| /external/tensorflow/tensorflow/contrib/lite/toco/graph_transformations/ |
| resolve_mean_attributes.cc | 21 #include "tensorflow/contrib/lite/toco/model.h"
27 bool ResolveMeanAttributes::Run(Model* model, std::size_t op_index) {
28 auto* mean_op = model->operators[op_index].get();
37 if (!IsConstantParameterArray(*model, op->inputs[1])) return false;
39 const auto& indices_array = model->GetArray(op->inputs[1]);
|
| resolve_reshape_attributes.cc | 22 #include "tensorflow/contrib/lite/toco/model.h"
28 bool ResolveReshapeAttributes::Run(Model* model, std::size_t op_index) {
29 const auto reshape_it = model->operators.begin() + op_index;
39 if (IsConstantParameterArray(*model, reshape_op->inputs[1])) {
40 const auto& constant_input_array = model->GetArray(reshape_op->inputs[1]);
|
| resolve_squeeze_attributes.cc | 22 #include "tensorflow/contrib/lite/toco/model.h"
28 bool ResolveSqueezeAttributes::Run(Model* model, std::size_t op_index) {
29 auto* squeeze_op = model->operators[op_index].get();
37 if (CountOpsWithInput(*model, squeeze_op->outputs[0]) == 1) {
38 const auto* next_op = GetOpWithInput(*model, squeeze_op->outputs[0]);
45 return RemoveTrivialPassthroughOp(this, model, op_index);
|
| /external/testng/src/main/java/org/testng/reporters/jq/ |
| BannerPanel.java | 7 public BannerPanel(Model model) {
8 super(model);
|
| /frameworks/ml/nn/runtime/test/generated/models/ |
| dequantize.model.cpp | 2 void CreateModel(Model *model) {
6 auto op1 = model->addOperand(&type0);
7 auto op2 = model->addOperand(&type1);
9 model->addOperation(ANEURALNETWORKS_DEQUANTIZE, {op1}, {op2});
11 model->identifyInputsAndOutputs(
14 assert(model->isValid());
|
| dequantize_relaxed.model.cpp | 2 void CreateModel(Model *model) {
6 auto op1 = model->addOperand(&type0);
7 auto op2 = model->addOperand(&type1);
9 model->addOperation(ANEURALNETWORKS_DEQUANTIZE, {op1}, {op2});
11 model->identifyInputsAndOutputs(
15 model->relaxComputationFloat32toFloat16(true);
16 assert(model->isValid());
|
| embedding_lookup.model.cpp | 2 void CreateModel(Model *model) {
6 auto index = model->addOperand(&type0);
7 auto value = model->addOperand(&type1);
8 auto output = model->addOperand(&type1);
10 model->addOperation(ANEURALNETWORKS_EMBEDDING_LOOKUP, {index, value}, {output});
12 model->identifyInputsAndOutputs(
15 assert(model->isValid());
|
| embedding_lookup_relaxed.model.cpp | 2 void CreateModel(Model *model) {
6 auto index = model->addOperand(&type0);
7 auto value = model->addOperand(&type1);
8 auto output = model->addOperand(&type1);
10 model->addOperation(ANEURALNETWORKS_EMBEDDING_LOOKUP, {index, value}, {output});
12 model->identifyInputsAndOutputs(
16 model->relaxComputationFloat32toFloat16(true);
17 assert(model->isValid());
|
| logistic_quant8_1.model.cpp | 2 void CreateModel(Model *model) {
6 auto op1 = model->addOperand(&type0);
7 auto op3 = model->addOperand(&type1);
9 model->addOperation(ANEURALNETWORKS_LOGISTIC, {op1}, {op3});
11 model->identifyInputsAndOutputs(
14 assert(model->isValid());
|
| logistic_quant8_2.model.cpp | 2 void CreateModel(Model *model) {
6 auto input = model->addOperand(&type0);
7 auto output = model->addOperand(&type1);
9 model->addOperation(ANEURALNETWORKS_LOGISTIC, {input}, {output});
11 model->identifyInputsAndOutputs(
14 assert(model->isValid());
|
| reshape_quant8_weights_as_inputs.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);
11 model->addOperation(ANEURALNETWORKS_RESHAPE, {op1, op2}, {op3});
13 model->identifyInputsAndOutputs(
16 assert(model->isValid());
|
| reshape_weights_as_inputs.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);
11 model->addOperation(ANEURALNETWORKS_RESHAPE, {op1, op2}, {op3});
13 model->identifyInputsAndOutputs(
16 assert(model->isValid());
|
| reshape_weights_as_inputs_relaxed.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);
11 model->addOperation(ANEURALNETWORKS_RESHAPE, {op1, op2}, {op3});
13 model->identifyInputsAndOutputs(
17 model->relaxComputationFloat32toFloat16(true);
18 assert(model->isValid());
|
| softmax_float_1.model.cpp | 2 void CreateModel(Model *model) {
6 auto input = model->addOperand(&type0);
7 auto beta = model->addOperand(&type1);
8 auto output = model->addOperand(&type0);
11 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
12 model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output});
14 model->identifyInputsAndOutputs(
17 assert(model->isValid());
|
| softmax_float_1_relaxed.model.cpp | 2 void CreateModel(Model *model) {
6 auto input = model->addOperand(&type0);
7 auto beta = model->addOperand(&type1);
8 auto output = model->addOperand(&type0);
11 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
12 model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output});
14 model->identifyInputsAndOutputs(
18 model->relaxComputationFloat32toFloat16(true);
19 assert(model->isValid())
[all...] |
| softmax_float_2.model.cpp | 2 void CreateModel(Model *model) {
6 auto input = model->addOperand(&type0);
7 auto beta = model->addOperand(&type1);
8 auto output = model->addOperand(&type0);
11 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
12 model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output});
14 model->identifyInputsAndOutputs(
17 assert(model->isValid());
|
| softmax_float_2_relaxed.model.cpp | 2 void CreateModel(Model *model) {
6 auto input = model->addOperand(&type0);
7 auto beta = model->addOperand(&type1);
8 auto output = model->addOperand(&type0);
11 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
12 model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output});
14 model->identifyInputsAndOutputs(
18 model->relaxComputationFloat32toFloat16(true);
19 assert(model->isValid())
[all...] |
| transpose.model.cpp | 2 void CreateModel(Model *model) {
6 auto input = model->addOperand(&type0);
7 auto perms = model->addOperand(&type1);
8 auto output = model->addOperand(&type0);
11 model->setOperandValue(perms, perms_init, sizeof(int32_t) * 4);
12 model->addOperation(ANEURALNETWORKS_TRANSPOSE, {input, perms}, {output});
14 model->identifyInputsAndOutputs(
17 assert(model->isValid());
|
| transpose_relaxed.model.cpp | 2 void CreateModel(Model *model) {
6 auto input = model->addOperand(&type0);
7 auto perms = model->addOperand(&type1);
8 auto output = model->addOperand(&type0);
11 model->setOperandValue(perms, perms_init, sizeof(int32_t) * 4);
12 model->addOperation(ANEURALNETWORKS_TRANSPOSE, {input, perms}, {output});
14 model->identifyInputsAndOutputs(
18 model->relaxComputationFloat32toFloat16(true);
19 assert(model->isValid())
[all...] |
| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| conv_1_h3_w2_SAME.mod.py | 0 model = Model()
10 model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) variable
1 model = Model() variable
|
| conv_1_h3_w2_VALID.mod.py | 0 model = Model()
10 model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) variable
1 model = Model() variable
|
| conv_3_h3_w2_SAME.mod.py | 0 model = Model()
10 model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) variable
1 model = Model() variable
|
| conv_3_h3_w2_VALID.mod.py | 0 model = Model()
10 model = model.Conv(i2, i0, i1, i4, i5, i6, i7).To(i3) variable
1 model = Model() variable
|
| depthwise_conv.mod.py | 0 model = Model()
11 model = model.DepthWiseConv(i2, i0, i1, i4, i5, i6, i7, i8).To(i3) variable
1 model = Model() variable
|
| depthwise_conv2d_float_large_2_weights_as_inputs.mod.py | 17 model = Model() variable
27 model = model.Operation("DEPTHWISE_CONV_2D", variable
|
