508 lines
18 KiB
C++
508 lines
18 KiB
C++
/* Copyright 2019 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/kernels/subgraph_test_util.h"
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#include <stddef.h>
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#include <stdint.h>
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#include <stdlib.h>
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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 "tensorflow/lite/builtin_ops.h"
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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/core/subgraph.h"
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#include "tensorflow/lite/kernels/builtin_op_kernels.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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namespace tflite {
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// Forward declaration for op kernels.
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namespace ops {
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namespace custom {
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TfLiteRegistration* Register_ASSIGN_VARIABLE();
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TfLiteRegistration* Register_READ_VARIABLE();
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namespace random_int {
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 0);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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TfLiteTensor* output = GetOutput(context, node, 0);
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TfLiteIntArray* outputSize = TfLiteIntArrayCreate(1);
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outputSize->data[0] = 1;
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// TODO(jaesung): Make output size be changeable depending on user's input to
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// make it generic.
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return context->ResizeTensor(context, output, outputSize);
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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TfLiteTensor& output = context->tensors[node->outputs->data[0]];
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std::random_device rd;
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std::uniform_int_distribution<int> dist(1, 32768);
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output.data.i32[0] = dist(rd);
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return kTfLiteOk;
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}
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} // namespace random_int
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TfLiteRegistration* Register_RANDOM_INT() {
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static TfLiteRegistration r = {nullptr, nullptr, random_int::Prepare,
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random_int::Eval};
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return &r;
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}
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} // namespace custom
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} // namespace ops
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namespace subgraph_test_util {
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namespace {
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void SetupTensor(Subgraph* subgraph, int tensor_index, TfLiteType type) {
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ASSERT_EQ(subgraph->SetTensorParametersReadWrite(tensor_index, type, "", 0,
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nullptr, {}, false),
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kTfLiteOk);
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}
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} // namespace
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SubgraphBuilder::~SubgraphBuilder() {
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for (auto buffer : buffers_) {
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free(buffer);
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}
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}
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void SubgraphBuilder::BuildAddSubgraph(Subgraph* subgraph) {
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const int kInput1 = 0;
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const int kInput2 = 1;
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const int kOutput = 2;
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const int kTensorCount = 3;
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// kInput1(0) --> +---+
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// |ADD| --> kOutput(2)
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// kInput2(1) --> +---+
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int first_new_tensor_index;
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ASSERT_EQ(subgraph->AddTensors(kTensorCount, &first_new_tensor_index),
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kTfLiteOk);
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ASSERT_EQ(first_new_tensor_index, 0);
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ASSERT_EQ(subgraph->SetInputs({kInput1, kInput2}), kTfLiteOk);
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ASSERT_EQ(subgraph->SetOutputs({kOutput}), kTfLiteOk);
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SetupTensor(subgraph, kInput1, kTfLiteInt32);
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SetupTensor(subgraph, kInput2, kTfLiteInt32);
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SetupTensor(subgraph, kOutput, kTfLiteInt32);
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TfLiteAddParams* params =
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reinterpret_cast<TfLiteAddParams*>(malloc(sizeof(TfLiteAddParams)));
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params->activation = kTfLiteActNone;
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auto* add_reg = ops::builtin::Register_ADD();
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add_reg->builtin_code = kTfLiteBuiltinAdd;
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int node_index;
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subgraph->AddNodeWithParameters({kInput1, kInput2}, {kOutput}, {}, nullptr, 0,
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params, add_reg, &node_index);
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}
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// Build a subgraph with an mul op. Helper function for testing.
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void SubgraphBuilder::BuildMulSubgraph(Subgraph* subgraph) {
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const int kInput1 = 0;
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const int kInput2 = 1;
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const int kOutput = 2;
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const int kTensorCount = 3;
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// kInput1(0) --> +---+
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// |MUL| --> kOutput(2)
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// kInput2(1) --> +---+
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int first_new_tensor_index;
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ASSERT_EQ(subgraph->AddTensors(kTensorCount, &first_new_tensor_index),
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kTfLiteOk);
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ASSERT_EQ(first_new_tensor_index, 0);
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ASSERT_EQ(subgraph->SetInputs({kInput1, kInput2}), kTfLiteOk);
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ASSERT_EQ(subgraph->SetOutputs({kOutput}), kTfLiteOk);
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SetupTensor(subgraph, kInput1, kTfLiteInt32);
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SetupTensor(subgraph, kInput2, kTfLiteInt32);
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SetupTensor(subgraph, kOutput, kTfLiteInt32);
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TfLiteMulParams* params =
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reinterpret_cast<TfLiteMulParams*>(malloc(sizeof(TfLiteMulParams)));
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params->activation = kTfLiteActNone;
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auto* mul_reg = ops::builtin::Register_MUL();
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mul_reg->builtin_code = kTfLiteBuiltinMul;
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int node_index;
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subgraph->AddNodeWithParameters({kInput1, kInput2}, {kOutput}, {}, nullptr, 0,
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params, mul_reg, &node_index);
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}
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// Build a subgraph with a pad op. Helper function for testing.
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void SubgraphBuilder::BuildPadSubgraph(Subgraph* subgraph) {
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const int kInput1 = 0;
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const int kInput2 = 1;
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const int kOutput = 2;
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const int kTensorCount = 3;
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// kInput1(0) --> +---+
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// |PAD| --> kOutput(2)
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// kInput2(1) --> +---+
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int first_new_tensor_index;
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ASSERT_EQ(subgraph->AddTensors(kTensorCount, &first_new_tensor_index),
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kTfLiteOk);
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ASSERT_EQ(first_new_tensor_index, 0);
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ASSERT_EQ(subgraph->SetInputs({kInput1, kInput2}), kTfLiteOk);
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ASSERT_EQ(subgraph->SetOutputs({kOutput}), kTfLiteOk);
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SetupTensor(subgraph, kInput1, kTfLiteInt32);
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SetupTensor(subgraph, kInput2, kTfLiteInt32);
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SetupTensor(subgraph, kOutput, kTfLiteInt32);
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TfLitePadParams* params =
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reinterpret_cast<TfLitePadParams*>(malloc(sizeof(TfLitePadParams)));
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auto* pad_reg = ops::builtin::Register_PAD();
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pad_reg->builtin_code = kTfLiteBuiltinPad;
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int node_index;
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subgraph->AddNodeWithParameters({kInput1, kInput2}, {kOutput}, {}, nullptr, 0,
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params, pad_reg, &node_index);
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}
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void SubgraphBuilder::BuildIfSubgraph(Subgraph* subgraph) {
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const int kCondInput = 0;
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const int kInput1 = 1;
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const int kInput2 = 2;
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const int kOutput = 3;
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const int kTensorCount = 4;
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// kCondInput(0) --> +----+
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// kInput1(1) ----> | IF | --> kOutput(3)
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// kInput2(2) ----> +----+
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int first_new_tensor_index;
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ASSERT_EQ(subgraph->AddTensors(kTensorCount, &first_new_tensor_index),
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kTfLiteOk);
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ASSERT_EQ(first_new_tensor_index, 0);
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ASSERT_EQ(subgraph->SetInputs({kCondInput, kInput1, kInput2}), kTfLiteOk);
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ASSERT_EQ(subgraph->SetOutputs({kOutput}), kTfLiteOk);
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SetupTensor(subgraph, kCondInput, kTfLiteBool);
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SetupTensor(subgraph, kInput1, kTfLiteInt32);
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SetupTensor(subgraph, kInput2, kTfLiteInt32);
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SetupTensor(subgraph, kOutput, kTfLiteInt32);
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TfLiteIfParams* params =
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reinterpret_cast<TfLiteIfParams*>(malloc(sizeof(TfLiteIfParams)));
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params->then_subgraph_index = 1;
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params->else_subgraph_index = 2;
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auto* if_reg = ops::builtin::Register_IF();
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if_reg->builtin_code = kTfLiteBuiltinIf;
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int node_index;
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subgraph->AddNodeWithParameters({kCondInput, kInput1, kInput2}, {kOutput}, {},
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nullptr, 0, params, if_reg, &node_index);
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}
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void SubgraphBuilder::BuildLessEqualCondSubgraph(Subgraph* subgraph, int rhs) {
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const int kInput1 = 0;
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const int kInput2 = 1;
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const int kOutput = 2;
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const int kConstRhs = 3;
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const int kTensorCount = 4;
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// kInput1(0) ----> +------------+
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// | LESS_EQUAL | --> kOutput(2)
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// kConstRhs(3) --> +------------+
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//
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// kInput2(1) --> (unused)
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int first_new_tensor_index;
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ASSERT_EQ(subgraph->AddTensors(kTensorCount, &first_new_tensor_index),
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kTfLiteOk);
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ASSERT_EQ(first_new_tensor_index, 0);
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ASSERT_EQ(subgraph->SetInputs({kInput1, kInput2}), kTfLiteOk);
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ASSERT_EQ(subgraph->SetOutputs({kOutput}), kTfLiteOk);
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SetupTensor(subgraph, kInput1, kTfLiteInt32);
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SetupTensor(subgraph, kInput2, kTfLiteInt32);
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SetupTensor(subgraph, kOutput, kTfLiteBool);
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auto* le_reg = ops::builtin::Register_LESS_EQUAL();
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le_reg->builtin_code = kTfLiteBuiltinLessEqual;
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CreateConstantInt32Tensor(subgraph, kConstRhs, {1}, {rhs});
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int node_index;
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subgraph->AddNodeWithParameters({kInput1, kConstRhs}, {kOutput}, {}, nullptr,
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0, nullptr, le_reg, &node_index);
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}
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void SubgraphBuilder::BuildAccumulateLoopBodySubgraph(Subgraph* subgraph) {
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const int kInputCounter = 0;
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const int kInputValue = 1;
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const int kOutputCounter = 2;
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const int kOutputValue = 3;
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const int kConstStep = 4;
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const int kTensorCount = 5;
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// kInputCounter(0) --> +-----+
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// | ADD | --> kOutputCounter(2)
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// kConstStep(4) -----> +-----+ |
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// |
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// v
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// +-----+
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// | ADD | --> kOutputValue(3)
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// kInputValue(1) ----------------------+-----+
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int first_new_tensor_index;
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ASSERT_EQ(subgraph->AddTensors(kTensorCount, &first_new_tensor_index),
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kTfLiteOk);
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ASSERT_EQ(first_new_tensor_index, 0);
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ASSERT_EQ(subgraph->SetInputs({kInputCounter, kInputValue}), kTfLiteOk);
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ASSERT_EQ(subgraph->SetOutputs({kOutputCounter, kOutputValue}), kTfLiteOk);
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SetupTensor(subgraph, kInputCounter, kTfLiteInt32);
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SetupTensor(subgraph, kInputValue, kTfLiteInt32);
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SetupTensor(subgraph, kOutputCounter, kTfLiteInt32);
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SetupTensor(subgraph, kOutputValue, kTfLiteInt32);
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CreateConstantInt32Tensor(subgraph, kConstStep, {1}, {1});
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int node_index;
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TfLiteAddParams* params =
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reinterpret_cast<TfLiteAddParams*>(malloc(sizeof(TfLiteAddParams)));
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params->activation = kTfLiteActNone;
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params->pot_scale_int16 = false;
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auto* add_reg = ops::builtin::Register_ADD();
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add_reg->builtin_code = kTfLiteBuiltinAdd;
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subgraph->AddNodeWithParameters({0, 4}, {2}, {}, nullptr, 0, params, add_reg,
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&node_index);
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params = reinterpret_cast<TfLiteAddParams*>(malloc(sizeof(TfLiteAddParams)));
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params->activation = kTfLiteActNone;
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params->pot_scale_int16 = false;
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subgraph->AddNodeWithParameters({2, 1}, {3}, {}, nullptr, 0, params, add_reg,
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&node_index);
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}
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void SubgraphBuilder::BuildPadLoopBodySubgraph(Subgraph* subgraph,
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const std::vector<int> padding) {
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const int kInputCounter = 0;
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const int kInputValue = 1;
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const int kOutputCounter = 2;
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const int kOutputValue = 3;
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const int kConstStep = 4;
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const int kConstPadding = 5;
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const int kTensorCount = 6;
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// kInputCounter(0) --> +-----+
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// | ADD | --> kOutputCounter(2)
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// kConstStep(4) -----> +-----+
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//
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// kInputValue(1) ----> +-----+
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// | PAD | --> kOutputValue(3)
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// kConstPadding(5) --> +-----+
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int first_new_tensor_index;
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ASSERT_EQ(subgraph->AddTensors(kTensorCount, &first_new_tensor_index),
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kTfLiteOk);
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ASSERT_EQ(first_new_tensor_index, 0);
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ASSERT_EQ(subgraph->SetInputs({kInputCounter, kInputValue}), kTfLiteOk);
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ASSERT_EQ(subgraph->SetOutputs({kOutputCounter, kOutputValue}), kTfLiteOk);
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SetupTensor(subgraph, kInputCounter, kTfLiteInt32);
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SetupTensor(subgraph, kInputValue, kTfLiteInt32);
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SetupTensor(subgraph, kOutputCounter, kTfLiteInt32);
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SetupTensor(subgraph, kOutputValue, kTfLiteInt32);
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CreateConstantInt32Tensor(subgraph, kConstStep, {1}, {1});
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ASSERT_EQ(padding.size() % 2, 0);
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int padding_dims = padding.size();
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CreateConstantInt32Tensor(subgraph, kConstPadding, {1, padding_dims},
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padding);
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int node_index;
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TfLiteAddParams* add_params =
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reinterpret_cast<TfLiteAddParams*>(malloc(sizeof(TfLiteAddParams)));
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add_params->activation = kTfLiteActNone;
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auto* add_reg = ops::builtin::Register_ADD();
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add_reg->builtin_code = kTfLiteBuiltinAdd;
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subgraph->AddNodeWithParameters({kInputCounter, kConstStep}, {kOutputCounter},
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{}, nullptr, 0, add_params, add_reg,
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&node_index);
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TfLitePadParams* pad_params =
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reinterpret_cast<TfLitePadParams*>(malloc(sizeof(TfLiteAddParams)));
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auto* pad_reg = ops::builtin::Register_PAD();
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pad_reg->builtin_code = kTfLiteBuiltinPad;
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subgraph->AddNodeWithParameters({kInputValue, kConstPadding}, {kOutputValue},
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{}, nullptr, 0, pad_params, pad_reg,
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&node_index);
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}
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void SubgraphBuilder::BuildWhileSubgraph(Subgraph* subgraph) {
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const int kInput1 = 0;
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const int kInput2 = 1;
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const int kOutput1 = 2;
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const int kOutput2 = 3;
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const int kTensorCount = 4;
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// kInput1(0) --> +-------+ --> kOutput1(2)
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// | WHILE |
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// kInput2(1) --> +-------+ --> kOutput2(3)
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int first_new_tensor_index;
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ASSERT_EQ(subgraph->AddTensors(kTensorCount, &first_new_tensor_index),
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kTfLiteOk);
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ASSERT_EQ(first_new_tensor_index, 0);
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ASSERT_EQ(subgraph->SetInputs({kInput1, kInput2}), kTfLiteOk);
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ASSERT_EQ(subgraph->SetOutputs({kOutput1, kOutput2}), kTfLiteOk);
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SetupTensor(subgraph, kInput1, kTfLiteInt32);
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SetupTensor(subgraph, kInput2, kTfLiteInt32);
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SetupTensor(subgraph, kOutput1, kTfLiteInt32);
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SetupTensor(subgraph, kOutput2, kTfLiteInt32);
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TfLiteWhileParams* params =
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reinterpret_cast<TfLiteWhileParams*>(malloc(sizeof(TfLiteWhileParams)));
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params->cond_subgraph_index = 1;
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params->body_subgraph_index = 2;
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auto* while_reg = ops::builtin::Register_WHILE();
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while_reg->builtin_code = kTfLiteBuiltinWhile;
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int node_index;
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subgraph->AddNodeWithParameters({0, 1}, {2, 3}, {}, nullptr, 0, params,
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while_reg, &node_index);
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}
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void SubgraphBuilder::BuildAssignRandomValueToVariableSubgraph(
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Subgraph* subgraph) {
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const int kConstResourceId = 0;
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const int kRandomValue = 1;
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const int kTensorCount = 3;
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// Construct a graph like ths:
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// %1 = random_int()
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// variable_assign(%0, %1)
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int first_new_tensor_index;
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ASSERT_EQ(subgraph->AddTensors(kTensorCount, &first_new_tensor_index),
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kTfLiteOk);
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ASSERT_EQ(subgraph->SetInputs({}), kTfLiteOk);
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ASSERT_EQ(subgraph->SetOutputs({}), kTfLiteOk);
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SetupTensor(subgraph, kRandomValue, kTfLiteInt32);
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CreateConstantInt32Tensor(subgraph, kConstResourceId, {1}, {1024});
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int node_index;
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subgraph->AddNodeWithParameters({}, {kRandomValue}, {}, nullptr, 0, nullptr,
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::tflite::ops::custom::Register_RANDOM_INT(),
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&node_index);
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subgraph->AddNodeWithParameters(
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{kConstResourceId, kRandomValue}, {}, {}, nullptr, 0, nullptr,
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::tflite::ops::custom::Register_ASSIGN_VARIABLE(), &node_index);
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}
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void SubgraphBuilder::BuildCallOnceAndReadVariableSubgraph(Subgraph* subgraph) {
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const int kConstResourceId = 0;
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const int kOutput = 1;
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const int kTensorCount = 2;
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// Construct a graph like ths:
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// Output: %1
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// %1 = read_variable(%0)
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int first_new_tensor_index;
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ASSERT_EQ(subgraph->AddTensors(kTensorCount, &first_new_tensor_index),
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kTfLiteOk);
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ASSERT_EQ(subgraph->SetInputs({}), kTfLiteOk);
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ASSERT_EQ(subgraph->SetOutputs({kOutput}), kTfLiteOk);
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SetupTensor(subgraph, kOutput, kTfLiteInt32);
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CreateConstantInt32Tensor(subgraph, kConstResourceId, {1}, {1024});
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TfLiteCallOnceParams* params = reinterpret_cast<TfLiteCallOnceParams*>(
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malloc(sizeof(TfLiteCallOnceParams)));
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params->init_subgraph_index = 1;
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int node_index;
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subgraph->AddNodeWithParameters({}, {}, {}, nullptr, 0, params,
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::tflite::ops::builtin::Register_CALL_ONCE(),
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&node_index);
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subgraph->AddNodeWithParameters(
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{kConstResourceId}, {kOutput}, {}, nullptr, 0, nullptr,
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::tflite::ops::custom::Register_READ_VARIABLE(), &node_index);
|
|
}
|
|
|
|
void SubgraphBuilder::CreateConstantInt32Tensor(Subgraph* subgraph,
|
|
int tensor_index,
|
|
const std::vector<int>& shape,
|
|
const std::vector<int>& data) {
|
|
ASSERT_GT(shape.size(), 0);
|
|
int num_elements = 1;
|
|
for (int dim : shape) {
|
|
num_elements *= dim;
|
|
}
|
|
ASSERT_EQ(data.size(), num_elements);
|
|
size_t size_in_bytes = sizeof(int32_t) * num_elements;
|
|
// Maybe aligned.
|
|
int32_t* buffer = reinterpret_cast<int32_t*>(malloc(size_in_bytes));
|
|
for (int i = 0; i < num_elements; ++i) {
|
|
buffer[i] = data[i];
|
|
}
|
|
buffers_.push_back(buffer);
|
|
ASSERT_EQ(subgraph->SetTensorParametersReadOnly(
|
|
tensor_index, kTfLiteInt32, "", shape, {},
|
|
reinterpret_cast<const char*>(buffer), size_in_bytes),
|
|
kTfLiteOk);
|
|
}
|
|
|
|
void FillIntTensor(TfLiteTensor* tensor, const std::vector<int32_t>& data) {
|
|
int count = NumElements(tensor);
|
|
ASSERT_EQ(count, data.size());
|
|
for (int i = 0; i < count; ++i) {
|
|
tensor->data.i32[i] = data[i];
|
|
}
|
|
}
|
|
|
|
void CheckIntTensor(const TfLiteTensor* tensor, const std::vector<int>& shape,
|
|
const std::vector<int32_t>& data) {
|
|
ASSERT_EQ(tensor->dims->size, shape.size());
|
|
for (int i = 0; i < tensor->dims->size; ++i) {
|
|
ASSERT_EQ(tensor->dims->data[i], shape[i]);
|
|
}
|
|
ASSERT_EQ(tensor->type, kTfLiteInt32);
|
|
int count = NumElements(tensor);
|
|
ASSERT_EQ(count, data.size());
|
|
for (int i = 0; i < count; ++i) {
|
|
EXPECT_EQ(tensor->data.i32[i], data[i]);
|
|
}
|
|
}
|
|
|
|
void CheckBoolTensor(const TfLiteTensor* tensor, const std::vector<int>& shape,
|
|
const std::vector<bool>& data) {
|
|
ASSERT_EQ(tensor->dims->size, shape.size());
|
|
for (int i = 0; i < tensor->dims->size; ++i) {
|
|
ASSERT_EQ(tensor->dims->data[i], shape[i]);
|
|
}
|
|
ASSERT_EQ(tensor->type, kTfLiteBool);
|
|
int count = NumElements(tensor);
|
|
ASSERT_EQ(count, data.size());
|
|
for (int i = 0; i < count; ++i) {
|
|
EXPECT_EQ(tensor->data.b[i], data[i]);
|
|
}
|
|
}
|
|
|
|
} // namespace subgraph_test_util
|
|
} // namespace tflite
|