/frameworks/ml/nn/runtime/test/specs/V1_1/ |
H A D | transpose_float_1_relaxed.mod.py | 17 model = Model() variable 22 model = model.Operation("TRANSPOSE", i1, perms).To(output) variable 23 model = model.RelaxedExecution(True) variable
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H A D | transpose_quant8_1.mod.py | 0 model = Model() 6 model = model.Operation("TRANSPOSE", i1, perms).To(output) variable 1 model = Model() variable
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H A D | transpose_relaxed.mod.py | 17 model = Model() variable 22 model = model.Operation("TRANSPOSE", i1, perms).To(output) variable 23 model = model.RelaxedExecution(True) variable
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/frameworks/ml/nn/tools/test_generator/tests/P_full/ |
H A D | addfloat.mod.py | 0 # model 2 model = Model() variable 7 model = model.Operation("ADD", i1, i2, b0).To(i3) variable
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/frameworks/ml/nn/tools/test_generator/tests/P_quantized_avgpool/ |
H A D | averpoolfloat.mod.py | 0 # model 2 model = Model() variable 7 model = model.Operation("AVERAGE_POOL", i1, cons1, cons1, cons1, cons1, cons1, act).To(o) variable
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/frameworks/ml/nn/tools/test_generator/tests/P_vts_full/ |
H A D | vts_full.mod.py | 3 # model 4 model = Model() variable 10 model.Operation("ADD", i0, p0, b0).To(o)
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/frameworks/support/work/workmanager/src/main/java/androidx/work/impl/model/ |
H A D | Dependency.java | 17 package androidx.work.impl.model;
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H A D | WorkName.java | 17 package androidx.work.impl.model;
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H A D | WorkTag.java | 17 package androidx.work.impl.model;
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/frameworks/base/packages/PrintSpooler/src/com/android/printspooler/model/ |
H A D | OpenDocumentCallback.java | 17 package com.android.printspooler.model;
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/frameworks/ml/nn/runtime/test/ |
H A D | Bridge.cpp | 30 void graphDump(const char* name, const ModelBuilder* model, std::ostream& outStream) { argument 32 model->setHidlModel(&hidlModel);
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/frameworks/ml/nn/runtime/test/generated/models/ |
H A D | add.model.cpp | 2 void CreateModel(Model *model) { argument 6 auto op1 = model->addOperand(&type0); 7 auto op2 = model->addOperand(&type0); 8 auto act = model->addOperand(&type1); 9 auto op3 = model->addOperand(&type0); 12 model->setOperandValue(act, act_init, sizeof(int32_t) * 1); 13 model->addOperation(ANEURALNETWORKS_ADD, {op1, op2, act}, {op3}); 15 model->identifyInputsAndOutputs( 18 assert(model->isValid());
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H A D | add_broadcast_quant8.model.cpp | 2 void CreateModel(Model *model) { argument 7 auto op1 = model->addOperand(&type0); 8 auto op2 = model->addOperand(&type1); 9 auto act = model->addOperand(&type2); 10 auto op3 = model->addOperand(&type1); 13 model->setOperandValue(act, act_init, sizeof(int32_t) * 1); 14 model->addOperation(ANEURALNETWORKS_ADD, {op1, op2, act}, {op3}); 16 model->identifyInputsAndOutputs( 19 assert(model->isValid());
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H A D | add_quant8.model.cpp | 2 void CreateModel(Model *model) { argument 7 auto op1 = model->addOperand(&type0); 8 auto op2 = model->addOperand(&type1); 9 auto act = model->addOperand(&type2); 10 auto op3 = model->addOperand(&type1); 13 model->setOperandValue(act, act_init, sizeof(int32_t) * 1); 14 model->addOperation(ANEURALNETWORKS_ADD, {op1, op2, act}, {op3}); 16 model->identifyInputsAndOutputs( 19 assert(model->isValid());
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H A D | add_relaxed.model.cpp | 2 void CreateModel(Model *model) { argument 6 auto op1 = model->addOperand(&type0); 7 auto op2 = model->addOperand(&type0); 8 auto act = model->addOperand(&type1); 9 auto op3 = model->addOperand(&type0); 12 model->setOperandValue(act, act_init, sizeof(int32_t) * 1); 13 model->addOperation(ANEURALNETWORKS_ADD, {op1, op2, act}, {op3}); 15 model->identifyInputsAndOutputs( 19 model->relaxComputationFloat32toFloat16(true); 20 assert(model [all...] |
H A D | batch_to_space.model.cpp | 2 void CreateModel(Model *model) { argument 7 auto input = model->addOperand(&type0); 8 auto block_size = model->addOperand(&type1); 9 auto output = model->addOperand(&type2); 12 model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); 13 model->addOperation(ANEURALNETWORKS_BATCH_TO_SPACE_ND, {input, block_size}, {output}); 15 model->identifyInputsAndOutputs( 18 assert(model->isValid());
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H A D | batch_to_space_float_1.model.cpp | 2 void CreateModel(Model *model) { argument 7 auto input = model->addOperand(&type0); 8 auto block_size = model->addOperand(&type1); 9 auto output = model->addOperand(&type2); 12 model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); 13 model->addOperation(ANEURALNETWORKS_BATCH_TO_SPACE_ND, {input, block_size}, {output}); 15 model->identifyInputsAndOutputs( 18 assert(model->isValid());
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H A D | batch_to_space_float_1_relaxed.model.cpp | 2 void CreateModel(Model *model) { argument 7 auto input = model->addOperand(&type0); 8 auto block_size = model->addOperand(&type1); 9 auto output = model->addOperand(&type2); 12 model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); 13 model->addOperation(ANEURALNETWORKS_BATCH_TO_SPACE_ND, {input, block_size}, {output}); 15 model->identifyInputsAndOutputs( 19 model->relaxComputationFloat32toFloat16(true); 20 assert(model->isValid());
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H A D | batch_to_space_quant8_1.model.cpp | 2 void CreateModel(Model *model) { argument 7 auto input = model->addOperand(&type0); 8 auto block_size = model->addOperand(&type1); 9 auto output = model->addOperand(&type2); 12 model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); 13 model->addOperation(ANEURALNETWORKS_BATCH_TO_SPACE_ND, {input, block_size}, {output}); 15 model->identifyInputsAndOutputs( 18 assert(model->isValid());
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H A D | batch_to_space_relaxed.model.cpp | 2 void CreateModel(Model *model) { argument 7 auto input = model->addOperand(&type0); 8 auto block_size = model->addOperand(&type1); 9 auto output = model->addOperand(&type2); 12 model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); 13 model->addOperation(ANEURALNETWORKS_BATCH_TO_SPACE_ND, {input, block_size}, {output}); 15 model->identifyInputsAndOutputs( 19 model->relaxComputationFloat32toFloat16(true); 20 assert(model->isValid());
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H A D | concat_float_1.model.cpp | 2 void CreateModel(Model *model) { argument 7 auto op1 = model->addOperand(&type0); 8 auto op2 = model->addOperand(&type0); 9 auto axis0 = model->addOperand(&type1); 10 auto result = model->addOperand(&type2); 13 model->setOperandValue(axis0, axis0_init, sizeof(int32_t) * 1); 14 model->addOperation(ANEURALNETWORKS_CONCATENATION, {op1, op2, axis0}, {result}); 16 model->identifyInputsAndOutputs( 19 assert(model->isValid());
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H A D | concat_float_1_relaxed.model.cpp | 2 void CreateModel(Model *model) { argument 7 auto op1 = model->addOperand(&type0); 8 auto op2 = model->addOperand(&type0); 9 auto axis0 = model->addOperand(&type1); 10 auto result = model->addOperand(&type2); 13 model->setOperandValue(axis0, axis0_init, sizeof(int32_t) * 1); 14 model->addOperation(ANEURALNETWORKS_CONCATENATION, {op1, op2, axis0}, {result}); 16 model->identifyInputsAndOutputs( 20 model->relaxComputationFloat32toFloat16(true); 21 assert(model [all...] |
H A D | concat_float_2.model.cpp | 2 void CreateModel(Model *model) { argument 8 auto input1 = model->addOperand(&type0); 9 auto input2 = model->addOperand(&type1); 10 auto axis0 = model->addOperand(&type2); 11 auto output = model->addOperand(&type3); 14 model->setOperandValue(axis0, axis0_init, sizeof(int32_t) * 1); 15 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); 17 model->identifyInputsAndOutputs( 20 assert(model->isValid());
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H A D | concat_float_2_relaxed.model.cpp | 2 void CreateModel(Model *model) { argument 8 auto input1 = model->addOperand(&type0); 9 auto input2 = model->addOperand(&type1); 10 auto axis0 = model->addOperand(&type2); 11 auto output = model->addOperand(&type3); 14 model->setOperandValue(axis0, axis0_init, sizeof(int32_t) * 1); 15 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); 17 model->identifyInputsAndOutputs( 21 model->relaxComputationFloat32toFloat16(true); 22 assert(model [all...] |
H A D | concat_float_3.model.cpp | 2 void CreateModel(Model *model) { argument 8 auto input1 = model->addOperand(&type0); 9 auto input2 = model->addOperand(&type1); 10 auto axis1 = model->addOperand(&type2); 11 auto output = model->addOperand(&type3); 14 model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); 15 model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); 17 model->identifyInputsAndOutputs( 20 assert(model->isValid());
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