Searched defs:bias (Results 1 - 25 of 33) sorted by last modified time

12

/frameworks/native/include/android/
H A Dsensor.h234 float bias[3]; member in union:AUncalibratedEvent::__anon1565
/frameworks/native/include_sensor/android/
H A Dsensor.h234 float bias[3]; member in union:AUncalibratedEvent::__anon1668
/frameworks/native/services/surfaceflinger/
H A DSurfaceFlinger.cpp1603 nsecs_t bias = vsyncInterval / 2; local
1605 (compositeToPresentLatency - idealLatency + bias) / vsyncInterval;
H A DSurfaceFlinger_hwc1.cpp1243 nsecs_t bias = vsyncInterval / 2; local
1245 (compositeToPresentLatency - idealLatency + bias) / vsyncInterval;
/frameworks/ml/nn/common/
H A DCpuExecutor.cpp398 const RunTimeOperandInfo& bias = mOperands[ins[2]]; local
440 success = depthwiseConvPrepare(input.shape(), filter.shape(), bias.shape(),
450 reinterpret_cast<const float*>(bias.buffer),
451 bias.shape(),
459 success = depthwiseConvPrepare(input.shape(), filter.shape(), bias.shape(),
469 reinterpret_cast<const int32_t*>(bias.buffer),
470 bias.shape(),
488 const RunTimeOperandInfo& bias = mOperands[ins[2]]; local
527 success = convPrepare(input.shape(), filter.shape(), bias.shape(),
535 reinterpret_cast<const float*>(bias
936 RunTimeOperandInfo& bias = mOperands[ins[2]]; local
1041 float bias = getScalarData<float>(mOperands[ins[2]]); local
[all...]
/frameworks/ml/nn/common/operations/
H A DNormalization.cpp45 int32_t radius, float bias, float alpha, float beta,
49 radius, bias, alpha, beta,
44 localResponseNormFloat32(const float* inputData, const Shape& inputShape, int32_t radius, float bias, float alpha, float beta, float* outputData, const Shape& outputShape) argument
H A DRNN.cpp55 const RunTimeOperandInfo *bias = local
63 NN_CHECK_EQ(SizeOfDimension(input_weights, 0), SizeOfDimension(bias, 0));
64 NN_CHECK_EQ(SizeOfDimension(recurrent_weights, 0), SizeOfDimension(bias, 0));
65 NN_CHECK_EQ(SizeOfDimension(recurrent_weights, 1), SizeOfDimension(bias, 0));
92 // Initialize the pointer to input, output and bias.
107 // Output = bias
H A DSVDF.cpp84 const RunTimeOperandInfo *bias = local
86 if (!IsNullInput(bias)) {
87 NN_CHECK_EQ(SizeOfDimension(bias, 0), num_units);
120 // Initialize the pointer to input, output and bias.
139 // Apply bias if bias tensor exists.
/frameworks/ml/nn/runtime/test/
H A DTestTrivialModel.cpp77 void CreateAddThreeTensorModel(Model* model, const Matrix3x4 bias) { argument
87 model->setOperandValue(e, bias, sizeof(Matrix3x4));
/frameworks/ml/nn/runtime/test/generated/models/
H A Dlocal_response_norm_float_1.model.cpp9 auto bias = model->addOperand(&type2); local
17 model->setOperandValue(bias, bias_init, sizeof(float) * 1);
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
H A Dlocal_response_norm_float_2.model.cpp9 auto bias = model->addOperand(&type2); local
17 model->setOperandValue(bias, bias_init, sizeof(float) * 1);
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
H A Dlocal_response_norm_float_3.model.cpp9 auto bias = model->addOperand(&type2); local
17 model->setOperandValue(bias, bias_init, sizeof(float) * 1);
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
H A Dlocal_response_norm_float_4.model.cpp9 auto bias = model->addOperand(&type2); local
17 model->setOperandValue(bias, bias_init, sizeof(float) * 1);
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
H A Drnn.model.cpp13 auto bias = model->addOperand(&type3); local
19 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
22 {input, weights, recurrent_weights, bias, hidden_state_in, activation_param},
H A Drnn_state.model.cpp13 auto bias = model->addOperand(&type3); local
19 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
22 {input, weights, recurrent_weights, bias, hidden_state_in, activation_param},
H A Dsvdf.model.cpp14 auto bias = model->addOperand(&type3); local
21 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
24 {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param},
H A Dsvdf_state.model.cpp14 auto bias = model->addOperand(&type3); local
21 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
24 {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param},
/frameworks/ml/nn/runtime/test/specs/
H A Dfully_connected_float.mod.py20 bias = Parameter("b0", "TENSOR_FLOAT32", "{1}", [4]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
H A Dfully_connected_float_large.mod.py20 bias = Parameter("b0", "TENSOR_FLOAT32", "{1}", [900000]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
H A Dfully_connected_float_large_weights_as_inputs.mod.py20 bias = Input("b0", "TENSOR_FLOAT32", "{1}") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
30 bias:
H A Dfully_connected_float_weights_as_inputs.mod.py20 bias = Input("b0", "TENSOR_FLOAT32", "{1}") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
29 bias: [4]}
H A Dfully_connected_quant8.mod.py20 bias = Parameter("b0", "TENSOR_INT32", "{1}, 0.25f, 0", [4]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
H A Dfully_connected_quant8_large.mod.py20 bias = Parameter("b0", "TENSOR_INT32", "{1}, 0.04, 0", [10]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
H A Dfully_connected_quant8_large_weights_as_inputs.mod.py20 bias = Input("b0", "TENSOR_INT32", "{1}, 0.04, 0") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
30 bias:
H A Dfully_connected_quant8_weights_as_inputs.mod.py20 bias = Input("b0", "TENSOR_INT32", "{1}, 0.25f, 0") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
29 bias: [4]}

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