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Update test case for modify model interface. The input and output float tensors are in the beginning and end of the model respectively.

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PiperOrigin-RevId: 315337239
Change-Id: I9f0efe0b3abfa82214fc9ac5ce35b4c340b7e24d
This commit is contained in:
Meghna Natraj 2020-06-08 13:09:27 -07:00 committed by TensorFlower Gardener
parent 05355c404a
commit 10ff91a6dc
1 changed files with 196 additions and 194 deletions
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390
tensorflow/lite/tools/optimize/modify_model_interface_test.cc
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@ -26,10 +26,8 @@ namespace tflite {
namespace optimize {
namespace {
using ::testing::ElementsAreArray;
// Create a model with 1 quant, 1 FC, 1 dequant
std::unique_ptr<ModelT> CreateModelSingleInputOutput() {
// Create a quantized model with 1 quant, 1 FC, 1 dequant
std::unique_ptr<ModelT> CreateQuantizedModelSingleInputOutput() {
auto model = absl::make_unique<ModelT>();
auto subgraph = absl::make_unique<tflite::SubGraphT>();
auto buffer = absl::make_unique<tflite::BufferT>();
@ -54,15 +52,15 @@ std::unique_ptr<ModelT> CreateModelSingleInputOutput() {
// Op.
quant_op->opcode_index = 0;
quant_op->inputs = {2};
quant_op->outputs = {0};
quant_op->inputs = {0};
quant_op->outputs = {1};
fc_op->opcode_index = 1;
fc_op->inputs = {0};
fc_op->outputs = {1};
fc_op->inputs = {1};
fc_op->outputs = {2};
dequant_op->opcode_index = 2;
dequant_op->inputs = {1};
dequant_op->inputs = {2};
dequant_op->outputs = {3};
model->subgraphs[0]->operators.push_back(std::move(quant_op));
@ -73,32 +71,31 @@ std::unique_ptr<ModelT> CreateModelSingleInputOutput() {
model->operator_codes.push_back(std::move(fc_op_code));
model->operator_codes.push_back(std::move(dequant_op_code));
// Model input/otuput.
model->subgraphs[0]->inputs = {2};
// Model input/output.
model->subgraphs[0]->inputs = {0};
model->subgraphs[0]->outputs = {3};
// Tensors. Float tensors are at the end of the tensor list.
// Tensors
auto tensor_0 = absl::make_unique<TensorT>();
tensor_0->quantization = absl::make_unique<QuantizationParametersT>();
tensor_0->quantization->scale.push_back(0.35);
tensor_0->quantization->zero_point.push_back(28);
tensor_0->name = "tensor_0";
tensor_0->shape = {};
tensor_0->type = TensorType_INT8;
tensor_0->type = TensorType_FLOAT32;
auto tensor_1 = absl::make_unique<TensorT>();
tensor_1->quantization = absl::make_unique<QuantizationParametersT>();
tensor_1->quantization->scale.push_back(0.12);
tensor_1->quantization->zero_point.push_back(50);
tensor_1->quantization->scale.push_back(0.35);
tensor_1->quantization->zero_point.push_back(28);
tensor_1->name = "tensor_1";
tensor_1->shape = {};
tensor_1->type = TensorType_INT8;
auto tensor_2 = absl::make_unique<TensorT>();
tensor_2->quantization = absl::make_unique<QuantizationParametersT>();
tensor_2->quantization->scale.push_back(0.12);
tensor_2->quantization->zero_point.push_back(50);
tensor_2->name = "tensor_2";
tensor_2->shape = {};
tensor_2->type = TensorType_FLOAT32;
tensor_2->type = TensorType_INT8;
auto tensor_3 = absl::make_unique<TensorT>();
tensor_3->name = "tensor_3";
@ -116,9 +113,8 @@ std::unique_ptr<ModelT> CreateModelSingleInputOutput() {
return model;
}
// Create a model with 2 quant, 1 FC, 2 dequant
// The model mimics the behavior of the quantize_model.cc.
std::unique_ptr<ModelT> CreateModelMultipleInputOutput() {
// Create a quantized model with 2 quant, 1 FC, 2 dequant
std::unique_ptr<ModelT> CreateQuantizedModelMultipleInputOutput() {
auto model = absl::make_unique<ModelT>();
auto subgraph = absl::make_unique<tflite::SubGraphT>();
auto buffer = absl::make_unique<tflite::BufferT>();
@ -145,21 +141,21 @@ std::unique_ptr<ModelT> CreateModelMultipleInputOutput() {
// Op.
quant_op_1->opcode_index = 0;
quant_op_1->inputs = {4};
quant_op_1->outputs = {0};
quant_op_1->inputs = {0};
quant_op_1->outputs = {2};
quant_op_2->opcode_index = 0;
quant_op_2->inputs = {5};
quant_op_2->outputs = {1};
quant_op_2->inputs = {1};
quant_op_2->outputs = {3};
fc_op->opcode_index = 1;
fc_op->inputs = {0, 1};
fc_op->outputs = {2, 3};
fc_op->inputs = {2, 3};
fc_op->outputs = {4, 5};
dequant_op_1->opcode_index = 2;
dequant_op_1->inputs = {2};
dequant_op_1->inputs = {4};
dequant_op_1->outputs = {6};
dequant_op_2->opcode_index = 2;
dequant_op_2->inputs = {3};
dequant_op_2->inputs = {5};
dequant_op_2->outputs = {7};
model->subgraphs[0]->operators.push_back(std::move(quant_op_1));
@ -172,30 +168,24 @@ std::unique_ptr<ModelT> CreateModelMultipleInputOutput() {
model->operator_codes.push_back(std::move(fc_op_code));
model->operator_codes.push_back(std::move(dequant_op_code));
// Model input/otuput.
model->subgraphs[0]->inputs = {4, 5};
// Model input/output.
model->subgraphs[0]->inputs = {0, 1};
model->subgraphs[0]->outputs = {6, 7};
// Tensors
auto tensor_0 = absl::make_unique<TensorT>();
tensor_0->quantization = absl::make_unique<QuantizationParametersT>();
tensor_0->quantization->scale.push_back(0.35);
tensor_0->quantization->zero_point.push_back(28);
tensor_0->name = "tensor_0";
tensor_0->shape = {};
tensor_0->type = TensorType_INT8;
tensor_0->type = TensorType_FLOAT32;
auto tensor_1 = absl::make_unique<TensorT>();
tensor_1->quantization = absl::make_unique<QuantizationParametersT>();
tensor_1->quantization->scale.push_back(0.12);
tensor_1->quantization->zero_point.push_back(50);
tensor_1->name = "tensor_1";
tensor_1->shape = {};
tensor_1->type = TensorType_INT8;
tensor_1->type = TensorType_FLOAT32;
auto tensor_2 = absl::make_unique<TensorT>();
tensor_2->quantization = absl::make_unique<QuantizationParametersT>();
tensor_2->quantization->scale.push_back(0.45);
tensor_2->quantization->scale.push_back(0.35);
tensor_2->quantization->zero_point.push_back(28);
tensor_2->name = "tensor_2";
tensor_2->shape = {};
@ -203,21 +193,27 @@ std::unique_ptr<ModelT> CreateModelMultipleInputOutput() {
auto tensor_3 = absl::make_unique<TensorT>();
tensor_3->quantization = absl::make_unique<QuantizationParametersT>();
tensor_3->quantization->scale.push_back(0.22);
tensor_3->quantization->scale.push_back(0.12);
tensor_3->quantization->zero_point.push_back(50);
tensor_3->name = "tensor_3";
tensor_3->shape = {};
tensor_3->type = TensorType_INT8;
auto tensor_4 = absl::make_unique<TensorT>();
tensor_4->quantization = absl::make_unique<QuantizationParametersT>();
tensor_4->quantization->scale.push_back(0.45);
tensor_4->quantization->zero_point.push_back(28);
tensor_4->name = "tensor_4";
tensor_4->shape = {};
tensor_4->type = TensorType_FLOAT32;
tensor_4->type = TensorType_INT8;
auto tensor_5 = absl::make_unique<TensorT>();
tensor_5->quantization = absl::make_unique<QuantizationParametersT>();
tensor_5->quantization->scale.push_back(0.22);
tensor_5->quantization->zero_point.push_back(50);
tensor_5->name = "tensor_5";
tensor_5->shape = {};
tensor_5->type = TensorType_FLOAT32;
tensor_5->type = TensorType_INT8;
auto tensor_6 = absl::make_unique<TensorT>();
tensor_6->name = "tensor_6";
@ -266,7 +262,7 @@ std::unique_ptr<ModelT> CreateFloatModel() {
model->subgraphs[0]->operators.push_back(std::move(fc_op));
model->operator_codes.push_back(std::move(fc_op_code));
// Model input/otuput.
// Model input/output.
model->subgraphs[0]->inputs = {0};
model->subgraphs[0]->outputs = {1};
@ -291,12 +287,7 @@ std::unique_ptr<ModelT> CreateFloatModel() {
}
TEST(ModelInterface, Uint8SingleInputOutput) {
auto model = CreateModelSingleInputOutput();
// Ops.
EXPECT_EQ(model->subgraphs[0]->operators[0]->opcode_index, 0);
EXPECT_EQ(model->subgraphs[0]->operators[1]->opcode_index, 1);
EXPECT_EQ(model->subgraphs[0]->operators[2]->opcode_index, 2);
auto model = CreateQuantizedModelSingleInputOutput();
// Change model type.
flatbuffers::FlatBufferBuilder builder;
@ -305,32 +296,35 @@ TEST(ModelInterface, Uint8SingleInputOutput) {
kTfLiteOk);
// Verify results.
EXPECT_EQ(model->operator_codes.size(), 3);
EXPECT_EQ(model->subgraphs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->operators.size(), 3);
EXPECT_EQ(model->subgraphs[0]->tensors.size(), 4);
EXPECT_EQ(model->buffers.size(), 1);
EXPECT_EQ(model->subgraphs[0]->tensors[2]->name, "tensor_2");
EXPECT_EQ(model->subgraphs[0]->tensors[2]->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(model->subgraphs[0]->tensors[2]->quantization->scale[0],
0.35);
EXPECT_EQ(model->subgraphs[0]->tensors[2]->quantization->zero_point[0], 156);
EXPECT_EQ(model->subgraphs[0]->tensors[3]->name, "tensor_3");
EXPECT_EQ(model->subgraphs[0]->tensors[3]->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(model->subgraphs[0]->tensors[3]->quantization->scale[0],
0.12);
EXPECT_EQ(model->subgraphs[0]->tensors[3]->quantization->zero_point[0], 178);
// Ops.
EXPECT_EQ(model->subgraphs[0]->inputs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->inputs[0], 0);
EXPECT_EQ(model->subgraphs[0]->outputs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->outputs[0], 3);
EXPECT_EQ(model->operator_codes.size(), 3);
EXPECT_EQ(model->subgraphs[0]->operators.size(), 3);
EXPECT_EQ(model->subgraphs[0]->operators[0]->opcode_index, 0);
EXPECT_EQ(model->subgraphs[0]->operators[1]->opcode_index, 1);
EXPECT_EQ(model->subgraphs[0]->operators[2]->opcode_index, 0);
auto input_quant_op = model->subgraphs[0]->operators[0].get();
auto input = model->subgraphs[0]->tensors[input_quant_op->inputs[0]].get();
EXPECT_EQ(input->name, "tensor_0");
EXPECT_EQ(input->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(input->quantization->scale[0], 0.35);
EXPECT_EQ(input->quantization->zero_point[0], 156);
auto output_quant_op = model->subgraphs[0]->operators[2].get();
auto output = model->subgraphs[0]->tensors[output_quant_op->outputs[0]].get();
EXPECT_EQ(output->name, "tensor_3");
EXPECT_EQ(output->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(output->quantization->scale[0], 0.12);
EXPECT_EQ(output->quantization->zero_point[0], 178);
}
TEST(ModelInterface, Int8SingleInputOutput) {
auto model = CreateModelSingleInputOutput();
auto model = CreateQuantizedModelSingleInputOutput();
// Change model type.
flatbuffers::FlatBufferBuilder builder;
@ -339,20 +333,35 @@ TEST(ModelInterface, Int8SingleInputOutput) {
kTfLiteOk);
// Verify results.
EXPECT_EQ(model->operator_codes.size(), 3);
EXPECT_EQ(model->subgraphs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->operators.size(), 1);
EXPECT_EQ(model->subgraphs[0]->tensors.size(), 2);
EXPECT_EQ(model->buffers.size(), 1);
// TODO (b/158254056): Remove unused inputs and outputs from tensor list
// EXPECT_EQ(model->subgraphs[0]->tensors.size(), 2);
EXPECT_EQ(model->subgraphs[0]->tensors.size(), 3);
EXPECT_EQ(model->subgraphs[0]->inputs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->inputs[0], 0);
EXPECT_EQ(model->subgraphs[0]->inputs[0], 1);
EXPECT_EQ(model->subgraphs[0]->outputs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->outputs[0], 1);
EXPECT_EQ(model->subgraphs[0]->outputs[0], 2);
EXPECT_EQ(model->operator_codes.size(), 3);
EXPECT_EQ(model->subgraphs[0]->operators.size(), 1);
EXPECT_EQ(model->subgraphs[0]->operators[0]->opcode_index, 1);
auto fc_op = model->subgraphs[0]->operators[0].get();
auto input = model->subgraphs[0]->tensors[fc_op->inputs[0]].get();
EXPECT_EQ(input->name, "tensor_1");
EXPECT_EQ(input->type, TensorType_INT8);
EXPECT_FLOAT_EQ(input->quantization->scale[0], 0.35);
EXPECT_EQ(input->quantization->zero_point[0], 28);
auto output = model->subgraphs[0]->tensors[fc_op->outputs[0]].get();
EXPECT_EQ(output->name, "tensor_2");
EXPECT_EQ(output->type, TensorType_INT8);
EXPECT_FLOAT_EQ(output->quantization->scale[0], 0.12);
EXPECT_EQ(output->quantization->zero_point[0], 50);
}
TEST(ModelInterface, MixedTypeSingleInputOutput) {
auto model = CreateModelSingleInputOutput();
auto model = CreateQuantizedModelSingleInputOutput();
// Change model type.
flatbuffers::FlatBufferBuilder builder;
@ -361,27 +370,34 @@ TEST(ModelInterface, MixedTypeSingleInputOutput) {
kTfLiteOk);
// Verify results.
EXPECT_EQ(model->operator_codes.size(), 3);
EXPECT_EQ(model->subgraphs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->operators.size(), 2);
EXPECT_EQ(model->subgraphs[0]->tensors.size(), 3);
EXPECT_EQ(model->buffers.size(), 1);
EXPECT_EQ(model->subgraphs[0]->inputs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->inputs[0], 2);
EXPECT_EQ(model->subgraphs[0]->inputs[0], 0);
EXPECT_EQ(model->subgraphs[0]->outputs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->outputs[0], 1);
EXPECT_EQ(model->subgraphs[0]->outputs[0], 2);
EXPECT_EQ(model->operator_codes.size(), 3);
EXPECT_EQ(model->subgraphs[0]->operators.size(), 2);
EXPECT_EQ(model->subgraphs[0]->operators[0]->opcode_index, 0);
EXPECT_EQ(model->subgraphs[0]->operators[1]->opcode_index, 1);
auto quant_op = model->subgraphs[0]->operators[0].get();
auto input = model->subgraphs[0]->tensors[quant_op->inputs[0]].get();
EXPECT_EQ(input->name, "tensor_0");
EXPECT_EQ(input->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(input->quantization->scale[0], 0.35);
EXPECT_EQ(input->quantization->zero_point[0], 156);
auto fc_op = model->subgraphs[0]->operators[1].get();
auto output = model->subgraphs[0]->tensors[fc_op->outputs[0]].get();
EXPECT_EQ(output->name, "tensor_2");
EXPECT_EQ(output->type, TensorType_INT8);
EXPECT_FLOAT_EQ(output->quantization->scale[0], 0.12);
EXPECT_EQ(output->quantization->zero_point[0], 50);
}
TEST(ModelInterface, Uint8MutipleInputOutput) {
auto model = CreateModelMultipleInputOutput();
// Ops.
EXPECT_EQ(model->subgraphs[0]->operators[0]->opcode_index, 0);
EXPECT_EQ(model->subgraphs[0]->operators[1]->opcode_index, 0);
EXPECT_EQ(model->subgraphs[0]->operators[2]->opcode_index, 1);
EXPECT_EQ(model->subgraphs[0]->operators[3]->opcode_index, 2);
EXPECT_EQ(model->subgraphs[0]->operators[4]->opcode_index, 2);
auto model = CreateQuantizedModelMultipleInputOutput();
// Change model type.
flatbuffers::FlatBufferBuilder builder;
@ -390,54 +406,55 @@ TEST(ModelInterface, Uint8MutipleInputOutput) {
kTfLiteOk);
// Verify results.
// Model.
EXPECT_EQ(model->operator_codes.size(), 3);
EXPECT_EQ(model->subgraphs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->operators.size(), 5);
EXPECT_EQ(model->subgraphs[0]->tensors.size(), 8);
EXPECT_EQ(model->subgraphs[0]->inputs.size(), 2);
EXPECT_EQ(model->subgraphs[0]->inputs[0], 4);
EXPECT_EQ(model->subgraphs[0]->inputs[1], 5);
EXPECT_EQ(model->subgraphs[0]->inputs[0], 0);
EXPECT_EQ(model->subgraphs[0]->inputs[1], 1);
EXPECT_EQ(model->subgraphs[0]->outputs.size(), 2);
EXPECT_EQ(model->subgraphs[0]->outputs[0], 6);
EXPECT_EQ(model->subgraphs[0]->outputs[1], 7);
EXPECT_EQ(model->buffers.size(), 1);
// Tensors,
EXPECT_EQ(model->subgraphs[0]->tensors[4]->name, "tensor_4");
EXPECT_EQ(model->subgraphs[0]->tensors[4]->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(model->subgraphs[0]->tensors[4]->quantization->scale[0],
0.35);
EXPECT_EQ(model->subgraphs[0]->tensors[4]->quantization->zero_point[0], 156);
EXPECT_EQ(model->subgraphs[0]->tensors[5]->name, "tensor_5");
EXPECT_EQ(model->subgraphs[0]->tensors[5]->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(model->subgraphs[0]->tensors[5]->quantization->scale[0],
0.12);
EXPECT_EQ(model->subgraphs[0]->tensors[5]->quantization->zero_point[0], 178);
EXPECT_EQ(model->subgraphs[0]->tensors[6]->name, "tensor_6");
EXPECT_EQ(model->subgraphs[0]->tensors[6]->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(model->subgraphs[0]->tensors[6]->quantization->scale[0],
0.45);
EXPECT_EQ(model->subgraphs[0]->tensors[6]->quantization->zero_point[0], 156);
EXPECT_EQ(model->subgraphs[0]->tensors[7]->name, "tensor_7");
EXPECT_EQ(model->subgraphs[0]->tensors[7]->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(model->subgraphs[0]->tensors[7]->quantization->scale[0],
0.22);
EXPECT_EQ(model->subgraphs[0]->tensors[7]->quantization->zero_point[0], 178);
// Ops.
EXPECT_EQ(model->operator_codes.size(), 3);
EXPECT_EQ(model->subgraphs[0]->operators.size(), 5);
EXPECT_EQ(model->subgraphs[0]->operators[0]->opcode_index, 0);
EXPECT_EQ(model->subgraphs[0]->operators[1]->opcode_index, 0);
EXPECT_EQ(model->subgraphs[0]->operators[2]->opcode_index, 1);
EXPECT_EQ(model->subgraphs[0]->operators[3]->opcode_index, 0);
EXPECT_EQ(model->subgraphs[0]->operators[4]->opcode_index, 0);
auto input_quant_1 = model->subgraphs[0]->operators[0].get();
auto input_1 = model->subgraphs[0]->tensors[input_quant_1->inputs[0]].get();
EXPECT_EQ(input_1->name, "tensor_0");
EXPECT_EQ(input_1->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(input_1->quantization->scale[0], 0.35);
EXPECT_EQ(input_1->quantization->zero_point[0], 156);
auto input_quant_2 = model->subgraphs[0]->operators[1].get();
auto input_2 = model->subgraphs[0]->tensors[input_quant_2->inputs[0]].get();
EXPECT_EQ(input_2->name, "tensor_1");
EXPECT_EQ(input_2->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(input_2->quantization->scale[0], 0.12);
EXPECT_EQ(input_2->quantization->zero_point[0], 178);
auto output_quant_1 = model->subgraphs[0]->operators[3].get();
auto output_1 =
model->subgraphs[0]->tensors[output_quant_1->outputs[0]].get();
EXPECT_EQ(output_1->name, "tensor_6");
EXPECT_EQ(output_1->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(output_1->quantization->scale[0], 0.45);
EXPECT_EQ(output_1->quantization->zero_point[0], 156);
auto output_quant_2 = model->subgraphs[0]->operators[4].get();
auto output_2 =
model->subgraphs[0]->tensors[output_quant_2->outputs[0]].get();
EXPECT_EQ(output_2->name, "tensor_7");
EXPECT_EQ(output_2->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(output_2->quantization->scale[0], 0.22);
EXPECT_EQ(output_2->quantization->zero_point[0], 178);
}
TEST(ModelInterface, Int8MutipleInputOutput) {
auto model = CreateModelMultipleInputOutput();
auto model = CreateQuantizedModelMultipleInputOutput();
// Change model type.
flatbuffers::FlatBufferBuilder builder;
@ -446,45 +463,45 @@ TEST(ModelInterface, Int8MutipleInputOutput) {
kTfLiteOk);
// Verify results.
EXPECT_EQ(model->operator_codes.size(), 3);
EXPECT_EQ(model->subgraphs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->operators.size(), 1);
EXPECT_EQ(model->subgraphs[0]->tensors.size(), 4);
// TODO (b/158254056): Remove unused inputs and outputs from tensor list
// EXPECT_EQ(model->subgraphs[0]->tensors.size(), 4);
EXPECT_EQ(model->subgraphs[0]->tensors.size(), 6);
EXPECT_EQ(model->subgraphs[0]->inputs.size(), 2);
EXPECT_EQ(model->subgraphs[0]->inputs[0], 0);
EXPECT_EQ(model->subgraphs[0]->inputs[1], 1);
EXPECT_EQ(model->subgraphs[0]->inputs[0], 2);
EXPECT_EQ(model->subgraphs[0]->inputs[1], 3);
EXPECT_EQ(model->subgraphs[0]->outputs.size(), 2);
EXPECT_EQ(model->subgraphs[0]->outputs[0], 2);
EXPECT_EQ(model->subgraphs[0]->outputs[1], 3);
EXPECT_EQ(model->buffers.size(), 1);
// Tensors,
EXPECT_EQ(model->subgraphs[0]->tensors[0]->name, "tensor_0");
EXPECT_EQ(model->subgraphs[0]->tensors[0]->type, TensorType_INT8);
EXPECT_FLOAT_EQ(model->subgraphs[0]->tensors[0]->quantization->scale[0],
0.35);
EXPECT_EQ(model->subgraphs[0]->tensors[0]->quantization->zero_point[0], 28);
EXPECT_EQ(model->subgraphs[0]->tensors[1]->name, "tensor_1");
EXPECT_EQ(model->subgraphs[0]->tensors[1]->type, TensorType_INT8);
EXPECT_FLOAT_EQ(model->subgraphs[0]->tensors[1]->quantization->scale[0],
0.12);
EXPECT_EQ(model->subgraphs[0]->tensors[1]->quantization->zero_point[0], 50);
EXPECT_EQ(model->subgraphs[0]->tensors[2]->name, "tensor_2");
EXPECT_EQ(model->subgraphs[0]->tensors[2]->type, TensorType_INT8);
EXPECT_FLOAT_EQ(model->subgraphs[0]->tensors[2]->quantization->scale[0],
0.45);
EXPECT_EQ(model->subgraphs[0]->tensors[2]->quantization->zero_point[0], 28);
EXPECT_EQ(model->subgraphs[0]->tensors[3]->name, "tensor_3");
EXPECT_EQ(model->subgraphs[0]->tensors[3]->type, TensorType_INT8);
EXPECT_FLOAT_EQ(model->subgraphs[0]->tensors[3]->quantization->scale[0],
0.22);
EXPECT_EQ(model->subgraphs[0]->tensors[3]->quantization->zero_point[0], 50);
// Ops.
EXPECT_EQ(model->subgraphs[0]->outputs[0], 4);
EXPECT_EQ(model->subgraphs[0]->outputs[1], 5);
EXPECT_EQ(model->operator_codes.size(), 3);
EXPECT_EQ(model->subgraphs[0]->operators.size(), 1);
EXPECT_EQ(model->subgraphs[0]->operators[0]->opcode_index, 1);
auto fc_op = model->subgraphs[0]->operators[0].get();
auto input_1 = model->subgraphs[0]->tensors[fc_op->inputs[0]].get();
EXPECT_EQ(input_1->name, "tensor_2");
EXPECT_EQ(input_1->type, TensorType_INT8);
EXPECT_FLOAT_EQ(input_1->quantization->scale[0], 0.35);
EXPECT_EQ(input_1->quantization->zero_point[0], 28);
auto input_2 = model->subgraphs[0]->tensors[fc_op->inputs[1]].get();
EXPECT_EQ(input_2->name, "tensor_3");
EXPECT_EQ(input_2->type, TensorType_INT8);
EXPECT_FLOAT_EQ(input_2->quantization->scale[0], 0.12);
EXPECT_EQ(input_2->quantization->zero_point[0], 50);
auto output_1 = model->subgraphs[0]->tensors[fc_op->outputs[0]].get();
EXPECT_EQ(output_1->name, "tensor_4");
EXPECT_EQ(output_1->type, TensorType_INT8);
EXPECT_FLOAT_EQ(output_1->quantization->scale[0], 0.45);
EXPECT_EQ(output_1->quantization->zero_point[0], 28);
auto output_2 = model->subgraphs[0]->tensors[fc_op->outputs[1]].get();
EXPECT_EQ(output_2->name, "tensor_5");
EXPECT_EQ(output_2->type, TensorType_INT8);
EXPECT_FLOAT_EQ(output_2->quantization->scale[0], 0.22);
EXPECT_EQ(output_2->quantization->zero_point[0], 50);
}
TEST(ModelInterface, Float) {
@ -510,47 +527,32 @@ TEST(ModelInterface, Float) {
model.reset(output_model->UnPack());
// Verify results.
EXPECT_EQ(model->operator_codes.size(), 3);
EXPECT_EQ(model->subgraphs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->operators.size(), 3);
EXPECT_EQ(model->subgraphs[0]->tensors.size(), 4);
EXPECT_EQ(model->buffers.size(), 1);
// Ops.
EXPECT_EQ(model->subgraphs[0]->inputs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->inputs[0], 0);
EXPECT_EQ(model->subgraphs[0]->outputs.size(), 1);
EXPECT_EQ(model->subgraphs[0]->outputs[0], 1);
EXPECT_EQ(model->operator_codes.size(), 3);
EXPECT_EQ(model->operator_codes[0]->builtin_code,
BuiltinOperator_FULLY_CONNECTED);
EXPECT_EQ(model->operator_codes[1]->builtin_code, BuiltinOperator_DEQUANTIZE);
EXPECT_EQ(model->operator_codes[2]->builtin_code, BuiltinOperator_QUANTIZE);
EXPECT_EQ(model->subgraphs[0]->operators.size(), 3);
EXPECT_EQ(model->subgraphs[0]->operators[0]->opcode_index, 1);
EXPECT_EQ(model->subgraphs[0]->operators[1]->opcode_index, 0);
EXPECT_EQ(model->subgraphs[0]->operators[2]->opcode_index, 2);
auto dequantize_op = model->subgraphs[0]->operators[0].get();
auto input = model->subgraphs[0]->tensors[dequantize_op->inputs[0]].get();
EXPECT_EQ(input->name, "tensor_0_uint8");
EXPECT_EQ(input->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(input->quantization->scale[0], 0.4);
EXPECT_EQ(input->quantization->zero_point[0], 2);
EXPECT_THAT(model->subgraphs[0]->operators[0]->inputs, ElementsAreArray({2}));
EXPECT_THAT(model->subgraphs[0]->operators[0]->outputs,
ElementsAreArray({0}));
EXPECT_THAT(model->subgraphs[0]->operators[1]->inputs, ElementsAreArray({0}));
EXPECT_THAT(model->subgraphs[0]->operators[1]->outputs,
ElementsAreArray({1}));
EXPECT_THAT(model->subgraphs[0]->operators[2]->inputs, ElementsAreArray({1}));
EXPECT_THAT(model->subgraphs[0]->operators[2]->outputs,
ElementsAreArray({3}));
// Tensors.
EXPECT_EQ(model->subgraphs[0]->tensors[0]->name, "tensor_0");
EXPECT_EQ(model->subgraphs[0]->tensors[0]->type, TensorType_FLOAT32);
EXPECT_EQ(model->subgraphs[0]->tensors[1]->name, "tensor_1");
EXPECT_EQ(model->subgraphs[0]->tensors[1]->type, TensorType_FLOAT32);
EXPECT_EQ(model->subgraphs[0]->tensors[2]->name, "tensor_0_uint8");
EXPECT_EQ(model->subgraphs[0]->tensors[2]->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(model->subgraphs[0]->tensors[2]->quantization->scale[0], 0.4);
EXPECT_EQ(model->subgraphs[0]->tensors[2]->quantization->zero_point[0], 2);
EXPECT_EQ(model->subgraphs[0]->tensors[3]->name, "tensor_1_uint8");
EXPECT_EQ(model->subgraphs[0]->tensors[3]->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(model->subgraphs[0]->tensors[3]->quantization->scale[0], 0.5);
EXPECT_EQ(model->subgraphs[0]->tensors[3]->quantization->zero_point[0], -5);
auto quantize_op = model->subgraphs[0]->operators[2].get();
auto output = model->subgraphs[0]->tensors[quantize_op->outputs[0]].get();
EXPECT_EQ(output->name, "tensor_1_uint8");
EXPECT_EQ(output->type, TensorType_UINT8);
EXPECT_FLOAT_EQ(output->quantization->scale[0], 0.5);
EXPECT_EQ(output->quantization->zero_point[0], -5);
}
} // namespace