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
304 lines
12 KiB
C++
304 lines
12 KiB
C++
/* Copyright 2017 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 TestMaxMinFloat(tflite::BuiltinOperator op,
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std::initializer_list<int> input1_dims_data,
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std::initializer_list<float> input1_data,
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std::initializer_list<int> input2_dims_data,
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std::initializer_list<float> input2_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* input1_dims = IntArrayFromInitializer(input1_dims_data);
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TfLiteIntArray* input2_dims = IntArrayFromInitializer(input2_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(input1_data, input1_dims),
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CreateFloatTensor(input2_data, input2_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 = resolver.FindOp(op);
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TF_LITE_MICRO_EXPECT_NE(nullptr, registration);
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TfLiteIntArray* inputs_array = IntArrayFromInitializer({2, 0, 1});
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TfLiteIntArray* outputs_array = IntArrayFromInitializer({1, 2});
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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 = nullptr;
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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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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-5);
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}
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}
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void TestMaxMinQuantized(
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tflite::BuiltinOperator op, std::initializer_list<int> input1_dims_data,
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std::initializer_list<uint8_t> input1_data, float input1_min,
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float input1_max, std::initializer_list<int> input2_dims_data,
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std::initializer_list<uint8_t> input2_data, float input2_min,
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float input2_max, std::initializer_list<uint8_t> expected_output_data,
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float output_min, float output_max,
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std::initializer_list<int> output_dims_data, uint8_t* output_data) {
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TfLiteIntArray* input1_dims = IntArrayFromInitializer(input1_dims_data);
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TfLiteIntArray* input2_dims = IntArrayFromInitializer(input2_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(input1_data, input1_dims, input1_min, input1_max),
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CreateQuantizedTensor(input2_data, input2_dims, input2_min, input2_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 = resolver.FindOp(op);
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TF_LITE_MICRO_EXPECT_NE(nullptr, registration);
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TfLiteIntArray* inputs_array = IntArrayFromInitializer({2, 0, 1});
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TfLiteIntArray* outputs_array = IntArrayFromInitializer({1, 2});
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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 = nullptr;
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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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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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void TestMaxMinQuantizedInt32(
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tflite::BuiltinOperator op, std::initializer_list<int> input1_dims_data,
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std::initializer_list<int32_t> input1_data, float input1_scale,
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std::initializer_list<int> input2_dims_data,
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std::initializer_list<int32_t> input2_data, float input2_scale,
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std::initializer_list<int32_t> expected_output_data, float output_scale,
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std::initializer_list<int> output_dims_data, int32_t* output_data) {
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TfLiteIntArray* input1_dims = IntArrayFromInitializer(input1_dims_data);
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TfLiteIntArray* input2_dims = IntArrayFromInitializer(input2_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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CreateQuantized32Tensor(input1_data, input1_dims, input1_scale),
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CreateQuantized32Tensor(input2_data, input2_dims, input2_scale),
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CreateQuantized32Tensor(output_data, output_dims, output_scale),
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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 = resolver.FindOp(op);
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TF_LITE_MICRO_EXPECT_NE(nullptr, registration);
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TfLiteIntArray* inputs_array = IntArrayFromInitializer({2, 0, 1});
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TfLiteIntArray* outputs_array = IntArrayFromInitializer({1, 2});
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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 = nullptr;
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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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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(FloatTest) {
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std::initializer_list<float> data1 = {1.0, 0.0, -1.0, 11.0, -2.0, -1.44};
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std::initializer_list<float> data2 = {-1.0, 0.0, 1.0, 12.0, -3.0, -1.43};
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float output_data[6];
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tflite::testing::TestMaxMinFloat(
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tflite::BuiltinOperator_MAXIMUM, {3, 3, 1, 2},
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data1, // input1 shape and data
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{3, 3, 1, 2}, data2, // input2 shape and data
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{1.0, 0.0, 1.0, 12.0, -2.0, -1.43}, // expected output
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{3, 3, 1, 2}, output_data); // output shape and data buffer
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tflite::testing::TestMaxMinFloat(
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tflite::BuiltinOperator_MINIMUM, {3, 3, 1, 2},
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data1, // input1 shape and data
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{3, 3, 1, 2}, data2, // input2 shape and data
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{-1.0, 0.0, -1.0, 11.0, -3.0, -1.44}, // expected output
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{3, 3, 1, 2}, output_data); // output shape and data buffer
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}
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TF_LITE_MICRO_TEST(Uint8Test) {
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std::initializer_list<uint8_t> data1 = {1, 0, 2, 11, 2, 23};
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std::initializer_list<uint8_t> data2 = {0, 0, 1, 12, 255, 1};
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const float input1_min = -63.5;
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const float input1_max = 64;
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const float input2_min = -63.5;
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const float input2_max = 64;
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const float output_min = -63.5;
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const float output_max = 64;
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uint8_t output_data[6];
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tflite::testing::TestMaxMinQuantized(
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tflite::BuiltinOperator_MAXIMUM,
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// input1 shape, data and bounds
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{3, 3, 1, 2}, data1, input1_min, input1_max,
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// input2 shape, data and bounds
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{3, 3, 1, 2}, data2, input2_min, input2_max,
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// expected output
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{1, 0, 2, 12, 255, 23},
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// output bounds, shape and data buffer
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output_min, output_max, {3, 3, 1, 2}, output_data);
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tflite::testing::TestMaxMinQuantized(
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tflite::BuiltinOperator_MINIMUM,
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// input1 shape, data and bounds
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{3, 3, 1, 2}, data1, input1_min, input1_max,
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// input2 shape, data and bounds
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{3, 3, 1, 2}, data2, input2_min, input2_max,
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// expected output
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{0, 0, 1, 11, 2, 1},
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// output bounds, shape and data buffer
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output_min, output_max, {3, 3, 1, 2}, output_data);
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}
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TF_LITE_MICRO_TEST(FloatWithBroadcastTest) {
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std::initializer_list<float> data1 = {1.0, 0.0, -1.0, -2.0, -1.44, 11.0};
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std::initializer_list<float> data2 = {0.5, 2.0};
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float output_data[6];
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tflite::testing::TestMaxMinFloat(
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tflite::BuiltinOperator_MAXIMUM, {3, 3, 1, 2},
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data1, // input1 shape and data
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{1, 2}, data2, // input2 shape and data
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{1.0, 2.0, 0.5, 2.0, 0.5, 11.0}, // expected output
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{3, 3, 1, 2}, output_data); // output shape and data buffer
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tflite::testing::TestMaxMinFloat(
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tflite::BuiltinOperator_MINIMUM, {3, 3, 1, 2},
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data1, // input1 shape and data
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{1, 2}, data2, // input2 shape and data
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{0.5, 0.0, -1.0, -2.0, -1.44, 2.0}, // expected output
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{3, 3, 1, 2}, output_data); // output shape and data buffer
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}
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TF_LITE_MICRO_TEST(Int32WithBroadcastTest) {
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const float input1_scale = 0.5;
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const float input2_scale = 0.5;
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const float output_scale = 0.5;
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std::initializer_list<int32_t> data1 = {1, 0, -1, -2, 3, 11};
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std::initializer_list<int32_t> data2 = {2};
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int32_t output_data[6];
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tflite::testing::TestMaxMinQuantizedInt32(
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tflite::BuiltinOperator_MAXIMUM,
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// input1 shape, data and scale
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{3, 3, 1, 2}, data1, input1_scale,
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// input2 shape, data and scale
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{1, 1}, data2, input2_scale,
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// expected output
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{2, 2, 2, 2, 3, 11},
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// output scale, shape and data buffer
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output_scale, {3, 3, 1, 2}, output_data);
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tflite::testing::TestMaxMinQuantizedInt32(
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tflite::BuiltinOperator_MINIMUM,
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// input1 shape, data and scale
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{3, 3, 1, 2}, data1, input1_scale,
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// input2 shape, data and scale
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{1, 1}, data2, input2_scale,
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// expected output
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{1, 0, -1, -2, 2, 2},
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// output scale, shape and data buffer
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output_scale, {3, 3, 1, 2}, output_data);
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}
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TF_LITE_MICRO_TESTS_END
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