| /frameworks/ml/nn/runtime/test/generated/models/ |
| concat_float_3.model.cpp | 2 void CreateModel(Model *model) {
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())
[all...] |
| concat_quant8_1.model.cpp | 2 void CreateModel(Model *model) {
7 auto op1 = model->addOperand(&type0);
8 auto op2 = model->addOperand(&type0);
9 auto axis1 = model->addOperand(&type1);
10 auto result = model->addOperand(&type2);
13 model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1);
14 model->addOperation(ANEURALNETWORKS_CONCATENATION, {op1, op2, axis1}, {result});
16 model->identifyInputsAndOutputs(
19 assert(model->isValid())
[all...] |
| concat_quant8_2.model.cpp | 2 void CreateModel(Model *model) {
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())
[all...] |
| concat_quant8_3.model.cpp | 2 void CreateModel(Model *model) {
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())
[all...] |
| depth_to_space_float_1.model.cpp | 2 void CreateModel(Model *model) {
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) * 1);
13 model->addOperation(ANEURALNETWORKS_DEPTH_TO_SPACE, {input, block_size}, {output});
15 model->identifyInputsAndOutputs(
18 assert(model->isValid());
|
| depth_to_space_float_2.model.cpp | 2 void CreateModel(Model *model) {
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) * 1);
13 model->addOperation(ANEURALNETWORKS_DEPTH_TO_SPACE, {input, block_size}, {output});
15 model->identifyInputsAndOutputs(
18 assert(model->isValid());
|
| depth_to_space_float_3.model.cpp | 2 void CreateModel(Model *model) {
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) * 1);
13 model->addOperation(ANEURALNETWORKS_DEPTH_TO_SPACE, {input, block_size}, {output});
15 model->identifyInputsAndOutputs(
18 assert(model->isValid());
|
| depth_to_space_quant8_1.model.cpp | 2 void CreateModel(Model *model) {
7 auto input = model->addOperand(&type0);
8 auto radius = model->addOperand(&type1);
9 auto output = model->addOperand(&type2);
12 model->setOperandValue(radius, radius_init, sizeof(int32_t) * 1);
13 model->addOperation(ANEURALNETWORKS_DEPTH_TO_SPACE, {input, radius}, {output});
15 model->identifyInputsAndOutputs(
18 assert(model->isValid());
|
| depth_to_space_quant8_2.model.cpp | 2 void CreateModel(Model *model) {
7 auto input = model->addOperand(&type0);
8 auto radius = model->addOperand(&type1);
9 auto output = model->addOperand(&type2);
12 model->setOperandValue(radius, radius_init, sizeof(int32_t) * 1);
13 model->addOperation(ANEURALNETWORKS_DEPTH_TO_SPACE, {input, radius}, {output});
15 model->identifyInputsAndOutputs(
18 assert(model->isValid());
|
| fully_connected_float_large_weights_as_inputs.model.cpp | 2 void CreateModel(Model *model) {
8 auto op1 = model->addOperand(&type0);
9 auto op2 = model->addOperand(&type0);
10 auto b0 = model->addOperand(&type1);
11 auto op3 = model->addOperand(&type2);
12 auto act = model->addOperand(&type3);
15 model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
16 model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3});
18 model->identifyInputsAndOutputs
[all...] |
| fully_connected_float_weights_as_inputs.model.cpp | 2 void CreateModel(Model *model) {
8 auto op1 = model->addOperand(&type0);
9 auto op2 = model->addOperand(&type1);
10 auto b0 = model->addOperand(&type2);
11 auto op3 = model->addOperand(&type0);
12 auto act = model->addOperand(&type3);
15 model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
16 model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3});
18 model->identifyInputsAndOutputs
[all...] |
| fully_connected_quant8_large_weights_as_inputs.model.cpp | 2 void CreateModel(Model *model) {
8 auto op1 = model->addOperand(&type0);
9 auto op2 = model->addOperand(&type0);
10 auto b0 = model->addOperand(&type1);
11 auto op3 = model->addOperand(&type2);
12 auto act = model->addOperand(&type3);
15 model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
16 model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3});
18 model->identifyInputsAndOutputs
[all...] |
| fully_connected_quant8_weights_as_inputs.model.cpp | 2 void CreateModel(Model *model) {
9 auto op1 = model->addOperand(&type0);
10 auto op2 = model->addOperand(&type1);
11 auto b0 = model->addOperand(&type2);
12 auto op3 = model->addOperand(&type3);
13 auto act = model->addOperand(&type4);
16 model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
17 model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3});
19 model->identifyInputsAndOutputs
[all...] |
| hashtable_lookup_float.model.cpp | 2 void CreateModel(Model *model) {
9 auto lookup = model->addOperand(&type0);
10 auto key = model->addOperand(&type1);
11 auto value = model->addOperand(&type2);
12 auto output = model->addOperand(&type3);
13 auto hits = model->addOperand(&type4);
15 model->addOperation(ANEURALNETWORKS_HASHTABLE_LOOKUP, {lookup, key, value}, {output, hits});
17 model->identifyInputsAndOutputs(
20 assert(model->isValid())
[all...] |
| hashtable_lookup_quant8.model.cpp | 2 void CreateModel(Model *model) {
9 auto lookup = model->addOperand(&type0);
10 auto key = model->addOperand(&type1);
11 auto value = model->addOperand(&type2);
12 auto output = model->addOperand(&type3);
13 auto hits = model->addOperand(&type4);
15 model->addOperation(ANEURALNETWORKS_HASHTABLE_LOOKUP, {lookup, key, value}, {output, hits});
17 model->identifyInputsAndOutputs(
20 assert(model->isValid())
[all...] |
| lsh_projection_weights_as_inputs.model.cpp | 2 void CreateModel(Model *model) {
9 auto hash = model->addOperand(&type0);
10 auto lookup = model->addOperand(&type1);
11 auto weight = model->addOperand(&type2);
12 auto type_param = model->addOperand(&type3);
13 auto output = model->addOperand(&type4);
15 model->addOperation(ANEURALNETWORKS_LSH_PROJECTION, {hash, lookup, weight, type_param}, {output});
17 model->identifyInputsAndOutputs(
20 assert(model->isValid())
[all...] |
| mul.model.cpp | 2 void CreateModel(Model *model) {
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_MUL, {op1, op2, act}, {op3});
15 model->identifyInputsAndOutputs(
18 assert(model->isValid())
[all...] |
| mul_broadcast_quant8.model.cpp | 2 void CreateModel(Model *model) {
8 auto op1 = model->addOperand(&type0);
9 auto op2 = model->addOperand(&type1);
10 auto act = model->addOperand(&type2);
11 auto op3 = model->addOperand(&type3);
14 model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
15 model->addOperation(ANEURALNETWORKS_MUL, {op1, op2, act}, {op3});
17 model->identifyInputsAndOutputs(
20 assert(model->isValid())
[all...] |
| mul_quant8.model.cpp | 2 void CreateModel(Model *model) {
7 auto op1 = model->addOperand(&type0);
8 auto op2 = model->addOperand(&type0);
9 auto act = model->addOperand(&type1);
10 auto op3 = model->addOperand(&type2);
13 model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
14 model->addOperation(ANEURALNETWORKS_MUL, {op1, op2, act}, {op3});
16 model->identifyInputsAndOutputs(
19 assert(model->isValid())
[all...] |
| mul_relu.model.cpp | 2 void CreateModel(Model *model) {
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_MUL, {op1, op2, act}, {op3});
15 model->identifyInputsAndOutputs(
18 assert(model->isValid())
[all...] |
| reshape.model.cpp | 2 void CreateModel(Model *model) {
7 auto op1 = model->addOperand(&type0);
8 auto op2 = model->addOperand(&type1);
9 auto op3 = model->addOperand(&type2);
12 model->setOperandValue(op2, op2_init, sizeof(int32_t) * 1);
13 model->addOperation(ANEURALNETWORKS_RESHAPE, {op1, op2}, {op3});
15 model->identifyInputsAndOutputs(
18 assert(model->isValid());
|
| reshape_quant8.model.cpp | 2 void CreateModel(Model *model) {
7 auto op1 = model->addOperand(&type0);
8 auto op2 = model->addOperand(&type1);
9 auto op3 = model->addOperand(&type2);
12 model->setOperandValue(op2, op2_init, sizeof(int32_t) * 1);
13 model->addOperation(ANEURALNETWORKS_RESHAPE, {op1, op2}, {op3});
15 model->identifyInputsAndOutputs(
18 assert(model->isValid());
|
| resize_bilinear.model.cpp | 2 void CreateModel(Model *model) {
7 auto op1 = model->addOperand(&type0);
8 auto op2 = model->addOperand(&type1);
9 auto width = model->addOperand(&type2);
10 auto height = model->addOperand(&type2);
13 model->setOperandValue(width, width_init, sizeof(int32_t) * 1);
15 model->setOperandValue(height, height_init, sizeof(int32_t) * 1);
16 model->addOperation(ANEURALNETWORKS_RESIZE_BILINEAR, {op1, width, height}, {op2});
18 model->identifyInputsAndOutputs
[all...] |
| softmax_quant8_1.model.cpp | 2 void CreateModel(Model *model) {
7 auto input = model->addOperand(&type0);
8 auto beta = model->addOperand(&type1);
9 auto output = model->addOperand(&type2);
12 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
13 model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output});
15 model->identifyInputsAndOutputs(
18 assert(model->isValid());
|
| softmax_quant8_2.model.cpp | 2 void CreateModel(Model *model) {
7 auto input = model->addOperand(&type0);
8 auto beta = model->addOperand(&type1);
9 auto output = model->addOperand(&type2);
12 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
13 model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output});
15 model->identifyInputsAndOutputs(
18 assert(model->isValid());
|
