12 auto activation = model->addOperand(&type1); in CreateModel() local
22 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel()
23 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel()
45 auto activation = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
55 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
56 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel_dynamic_output_shape()
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| D | avg_pool_quant8_3.model.cpp | 12 auto activation = model->addOperand(&type1); in CreateModel() local
22 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel()
23 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel()
45 auto activation = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
55 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
56 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel_dynamic_output_shape()
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| D | avg_pool_float_2.model.cpp | 12 auto activation = model->addOperand(&type1); in CreateModel() local
22 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel()
23 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel()
45 auto activation = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
55 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
56 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel_dynamic_output_shape()
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| D | avg_pool_float_3.model.cpp | 12 auto activation = model->addOperand(&type1); in CreateModel() local
22 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel()
23 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel()
45 auto activation = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
55 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
56 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel_dynamic_output_shape()
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| D | avg_pool_float_3_relaxed.model.cpp | 12 auto activation = model->addOperand(&type1); in CreateModel() local
22 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel()
23 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel()
47 auto activation = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
57 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
58 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel_dynamic_output_shape()
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| D | avg_pool_float_2_relaxed.model.cpp | 12 auto activation = model->addOperand(&type1); in CreateModel() local
22 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel()
23 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel()
47 auto activation = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
57 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
58 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel_dynamic_output_shape()
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| D | max_pool_float_2_relaxed.model.cpp | 12 auto activation = model->addOperand(&type1); in CreateModel() local
22 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel()
23 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel()
47 auto activation = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
57 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
58 …D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); in CreateModel_dynamic_output_shape()
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| D | unidirectional_sequence_rnn.model.cpp | 17 auto activation = model->addOperand(&type6); in CreateModel() local
22 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel()
25 …_SEQUENCE_RNN, {input, weights, recurrent_weights, bias, hidden_state, activation, time_major}, {o… in CreateModel()
52 auto activation = model->addOperand(&type6); in CreateModel_relaxed() local
57 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel_relaxed()
60 …_SEQUENCE_RNN, {input, weights, recurrent_weights, bias, hidden_state, activation, time_major}, {o… in CreateModel_relaxed()
89 auto activation = model->addOperand(&type6); in CreateModel_float16() local
94 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); in CreateModel_float16()
97 …_SEQUENCE_RNN, {input, weights, recurrent_weights, bias, hidden_state, activation, time_major}, {o… in CreateModel_float16()
124 auto activation = model->addOperand(&type6); in CreateModel_dynamic_output_shape() local
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| /frameworks/ml/nn/common/include/ |
| D | Operations.h | 54 int32_t dilationHeightFactor, int32_t depthMultiplier, int32_t activation,
61 int32_t depthMultiplier, int32_t activation, float* outputData,
68 int32_t dilationHeightFactor, int32_t depthMultiplier, int32_t activation,
77 int32_t depthMultiplier, int32_t activation, uint8_t* outputData,
146 int32_t activation, _Float16* outputData, const Shape& outputShape);
152 int32_t stride_height, int32_t activation, float* outputData,
159 int32_t stride_height, int32_t activation, uint8_t* outputData,
168 int32_t activation, uint8_t* outputData, const Shape& outputShape);
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| /frameworks/base/cmds/statsd/src/ |
| D | active_config_list.proto | 27 // Time left in activation. When this proto is loaded after device boot,
28 // the activation should be set to active for this duration.
45 repeated ActiveEventActivation activation = 2; field
|