/frameworks/native/include/android/ |
H A D | sensor.h | 234 float bias[3]; member in union:AUncalibratedEvent::__anon1565
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/frameworks/native/include_sensor/android/ |
H A D | sensor.h | 234 float bias[3]; member in union:AUncalibratedEvent::__anon1668
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/frameworks/native/services/surfaceflinger/ |
H A D | SurfaceFlinger.cpp | 1603 nsecs_t bias = vsyncInterval / 2; local 1605 (compositeToPresentLatency - idealLatency + bias) / vsyncInterval;
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H A D | SurfaceFlinger_hwc1.cpp | 1243 nsecs_t bias = vsyncInterval / 2; local 1245 (compositeToPresentLatency - idealLatency + bias) / vsyncInterval;
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/frameworks/ml/nn/common/ |
H A D | CpuExecutor.cpp | 398 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 D | Normalization.cpp | 45 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
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H A D | RNN.cpp | 55 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
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H A D | SVDF.cpp | 84 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.
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/frameworks/ml/nn/runtime/test/ |
H A D | TestTrivialModel.cpp | 77 void CreateAddThreeTensorModel(Model* model, const Matrix3x4 bias) { argument 87 model->setOperandValue(e, bias, sizeof(Matrix3x4));
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/frameworks/ml/nn/runtime/test/generated/models/ |
H A D | local_response_norm_float_1.model.cpp | 9 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});
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H A D | local_response_norm_float_2.model.cpp | 9 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});
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H A D | local_response_norm_float_3.model.cpp | 9 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});
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H A D | local_response_norm_float_4.model.cpp | 9 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});
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H A D | rnn.model.cpp | 13 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},
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H A D | rnn_state.model.cpp | 13 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},
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H A D | svdf.model.cpp | 14 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},
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H A D | svdf_state.model.cpp | 14 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},
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/frameworks/ml/nn/runtime/test/specs/ |
H A D | fully_connected_float.mod.py | 20 bias = Parameter("b0", "TENSOR_FLOAT32", "{1}", [4]) variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
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H A D | fully_connected_float_large.mod.py | 20 bias = Parameter("b0", "TENSOR_FLOAT32", "{1}", [900000]) variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
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H A D | fully_connected_float_large_weights_as_inputs.mod.py | 20 bias = Input("b0", "TENSOR_FLOAT32", "{1}") variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0) 30 bias:
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H A D | fully_connected_float_weights_as_inputs.mod.py | 20 bias = Input("b0", "TENSOR_FLOAT32", "{1}") variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0) 29 bias: [4]}
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H A D | fully_connected_quant8.mod.py | 20 bias = Parameter("b0", "TENSOR_INT32", "{1}, 0.25f, 0", [4]) variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
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H A D | fully_connected_quant8_large.mod.py | 20 bias = Parameter("b0", "TENSOR_INT32", "{1}, 0.04, 0", [10]) variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
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H A D | fully_connected_quant8_large_weights_as_inputs.mod.py | 20 bias = Input("b0", "TENSOR_INT32", "{1}, 0.04, 0") variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0) 30 bias:
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H A D | fully_connected_quant8_weights_as_inputs.mod.py | 20 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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