| /frameworks/ml/nn/runtime/test/generated/models/ |
| D | concat_quant8_1.model.cpp | 10 auto axis1 = model->addOperand(&type1); in CreateModel() local
14 model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); in CreateModel()
15 model->addOperation(ANEURALNETWORKS_CONCATENATION, {op1, op2, axis1}, {result}); in CreateModel()
35 auto axis1 = model->addOperand(&type1); in CreateModel_dynamic_output_shape() local
39 model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
40 model->addOperation(ANEURALNETWORKS_CONCATENATION, {op1, op2, axis1}, {result}); in CreateModel_dynamic_output_shape()
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| D | concat_float_3.model.cpp | 11 auto axis1 = model->addOperand(&type2); in CreateModel() local
15 model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); in CreateModel()
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel()
37 auto axis1 = model->addOperand(&type2); in CreateModel_dynamic_output_shape() local
41 model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
42 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel_dynamic_output_shape()
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| D | concat_quant8_3.model.cpp | 11 auto axis1 = model->addOperand(&type2); in CreateModel() local
15 model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); in CreateModel()
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel()
37 auto axis1 = model->addOperand(&type2); in CreateModel_dynamic_output_shape() local
41 model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
42 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel_dynamic_output_shape()
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| D | concat_float16_3.model.cpp | 11 auto axis1 = model->addOperand(&type2); in CreateModel() local
15 model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); in CreateModel()
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel()
37 auto axis1 = model->addOperand(&type2); in CreateModel_dynamic_output_shape() local
41 model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
42 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel_dynamic_output_shape()
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| D | concat_float_3_relaxed.model.cpp | 11 auto axis1 = model->addOperand(&type2); in CreateModel() local
15 model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); in CreateModel()
16 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel()
39 auto axis1 = model->addOperand(&type2); in CreateModel_dynamic_output_shape() local
43 model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); in CreateModel_dynamic_output_shape()
44 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); in CreateModel_dynamic_output_shape()
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| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| D | concat_quant8_1.mod.py | 21 axis1 = Int32Scalar("axis1", 1) variable
23 model = model.Operation("CONCATENATION", i1, i2, axis1).To(r)
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| D | concat_float_3.mod.py | 27 axis1 = Int32Scalar("axis1", 1) variable
29 model = model.Operation("CONCATENATION", input1, input2, axis1).To(output)
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| D | concat_quant8_3.mod.py | 27 axis1 = Int32Scalar("axis1", 1) variable
29 model = model.Operation("CONCATENATION", input1, input2, axis1).To(output)
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| /frameworks/ml/nn/runtime/test/specs/V1_2/ |
| D | concat_float16_3.mod.py | 27 axis1 = Int32Scalar("axis1", 1) variable
29 model = model.Operation("CONCATENATION", input1, input2, axis1).To(output)
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| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| D | concat_float_3_relaxed.mod.py | 27 axis1 = Int32Scalar("axis1", 1) variable
29 model = model.Operation("CONCATENATION", input1, input2, axis1).To(output)
|