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Searched refs:weights (Results 1 – 25 of 49) sorted by relevance

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/frameworks/ml/nn/runtime/test/specs/V1_3/
Dfully_connected_quant8_signed.mod.py19 weights = Parameter("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{3, 10}, 0.5f, -1", variable
26 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act_relu).To(out0)
42 weights = Parameter("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 5}, 0.2, -128", [-118, -108, -108, -1… variable
46 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
61 weights = Input("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 5}, 0.2, -128") # num_units = 1, input_si… variable
65 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
70 weights:
84 weights = Parameter("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 1}, 0.5f, -128", [-126]) variable
88 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
103 weights = Input("op2", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 1}, 0.5f, -128") variable
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Dunidirectional_sequence_rnn.mod.py19 def test(name, input, weights, recurrent_weights, bias, hidden_state, argument
25 model = Model().Operation("UNIDIRECTIONAL_SEQUENCE_RNN", input, weights,
31 weights: weights_data,
181 weights=Input("weights", "TENSOR_FLOAT32",
207 weights=Input("weights", "TENSOR_FLOAT32",
/frameworks/base/services/core/java/com/android/server/display/whitebalance/
DAmbientFilter.java208 final float[] weights = getWeights(time, buffer); in filter() local
210 Slog.v(mTag, "filter: " + buffer + " => " + Arrays.toString(weights)); in filter()
212 for (int i = 0; i < weights.length; i++) { in filter()
214 final float weight = weights[i]; in filter()
231 float[] weights = new float[buffer.size()]; in getWeights() local
234 for (int i = 1; i < weights.length; i++) { in getWeights()
237 weights[i - 1] = weight; in getWeights()
242 weights[weights.length - 1] = lastWeight; in getWeights()
243 return weights; in getWeights()
/frameworks/ml/nn/common/operations/
DQuantizedLSTM.cpp211 uint8_t* weights) { in assignWeightsSubmatrix() argument
217 weights[(row + offset_row) * weightsDims[1] + column + offset_column] = submatrixValues[i]; in assignWeightsSubmatrix()
269 auto checkWeightsShape = [&](const RunTimeOperandInfo* weights, uint32_t columns) -> bool { in prepare() argument
270 NN_RET_CHECK_EQ(NumDimensions(weights), 2); in prepare()
271 NN_RET_CHECK_EQ(SizeOfDimension(weights, 0), outputSize); in prepare()
272 NN_RET_CHECK_EQ(SizeOfDimension(weights, 1), columns); in prepare()
273 NN_RET_CHECK_EQ(weights->scale, weightsScale); in prepare()
274 NN_RET_CHECK_EQ(weights->zeroPoint, weightsZeroPoint); in prepare()
353 uint8_t* weights) { in concatenateWeights() argument
356 assignWeightsSubmatrix(inputToInputWeights_, 0 * outputSize, outputSize, weightsDims, weights); in concatenateWeights()
[all …]
DFullyConnected.cpp183 bool validateShapes(const Shape& input, const Shape& weights, const Shape& bias, in validateShapes() argument
187 NN_RET_CHECK(weights.type == input.type); in validateShapes()
198 NN_RET_CHECK_EQ(getNumberOfDimensions(weights), 2); in validateShapes()
201 uint32_t num_units = getSizeOfDimension(weights, 0); in validateShapes()
202 uint32_t input_size = getSizeOfDimension(weights, 1); in validateShapes()
283 Shape weights = context->getInputShape(kWeightsTensor); in validate() local
285 if (hasKnownRank(input) && hasKnownRank(weights) && hasKnownRank(bias)) { in validate()
286 NN_RET_CHECK(validateShapes(input, weights, bias)); in validate()
294 Shape weights = context->getInputShape(kWeightsTensor); in prepare() local
297 NN_RET_CHECK(validateShapes(input, weights, bias, &output)); in prepare()
/frameworks/base/libs/hwui/utils/
DBlur.cpp61 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()
DBlur.h37 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,
/frameworks/ml/nn/runtime/test/
DTestMemory.cpp55 WrapperMemory weights(offsetForMatrix3 + sizeof(matrix3), PROT_READ, fd, 0); in TEST_F() local
56 ASSERT_TRUE(weights.isValid()); in TEST_F()
69 model.setOperandValueFromMemory(e, &weights, offsetForMatrix2, sizeof(Matrix3x4)); in TEST_F()
70 model.setOperandValueFromMemory(a, &weights, offsetForMatrix3, sizeof(Matrix3x4)); in TEST_F()
113 WrapperMemory weights(buffer); in TEST_F() local
114 ASSERT_TRUE(weights.isValid()); in TEST_F()
127 model.setOperandValueFromMemory(e, &weights, offsetForMatrix2, sizeof(Matrix3x4)); in TEST_F()
128 model.setOperandValueFromMemory(a, &weights, offsetForMatrix3, sizeof(Matrix3x4)); in TEST_F()
/frameworks/ml/nn/runtime/test/specs/V1_2/
Dunidirectional_sequence_rnn.mod.py19 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(
Drnn_float16.mod.py24 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: [
Dfully_connected_v1_2.mod.py20 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),
/frameworks/ml/nn/runtime/test/specs/V1_0/
Dfully_connected_quant8_large_weights_as_inputs.mod.py19 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:
Dfully_connected_float_large_weights_as_inputs.mod.py19 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:
Dfully_connected_quant8_weights_as_inputs.mod.py19 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],
Dfully_connected_float_weights_as_inputs.mod.py19 weights = Input("op2", "TENSOR_FLOAT32", "{1, 1}") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
28 weights: [2],
Drnn_state.mod.py24 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: [
Drnn.mod.py24 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: [
Dfully_connected_float_large.mod.py19 weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 5}", [2, 3, 4, 5, 6]) # num_units = 1, input_size… variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
Dfully_connected_quant8_2.mod.py19 weights = Parameter("op2", "TENSOR_QUANT8_ASYMM", "{3, 10}, 0.5f, 127", variable
26 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act_relu).To(out0)
Dfully_connected_float.mod.py19 weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 1}", [2]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
Dfully_connected_quant8_large.mod.py19 weights = Parameter("op2", "TENSOR_QUANT8_ASYMM", "{1, 5}, 0.2, 0", [10, 20, 20, 20, 10]) # num_uni… variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
/frameworks/ml/nn/runtime/test/specs/V1_1/
Dfully_connected_float_large_weights_as_inputs_relaxed.mod.py19 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:
Dfully_connected_float_weights_as_inputs_relaxed.mod.py19 weights = Input("op2", "TENSOR_FLOAT32", "{1, 1}") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
29 weights: [2],
Drnn_state_relaxed.mod.py24 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: [
Drnn_relaxed.mod.py24 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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