| /frameworks/ml/nn/tools/test_generator/tests/P_quantized_avgpool/ |
| averpoolfloat.mod.py | 4 cons1 = Int32Scalar("cons1", 1) variable
7 model = model.Operation("AVERAGE_POOL", i1, cons1, cons1, cons1, cons1, cons1, act).To(o)
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| /frameworks/ml/nn/runtime/test/specs/ |
| avg_pool_float_1.mod.py | 20 cons1 = Int32Scalar("cons1", 1) variable
24 model = model.Operation("AVERAGE_POOL_2D", i1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act).To(i3)
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| avg_pool_quant8_1.mod.py | 20 cons1 = Int32Scalar("cons1", 1) variable
24 model = model.Operation("AVERAGE_POOL_2D", i1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act).To(o)
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| avg_pool_quant8_4.mod.py | 20 cons1 = Int32Scalar("cons1", 1) variable
24 model = model.Operation("AVERAGE_POOL_2D", i1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act2).To(o)
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| l2_pool_float.mod.py | 19 cons1 = Int32Scalar("cons1", 1) variable
23 model = model.Operation("L2_POOL_2D", i1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act).To(i3)
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| max_pool_float_1.mod.py | 20 cons1 = Int32Scalar("cons1", 1) variable
24 model = model.Operation("MAX_POOL_2D", i1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act).To(i3)
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| max_pool_quant8_1.mod.py | 20 cons1 = Int32Scalar("cons1", 1) variable
24 model = model.Operation("MAX_POOL_2D", i1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act).To(i3)
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| /frameworks/ml/nn/runtime/test/generated/models/ |
| avg_pool_float_1.model.cpp | 7 auto cons1 = model->addOperand(&type1); local
13 model->setOperandValue(cons1, cons1_init, sizeof(int32_t) * 1);
18 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {op1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act}, {op3});
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| avg_pool_quant8_1.model.cpp | 7 auto cons1 = model->addOperand(&type1); local
13 model->setOperandValue(cons1, cons1_init, sizeof(int32_t) * 1);
18 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {op1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act}, {op3});
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| avg_pool_quant8_4.model.cpp | 7 auto cons1 = model->addOperand(&type1); local
13 model->setOperandValue(cons1, cons1_init, sizeof(int32_t) * 1);
18 model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {op1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, relu1_activitation}, {op3});
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| l2_pool_float.model.cpp | 7 auto cons1 = model->addOperand(&type1); local
13 model->setOperandValue(cons1, cons1_init, sizeof(int32_t) * 1);
18 model->addOperation(ANEURALNETWORKS_L2_POOL_2D, {op1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act}, {op3});
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| max_pool_float_1.model.cpp | 7 auto cons1 = model->addOperand(&type1); local
13 model->setOperandValue(cons1, cons1_init, sizeof(int32_t) * 1);
18 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {op1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act}, {op3});
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| max_pool_quant8_1.model.cpp | 7 auto cons1 = model->addOperand(&type1); local
13 model->setOperandValue(cons1, cons1_init, sizeof(int32_t) * 1);
18 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {op1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act}, {op3});
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| /external/v8/src/runtime/ |
| runtime-strings.cc | 91 Handle<String> cons1; local
93 isolate, cons1, isolate->factory()->NewConsString(first, replace),
97 return isolate->factory()->NewConsString(cons1, second);
|
