/frameworks/ml/nn/common/ |
H A D | CpuExecutor.cpp | 297 int32_t activation = getScalarData<int32_t>(mOperands[ins[2]]); local 309 activation, 319 activation, 330 int32_t activation = getScalarData<int32_t>(mOperands[ins[2]]); local 342 activation, 352 activation, 404 int32_t activation; local 414 activation = getScalarData<int32_t>(mOperands[ins[10]]); 420 activation = getScalarData<int32_t>(mOperands[ins[7]]); 455 depth_multiplier, activation, 493 int32_t activation; local 572 int32_t activation; local 652 int32_t activation; local 716 int32_t activation; local 938 int32_t activation = getScalarData<int32_t>(mOperands[ins[3]]); local [all...] |
H A D | OperationsUtils.cpp | 128 void CalculateActivationRangeUint8(int32_t activation, 142 if (activation == kActivationRelu) { 145 } else if (activation == kActivationRelu6) { 148 } else if (activation == kActivationRelu1) {
|
/frameworks/ml/nn/common/include/ |
H A D | Operations.h | 39 int32_t activation, 43 int32_t activation, 48 int32_t activation, 52 int32_t activation, 69 int32_t depth_multiplier, int32_t activation, 77 int32_t depth_multiplier, int32_t activation, 86 int32_t activation, 94 int32_t activation, 101 int32_t filter_width, int32_t filter_height, int32_t activation, 107 int32_t filter_width, int32_t filter_height, int32_t activation, [all...] |
H A D | OperationsUtils.h | 92 void CalculateActivationRangeUint8(int32_t activation, 211 switch (activation) { \ 225 LOG(ERROR) << "Unsupported fused activation function type"; \
|
/frameworks/ml/nn/common/operations/ |
H A D | Activation.cpp | 93 #define ANDROID_NN_RELUX_QUANT8(activation) \ 98 CalculateActivationRangeUint8(activation, inputShape, \
|
H A D | Conv2D.cpp | 69 int32_t activation, 74 #define ANDROID_NN_CONV(activation) \ 75 optimized_ops::Conv<FusedActivationFunctionType::activation>( \ 98 int32_t activation, 119 CalculateActivationRangeUint8(activation, outputShape, 127 #define ANDROID_NN_CONV(activation) \ 128 optimized_ops::Conv<FusedActivationFunctionType::activation>( \ 63 convFloat32(const float* inputData, const Shape& inputShape, const float* filterData, const Shape& filterShape, const float* biasData, const Shape& biasShape, int32_t padding_left, int32_t padding_right, int32_t padding_top, int32_t padding_bottom, int32_t stride_width, int32_t stride_height, int32_t activation, float* outputData, const Shape& outputShape) argument 92 convQuant8(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData, const Shape& filterShape, const int32_t* biasData, const Shape& biasShape, int32_t padding_left, int32_t padding_right, int32_t padding_top, int32_t padding_bottom, int32_t stride_width, int32_t stride_height, int32_t activation, uint8_t* outputData, const Shape& outputShape) argument
|
H A D | DepthwiseConv2D.cpp | 43 int32_t depth_multiplier, int32_t activation, 48 #define ANDROID_NN_DEPTHWISE_CONV(activation) \ 49 optimized_ops::DepthwiseConv<FusedActivationFunctionType::activation>( \ 70 int32_t depth_multiplier, int32_t activation, 88 CalculateActivationRangeUint8(activation, outputShape, 95 #define ANDROID_NN_DEPTHWISE_CONV(activation) \ 96 optimized_ops::DepthwiseConv<FusedActivationFunctionType::activation>( \ 37 depthwiseConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData, const Shape& filterShape, const float* biasData, const Shape& biasShape, int32_t padding_left, int32_t padding_right, int32_t padding_top, int32_t padding_bottom, int32_t stride_width, int32_t stride_height, int32_t depth_multiplier, int32_t activation, float* outputData, const Shape& outputShape) argument 64 depthwiseConvQuant8(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData, const Shape& filterShape, const int32_t* biasData, const Shape& biasShape, int32_t padding_left, int32_t padding_right, int32_t padding_top, int32_t padding_bottom, int32_t stride_width, int32_t stride_height, int32_t depth_multiplier, int32_t activation, uint8_t* outputData, const Shape& outputShape) argument
|
H A D | FullyConnected.cpp | 28 int32_t activation, 31 #define ANDROID_NN_FULLY_CONNECTED(activation) \ 32 optimized_ops::FullyConnected<FusedActivationFunctionType::activation>( \ 46 int32_t activation, 64 CalculateActivationRangeUint8(activation, outputShape, 72 #define ANDROID_NN_FULLY_CONNECTED(activation) \ 73 optimized_ops::FullyConnected<FusedActivationFunctionType::activation>( \ 25 fullyConnectedFloat32(const float* inputData, const Shape& inputShape, const float* weightsData, const Shape& weightsShape, const float* biasData, const Shape& biasShape, int32_t activation, float* outputData, const Shape& outputShape) argument 43 fullyConnectedQuant8(const uint8_t* inputData, const Shape& inputShape, const uint8_t* weightsData, const Shape& weightsShape, const int32_t* biasData, const Shape& biasShape, int32_t activation, uint8_t* outputData, const Shape& outputShape) argument
|
H A D | Pooling.cpp | 38 int32_t filter_width, int32_t filter_height, int32_t activation, 43 #define ANDROID_NN_AVERAGE_POOL(activation) \ 44 optimized_ops::AveragePool<FusedActivationFunctionType::activation>( \ 60 int32_t filter_width, int32_t filter_height, int32_t activation, 68 CalculateActivationRangeUint8(activation, outputShape, 72 #define ANDROID_NN_AVERAGE_POOL(activation) \ 73 optimized_ops::AveragePool<FusedActivationFunctionType::activation>( \ 90 int32_t filter_width, int32_t filter_height, int32_t activation, 95 #define ANDROID_NN_L2_POOL(activation) \ 96 optimized_ops::L2Pool<FusedActivationFunctionType::activation>( \ 34 averagePoolFloat32(const float* inputData, const Shape& inputShape, int32_t padding_left, int32_t padding_right, int32_t padding_top, int32_t padding_bottom, int32_t stride_width, int32_t stride_height, int32_t filter_width, int32_t filter_height, int32_t activation, float* outputData, const Shape& outputShape) argument 56 averagePoolQuant8(const uint8_t* inputData, const Shape& inputShape, int32_t padding_left, int32_t padding_right, int32_t padding_top, int32_t padding_bottom, int32_t stride_width, int32_t stride_height, int32_t filter_width, int32_t filter_height, int32_t activation, uint8_t* outputData, const Shape& outputShape) argument 86 l2PoolFloat32(const float* inputData, const Shape& inputShape, int32_t padding_left, int32_t padding_right, int32_t padding_top, int32_t padding_bottom, int32_t stride_width, int32_t stride_height, int32_t filter_width, int32_t filter_height, int32_t activation, float* outputData, const Shape& outputShape) argument 108 maxPoolFloat32(const float* inputData, const Shape& inputShape, int32_t padding_left, int32_t padding_right, int32_t padding_top, int32_t padding_bottom, int32_t stride_width, int32_t stride_height, int32_t filter_width, int32_t filter_height, int32_t activation, float* outputData, const Shape& outputShape) argument 130 maxPoolQuant8(const uint8_t* inputData, const Shape& inputShape, int32_t padding_left, int32_t padding_right, int32_t padding_top, int32_t padding_bottom, int32_t stride_width, int32_t stride_height, int32_t filter_width, int32_t filter_height, int32_t activation, uint8_t* outputData, const Shape& outputShape) argument [all...] |
H A D | SVDF.cpp | 128 float activation = 0.0; local 132 activation += input_ptr_batch[j] * weights_feature_ptr[j]; 143 output_ptr_batch[c] += weights_time_ptr[memory_size - 1] * activation; 148 // Apply activation. 152 // Right shift the state and concatenate with activation. 153 svdf_right_shift_state(state_in_ptr, memory_size - 1, activation,
|
H A D | SVDFTest.cpp | 221 int activation = ActivationFn::kActivationNone; local 222 ASSERT_EQ(execution.setInput(SVDF::kActivationParam, &activation, 223 sizeof(activation)),
|
H A D | SimpleMath.cpp | 30 int32_t activation, 34 #define ANDROID_NN_NORMAL_ADD(activation) \ 35 optimized_ops::Add<FusedActivationFunctionType::activation>( \ 40 #define ANDROID_NN_BROADCAST_ADD(activation) \ 41 optimized_ops::BroadcastAdd<FusedActivationFunctionType::activation>( \ 59 int32_t activation, 94 CalculateActivationRangeUint8(activation, shapeOut, 98 #define ANDROID_NN_NORMAL_ADD(activation) \ 99 optimized_ops::Add<FusedActivationFunctionType::activation>( \ 109 #define ANDROID_NN_BROADCAST_ADD(activation) \ 28 addFloat32(const float* in1, const Shape& shape1, const float* in2, const Shape& shape2, int32_t activation, float* out, const Shape& shapeOut) argument 57 addQuant8(const uint8_t* in1, const Shape& shape1, const uint8_t* in2, const Shape& shape2, int32_t activation, uint8_t* out, const Shape& shapeOut) argument 131 mulFloat32(const float* in1, const Shape& shape1, const float* in2, const Shape& shape2, int32_t activation, float* out, const Shape& shapeOut) argument 160 mulQuant8(const uint8_t* in1, const Shape& shape1, const uint8_t* in2, const Shape& shape2, int32_t activation, uint8_t* out, const Shape& shapeOut) argument [all...] |
/frameworks/ml/nn/common/operations/internal/optimized/ |
H A D | neon_tensor_utils.h | 78 ActivationFn activation, float* result) { 79 PortableApplyActivationToVector(vector, v_size, activation, result); 77 ApplyActivationToVector(const float* vector, int v_size, ActivationFn activation, float* result) argument
|
H A D | tensor_utils_impl.h | 101 // Apply activation function to elements of a vector. 103 ActivationFn activation,
|
/frameworks/ml/nn/common/operations/internal/reference/ |
H A D | portable_tensor_utils.cc | 117 ActivationFn activation, 119 auto activation_func = ActivationFunctor(activation); 116 PortableApplyActivationToVector(const float* vector, int v_size, ActivationFn activation, float* result) argument
|
H A D | portable_tensor_utils.h | 75 // Apply activation function to elements of a vector. 77 ActivationFn activation, 158 ActivationFn activation, float* result) { 159 PortableApplyActivationToVector(vector, v_size, activation, result); 157 ApplyActivationToVector(const float* vector, int v_size, ActivationFn activation, float* result) argument
|
/frameworks/ml/nn/common/operations/internal/ |
H A D | tensor_utils.h | 88 // Apply activation function to elements of a vector. 90 ActivationFn activation, float* result);
|
/frameworks/ml/nn/runtime/test/ |
H A D | TestMemory.cpp | 89 int32_t activation(0); 99 model.setOperandValue(f, &activation, sizeof(activation)); 161 int32_t activation(0); 171 model.setOperandValue(f, &activation, sizeof(activation));
|
H A D | TestTrivialModel.cpp | 63 int32_t activation(ANEURALNETWORKS_FUSED_NONE); 68 model->setOperandValue(d, &activation, sizeof(activation)); 80 int32_t activation(ANEURALNETWORKS_FUSED_NONE); 88 model->setOperandValue(f, &activation, sizeof(activation)); 159 // activation: NONE. 162 auto activation = modelBroadcastAdd2.addOperand(&scalarType); local 163 modelBroadcastAdd2.setOperandValue(activation, activation_init, sizeof(int32_t) * 1); 171 modelBroadcastAdd2.addOperation(ANEURALNETWORKS_ADD, {a, b, activation}, { 194 auto activation = modelBroadcastMul2.addOperand(&scalarType); local [all...] |
/frameworks/ml/nn/runtime/test/generated/models/ |
H A D | avg_pool_float_2.model.cpp | 11 auto activation = model->addOperand(&type1); local 21 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
|
H A D | avg_pool_float_3.model.cpp | 11 auto activation = model->addOperand(&type1); local 21 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
|
H A D | avg_pool_quant8_2.model.cpp | 11 auto activation = model->addOperand(&type1); local 21 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
|
H A D | avg_pool_quant8_3.model.cpp | 11 auto activation = model->addOperand(&type1); local 21 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
|
H A D | max_pool_float_2.model.cpp | 11 auto activation = model->addOperand(&type1); local 21 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
|
H A D | max_pool_quant8_2.model.cpp | 11 auto activation = model->addOperand(&type1); local 21 model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
|