wehub-resource-sync
82 lines
3.2 KiB
C++
82 lines
3.2 KiB
C++
/* Copyright 2021 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 <string>
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#include <gtest/gtest.h>
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#include "tensorflow/lite/core/interpreter.h"
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#include "tensorflow/lite/core/interpreter_builder.h"
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#include "tensorflow/lite/core/kernels/register.h"
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#include "tensorflow/lite/core/macros.h"
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#include "tensorflow/lite/core/model_builder.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/tools/logging.h"
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namespace tflite {
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void TestMemoryThreshold(const std::string& model_path,
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size_t threshold_in_kb) {
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// The Im2Col optimization is only applied on mobile platforms, so only
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// validate on such platforms.
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if (!IsMobilePlatform()) {
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return;
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}
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// The model has a conv op will require a huge temporary tensor if
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// im2col is performed and it's possible to cause OOM on devices. To prevent
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// this from happening, a size cap (i.e. kMaxIm2colBufferSizeMobile) of
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// to-be-allocated im2col data is used to determine whether to disable
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// im2col. This test will check the memory footprint before/after
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// interpreter Invoke to ensure the size cap is correctly enforced on mobile
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// platforms.
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auto model = FlatBufferModel::BuildFromFile(model_path.c_str());
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ASSERT_TRUE(model);
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std::unique_ptr<Interpreter> interpreter;
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// Note that we explicitly set 1 thread here to avoid extra memory footprint
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// caused by multithreading, which will make the memory usage threshold
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// check later more reliable.
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ASSERT_EQ(InterpreterBuilder(*model, ops::builtin::BuiltinOpResolver())(
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&interpreter, /*num_threads*/ 1),
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kTfLiteOk);
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ASSERT_TRUE(interpreter);
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ASSERT_EQ(interpreter->AllocateTensors(), kTfLiteOk);
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// Memory required for all tensors should be smaller than the threshold.
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int64_t accumulate_tensor_memory = 0;
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for (int i = 0; i < interpreter->tensors_size(); ++i) {
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accumulate_tensor_memory += interpreter->tensor(i)->bytes;
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}
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EXPECT_LE(accumulate_tensor_memory, threshold_in_kb * 1024);
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}
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TEST(ConvMemUsage, HugeIm2ColData) {
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TestMemoryThreshold(
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// The model has a conv op will require a temporary tensor of ~3.5GB if
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// im2col is performed.
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"tensorflow/lite/testdata/conv_huge_im2col.bin",
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/*threshold_in_kb=*/3 * 1024 * 1024);
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}
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TEST(Conv3DMemUsage, HugeIm2ColData) {
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TestMemoryThreshold(
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// The model has a Conv3D op will require a temporary tensor of ~1.3GB if
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// im2col is performed.If not, it will use about 450MB.
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"tensorflow/lite/testdata/conv3d_huge_im2col.bin",
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/*threshold_in_kb=*/1 * 1024 * 1024);
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}
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} // namespace tflite
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