b6d13bb0a8
The TFLM team is preparing to provide an "optimized" memory build option. This build option will eliminate non-needed/essential fields from core TFLite structs. The first big change is to reduce the number of pointers on TfLiteTensor. Many models have multiple tensors (e.g. benchmark keyword has 54) and each pointer adds up for TFLM. This cleanup pass removes the soon to be un-used 'name' field from TfLiteTensor. PiperOrigin-RevId: 316000388 Change-Id: I230865014d5a59b78c1c1c9f5eda784f6d611e77
229 lines
10 KiB
C++
229 lines
10 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/c/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/micro/all_ops_resolver.h"
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#include "tensorflow/lite/micro/testing/micro_test.h"
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#include "tensorflow/lite/micro/testing/test_utils.h"
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namespace tflite {
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namespace testing {
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namespace {
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void TestPreluFloat(std::initializer_list<int> input_dims_data,
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std::initializer_list<float> input_data,
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std::initializer_list<int> alpha_dims_data,
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std::initializer_list<float> alpha_data,
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std::initializer_list<float> expected_output_data,
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std::initializer_list<int> output_dims_data,
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float* output_data) {
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TfLiteIntArray* input_dims = IntArrayFromInitializer(input_dims_data);
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TfLiteIntArray* alpha_dims = IntArrayFromInitializer(alpha_dims_data);
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TfLiteIntArray* output_dims = IntArrayFromInitializer(output_dims_data);
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const int output_dims_count = ElementCount(*output_dims);
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constexpr int inputs_size = 2;
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constexpr int outputs_size = 1;
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constexpr int tensors_size = inputs_size + outputs_size;
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TfLiteTensor tensors[tensors_size] = {
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CreateFloatTensor(input_data, input_dims),
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CreateFloatTensor(alpha_data, alpha_dims),
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CreateFloatTensor(output_data, output_dims),
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};
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TfLiteContext context;
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PopulateContext(tensors, tensors_size, micro_test::reporter, &context);
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::tflite::AllOpsResolver resolver;
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const TfLiteRegistration* registration =
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resolver.FindOp(tflite::BuiltinOperator_PRELU);
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TF_LITE_MICRO_EXPECT_NE(nullptr, registration);
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size_t init_data_size = 0;
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void* user_data = nullptr;
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if (registration->init) {
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user_data = registration->init(&context, nullptr, init_data_size);
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}
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int inputs_array_data[] = {2, 0, 1};
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TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data);
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int outputs_array_data[] = {1, 2};
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TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
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TfLiteIntArray* temporaries_array = IntArrayFromInitializer({0});
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TfLiteNode node;
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node.inputs = inputs_array;
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node.outputs = outputs_array;
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node.temporaries = temporaries_array;
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node.user_data = user_data;
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node.builtin_data = nullptr;
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node.custom_initial_data = nullptr;
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node.custom_initial_data_size = 0;
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node.delegate = nullptr;
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if (registration->prepare) {
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->prepare(&context, &node));
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}
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TF_LITE_MICRO_EXPECT_NE(nullptr, registration->invoke);
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->invoke(&context, &node));
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if (registration->free) {
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registration->free(&context, user_data);
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}
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for (int i = 0; i < output_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_NEAR(expected_output_data.begin()[i], output_data[i],
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1e-5f);
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}
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}
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// Template argument T can be either uint8_t or int8_t depending on which type
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// of quantization required to be tested.
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template <typename T>
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void TestPreluQuantized(std::initializer_list<int> input_dims_data,
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std::initializer_list<T> input_data, float input_min,
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float input_max,
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std::initializer_list<int> alpha_dims_data,
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std::initializer_list<T> alpha_data, float alpha_min,
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float alpha_max,
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std::initializer_list<T> expected_output_data,
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std::initializer_list<int> output_dims_data,
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float output_min, float output_max, T* output_data) {
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TfLiteIntArray* input_dims = IntArrayFromInitializer(input_dims_data);
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TfLiteIntArray* alpha_dims = IntArrayFromInitializer(alpha_dims_data);
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TfLiteIntArray* output_dims = IntArrayFromInitializer(output_dims_data);
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const int output_dims_count = ElementCount(*output_dims);
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constexpr int inputs_size = 2;
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constexpr int outputs_size = 1;
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constexpr int tensors_size = inputs_size + outputs_size;
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TfLiteTensor tensors[tensors_size] = {
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CreateQuantizedTensor(input_data, input_dims, input_min, input_max),
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CreateQuantizedTensor(alpha_data, alpha_dims, alpha_min, alpha_max),
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CreateQuantizedTensor(output_data, output_dims, output_min, output_max),
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};
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TfLiteContext context;
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PopulateContext(tensors, tensors_size, micro_test::reporter, &context);
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::tflite::AllOpsResolver resolver;
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const TfLiteRegistration* registration =
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resolver.FindOp(tflite::BuiltinOperator_PRELU);
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TF_LITE_MICRO_EXPECT_NE(nullptr, registration);
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size_t init_data_size = 0;
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void* user_data = nullptr;
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if (registration->init) {
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user_data = registration->init(&context, nullptr, init_data_size);
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}
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int inputs_array_data[] = {2, 0, 1};
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TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data);
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int outputs_array_data[] = {1, 2};
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TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
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TfLiteIntArray* temporaries_array = IntArrayFromInitializer({0});
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TfLiteNode node;
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node.inputs = inputs_array;
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node.outputs = outputs_array;
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node.temporaries = temporaries_array;
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node.user_data = user_data;
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node.builtin_data = nullptr;
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node.custom_initial_data = nullptr;
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node.custom_initial_data_size = 0;
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node.delegate = nullptr;
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if (registration->prepare) {
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->prepare(&context, &node));
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}
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TF_LITE_MICRO_EXPECT_NE(nullptr, registration->invoke);
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->invoke(&context, &node));
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if (registration->free) {
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registration->free(&context, user_data);
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}
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for (int i = 0; i < output_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_EQ(expected_output_data.begin()[i], output_data[i]);
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}
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}
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} // namespace
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} // namespace testing
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} // namespace tflite
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TF_LITE_MICRO_TESTS_BEGIN
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TF_LITE_MICRO_TEST(FloatPreluActivationsOpTest) {
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const int output_dims_count = 12;
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float output_data[output_dims_count];
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tflite::testing::TestPreluFloat({3, 2, 2, 3}, // input shape
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{
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0.0f, 0.0f, 0.0f, // Row 1, Column 1
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1.0f, 1.0f, 1.0f, // Row 1, Column 2
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-1.0f, -1.0f, -1.0f, // Row 2, Column 1
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-2.0f, -2.0f, -2.0f, // Row 1, Column 2
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},
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{3, 1, 1, 3}, // alpha shape
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{0.0f, 1.0f, 2.0f}, // alpha values
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{
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0.0f, 0.0f, 0.0f, // Row 1, Column 1
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1.0f, 1.0f, 1.0f, // Row 1, Column 2
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0.0f, -1.0f, -2.0f, // Row 2, Column 1
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0.0f, -2.0f, -4.0f, // Row 1, Column 2
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},
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{3, 2, 2, 3}, // output shape
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output_data);
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}
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TF_LITE_MICRO_TEST(QuantizedUint8PreluActivationsOpTest) {
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using tflite::testing::F2Q;
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const float kMin = -4;
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const float kMax = 127.f / 32.f;
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const float kAlphaMin = -0.5f;
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const float kAlphaMax = 0.5f;
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const int output_dims_count = 12;
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uint8_t output_data[output_dims_count];
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tflite::testing::TestPreluQuantized(
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{3, 2, 2, 3}, // input shape
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{F2Q(0.0f, kMin, kMax), F2Q(0.0f, kMin, kMax), F2Q(0.0f, kMin, kMax),
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F2Q(0.5f, kMin, kMax), F2Q(0.5f, kMin, kMax), F2Q(0.5f, kMin, kMax),
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F2Q(-1.0f, kMin, kMax), F2Q(-1.0f, kMin, kMax), F2Q(-1.0f, kMin, kMax),
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F2Q(-0.25f, kMin, kMax), F2Q(-0.25f, kMin, kMax),
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F2Q(-0.25f, kMin, kMax)},
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kMin, kMax, {3, 1, 1, 3}, // alpha shape
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{F2Q(0.0f, kMin, kMax), F2Q(0.5f, kMin, kMax), F2Q(-0.5f, kMin, kMax)},
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kMin, kMax,
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{F2Q(0.0f, kMin, kMax), F2Q(0.0f, kMin, kMax), F2Q(0.0f, kMin, kMax),
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F2Q(0.5f, kMin, kMax), F2Q(0.5f, kMin, kMax), F2Q(0.5f, kMin, kMax),
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F2Q(0.0f, kMin, kMax), F2Q(-0.5f, kMin, kMax), F2Q(0.5f, kMin, kMax),
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F2Q(0.0f, kMin, kMax), F2Q(-0.125f, kMin, kMax),
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F2Q(0.125f, kMin, kMax)},
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{3, 2, 2, 3}, // output shape
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kMin, kMax, output_data);
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}
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TF_LITE_MICRO_TEST(QuantizedInt8PreluActivationsOpTest) {
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using tflite::testing::F2QS;
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const float kMin = -1;
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const float kMax = 127.f / 128.f;
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const float kAlphaMin = -0.5f;
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const float kAlphaMax = 0.5f;
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const int output_dims_count = 12;
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int8_t output_data[output_dims_count];
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tflite::testing::TestPreluQuantized(
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{3, 2, 2, 3}, // input shape
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{F2QS(0.0f, kMin, kMax), F2QS(0.0f, kMin, kMax), F2QS(0.0f, kMin, kMax),
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F2QS(0.5f, kMin, kMax), F2QS(0.5f, kMin, kMax), F2QS(0.5f, kMin, kMax),
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F2QS(-1.0f, kMin, kMax), F2QS(-1.0f, kMin, kMax),
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F2QS(-1.0f, kMin, kMax), F2QS(-0.25f, kMin, kMax),
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F2QS(-0.25f, kMin, kMax), F2QS(-0.25f, kMin, kMax)},
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kMin, kMax, {3, 1, 1, 3}, // alpha shape
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{F2QS(0.0f, kMin, kMax), F2QS(0.5f, kMin, kMax), F2QS(-0.5f, kMin, kMax)},
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kMin, kMax,
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{F2QS(0.0f, kMin, kMax), F2QS(0.0f, kMin, kMax), F2QS(0.0f, kMin, kMax),
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F2QS(0.5f, kMin, kMax), F2QS(0.5f, kMin, kMax), F2QS(0.5f, kMin, kMax),
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F2QS(0.0f, kMin, kMax), F2QS(-0.5f, kMin, kMax), F2QS(0.5f, kMin, kMax),
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F2QS(0.0f, kMin, kMax), F2QS(-0.125f, kMin, kMax),
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F2QS(0.125f, kMin, kMax)},
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{3, 2, 2, 3}, // output shape
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kMin, kMax, output_data);
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}
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TF_LITE_MICRO_TESTS_END
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