Home
last modified time | relevance | path

Searched refs:input1 (Results 1 – 25 of 89) sorted by relevance

1234

/frameworks/ml/nn/runtime/test/generated/models/
Dsub_v1_2_broadcast.model.cpp9 auto input1 = model->addOperand(&type1); in CreateModel_none() local
15 model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0}); in CreateModel_none()
18 {input0, input1}, in CreateModel_none()
34 auto input1 = model->addOperand(&type1); in CreateModel_relu() local
40 model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0}); in CreateModel_relu()
43 {input0, input1}, in CreateModel_relu()
59 auto input1 = model->addOperand(&type1); in CreateModel_relu1() local
65 model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0}); in CreateModel_relu1()
68 {input0, input1}, in CreateModel_relu1()
84 auto input1 = model->addOperand(&type1); in CreateModel_relu6() local
[all …]
Dminimum.model.cpp7 auto input1 = model->addOperand(&type0); in CreateModel() local
10 model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); in CreateModel()
13 {input0, input1}, in CreateModel()
27 auto input1 = model->addOperand(&type0); in CreateModel_relaxed() local
30 model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); in CreateModel_relaxed()
33 {input0, input1}, in CreateModel_relaxed()
49 auto input1 = model->addOperand(&type4); in CreateModel_float16() local
52 model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); in CreateModel_float16()
55 {input0, input1}, in CreateModel_float16()
69 auto input1 = model->addOperand(&type5); in CreateModel_int32() local
[all …]
Dmaximum.model.cpp7 auto input1 = model->addOperand(&type0); in CreateModel() local
10 model->addOperation(ANEURALNETWORKS_MAXIMUM, {input0, input1}, {output0}); in CreateModel()
13 {input0, input1}, in CreateModel()
27 auto input1 = model->addOperand(&type0); in CreateModel_relaxed() local
30 model->addOperation(ANEURALNETWORKS_MAXIMUM, {input0, input1}, {output0}); in CreateModel_relaxed()
33 {input0, input1}, in CreateModel_relaxed()
49 auto input1 = model->addOperand(&type4); in CreateModel_float16() local
52 model->addOperation(ANEURALNETWORKS_MAXIMUM, {input0, input1}, {output0}); in CreateModel_float16()
55 {input0, input1}, in CreateModel_float16()
69 auto input1 = model->addOperand(&type5); in CreateModel_int32() local
[all …]
Dconcat_mixed_quant.model.cpp12 auto input1 = model->addOperand(&type4); 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()
42 auto input1 = model->addOperand(&type4); 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()
72 auto input1 = model->addOperand(&type4); 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()
102 auto input1 = model->addOperand(&type4); in CreateModel_dynamic_output_shape_quant8_2() local
[all …]
Dconcat_float_2.model.cpp9 auto input1 = model->addOperand(&type0); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
35 auto input1 = model->addOperand(&type0); 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_2.model.cpp9 auto input1 = model->addOperand(&type0); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
35 auto input1 = model->addOperand(&type0); 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.cpp9 auto input1 = model->addOperand(&type0); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
35 auto input1 = model->addOperand(&type0); 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.cpp9 auto input1 = model->addOperand(&type0); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
35 auto input1 = model->addOperand(&type0); 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.model.cpp9 auto input1 = model->addOperand(&type0); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
35 auto input1 = model->addOperand(&type0); 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.cpp9 auto input1 = model->addOperand(&type0); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
35 auto input1 = model->addOperand(&type0); 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_float_2_relaxed.model.cpp9 auto input1 = model->addOperand(&type0); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
37 auto input1 = model->addOperand(&type0); 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()
Dconcat_float_3_relaxed.model.cpp9 auto input1 = model->addOperand(&type0); in CreateModel() local
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel()
19 {input1, input2}, in CreateModel()
37 auto input1 = model->addOperand(&type0); 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()
/frameworks/ml/nn/runtime/test/specs/V1_2/
Dmaximum.mod.py17 def test(name, input0, input1, output0, input0_data, input1_data, output_data): argument
18 model = Model().Operation("MAXIMUM", input0, input1).To(output0)
22 input1: ["TENSOR_QUANT8_ASYMM", 1.0, 100],
28 input1: input1_data,
36 input1=Input("input1", "TENSOR_FLOAT32", "{3, 1, 2}"),
46 input1=Input("input1", "TENSOR_FLOAT32", "{2}"),
56 input1 = Input("input1", "TENSOR_QUANT8_ASYMM", "{2}, 1.0f, 128") variable
58 model = Model().Operation("MAXIMUM", input0, input1).To(output0)
62 input1: [128, 200],
Dminimum.mod.py17 def test(name, input0, input1, output0, input0_data, input1_data, output_data): argument
18 model = Model().Operation("MINIMUM", input0, input1).To(output0)
22 input1: ["TENSOR_QUANT8_ASYMM", 1.0, 100],
28 input1: input1_data,
36 input1=Input("input1", "TENSOR_FLOAT32", "{3, 1, 2}"),
46 input1=Input("input1", "TENSOR_FLOAT32", "{2}"),
56 input1 = Input("input1", "TENSOR_QUANT8_ASYMM", "{2}, 1.0f, 128") variable
58 model = Model().Operation("MINIMUM", input0, input1).To(output0)
62 input1: [128, 200],
Dless_equal.mod.py16 def test(name, input0, input1, output0, input0_data, input1_data, output_data, do_variations=True): argument
17 model = Model().Operation("LESS_EQUAL", input0, input1).To(output0)
20 input1: input1_data,
29 input1=Input("input1", "TENSOR_FLOAT32", "{3}"),
39 input1=Input("input1", "TENSOR_FLOAT32", "{2}"),
49 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 2.0, 128)),
60 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.0, 129)),
71 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.49725, 240)),
82 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.64771, 31)),
93 input1=Input("input1", "TENSOR_BOOL8", "{4}"),
Dgreater.mod.py16 def test(name, input0, input1, output0, input0_data, input1_data, output_data, do_variations=True): argument
17 model = Model().Operation("GREATER", input0, input1).To(output0)
20 input1: input1_data,
29 input1=Input("input1", "TENSOR_FLOAT32", "{3}"),
39 input1=Input("input1", "TENSOR_FLOAT32", "{2}"),
49 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 2.0, 128)),
60 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.0, 129)),
71 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.49725, 240)),
82 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.64771, 31)),
93 input1=Input("input1", "TENSOR_BOOL8", "{4}"),
Dnot_equal.mod.py16 def test(name, input0, input1, output0, input0_data, input1_data, output_data, do_variations=True): argument
17 model = Model().Operation("NOT_EQUAL", input0, input1).To(output0)
20 input1: input1_data,
29 input1=Input("input1", "TENSOR_FLOAT32", "{3}"),
39 input1=Input("input1", "TENSOR_FLOAT32", "{2}"),
49 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 2.0, 128)),
60 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.0, 129)),
71 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.49725, 240)),
82 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.64771, 31)),
93 input1=Input("input1", "TENSOR_BOOL8", "{4}"),
Dless.mod.py16 def test(name, input0, input1, output0, input0_data, input1_data, output_data, do_variations=True): argument
17 model = Model().Operation("LESS", input0, input1).To(output0)
20 input1: input1_data,
29 input1=Input("input1", "TENSOR_FLOAT32", "{3}"),
39 input1=Input("input1", "TENSOR_FLOAT32", "{2}"),
49 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 2.0, 128)),
60 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.0, 129)),
71 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.49725, 240)),
82 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.64771, 31)),
93 input1=Input("input1", "TENSOR_BOOL8", "{4}"),
Dequal.mod.py16 def test(name, input0, input1, output0, input0_data, input1_data, output_data, do_variations=True): argument
17 model = Model().Operation("EQUAL", input0, input1).To(output0)
20 input1: input1_data,
29 input1=Input("input1", "TENSOR_FLOAT32", "{3}"),
39 input1=Input("input1", "TENSOR_FLOAT32", "{2}"),
49 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 2.0, 128)),
60 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.0, 129)),
71 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.49725, 240)),
82 input1=Input("input1", ("TENSOR_QUANT8_ASYMM", [1], 1.64771, 31)),
93 input1=Input("input1", "TENSOR_BOOL8", "{4}"),
Dconcat_mixed_quant.mod.py20 input1 = Input("input1", "TENSOR_FLOAT32", "{2, 1, 2}") variable
26 model = Model().Operation("CONCATENATION", input0, input1, input2, input3, axis).To(output0)
31 input1: [1.1, 3.1, 4.1, 7.1],
37 input1: ["TENSOR_QUANT8_ASYMM", 0.05, 0],
46 input1: [1.1, 3.1, 4.1, 7.1],
52 input1: ["TENSOR_QUANT8_ASYMM", 0.05, 0],
Dsub_v1_2_broadcast.mod.py19 input1 = Input("input1", "TENSOR_FLOAT32", "{2, 2}") variable
23 model = Model().Operation("SUB", input0, input1, activation).To(output0)
33 input1: input1_values,
40 input1 = Input("input1", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.0, 0") variable
44 model = Model("quant8").Operation("SUB", input0, input1, activation).To(output0)
54 input1: input1_values,
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)
19 input1: ["TENSOR_QUANT8_ASYMM", 1.5, 129],
25 input1: input1_data,
33 input1=Input("input1", "TENSOR_FLOAT32", "{3}"),
45 input1=Input("input1", "TENSOR_FLOAT32", "{2, 2}"),
57 input1=Input("input1", "TENSOR_FLOAT32", "{2, 1, 2, 1, 2}"),
Dlogical_or.mod.py17 def test(name, input0, input1, output0, input0_data, input1_data, output_data): argument
18 model = Model().Operation("LOGICAL_OR", input0, input1).To(output0)
21 input1: input1_data,
28 input1=Input("input1", "TENSOR_BOOL8", "{1, 1, 1, 4}"),
38 input1=Input("input1", "TENSOR_BOOL8", "{1, 1}"),
Dlogical_and.mod.py17 def test(name, input0, input1, output0, input0_data, input1_data, output_data): argument
18 model = Model().Operation("LOGICAL_AND", input0, input1).To(output0)
21 input1: input1_data,
28 input1=Input("input1", "TENSOR_BOOL8", "{1, 1, 1, 4}"),
38 input1=Input("input1", "TENSOR_BOOL8", "{1, 1}"),
Dsub_v1_2.mod.py23 input1 = Input("input1", "TENSOR_FLOAT32", "{1, 2, 2, 1}") variable
27 model = Model().Operation("SUB", input0, input1, activation).To(output0)
31 input1: [2.0, -2.0, -4.0, 4.0],
39 input1 = Input("input1", "TENSOR_QUANT8_ASYMM", shape) variable
43 model = Model("quant8").Operation("SUB", input0, input1, activation).To(output0)
52 input1: input1_values,

1234