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/test_helpers.h"
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#include "tensorflow/lite/micro/testing/micro_test.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(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(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(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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