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android / platform / packages / modules / NeuralNetworks / c49850065cf80a64f1a99307fdd68f1301fe2582 / . / runtime / test / generated / spec_V1_0 / cts / conv_1_h3_w2_SAME.model.cpp
blob: 1f644463ab3d133532438834bd89691e2a9bba45 [file] [log] [blame]
// Generated from conv_1_h3_w2_SAME.mod.py
// DO NOT EDIT
// clang-format off
#include "TestGenerated.h"
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type2(Type::TENSOR_FLOAT32, {1, 8, 8, 1});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type2);
// Phase 2, operations
static float op0_init[] = {-0.966213f, -0.467474f, -0.82203f, -0.579455f, 0.0278809f, -0.79946f, -0.684259f, 0.563238f, 0.37289f, 0.738216f, 0.386045f, -0.917775f, 0.184325f, -0.270568f, 0.82236f, 0.0973683f, -0.941308f, -0.144706f};
model->setOperandValue(op0, op0_init, sizeof(float) * 18);
static float op1_init[] = {0.0f};
model->setOperandValue(op1, op1_init, sizeof(float) * 1);
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2},
{op3});
assert(model->isValid());
}
bool is_ignored(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_dynamic_output_shape(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type5);
// Phase 2, operations
static float op0_init[] = {-0.966213f, -0.467474f, -0.82203f, -0.579455f, 0.0278809f, -0.79946f, -0.684259f, 0.563238f, 0.37289f, 0.738216f, 0.386045f, -0.917775f, 0.184325f, -0.270568f, 0.82236f, 0.0973683f, -0.941308f, -0.144706f};
model->setOperandValue(op0, op0_init, sizeof(float) * 18);
static float op1_init[] = {0.0f};
model->setOperandValue(op1, op1_init, sizeof(float) * 1);
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2},
{op3});
assert(model->isValid());
}
bool is_ignored_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_all_inputs_as_internal(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type2(Type::TENSOR_FLOAT32, {1, 8, 8, 1});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type2);
auto op2_tmp = model->addOperand(&type1);
auto dummy = model->addOperand(&type4);
auto param = model->addOperand(&type0);
// Phase 2, operations
static float op0_init[] = {-0.966213f, -0.467474f, -0.82203f, -0.579455f, 0.0278809f, -0.79946f, -0.684259f, 0.563238f, 0.37289f, 0.738216f, 0.386045f, -0.917775f, 0.184325f, -0.270568f, 0.82236f, 0.0973683f, -0.941308f, -0.144706f};
model->setOperandValue(op0, op0_init, sizeof(float) * 18);
static float op1_init[] = {0.0f};
model->setOperandValue(op1, op1_init, sizeof(float) * 1);
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
static float dummy_init[] = {0.0f};
model->setOperandValue(dummy, dummy_init, sizeof(float) * 1);
static int32_t param_init[] = {0};
model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {op2_tmp, dummy, param}, {op2});
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2_tmp},
{op3});
assert(model->isValid());
}
bool is_ignored_all_inputs_as_internal(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_all_inputs_as_internal_dynamic_output_shape(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type5);
auto op2_tmp = model->addOperand(&type1);
auto dummy1 = model->addOperand(&type4);
auto param1 = model->addOperand(&type0);
// Phase 2, operations
static float op0_init[] = {-0.966213f, -0.467474f, -0.82203f, -0.579455f, 0.0278809f, -0.79946f, -0.684259f, 0.563238f, 0.37289f, 0.738216f, 0.386045f, -0.917775f, 0.184325f, -0.270568f, 0.82236f, 0.0973683f, -0.941308f, -0.144706f};
model->setOperandValue(op0, op0_init, sizeof(float) * 18);
static float op1_init[] = {0.0f};
model->setOperandValue(op1, op1_init, sizeof(float) * 1);
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
static float dummy1_init[] = {0.0f};
model->setOperandValue(dummy1, dummy1_init, sizeof(float) * 1);
static int32_t param1_init[] = {0};
model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {op2_tmp, dummy1, param1}, {op2});
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2_tmp},
{op3});
assert(model->isValid());
}
bool is_ignored_all_inputs_as_internal_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_all_tensors_as_inputs(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type2(Type::TENSOR_FLOAT32, {1, 8, 8, 1});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type2);
// Phase 2, operations
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2, op0, op1},
{op3});
assert(model->isValid());
}
bool is_ignored_all_tensors_as_inputs(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_all_tensors_as_inputs_dynamic_output_shape(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type5);
// Phase 2, operations
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2, op0, op1},
{op3});
assert(model->isValid());
}
bool is_ignored_all_tensors_as_inputs_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_all_tensors_as_inputs_all_inputs_as_internal(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type2(Type::TENSOR_FLOAT32, {1, 8, 8, 1});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type2);
auto op2_tmp = model->addOperand(&type1);
auto dummy2 = model->addOperand(&type4);
auto param2 = model->addOperand(&type0);
auto op0_tmp = model->addOperand(&type3);
auto dummy3 = model->addOperand(&type4);
auto param3 = model->addOperand(&type0);
auto op1_tmp = model->addOperand(&type4);
auto dummy4 = model->addOperand(&type4);
auto param4 = model->addOperand(&type0);
// Phase 2, operations
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
static float dummy2_init[] = {0.0f};
model->setOperandValue(dummy2, dummy2_init, sizeof(float) * 1);
static int32_t param2_init[] = {0};
model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1);
static float dummy3_init[] = {0.0f};
model->setOperandValue(dummy3, dummy3_init, sizeof(float) * 1);
static int32_t param3_init[] = {0};
model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
static float dummy4_init[] = {0.0f};
model->setOperandValue(dummy4, dummy4_init, sizeof(float) * 1);
static int32_t param4_init[] = {0};
model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {op2_tmp, dummy2, param2}, {op2});
model->addOperation(ANEURALNETWORKS_ADD, {op0_tmp, dummy3, param3}, {op0});
model->addOperation(ANEURALNETWORKS_ADD, {op1_tmp, dummy4, param4}, {op1});
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2_tmp, op0_tmp, op1_tmp},
{op3});
assert(model->isValid());
}
bool is_ignored_all_tensors_as_inputs_all_inputs_as_internal(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_all_tensors_as_inputs_all_inputs_as_internal_dynamic_output_shape(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type5);
auto op2_tmp = model->addOperand(&type1);
auto dummy5 = model->addOperand(&type4);
auto param5 = model->addOperand(&type0);
auto op0_tmp = model->addOperand(&type3);
auto dummy6 = model->addOperand(&type4);
auto param6 = model->addOperand(&type0);
auto op1_tmp = model->addOperand(&type4);
auto dummy7 = model->addOperand(&type4);
auto param7 = model->addOperand(&type0);
// Phase 2, operations
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
static float dummy5_init[] = {0.0f};
model->setOperandValue(dummy5, dummy5_init, sizeof(float) * 1);
static int32_t param5_init[] = {0};
model->setOperandValue(param5, param5_init, sizeof(int32_t) * 1);
static float dummy6_init[] = {0.0f};
model->setOperandValue(dummy6, dummy6_init, sizeof(float) * 1);
static int32_t param6_init[] = {0};
model->setOperandValue(param6, param6_init, sizeof(int32_t) * 1);
static float dummy7_init[] = {0.0f};
model->setOperandValue(dummy7, dummy7_init, sizeof(float) * 1);
static int32_t param7_init[] = {0};
model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {op2_tmp, dummy5, param5}, {op2});
model->addOperation(ANEURALNETWORKS_ADD, {op0_tmp, dummy6, param6}, {op0});
model->addOperation(ANEURALNETWORKS_ADD, {op1_tmp, dummy7, param7}, {op1});
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2_tmp, op0_tmp, op1_tmp},
{op3});
assert(model->isValid());
}
bool is_ignored_all_tensors_as_inputs_all_inputs_as_internal_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_2(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type2(Type::TENSOR_FLOAT32, {1, 8, 8, 1});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type2);
// Phase 2, operations
static float op0_init[] = {-0.966213f, -0.467474f, -0.82203f, -0.579455f, 0.0278809f, -0.79946f, -0.684259f, 0.563238f, 0.37289f, 0.738216f, 0.386045f, -0.917775f, 0.184325f, -0.270568f, 0.82236f, 0.0973683f, -0.941308f, -0.144706f};
model->setOperandValue(op0, op0_init, sizeof(float) * 18);
static float op1_init[] = {0.0f};
model->setOperandValue(op1, op1_init, sizeof(float) * 1);
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2},
{op3});
assert(model->isValid());
}
bool is_ignored_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type5);
// Phase 2, operations
static float op0_init[] = {-0.966213f, -0.467474f, -0.82203f, -0.579455f, 0.0278809f, -0.79946f, -0.684259f, 0.563238f, 0.37289f, 0.738216f, 0.386045f, -0.917775f, 0.184325f, -0.270568f, 0.82236f, 0.0973683f, -0.941308f, -0.144706f};
model->setOperandValue(op0, op0_init, sizeof(float) * 18);
static float op1_init[] = {0.0f};
model->setOperandValue(op1, op1_init, sizeof(float) * 1);
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2},
{op3});
assert(model->isValid());
}
bool is_ignored_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_all_inputs_as_internal_2(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type2(Type::TENSOR_FLOAT32, {1, 8, 8, 1});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type2);
auto op2_tmp = model->addOperand(&type1);
auto dummy8 = model->addOperand(&type4);
auto param8 = model->addOperand(&type0);
// Phase 2, operations
static float op0_init[] = {-0.966213f, -0.467474f, -0.82203f, -0.579455f, 0.0278809f, -0.79946f, -0.684259f, 0.563238f, 0.37289f, 0.738216f, 0.386045f, -0.917775f, 0.184325f, -0.270568f, 0.82236f, 0.0973683f, -0.941308f, -0.144706f};
model->setOperandValue(op0, op0_init, sizeof(float) * 18);
static float op1_init[] = {0.0f};
model->setOperandValue(op1, op1_init, sizeof(float) * 1);
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
static float dummy8_init[] = {0.0f};
model->setOperandValue(dummy8, dummy8_init, sizeof(float) * 1);
static int32_t param8_init[] = {0};
model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {op2_tmp, dummy8, param8}, {op2});
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2_tmp},
{op3});
assert(model->isValid());
}
bool is_ignored_all_inputs_as_internal_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_all_inputs_as_internal_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type5);
auto op2_tmp = model->addOperand(&type1);
auto dummy9 = model->addOperand(&type4);
auto param9 = model->addOperand(&type0);
// Phase 2, operations
static float op0_init[] = {-0.966213f, -0.467474f, -0.82203f, -0.579455f, 0.0278809f, -0.79946f, -0.684259f, 0.563238f, 0.37289f, 0.738216f, 0.386045f, -0.917775f, 0.184325f, -0.270568f, 0.82236f, 0.0973683f, -0.941308f, -0.144706f};
model->setOperandValue(op0, op0_init, sizeof(float) * 18);
static float op1_init[] = {0.0f};
model->setOperandValue(op1, op1_init, sizeof(float) * 1);
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
static float dummy9_init[] = {0.0f};
model->setOperandValue(dummy9, dummy9_init, sizeof(float) * 1);
static int32_t param9_init[] = {0};
model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {op2_tmp, dummy9, param9}, {op2});
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2_tmp},
{op3});
assert(model->isValid());
}
bool is_ignored_all_inputs_as_internal_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_all_tensors_as_inputs_2(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type2(Type::TENSOR_FLOAT32, {1, 8, 8, 1});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type2);
// Phase 2, operations
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2, op0, op1},
{op3});
assert(model->isValid());
}
bool is_ignored_all_tensors_as_inputs_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_all_tensors_as_inputs_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type5);
// Phase 2, operations
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2, op0, op1},
{op3});
assert(model->isValid());
}
bool is_ignored_all_tensors_as_inputs_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_all_tensors_as_inputs_all_inputs_as_internal_2(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type2(Type::TENSOR_FLOAT32, {1, 8, 8, 1});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type2);
auto op2_tmp = model->addOperand(&type1);
auto dummy10 = model->addOperand(&type4);
auto param10 = model->addOperand(&type0);
auto op0_tmp = model->addOperand(&type3);
auto dummy11 = model->addOperand(&type4);
auto param11 = model->addOperand(&type0);
auto op1_tmp = model->addOperand(&type4);
auto dummy12 = model->addOperand(&type4);
auto param12 = model->addOperand(&type0);
// Phase 2, operations
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
static float dummy10_init[] = {0.0f};
model->setOperandValue(dummy10, dummy10_init, sizeof(float) * 1);
static int32_t param10_init[] = {0};
model->setOperandValue(param10, param10_init, sizeof(int32_t) * 1);
static float dummy11_init[] = {0.0f};
model->setOperandValue(dummy11, dummy11_init, sizeof(float) * 1);
static int32_t param11_init[] = {0};
model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1);
static float dummy12_init[] = {0.0f};
model->setOperandValue(dummy12, dummy12_init, sizeof(float) * 1);
static int32_t param12_init[] = {0};
model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {op2_tmp, dummy10, param10}, {op2});
model->addOperation(ANEURALNETWORKS_ADD, {op0_tmp, dummy11, param11}, {op0});
model->addOperation(ANEURALNETWORKS_ADD, {op1_tmp, dummy12, param12}, {op1});
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2_tmp, op0_tmp, op1_tmp},
{op3});
assert(model->isValid());
}
bool is_ignored_all_tensors_as_inputs_all_inputs_as_internal_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME
namespace generated_tests::conv_1_h3_w2_SAME {
void CreateModel_all_tensors_as_inputs_all_inputs_as_internal_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::INT32, {});
OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3});
OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto op2 = model->addOperand(&type1);
auto op0 = model->addOperand(&type3);
auto op1 = model->addOperand(&type4);
auto b4 = model->addOperand(&type0);
auto b5 = model->addOperand(&type0);
auto b6 = model->addOperand(&type0);
auto b7 = model->addOperand(&type0);
auto op3 = model->addOperand(&type5);
auto op2_tmp = model->addOperand(&type1);
auto dummy13 = model->addOperand(&type4);
auto param13 = model->addOperand(&type0);
auto op0_tmp = model->addOperand(&type3);
auto dummy14 = model->addOperand(&type4);
auto param14 = model->addOperand(&type0);
auto op1_tmp = model->addOperand(&type4);
auto dummy15 = model->addOperand(&type4);
auto param15 = model->addOperand(&type0);
// Phase 2, operations
static int32_t b4_init[] = {1};
model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1);
static int32_t b5_init[] = {1};
model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1);
static int32_t b6_init[] = {1};
model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1);
static int32_t b7_init[] = {0};
model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1);
static float dummy13_init[] = {0.0f};
model->setOperandValue(dummy13, dummy13_init, sizeof(float) * 1);
static int32_t param13_init[] = {0};
model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1);
static float dummy14_init[] = {0.0f};
model->setOperandValue(dummy14, dummy14_init, sizeof(float) * 1);
static int32_t param14_init[] = {0};
model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1);
static float dummy15_init[] = {0.0f};
model->setOperandValue(dummy15, dummy15_init, sizeof(float) * 1);
static int32_t param15_init[] = {0};
model->setOperandValue(param15, param15_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {op2_tmp, dummy13, param13}, {op2});
model->addOperation(ANEURALNETWORKS_ADD, {op0_tmp, dummy14, param14}, {op0});
model->addOperation(ANEURALNETWORKS_ADD, {op1_tmp, dummy15, param15}, {op1});
model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{op2_tmp, op0_tmp, op1_tmp},
{op3});
assert(model->isValid());
}
bool is_ignored_all_tensors_as_inputs_all_inputs_as_internal_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::conv_1_h3_w2_SAME