wehub-resource-sync
224 lines
8.4 KiB
C++
224 lines
8.4 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
==============================================================================*/
|
|
|
|
#include "tensorflow/lite/delegates/xnnpack/pad_tester.h"
|
|
|
|
#include <algorithm>
|
|
#include <array>
|
|
#include <cstddef>
|
|
#include <cstdint>
|
|
#include <functional>
|
|
#include <memory>
|
|
#include <numeric>
|
|
#include <random>
|
|
#include <vector>
|
|
|
|
#include <gtest/gtest.h>
|
|
#include "fp16.h" // from @FP16
|
|
#include "flatbuffers/buffer.h" // from @flatbuffers
|
|
#include "flatbuffers/flatbuffer_builder.h" // from @flatbuffers
|
|
#include "flatbuffers/string.h" // from @flatbuffers
|
|
#include "tensorflow/compiler/mlir/lite/schema/schema_conversion_utils.h"
|
|
#include "tensorflow/lite/core/interpreter_builder.h"
|
|
#include "tensorflow/lite/core/kernels/register.h"
|
|
#include "tensorflow/lite/interpreter.h"
|
|
#include "tensorflow/lite/schema/schema_generated.h"
|
|
#include "tensorflow/lite/version.h"
|
|
|
|
namespace tflite {
|
|
namespace xnnpack {
|
|
|
|
std::vector<int32_t> PadTester::OutputShape() const {
|
|
std::vector<int32_t> output_shape;
|
|
output_shape.reserve(InputShape().size());
|
|
for (size_t i = 0; i < InputShape().size(); i++) {
|
|
int32_t output_dim = InputShape()[i];
|
|
if (i < InputPrePaddings().size()) {
|
|
output_dim += InputPrePaddings()[i];
|
|
}
|
|
if (i < InputPostPaddings().size()) {
|
|
output_dim += InputPostPaddings()[i];
|
|
}
|
|
output_shape.push_back(output_dim);
|
|
}
|
|
return output_shape;
|
|
}
|
|
|
|
void PadTester::Test(TfLiteDelegate* delegate) const {
|
|
ASSERT_EQ(InputPrePaddings().size(), InputPostPaddings().size());
|
|
ASSERT_LE(InputPrePaddings().size(), InputShape().size());
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto input_rng =
|
|
std::bind(std::uniform_real_distribution<float>(), std::ref(rng));
|
|
|
|
std::vector<char> buffer = CreateTfLiteModel();
|
|
const Model* model = GetModel(buffer.data());
|
|
|
|
std::unique_ptr<Interpreter> delegate_interpreter;
|
|
ASSERT_EQ(
|
|
InterpreterBuilder(
|
|
model,
|
|
::tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates())(
|
|
&delegate_interpreter),
|
|
kTfLiteOk);
|
|
std::unique_ptr<Interpreter> default_interpreter;
|
|
ASSERT_EQ(
|
|
InterpreterBuilder(
|
|
model,
|
|
::tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates())(
|
|
&default_interpreter),
|
|
kTfLiteOk);
|
|
|
|
ASSERT_TRUE(delegate_interpreter);
|
|
ASSERT_TRUE(default_interpreter);
|
|
|
|
ASSERT_EQ(delegate_interpreter->inputs().size(), 1);
|
|
ASSERT_EQ(default_interpreter->inputs().size(), 1);
|
|
|
|
ASSERT_EQ(delegate_interpreter->outputs().size(), 1);
|
|
ASSERT_EQ(default_interpreter->outputs().size(), 1);
|
|
|
|
ASSERT_EQ(delegate_interpreter->AllocateTensors(), kTfLiteOk);
|
|
ASSERT_EQ(default_interpreter->AllocateTensors(), kTfLiteOk);
|
|
|
|
ASSERT_EQ(delegate_interpreter->ModifyGraphWithDelegate(delegate), kTfLiteOk);
|
|
|
|
if (FP16()) {
|
|
TfLiteFloat16* default_input_data =
|
|
default_interpreter->typed_input_tensor<TfLiteFloat16>(0);
|
|
std::generate_n(
|
|
default_input_data, ComputeSize(InputShape()), [&]() -> TfLiteFloat16 {
|
|
return TfLiteFloat16{fp16_ieee_from_fp32_value(input_rng())};
|
|
});
|
|
|
|
TfLiteFloat16* delegate_input_data =
|
|
delegate_interpreter->typed_input_tensor<TfLiteFloat16>(0);
|
|
std::copy_n(default_input_data, ComputeSize(InputShape()),
|
|
delegate_input_data);
|
|
|
|
ASSERT_EQ(default_interpreter->Invoke(), kTfLiteOk);
|
|
ASSERT_EQ(delegate_interpreter->Invoke(), kTfLiteOk);
|
|
|
|
TfLiteFloat16* default_output_data =
|
|
default_interpreter->typed_output_tensor<TfLiteFloat16>(0);
|
|
TfLiteFloat16* delegate_output_data =
|
|
delegate_interpreter->typed_output_tensor<TfLiteFloat16>(0);
|
|
|
|
for (size_t i = 0; i < ComputeSize(OutputShape()); i++) {
|
|
ASSERT_EQ(default_output_data[i].data, delegate_output_data[i].data);
|
|
}
|
|
} else {
|
|
float* default_input_data =
|
|
default_interpreter->typed_input_tensor<float>(0);
|
|
std::generate_n(default_input_data, ComputeSize(InputShape()),
|
|
std::ref(input_rng));
|
|
|
|
float* delegate_input_data =
|
|
delegate_interpreter->typed_input_tensor<float>(0);
|
|
std::copy_n(default_input_data, ComputeSize(InputShape()),
|
|
delegate_input_data);
|
|
|
|
ASSERT_EQ(default_interpreter->Invoke(), kTfLiteOk);
|
|
ASSERT_EQ(delegate_interpreter->Invoke(), kTfLiteOk);
|
|
|
|
float* default_output_data =
|
|
default_interpreter->typed_output_tensor<float>(0);
|
|
float* delegate_output_data =
|
|
delegate_interpreter->typed_output_tensor<float>(0);
|
|
|
|
for (size_t i = 0; i < ComputeSize(OutputShape()); i++) {
|
|
ASSERT_EQ(default_output_data[i], delegate_output_data[i]);
|
|
}
|
|
}
|
|
}
|
|
|
|
std::vector<char> PadTester::CreateTfLiteModel() const {
|
|
flatbuffers::FlatBufferBuilder builder;
|
|
flatbuffers::Offset<OperatorCode> operator_code =
|
|
CreateOperatorCode(builder, BuiltinOperator_PAD);
|
|
|
|
std::vector<int32_t> paddings(InputPrePaddings().size() +
|
|
InputPostPaddings().size());
|
|
for (size_t i = 0; i < InputPrePaddings().size(); i++) {
|
|
paddings[i * 2] = InputPrePaddings()[i];
|
|
paddings[i * 2 + 1] = InputPostPaddings()[i];
|
|
}
|
|
const std::array<flatbuffers::Offset<Buffer>, 2> buffers{{
|
|
CreateBuffer(builder, builder.CreateVector({})),
|
|
CreateBuffer(builder,
|
|
builder.CreateVector(
|
|
reinterpret_cast<const uint8_t*>(paddings.data()),
|
|
sizeof(int32_t) * paddings.size())),
|
|
}};
|
|
|
|
const std::vector<int32_t> output_shape = OutputShape();
|
|
const std::array<int32_t, 2> paddings_shape{
|
|
{static_cast<int32_t>(InputPrePaddings().size()), 2}};
|
|
const std::array<flatbuffers::Offset<Tensor>, 3> tensors{{
|
|
CreateTensor(builder,
|
|
builder.CreateVector<int32_t>(InputShape().data(),
|
|
InputShape().size()),
|
|
FP16() ? TensorType_FLOAT16 : TensorType_FLOAT32),
|
|
CreateTensor(builder,
|
|
builder.CreateVector<int32_t>(paddings_shape.data(),
|
|
paddings_shape.size()),
|
|
TensorType_INT32, /*buffer=*/1),
|
|
CreateTensor(builder,
|
|
builder.CreateVector<int32_t>(output_shape.data(),
|
|
output_shape.size()),
|
|
FP16() ? TensorType_FLOAT16 : TensorType_FLOAT32),
|
|
}};
|
|
|
|
const std::array<int32_t, 2> op_inputs{{0, 1}};
|
|
const std::array<int32_t, 1> op_outputs{{2}};
|
|
flatbuffers::Offset<Operator> op = CreateOperator(
|
|
builder, /*opcode_index=*/0,
|
|
builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()),
|
|
builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size()));
|
|
|
|
const std::array<int32_t, 1> subgraph_inputs{{0}};
|
|
const std::array<int32_t, 1> subgraph_outputs{{2}};
|
|
flatbuffers::Offset<SubGraph> subgraph = CreateSubGraph(
|
|
builder, builder.CreateVector(tensors.data(), tensors.size()),
|
|
builder.CreateVector<int32_t>(subgraph_inputs.data(),
|
|
subgraph_inputs.size()),
|
|
builder.CreateVector<int32_t>(subgraph_outputs.data(),
|
|
subgraph_outputs.size()),
|
|
builder.CreateVector(&op, 1));
|
|
|
|
flatbuffers::Offset<flatbuffers::String> description =
|
|
builder.CreateString("Pad model");
|
|
|
|
flatbuffers::Offset<Model> model_buffer = CreateModel(
|
|
builder, TFLITE_SCHEMA_VERSION, builder.CreateVector(&operator_code, 1),
|
|
builder.CreateVector(&subgraph, 1), description,
|
|
builder.CreateVector(buffers.data(), buffers.size()));
|
|
|
|
builder.Finish(model_buffer);
|
|
|
|
return std::vector<char>(builder.GetBufferPointer(),
|
|
builder.GetBufferPointer() + builder.GetSize());
|
|
}
|
|
|
|
int32_t PadTester::ComputeSize(const std::vector<int32_t>& shape) {
|
|
return std::accumulate(shape.cbegin(), shape.cend(), 1,
|
|
std::multiplies<int32_t>());
|
|
}
|
|
|
|
} // namespace xnnpack
|
|
} // namespace tflite
|