mirror of
https://github.com/vee1e/tflite-micro.git
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8f1f3b2623
@tensorflow/micro Remove extraneous tensor copy operation after first invocation of condition subgraph. Move copy of operator inputs to outputs, such that it occurs before the first invocation of the condition subgraph. This preserves the operator inputs when one or more of them is the output of DECODE, and alternate decompression memory is in use. This is because the output of DECODE is for immediate consumption by the next operator in the graph (WHILE), yet it is possible for the WHILE subgraph invocations to share memory with the original DECODE output. Update the unit test for multiple invocations of the condition and body subgraphs. When copying tensors between operator inputs/outputs and subgraph inputs/outputs, check if the source and destination tensors share memory. bug=fixes #3632
101 lines
3.5 KiB
C++
101 lines
3.5 KiB
C++
/* Copyright 2022 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/c/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/micro/kernels/kernel_runner.h"
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#include "tensorflow/lite/micro/micro_arena_constants.h"
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#include "tensorflow/lite/micro/micro_interpreter.h"
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#include "tensorflow/lite/micro/mock_micro_graph.h"
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#include "tensorflow/lite/micro/test_helper_custom_ops.h"
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#include "tensorflow/lite/micro/test_helpers.h"
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#include "tensorflow/lite/micro/testing/micro_test_v2.h"
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TEST(WhileTest, WhileShouldNeverInvokeConditionFalse) {
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constexpr int kArenaSize = 5000;
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uint8_t arena[kArenaSize];
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const tflite::Model* model =
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tflite::testing::GetSimpleModelWithSubgraphsAndWhile();
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tflite::MicroMutableOpResolver<3> resolver;
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resolver.AddWhile();
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resolver.AddAdd();
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resolver.AddLess();
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tflite::MicroInterpreter interpreter(model, resolver, arena, kArenaSize);
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EXPECT_EQ(kTfLiteOk, interpreter.AllocateTensors());
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TfLiteTensor* input0 = interpreter.input(0);
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TfLiteTensor* input1 = interpreter.input(1);
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TfLiteTensor* output0 = interpreter.output(0);
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TfLiteTensor* output1 = interpreter.output(1);
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input0->data.f[0] = 3.0f;
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input1->data.f[0] = 2.0f;
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interpreter.Invoke();
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EXPECT_EQ(output0->data.f[0], 3.0f);
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EXPECT_EQ(output1->data.f[0], 2.0f);
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}
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TEST(WhileTest, WhileShouldInvokeOnce) {
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constexpr int kArenaSize = 5000;
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uint8_t arena[kArenaSize];
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const tflite::Model* model =
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tflite::testing::GetSimpleModelWithSubgraphsAndWhile();
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tflite::MicroMutableOpResolver<3> resolver;
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resolver.AddWhile();
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resolver.AddAdd();
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resolver.AddLess();
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tflite::MicroInterpreter interpreter(model, resolver, arena, kArenaSize);
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EXPECT_EQ(kTfLiteOk, interpreter.AllocateTensors());
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TfLiteTensor* input0 = interpreter.input(0);
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TfLiteTensor* input1 = interpreter.input(1);
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TfLiteTensor* output0 = interpreter.output(0);
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TfLiteTensor* output1 = interpreter.output(1);
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input0->data.f[0] = 2.0f;
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input1->data.f[0] = 3.0f;
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interpreter.Invoke();
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EXPECT_EQ(output0->data.f[0], 5.0f);
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EXPECT_EQ(output1->data.f[0], 3.0f);
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}
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TEST(WhileTest, WhileShouldInvokeMultiple) {
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constexpr int kArenaSize = 5000;
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uint8_t arena[kArenaSize];
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const tflite::Model* model =
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tflite::testing::GetSimpleModelWithSubgraphsAndWhile();
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tflite::MicroMutableOpResolver<3> resolver;
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resolver.AddWhile();
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resolver.AddAdd();
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resolver.AddLess();
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tflite::MicroInterpreter interpreter(model, resolver, arena, kArenaSize);
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EXPECT_EQ(kTfLiteOk, interpreter.AllocateTensors());
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TfLiteTensor* input0 = interpreter.input(0);
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TfLiteTensor* input1 = interpreter.input(1);
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TfLiteTensor* output0 = interpreter.output(0);
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TfLiteTensor* output1 = interpreter.output(1);
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input0->data.f[0] = -5.0f;
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input1->data.f[0] = 3.0f;
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interpreter.Invoke();
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EXPECT_EQ(output0->data.f[0], 4.0f);
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EXPECT_EQ(output1->data.f[0], 3.0f);
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}
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TF_LITE_MICRO_TESTS_MAIN
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