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/frameworks/ml/nn/runtime/test/specs/V1_2/
Dconcat_mixed_quant.mod.py21 input2 = Input("input2", "TENSOR_FLOAT32", "{2, 1, 2}") variable
26 model = Model().Operation("CONCATENATION", input0, input1, input2, input3, axis).To(output0)
32 input2: [1.2, -3.2, -4.2, 7.2],
38 input2: ["TENSOR_QUANT8_ASYMM", 0.089, 123],
47 input2: [1.2, -3.2, -4.2, 7.2],
53 input2: ["TENSOR_QUANT8_ASYMM", 0.089, 123],
Dselect_v1_2.mod.py16 def test(name, input0, input1, input2, output0, input0_data, input1_data, input2_data, output_data): argument
17 model = Model().Operation("SELECT", input0, input1, input2).To(output0)
20 input2: ["TENSOR_QUANT8_ASYMM", 0.5, 127],
26 input2: input2_data,
34 input2=Input("input2", "TENSOR_FLOAT32", "{3}"),
46 input2=Input("input2", "TENSOR_FLOAT32", "{2, 2}"),
58 input2=Input("input2", "TENSOR_FLOAT32", "{2, 1, 2, 1, 2}"),
Dconcat_float16_2.mod.py26 input2 = Input("input2", "TENSOR_FLOAT16", "{%d, %d}" % (row2, col)) # input tensor 2 variable
29 model = model.Operation("CONCATENATION", input1, input2, axis0).To(output)
36 input2: input2_values}
Dconcat_float16_3.mod.py26 input2 = Input("input2", "TENSOR_FLOAT16", "{%d, %d}" % (row, col2)) # input tensor 2 variable
29 model = model.Operation("CONCATENATION", input1, input2, axis1).To(output)
35 input2: input2_values}
/frameworks/ml/nn/runtime/test/generated/models/
Dconcat_mixed_quant.model.cpp13 auto input2 = model->addOperand(&type5); in CreateModel_quant8() local
20 …model->addOperation(ANEURALNETWORKS_CONCATENATION, {input0, input1, input2, input3, param}, {outpu… in CreateModel_quant8()
23 {input0, input1, input2, input3}, in CreateModel_quant8()
43 auto input2 = model->addOperand(&type5); in CreateModel_dynamic_output_shape_quant8() local
50 …model->addOperation(ANEURALNETWORKS_CONCATENATION, {input0, input1, input2, input3, param}, {outpu… in CreateModel_dynamic_output_shape_quant8()
53 {input0, input1, input2, input3}, in CreateModel_dynamic_output_shape_quant8()
73 auto input2 = model->addOperand(&type5); in CreateModel_quant8_2() local
80 …model->addOperation(ANEURALNETWORKS_CONCATENATION, {input0, input1, input2, input3, param}, {outpu… in CreateModel_quant8_2()
83 {input0, input1, input2, input3}, in CreateModel_quant8_2()
103 auto input2 = model->addOperand(&type5); in CreateModel_dynamic_output_shape_quant8_2() local
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Dconcat_float_3.model.cpp10 auto input2 = model->addOperand(&type1); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
36 auto input2 = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
42 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel_dynamic_output_shape()
45 {input1, input2}, in CreateModel_dynamic_output_shape()
Dconcat_quant8_3.model.cpp10 auto input2 = model->addOperand(&type1); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
36 auto input2 = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
42 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel_dynamic_output_shape()
45 {input1, input2}, in CreateModel_dynamic_output_shape()
Dconcat_float16_2.model.cpp10 auto input2 = model->addOperand(&type1); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
36 auto input2 = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
42 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); in CreateModel_dynamic_output_shape()
45 {input1, input2}, in CreateModel_dynamic_output_shape()
Dconcat_float_2.model.cpp10 auto input2 = model->addOperand(&type1); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
36 auto input2 = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
42 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); in CreateModel_dynamic_output_shape()
45 {input1, input2}, in CreateModel_dynamic_output_shape()
Dconcat_float16_3.model.cpp10 auto input2 = model->addOperand(&type1); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
36 auto input2 = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
42 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel_dynamic_output_shape()
45 {input1, input2}, in CreateModel_dynamic_output_shape()
Dconcat_quant8_2.model.cpp10 auto input2 = model->addOperand(&type1); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
36 auto input2 = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
42 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); in CreateModel_dynamic_output_shape()
45 {input1, input2}, in CreateModel_dynamic_output_shape()
Dconcat_float_3_relaxed.model.cpp10 auto input2 = model->addOperand(&type1); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
38 auto input2 = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
44 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel_dynamic_output_shape()
47 {input1, input2}, in CreateModel_dynamic_output_shape()
Dconcat_float_2_relaxed.model.cpp10 auto input2 = model->addOperand(&type1); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
38 auto input2 = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
44 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); in CreateModel_dynamic_output_shape()
47 {input1, input2}, in CreateModel_dynamic_output_shape()
Dselect_v1_2.model.cpp9 auto input2 = model->addOperand(&type1); in CreateModel() local
12 model->addOperation(ANEURALNETWORKS_SELECT, {input0, input1, input2}, {output0}); in CreateModel()
15 {input0, input1, input2}, in CreateModel()
31 auto input2 = model->addOperand(&type6); in CreateModel_int32() local
34 model->addOperation(ANEURALNETWORKS_SELECT, {input0, input1, input2}, {output0}); in CreateModel_int32()
37 {input0, input1, input2}, in CreateModel_int32()
53 auto input2 = model->addOperand(&type7); in CreateModel_float16() local
56 model->addOperation(ANEURALNETWORKS_SELECT, {input0, input1, input2}, {output0}); in CreateModel_float16()
59 {input0, input1, input2}, in CreateModel_float16()
75 auto input2 = model->addOperand(&type1); in CreateModel_relaxed() local
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Dtopk_v2.model.cpp326 auto input2 = model->addOperand(&type4); in CreateModel_3() local
333 model->addOperation(ANEURALNETWORKS_TOPK_V2, {input2, k2}, {out_values2, out_indices2}); in CreateModel_3()
336 {input2}, in CreateModel_3()
352 auto input2 = model->addOperand(&type4); in CreateModel_relaxed_3() local
359 model->addOperation(ANEURALNETWORKS_TOPK_V2, {input2, k2}, {out_values2, out_indices2}); in CreateModel_relaxed_3()
362 {input2}, in CreateModel_relaxed_3()
380 auto input2 = model->addOperand(&type16); in CreateModel_float16_3() local
387 model->addOperation(ANEURALNETWORKS_TOPK_V2, {input2, k2}, {out_values2, out_indices2}); in CreateModel_float16_3()
390 {input2}, in CreateModel_float16_3()
406 auto input2 = model->addOperand(&type4); in CreateModel_dynamic_output_shape_3() local
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/frameworks/ml/nn/runtime/test/specs/V1_0/
Dconcat_float_2.mod.py26 input2 = Input("input2", "TENSOR_FLOAT32", "{%d, %d}" % (row2, col)) # input tensor 2 variable
29 model = model.Operation("CONCATENATION", input1, input2, axis0).To(output)
36 input2: input2_values}
Dconcat_quant8_2.mod.py26 input2 = Input("input2", "TENSOR_QUANT8_ASYMM", "{%d, %d}, 0.5f, 0" % (row2, col)) variable
29 model = model.Operation("CONCATENATION", input1, input2, axis0).To(output)
36 input2: input2_values}
Dconcat_float_3.mod.py26 input2 = Input("input2", "TENSOR_FLOAT32", "{%d, %d}" % (row, col2)) # input tensor 2 variable
29 model = model.Operation("CONCATENATION", input1, input2, axis1).To(output)
35 input2: input2_values}
Dconcat_quant8_3.mod.py26 input2 = Input("input2", "TENSOR_QUANT8_ASYMM", "{%d, %d}, 0.5f, 0" % (row, col2)) variable
29 model = model.Operation("CONCATENATION", input1, input2, axis1).To(output)
35 input2: input2_values}
/frameworks/ml/nn/runtime/test/specs/V1_1/
Dconcat_float_2_relaxed.mod.py26 input2 = Input("input2", "TENSOR_FLOAT32", "{%d, %d}" % (row2, col)) # input tensor 2 variable
29 model = model.Operation("CONCATENATION", input1, input2, axis0).To(output)
37 input2: input2_values}
Dconcat_float_3_relaxed.mod.py26 input2 = Input("input2", "TENSOR_FLOAT32", "{%d, %d}" % (row, col2)) # input tensor 2 variable
29 model = model.Operation("CONCATENATION", input1, input2, axis1).To(output)
36 input2: input2_values}
/frameworks/ml/nn/runtime/test/
DTestIntrospectionControl.cpp230 float input2[2] = {3.0f, 4.0f}; in TEST_F() local
234 EXPECT_EQ(ANeuralNetworksExecution_setInput(mExecution, 1, nullptr, input2, sizeof(input2)), in TEST_F()
243 EXPECT_EQ(output[0], input1[0] + input2[0]); in TEST_F()
244 EXPECT_EQ(output[1], input1[1] + input2[1]); in TEST_F()
577 float input2[2] = {3.0f, 4.0f}; in TEST_P() local
581 EXPECT_EQ(ANeuralNetworksExecution_setInput(mExecution, 1, nullptr, input2, sizeof(input2)), in TEST_P()
837 float input2[2] = {3.0f, 4.0f}; in TEST_F() local
841 EXPECT_EQ(ANeuralNetworksExecution_setInput(mExecution, 1, nullptr, input2, sizeof(input2)), in TEST_F()
848 EXPECT_EQ(output[0], kSimpleMultiplier * (input1[0] + input2[0])); in TEST_F()
849 EXPECT_EQ(output[1], kSimpleMultiplier * (input1[1] + input2[1])); in TEST_F()
/frameworks/base/opengl/java/android/opengl/
DGLLogWrapper.java934 ByteBuffer input2 = (ByteBuffer) input; in toByteBuffer() local
935 int position = input2.position(); in toByteBuffer()
937 byteCount = input2.limit() - position; in toByteBuffer()
939 result = ByteBuffer.allocate(byteCount).order(input2.order()); in toByteBuffer()
941 result.put(input2.get()); in toByteBuffer()
943 input2.position(position); in toByteBuffer()
945 CharBuffer input2 = (CharBuffer) input; in toByteBuffer() local
946 int position = input2.position(); in toByteBuffer()
948 byteCount = (input2.limit() - position) * 2; in toByteBuffer()
950 result = ByteBuffer.allocate(byteCount).order(input2.order()); in toByteBuffer()
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/frameworks/av/services/audiopolicy/common/managerdefinitions/src/
DAudioProfile.cpp409 const T& input1, const T& input2, const Order& order) in intersectFilterAndOrder() argument
412 std::set<typename T::value_type> set2{input2.begin(), input2.end()}; in intersectFilterAndOrder()
427 const T& input1, const T& input2, Compare comp) in intersectAndOrder() argument
430 std::set<typename T::value_type, Compare> set2{input2.begin(), input2.end(), comp}; in intersectAndOrder()
/frameworks/ml/nn/common/operations/
DLogicalAndOr.cpp74 Shape input2 = context->getInputShape(kInputTensor2); in prepare() local
76 NN_RET_CHECK(calculateBroadcastedShape(input1, input2, &output)); in prepare()

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