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
107 lines
4.3 KiB
C++
107 lines
4.3 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/micro/testing/micro_test.h"
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#include "tensorflow/lite/micro/testing/test_utils.h"
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TF_LITE_MICRO_TESTS_BEGIN
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TF_LITE_MICRO_TEST(CreateQuantizedBiasTensor) {
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float input_scale = 0.5;
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float weight_scale = 0.5;
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constexpr int tensor_size = 12;
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int dims_arr[] = {4, 2, 3, 2, 1};
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int32_t quantized[tensor_size];
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float pre_quantized[] = {-10, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 10};
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int32_t expected_quantized_values[] = {-40, -20, -16, -12, -8, -4,
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0, 4, 8, 12, 16, 40};
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TfLiteIntArray* dims = tflite::testing::IntArrayFromInts(dims_arr);
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TfLiteTensor result = tflite::testing::CreateQuantizedBiasTensor(
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pre_quantized, quantized, dims, input_scale, weight_scale);
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TF_LITE_MICRO_EXPECT_EQ(result.bytes, tensor_size * sizeof(int32_t));
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TF_LITE_MICRO_EXPECT_EQ(result.dims, dims);
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TF_LITE_MICRO_EXPECT_EQ(result.params.scale, input_scale * weight_scale);
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for (int i = 0; i < tensor_size; i++) {
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TF_LITE_MICRO_EXPECT_EQ(expected_quantized_values[i], result.data.i32[i]);
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}
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}
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TF_LITE_MICRO_TEST(CreatePerChannelQuantizedBiasTensor) {
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float input_scale = 0.5;
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float weight_scales[] = {0.5, 1, 2, 4};
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constexpr int tensor_size = 12;
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const int channels = 4;
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int dims_arr[] = {4, 4, 3, 1, 1};
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int32_t quantized[tensor_size];
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float scales[channels + 1];
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int zero_points[] = {4, 0, 0, 0, 0};
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float pre_quantized[] = {-10, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 10};
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int32_t expected_quantized_values[] = {-40, -20, -16, -6, -4, -2,
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0, 1, 2, 2, 2, 5};
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TfLiteIntArray* dims = tflite::testing::IntArrayFromInts(dims_arr);
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TfLiteAffineQuantization quant;
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TfLiteTensor result = tflite::testing::CreatePerChannelQuantizedBiasTensor(
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pre_quantized, quantized, dims, input_scale, weight_scales, scales,
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zero_points, &quant, 0);
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// Values in scales array start at index 1 since index 0 is dedicated to
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// tracking the tensor size.
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for (int i = 0; i < channels; i++) {
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TF_LITE_MICRO_EXPECT_EQ(scales[i + 1], input_scale * weight_scales[i]);
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}
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TF_LITE_MICRO_EXPECT_EQ(result.bytes, tensor_size * sizeof(int32_t));
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TF_LITE_MICRO_EXPECT_EQ(result.dims, dims);
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for (int i = 0; i < tensor_size; i++) {
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TF_LITE_MICRO_EXPECT_EQ(expected_quantized_values[i], result.data.i32[i]);
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}
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}
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TF_LITE_MICRO_TEST(CreateSymmetricPerChannelQuantizedTensor) {
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const int tensor_size = 12;
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constexpr int channels = 2;
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const int dims_arr[] = {4, channels, 3, 2, 1};
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int8_t quantized[12];
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const float pre_quantized[] = {-127, -55, -4, -3, -2, -1,
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0, 1, 2, 3, 4, 63.5};
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const int8_t expected_quantized_values[] = {-127, -55, -4, -3, -2, -1,
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0, 2, 4, 6, 8, 127};
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float expected_scales[] = {1.0, 0.5};
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TfLiteIntArray* dims = tflite::testing::IntArrayFromInts(dims_arr);
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int zero_points[channels + 1];
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float scales[channels + 1];
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TfLiteAffineQuantization quant;
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TfLiteTensor result =
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tflite::testing::CreateSymmetricPerChannelQuantizedTensor(
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pre_quantized, quantized, dims, scales, zero_points, &quant, 0);
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TF_LITE_MICRO_EXPECT_EQ(result.bytes, tensor_size * sizeof(int8_t));
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TF_LITE_MICRO_EXPECT_EQ(result.dims, dims);
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TfLiteFloatArray* result_scales =
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static_cast<TfLiteAffineQuantization*>(result.quantization.params)->scale;
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for (int i = 0; i < channels; i++) {
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TF_LITE_MICRO_EXPECT_EQ(result_scales->data[i], expected_scales[i]);
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}
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for (int i = 0; i < tensor_size; i++) {
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TF_LITE_MICRO_EXPECT_EQ(expected_quantized_values[i], result.data.int8[i]);
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}
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}
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TF_LITE_MICRO_TESTS_END
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