| /frameworks/base/cmds/statsd/src/metrics/ |
| D | MetricProducer.cpp | 122 std::shared_ptr<Activation> activation = in addActivation() local
124 mEventActivationMap.emplace(activationTrackerIndex, activation); in addActivation()
127 deactivationList.push_back(activation); in addActivation()
136 auto& activation = it->second; in activateLocked() local
137 if (ACTIVATE_ON_BOOT == activation->activationType) { in activateLocked()
138 if (ActivationState::kNotActive == activation->state) { in activateLocked()
139 activation->state = ActivationState::kActiveOnBoot; in activateLocked()
144 activation->start_ns = elapsedTimestampNs; in activateLocked()
145 activation->state = ActivationState::kActive; in activateLocked()
169 const auto& activeEventActivation = activeMetric.activation(i); in loadActiveMetricLocked()
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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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| D | sub_quantized_different_scales.mod.py | 42 activation = 0
45 model = Model().Operation("SUB", input0, input1, activation).To(output0)
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| /frameworks/ml/nn/common/operations/ |
| D | Broadcast.cpp | 55 switch (activation) { \
75 int32_t activation, float* out, const Shape& shapeOut)>;
78 const Shape& shape2, int32_t activation, _Float16* out, in binaryOperationFloat16() argument
86 operationFloat32(in1_float32.data(), shape1, in2_float32.data(), shape2, activation, in binaryOperationFloat16()
94 int32_t activation, float* out, const Shape& shapeOut) { in addFloat32() argument
99 #define ANDROID_NN_BROADCAST_ADD(activation) \ in addFloat32() argument
100 tflite::optimized_ops::BroadcastAdd<tflite::FusedActivationFunctionType::activation>( \ in addFloat32()
108 #define ANDROID_NN_ADD(activation) \ in addFloat32() argument
109 tflite::optimized_ops::Add<tflite::FusedActivationFunctionType::activation>( \ in addFloat32()
121 int32_t activation, _Float16* out, const Shape& shapeOut) { in addFloat16() argument
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| D | Activation.cpp | 41 namespace activation { namespace
123 template <ActivationFn activation>
130 CalculateActivationRangeUint8(activation, inputShape, &output_activation_min, in reluXQuant8()
218 template <ActivationFn activation>
225 CalculateActivationRangeInt8(activation, inputShape, &output_activation_min, in reluXQuant8Signed()
620 NN_REGISTER_OPERATION(RELU, "RELU", std::bind(activation::validate, OperationType::RELU, _1),
621 std::bind(activation::prepare, OperationType::RELU, _1),
622 activation::executeRelu, .allowZeroSizedInput = true);
623 NN_REGISTER_OPERATION(RELU1, "RELU1", std::bind(activation::validate, OperationType::RELU1, _1),
624 std::bind(activation::prepare, OperationType::RELU1, _1),
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| D | DepthwiseConv2D.cpp | 53 int32_t activation; member
65 activation = context->getInputValue<int32_t>(7); in initialize()
82 activation = context->getInputValue<int32_t>(10); in initialize()
115 NN_RET_CHECK_GE(activation, 0); in initialize()
136 int32_t depthMultiplier, int32_t activation, float* outputData, in depthwiseConvNhwc() argument
143 CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max); in depthwiseConvNhwc()
170 int32_t dilationHeightFactor, int32_t depthMultiplier, int32_t activation, in depthwiseConvNhwc() argument
184 dilationHeightFactor, depthMultiplier, activation, outputDataFloat32.data(), in depthwiseConvNhwc()
196 int32_t depthMultiplier, int32_t activation, uint8_t* outputData, in depthwiseConvNhwc() argument
213 CalculateActivationRangeUint8(activation, outputShape, &output_activation_min, in depthwiseConvNhwc()
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| D | Conv2D.cpp | 68 int32_t activation; member
79 activation = context->getInputValue<int32_t>(6); in initialize()
95 activation = context->getInputValue<int32_t>(9); in initialize()
127 NN_RET_CHECK_GE(activation, 0); in initialize()
197 int32_t dilation_width_factor, int32_t dilation_height_factor, int32_t activation, in convNhwc() argument
204 CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max); in convNhwc()
226 int32_t dilation_width_factor, int32_t dilation_height_factor, int32_t activation, in convNhwc() argument
247 CalculateActivationRangeUint8(activation, outputShape, &output_activation_min, in convNhwc()
280 int32_t dilation_width_factor, int32_t dilation_height_factor, int32_t activation, in convNhwc() argument
298 dilation_height_factor, activation, unsignedOutput.data(), outputShape)); in convNhwc()
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| D | RNN.cpp | 123 const int32_t activation, T* outputData) { in RNNStep() argument
131 recurrentWeightsData, recurrentWeightsShape, activation, in RNNStep()
144 const Shape& recurrentWeightsShape, const int32_t activation, in RNNStep() argument
216 (ActivationFunctor(static_cast<ActivationFn>(activation)))(output_ptr_batch[o]); in RNNStep()
231 const Shape& recurrentWeightsShape, int32_t activation,
239 const Shape& recurrentWeightsShape, const int32_t activation,
247 const Shape& recurrentWeightsShape, int32_t activation,
255 const Shape& recurrentWeightsShape, int32_t activation,
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| D | GroupedConv2D.cpp | 50 int32_t numGroups, int32_t activation, float* outputData, in groupedConvFloat32() argument
56 CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max); in groupedConvFloat32()
108 int32_t numGroups, int32_t activation, T* outputData, in groupedConvQuant8() argument
127 CalculateActivationRange<T>(activation, outputShape, &output_activation_min, in groupedConvQuant8()
187 int32_t numGroups, int32_t activation, int8_t* outputData,
196 int32_t numGroups, int32_t activation, uint8_t* outputData,
206 int32_t activation, T* outputData, const Shape& outputShape) { in groupedConvQuant8PerChannel() argument
231 CalculateActivationRange<T>(activation, outputShape, &output_activation_min, in groupedConvQuant8PerChannel()
290 int32_t activation, _Float16* outputData, const Shape& outputShape) { in groupedConvFloat16() argument
304 padding_bottom, stride_width, stride_height, numGroups, activation, in groupedConvFloat16()
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| D | FullyConnected.cpp | 57 const float* biasData, const Shape& biasShape, int32_t activation, in fullyConnectedFloat32() argument
61 CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max); in fullyConnectedFloat32()
87 const _Float16* biasData, const Shape& biasShape, int32_t activation, in fullyConnectedFloat16() argument
99 weightsShape, biasDataFloat32.data(), biasShape, activation, in fullyConnectedFloat16()
108 const int32_t* biasData, const Shape& biasShape, int32_t activation, in fullyConnectedQuant8() argument
126 CalculateActivationRangeUint8(activation, outputShape, &outputActivationMin, in fullyConnectedQuant8()
149 const int32_t* biasData, const Shape& biasShape, int32_t activation, in fullyConnectedQuant8() argument
162 CalculateActivationRangeInt8(activation, outputShape, &outputActivationMin, in fullyConnectedQuant8()
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| D | RNN.h | 53 int32_t activation, T* outputData);
61 int32_t activation, uint32_t outputBatchStride, uint32_t outputBatchStep,
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| D | Pooling.cpp | 48 int32_t activation; member
63 activation = context->getInputValue<int32_t>(9); in initialize()
73 activation = context->getInputValue<int32_t>(6); in initialize()
95 NN_RET_CHECK_GE(activation, 0); in initialize()
117 CalculateActivationRangeUint8(activation, output, &output_activation_min, in toTfliteParam()
124 CalculateActivationRangeInt8(activation, output, &output_activation_min, in toTfliteParam()
130 CalculateActivationRangeFloat(activation, &output_activation_min, in toTfliteParam()
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| /frameworks/ml/nn/runtime/test/specs/V1_3/ |
| D | unidirectional_sequence_rnn.mod.py | 20 activation, time_major, output, output_state, input_data, weights_data, argument
23 activation = Int32Scalar("activation", activation)
26 recurrent_weights, bias, hidden_state, activation,
192 activation=1,
218 activation=1,
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| D | sub_quant8_signed.mod.py | 43 activation = 0
46 model = Model().Operation("SUB", input0, input1, activation).To(output0)
67 activation = 0 variable
70 model = Model("quant8").Operation("SUB", input0, input1, activation).To(output0)
89 activation = 0 variable
92 model = Model("quant8").Operation("SUB", input0, input1, activation).To(output0)
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| D | bidirectional_sequence_rnn_1_3.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,
244 activation=1,
294 activation=1,
346 activation=1,
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| D | bidirectional_sequence_rnn_state_output.mod.py | 44 fw_aux_weights, bw_aux_weights, activation, time_major, merge_outputs, argument
51 activation = Int32Scalar("activation", activation)
58 bw_aux_weights, activation, time_major,
241 activation=1,
309 activation=1,
356 activation=1,
419 activation=1,
484 activation=1,
554 activation=1,
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| /frameworks/ml/nn/common/include/ |
| D | Operations.h | 53 int32_t dilationHeightFactor, int32_t depthMultiplier, int32_t activation,
60 int32_t depthMultiplier, int32_t activation, float* outputData,
67 int32_t dilationHeightFactor, int32_t depthMultiplier, int32_t activation,
76 int32_t depthMultiplier, int32_t activation, uint8_t* outputData,
149 int32_t activation, _Float16* outputData, const Shape& outputShape);
155 int32_t stride_height, int32_t activation, float* outputData,
163 int32_t stride_height, int32_t activation, T* outputData,
173 int32_t activation, T* outputData, const Shape& outputShape);
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| D | CpuOperationUtils.h | 214 inline void CalculateActivationRange(int32_t activation, const Shape& outputShape,
218 inline void CalculateActivationRange<uint8_t>(int32_t activation, const Shape& outputShape,
221 CalculateActivationRangeUint8(activation, outputShape, outputActivationMin,
226 inline void CalculateActivationRange<int8_t>(int32_t activation, const Shape& outputShape,
229 CalculateActivationRangeInt8(activation, outputShape, outputActivationMin, outputActivationMax);
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| /frameworks/ml/nn/runtime/test/ |
| D | TestMemory.cpp | 61 int32_t activation(0); in TEST_F() local
71 model.setOperandValue(f, &activation, sizeof(activation)); in TEST_F()
119 int32_t activation(0); in TEST_F() local
129 model.setOperandValue(f, &activation, sizeof(activation)); in TEST_F()
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| D | TestValidateOperations.cpp | 1297 ANeuralNetworksOperandType activation = {.type = ANEURALNETWORKS_INT32, in simpleMathOpTest() local
1304 operationCode, {input1, input2, activation}, {output}, in simpleMathOpTest()
1390 ANeuralNetworksOperandType activation = {.type = ANEURALNETWORKS_INT32, in TEST() local
1396 OperationTestBase mulTest(ANEURALNETWORKS_MUL, {input1, input2, activation}, {output}); in TEST()
1847 ANeuralNetworksOperandType activation = scalar; in poolingOpTest() local
1851 strideHeight, filterWidth, filterHeight, activation}, in poolingOpTest()
1858 {input, padImplicit, strideWidth, strideHeight, filterWidth, filterHeight, activation}, in poolingOpTest()
1871 filterHeight, activation, layout}, in poolingOpTest()
1877 filterWidth, filterHeight, activation, layout}, in poolingOpTest()
2138 ANeuralNetworksOperandType activation = scalar; in convOpTest() local
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| /frameworks/ml/nn/common/ |
| D | OperationsUtils.cpp | 50 void CalculateActivationRangeImpl(int32_t activation, const Shape& outputShape, int32_t qmin, in CalculateActivationRangeImpl() argument
59 if (activation == kActivationRelu) { in CalculateActivationRangeImpl()
62 } else if (activation == kActivationRelu6) { in CalculateActivationRangeImpl()
65 } else if (activation == kActivationRelu1) { in CalculateActivationRangeImpl()
68 } else if (activation == kActivationNone) { in CalculateActivationRangeImpl()
273 void CalculateActivationRangeUint8(int32_t activation, const Shape& outputShape, int32_t* act_min, in CalculateActivationRangeUint8() argument
278 CalculateActivationRangeImpl(activation, outputShape, qmin, qmax, act_min, act_max); in CalculateActivationRangeUint8()
281 void CalculateActivationRangeInt8(int32_t activation, const Shape& outputShape, int32_t* act_min, in CalculateActivationRangeInt8() argument
286 CalculateActivationRangeImpl(activation, outputShape, qmin, qmax, act_min, act_max); in CalculateActivationRangeInt8()
289 void CalculateActivationRangeFloat(int32_t activation, float* activation_min, in CalculateActivationRangeFloat() argument
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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
|