| /frameworks/base/services/core/java/com/android/server/display/whitebalance/ |
| D | AmbientFilter.java | 207 final float[] weights = getWeights(time, buffer); in filter() local
209 Slog.v(mTag, "filter: " + buffer + " => " + Arrays.toString(weights)); in filter()
211 for (int i = 0; i < weights.length; i++) { in filter()
213 final float weight = weights[i]; in filter()
230 float[] weights = new float[buffer.size()]; in getWeights() local
233 for (int i = 1; i < weights.length; i++) { in getWeights()
236 weights[i - 1] = weight; in getWeights()
241 weights[weights.length - 1] = lastWeight; in getWeights()
242 return weights; in getWeights()
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| /frameworks/ml/nn/common/operations/ |
| D | QuantizedLSTM.cpp | 204 uint8_t* weights) { in assignWeightsSubmatrix() argument
210 weights[(row + offset_row) * weightsDims[1] + column + offset_column] = submatrixValues[i]; in assignWeightsSubmatrix()
264 auto checkWeightsShape = [&](const RunTimeOperandInfo* weights, uint32_t columns) -> bool { in prepare() argument
265 NN_RET_CHECK_EQ(NumDimensions(weights), 2); in prepare()
266 NN_RET_CHECK_EQ(SizeOfDimension(weights, 0), outputSize); in prepare()
267 NN_RET_CHECK_EQ(SizeOfDimension(weights, 1), columns); in prepare()
268 NN_RET_CHECK_EQ(weights->scale, weightsScale); in prepare()
269 NN_RET_CHECK_EQ(weights->zeroPoint, weightsZeroPoint); in prepare()
348 uint8_t* weights) { in concatenateWeights() argument
351 assignWeightsSubmatrix(inputToInputWeights_, 0 * outputSize, outputSize, weightsDims, weights); in concatenateWeights()
[all …]
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| D | UnidirectionalSequenceRNN.cpp | 60 const T* weights = context->getInputBuffer<T>(kWeightsTensor); in executeTyped() local
100 RNN::RNNStep<T>(input, fixedTimeInputShape, hiddenState, bias, weights, weightsShape, in executeTyped()
133 Shape weights = context->getInputShape(kWeightsTensor); in prepare() local
144 const uint32_t numUnits = getSizeOfDimension(weights, 0); in prepare()
148 NN_RET_CHECK_EQ(getNumberOfDimensions(weights), 2); in prepare()
153 NN_RET_CHECK_EQ(inputSize, getSizeOfDimension(weights, 1)); in prepare()
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| /frameworks/base/libs/hwui/utils/ |
| D | Blur.cpp | 61 void Blur::generateGaussianWeights(float* weights, float radius) { in generateGaussianWeights() argument
83 weights[r + intRadius] = coeff1 * pow(e, floatR * floatR * coeff2); in generateGaussianWeights()
84 normalizeFactor += weights[r + intRadius]; in generateGaussianWeights()
90 weights[r + intRadius] *= normalizeFactor; in generateGaussianWeights()
94 void Blur::horizontal(float* weights, int32_t radius, const uint8_t* source, uint8_t* dest, in horizontal() argument
105 const float* gPtr = weights; in horizontal()
137 void Blur::vertical(float* weights, int32_t radius, const uint8_t* source, uint8_t* dest, in vertical() argument
147 const float* gPtr = weights; in vertical()
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| D | Blur.h | 37 static void generateGaussianWeights(float* weights, float radius);
38 static void horizontal(float* weights, int32_t radius, const uint8_t* source, uint8_t* dest,
40 static void vertical(float* weights, int32_t radius, const uint8_t* source, uint8_t* dest,
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| /frameworks/ml/nn/runtime/test/generated/models/ |
| D | rnn_state.model.cpp | 12 auto weights = model->addOperand(&type1); in CreateModel() local
22 …model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in… in CreateModel()
25 {input, weights, recurrent_weights, bias, hidden_state_in}, in CreateModel()
45 auto weights = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
55 …model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in… in CreateModel_dynamic_output_shape()
58 {input, weights, recurrent_weights, bias, hidden_state_in}, in CreateModel_dynamic_output_shape()
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| D | rnn.model.cpp | 12 auto weights = model->addOperand(&type1); in CreateModel() local
22 …model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in… in CreateModel()
25 {input, weights, recurrent_weights, bias, hidden_state_in}, in CreateModel()
45 auto weights = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
55 …model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in… in CreateModel_dynamic_output_shape()
58 {input, weights, recurrent_weights, bias, hidden_state_in}, in CreateModel_dynamic_output_shape()
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| D | rnn_state_relaxed.model.cpp | 12 auto weights = model->addOperand(&type1); in CreateModel() local
22 …model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in… in CreateModel()
25 {input, weights, recurrent_weights, bias, hidden_state_in}, in CreateModel()
47 auto weights = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
57 …model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in… in CreateModel_dynamic_output_shape()
60 {input, weights, recurrent_weights, bias, hidden_state_in}, in CreateModel_dynamic_output_shape()
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| D | rnn_float16.model.cpp | 12 auto weights = model->addOperand(&type1); in CreateModel() local
22 …model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in… in CreateModel()
25 {input, weights, recurrent_weights, bias, hidden_state_in}, in CreateModel()
45 auto weights = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
55 …model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in… in CreateModel_dynamic_output_shape()
58 {input, weights, recurrent_weights, bias, hidden_state_in}, in CreateModel_dynamic_output_shape()
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| D | rnn_relaxed.model.cpp | 12 auto weights = model->addOperand(&type1); in CreateModel() local
22 …model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in… in CreateModel()
25 {input, weights, recurrent_weights, bias, hidden_state_in}, in CreateModel()
47 auto weights = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
57 …model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in… in CreateModel_dynamic_output_shape()
60 {input, weights, recurrent_weights, bias, hidden_state_in}, in CreateModel_dynamic_output_shape()
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| D | unidirectional_sequence_rnn.model.cpp | 13 auto weights = model->addOperand(&type1); in CreateModel() local
25 …model->addOperation(ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_RNN, {input, weights, recurrent_weight… in CreateModel()
28 {input, weights, recurrent_weights, bias, hidden_state}, in CreateModel()
48 auto weights = model->addOperand(&type1); in CreateModel_relaxed() local
60 …model->addOperation(ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_RNN, {input, weights, recurrent_weight… in CreateModel_relaxed()
63 {input, weights, recurrent_weights, bias, hidden_state}, in CreateModel_relaxed()
85 auto weights = model->addOperand(&type14); in CreateModel_float16() local
97 …model->addOperation(ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_RNN, {input, weights, recurrent_weight… in CreateModel_float16()
100 {input, weights, recurrent_weights, bias, hidden_state}, in CreateModel_float16()
120 auto weights = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
[all …]
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| /frameworks/ml/nn/runtime/test/ |
| D | TestMemory.cpp | 56 WrapperMemory weights(offsetForMatrix3 + sizeof(matrix3), PROT_READ, fd, 0); in TEST_F() local
57 ASSERT_TRUE(weights.isValid()); in TEST_F()
70 model.setOperandValueFromMemory(e, &weights, offsetForMatrix2, sizeof(Matrix3x4)); in TEST_F()
71 model.setOperandValueFromMemory(a, &weights, offsetForMatrix3, sizeof(Matrix3x4)); in TEST_F()
114 WrapperMemory weights(buffer); in TEST_F() local
115 ASSERT_TRUE(weights.isValid()); in TEST_F()
128 model.setOperandValueFromMemory(e, &weights, offsetForMatrix2, sizeof(Matrix3x4)); in TEST_F()
129 model.setOperandValueFromMemory(a, &weights, offsetForMatrix3, sizeof(Matrix3x4)); in TEST_F()
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| /frameworks/ml/nn/runtime/test/specs/V1_2/ |
| D | unidirectional_sequence_rnn.mod.py | 19 def test(name, input, weights, recurrent_weights, bias, hidden_state, argument
24 model = Model().Operation("UNIDIRECTIONAL_SEQUENCE_RNN", input, weights,
29 weights: weights_data,
143 weights=Input("weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(
165 weights=Input("weights", "TENSOR_FLOAT32", "{{{}, {}}}".format(
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| D | rnn_float16.mod.py | 24 weights = Input("weights", "TENSOR_FLOAT16", "{%d, %d}" % (units, input_size)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
38 weights: [
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| D | fully_connected_v1_2.mod.py | 20 weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 1}", [2]) variable
24 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
28 weights: ("TENSOR_QUANT8_ASYMM", 0.5, 120),
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| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| D | fully_connected_float_large_weights_as_inputs.mod.py | 19 weights = Input("op2", "TENSOR_FLOAT32", "{1, 5}") # num_units = 1, input_size = 5 variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
28 weights:
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| D | fully_connected_quant8_weights_as_inputs.mod.py | 19 weights = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 1}, 0.5f, 0") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
28 weights: [2],
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| D | fully_connected_quant8_large_weights_as_inputs.mod.py | 19 weights = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 5}, 0.2, 0") # num_units = 1, input_size = 5 variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
28 weights:
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| D | fully_connected_float_weights_as_inputs.mod.py | 19 weights = Input("op2", "TENSOR_FLOAT32", "{1, 1}") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
28 weights: [2],
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| D | rnn_state.mod.py | 24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
38 weights: [
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| D | rnn.mod.py | 24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
38 weights: [
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| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| D | fully_connected_float_weights_as_inputs_relaxed.mod.py | 19 weights = Input("op2", "TENSOR_FLOAT32", "{1, 1}") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
29 weights: [2],
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| D | fully_connected_float_large_weights_as_inputs_relaxed.mod.py | 19 weights = Input("op2", "TENSOR_FLOAT32", "{1, 5}") # num_units = 1, input_size = 5 variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
29 weights:
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| D | rnn_state_relaxed.mod.py | 24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
39 weights: [
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| D | rnn_relaxed.mod.py | 24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
39 weights: [
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