48c3bae94a
This enables TFLite Micro to selectively include these setter functions in unit tests. The APIs used in creating the flatbuffer introduce new and delete symbols which can cause issues for libraries not fully building with --gc-sections in linker flags. PiperOrigin-RevId: 339324965 Change-Id: I720b8dab6d80a94a47b7c8c427067966e2c42943
202 lines
8.1 KiB
C++
202 lines
8.1 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/delegates/xnnpack/pool_2d_tester.h"
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#include <array>
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#include <cstdint>
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#include <functional>
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#include <random>
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#include <vector>
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#include <gtest/gtest.h>
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#include "flatbuffers/flatbuffers.h" // from @flatbuffers
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#include "tensorflow/lite/interpreter.h"
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#include "tensorflow/lite/kernels/register.h"
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#include "tensorflow/lite/model.h"
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#include "tensorflow/lite/schema/schema_conversion_utils.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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#include "tensorflow/lite/version.h"
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namespace tflite {
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namespace xnnpack {
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void Pool2DTester::Test(tflite::BuiltinOperator pool_op,
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TfLiteDelegate* delegate) const {
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto range_rng = std::bind(
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std::uniform_real_distribution<float>(-25.0f, 25.0f), std::ref(rng));
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std::vector<char> buffer = CreateTfLiteModel(pool_op);
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const tflite::Model* model = tflite::GetModel(buffer.data());
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std::unique_ptr<tflite::Interpreter> delegate_interpreter;
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ASSERT_EQ(
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tflite::InterpreterBuilder(
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model,
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tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates())(
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&delegate_interpreter),
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kTfLiteOk);
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std::unique_ptr<tflite::Interpreter> default_interpreter;
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ASSERT_EQ(
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tflite::InterpreterBuilder(
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model,
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tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates())(
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&default_interpreter),
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kTfLiteOk);
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ASSERT_TRUE(delegate_interpreter);
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ASSERT_TRUE(default_interpreter);
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ASSERT_EQ(delegate_interpreter->inputs().size(), 1);
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ASSERT_EQ(default_interpreter->inputs().size(), 1);
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ASSERT_EQ(delegate_interpreter->outputs().size(), 1);
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ASSERT_EQ(default_interpreter->outputs().size(), 1);
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ASSERT_EQ(delegate_interpreter->AllocateTensors(), kTfLiteOk);
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ASSERT_EQ(default_interpreter->AllocateTensors(), kTfLiteOk);
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ASSERT_EQ(delegate_interpreter->ModifyGraphWithDelegate(delegate), kTfLiteOk);
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float* default_input_data = default_interpreter->typed_tensor<float>(
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default_interpreter->inputs()[0]);
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for (int32_t i = 0; i < BatchSize(); i++) {
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for (int32_t c = 0; c < Channels(); c++) {
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// Use the same range of all-positive or all-negative values to generate
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// all pixels within the same batch index & channel, but different ranges
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// for different channels or batches. This ensures that no catastrophic
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// cancellation occur, but test covers both positive and negative inputs.
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const float range = range_rng();
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auto value_rng =
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std::bind(std::uniform_real_distribution<float>(
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std::min(range, 0.0f), std::max(range, 0.0f)),
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std::ref(rng));
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for (int32_t y = 0; y < InputHeight(); y++) {
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for (int32_t x = 0; x < InputWidth(); x++) {
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const int32_t index =
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((i * InputHeight() + y) * InputWidth() + x) * Channels() + c;
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default_input_data[index] = value_rng();
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}
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}
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}
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}
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float* xnnpack_input_data = delegate_interpreter->typed_tensor<float>(
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delegate_interpreter->inputs()[0]);
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std::copy(default_input_data,
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default_input_data +
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BatchSize() * InputHeight() * InputWidth() * Channels(),
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xnnpack_input_data);
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ASSERT_EQ(default_interpreter->Invoke(), kTfLiteOk);
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ASSERT_EQ(delegate_interpreter->Invoke(), kTfLiteOk);
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float* default_output_data = default_interpreter->typed_tensor<float>(
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default_interpreter->outputs()[0]);
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float* xnnpack_output_data = delegate_interpreter->typed_tensor<float>(
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delegate_interpreter->outputs()[0]);
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for (int32_t i = 0; i < BatchSize(); i++) {
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for (int32_t y = 0; y < OutputHeight(); y++) {
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for (int32_t x = 0; x < OutputWidth(); x++) {
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for (int32_t c = 0; c < Channels(); c++) {
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const int32_t index =
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((i * OutputHeight() + y) * OutputWidth() + x) * Channels() + c;
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if (pool_op == BuiltinOperator_MAX_POOL_2D) {
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// MaxPooling results must be exact
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ASSERT_EQ(default_output_data[index], xnnpack_output_data[index])
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<< "batch " << i << " / " << BatchSize() << ", y position " << y
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<< " / " << OutputHeight() << ", x position " << x << " / "
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<< OutputWidth() << ", channel " << c << " / " << Channels();
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} else {
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ASSERT_NEAR(default_output_data[index], xnnpack_output_data[index],
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std::abs(default_output_data[index]) * 3.0e-6f)
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<< "batch " << i << " / " << BatchSize() << ", y position " << y
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<< " / " << OutputHeight() << ", x position " << x << " / "
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<< OutputWidth() << ", channel " << c << " / " << Channels();
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}
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}
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}
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}
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}
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}
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std::vector<char> Pool2DTester::CreateTfLiteModel(
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tflite::BuiltinOperator pool_op) const {
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flatbuffers::FlatBufferBuilder builder;
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flatbuffers::Offset<tflite::OperatorCode> operator_code =
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CreateOperatorCode(builder, pool_op, 0);
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flatbuffers::Offset<tflite::Pool2DOptions> pool_2d_options =
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CreatePool2DOptions(builder, Padding(), StrideWidth(), StrideHeight(),
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PoolingWidth(), PoolingHeight(), Activation());
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const flatbuffers::Offset<tflite::Buffer> null_buffer =
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tflite::CreateBuffer(builder, builder.CreateVector({}));
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const std::array<int32_t, 4> input_shape{
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{BatchSize(), InputHeight(), InputWidth(), Channels()}};
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const std::array<int32_t, 4> output_shape{
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{BatchSize(), OutputHeight(), OutputWidth(), Channels()}};
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const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{
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tflite::CreateTensor(
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builder,
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builder.CreateVector<int32_t>(input_shape.data(), input_shape.size()),
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tflite::TensorType_FLOAT32),
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tflite::CreateTensor(builder,
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builder.CreateVector<int32_t>(output_shape.data(),
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output_shape.size()),
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tflite::TensorType_FLOAT32),
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}};
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const std::array<int32_t, 1> op_inputs{{0}};
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const std::array<int32_t, 1> op_outputs{{1}};
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flatbuffers::Offset<tflite::Operator> op = CreateOperator(
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builder, /*opcode_index=*/0,
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builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()),
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builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size()),
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tflite::BuiltinOptions_Pool2DOptions, pool_2d_options.Union());
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const std::array<int32_t, 1> subgraph_inputs{{0}};
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const std::array<int32_t, 1> subgraph_outputs{{1}};
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flatbuffers::Offset<tflite::SubGraph> subgraph = CreateSubGraph(
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builder, builder.CreateVector(tensors.data(), tensors.size()),
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builder.CreateVector<int32_t>(subgraph_inputs.data(),
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subgraph_inputs.size()),
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builder.CreateVector<int32_t>(subgraph_outputs.data(),
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subgraph_outputs.size()),
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builder.CreateVector(&op, 1));
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flatbuffers::Offset<flatbuffers::String> description =
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builder.CreateString("Pool2D model");
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flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(
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builder, TFLITE_SCHEMA_VERSION, builder.CreateVector(&operator_code, 1),
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builder.CreateVector(&subgraph, 1), description,
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builder.CreateVector(&null_buffer, 1));
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builder.Finish(model_buffer);
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return std::vector<char>(builder.GetBufferPointer(),
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builder.GetBufferPointer() + builder.GetSize());
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}
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} // namespace xnnpack
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} // namespace tflite
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