First commit XiUOS
This commit is contained in:
xuetest
@@ -0,0 +1,2 @@
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*.h5
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*.tflite
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config USING_TFLITE_MNIST
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bool "mnist demo app for tflite micro"
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depends on INTELLIGENT_TFLITE
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default n
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@@ -0,0 +1,8 @@
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ifeq ($(CONFIG_USING_TFLITE_MNIST),y)
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SRC_FILES := \
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mnistapp.cpp \
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mnistmain.c
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CPPPATHS += -I.
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endif
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include $(KERNEL_ROOT)/compiler.mk
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@@ -0,0 +1,19 @@
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# MNIST 说明
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## 使用
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tools/mnist-train.py 训练生成 mnist 模型。
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tools/mnist-inference.py 使用 mnist 模型进行推理。
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tools/mnist-c-model.py 将 mnist 模型转换成 C 的数组保存在 model.h 中。
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tools/mnist-c-digit.py 将 mnist 数据集中的某个数字转成数组保存在 digit.h 中。
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## 参考资料
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https://tensorflow.google.cn/lite/performance/post_training_quantization
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https://tensorflow.google.cn/lite/performance/post_training_integer_quant
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https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/train/train_hello_world_model.ipynb
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@@ -0,0 +1,42 @@
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/*
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* Copyright (c) 2020 AIIT XUOS Lab
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* XiOS is licensed under Mulan PSL v2.
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* You can use this software according to the terms and conditions of the Mulan PSL v2.
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* You may obtain a copy of Mulan PSL v2 at:
|
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* http://license.coscl.org.cn/MulanPSL2
|
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* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
|
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* EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
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* MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
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* See the Mulan PSL v2 for more details.
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*/
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const float mnist_digit[] = {
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0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
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0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.33, 0.73, 0.62, 0.59, 0.24, 0.14, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
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0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.87, 1.00, 1.00, 1.00, 1.00, 0.95, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.67, 0.20, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
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0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.26, 0.45, 0.28, 0.45, 0.64, 0.89, 1.00, 0.88, 1.00, 1.00, 1.00, 0.98, 0.90, 1.00, 1.00, 0.55, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
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||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.07, 0.26, 0.05, 0.26, 0.26, 0.26, 0.23, 0.08, 0.93, 1.00, 0.42, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
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||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.33, 0.99, 0.82, 0.07, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.09, 0.91, 1.00, 0.33, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.51, 1.00, 0.93, 0.17, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.23, 0.98, 1.00, 0.24, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.52, 1.00, 0.73, 0.02, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.04, 0.80, 0.97, 0.23, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.49, 1.00, 0.71, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.29, 0.98, 0.94, 0.22, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.07, 0.87, 1.00, 0.65, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.01, 0.80, 1.00, 0.86, 0.14, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.15, 1.00, 1.00, 0.30, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.12, 0.88, 1.00, 0.45, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.52, 1.00, 1.00, 0.20, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.24, 0.95, 1.00, 1.00, 0.20, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.47, 1.00, 1.00, 0.86, 0.16, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
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0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.47, 1.00, 0.81, 0.07, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
|
||||
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00
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};
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const int mnist_label = 7;
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@@ -0,0 +1,103 @@
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/*
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* Copyright (c) 2020 AIIT XUOS Lab
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* XiOS is licensed under Mulan PSL v2.
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* You can use this software according to the terms and conditions of the Mulan PSL v2.
|
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* You may obtain a copy of Mulan PSL v2 at:
|
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* http://license.coscl.org.cn/MulanPSL2
|
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* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
|
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* EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
|
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* MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
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* See the Mulan PSL v2 for more details.
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*/
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#include <xiuos.h>
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#include "tensorflow/lite/micro/all_ops_resolver.h"
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#include "tensorflow/lite/micro/micro_error_reporter.h"
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#include "tensorflow/lite/micro/micro_interpreter.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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#include "tensorflow/lite/version.h"
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#include "digit.h"
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#include "model.h"
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namespace {
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tflite::ErrorReporter* error_reporter = nullptr;
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const tflite::Model* model = nullptr;
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tflite::MicroInterpreter* interpreter = nullptr;
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TfLiteTensor* input = nullptr;
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TfLiteTensor* output = nullptr;
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constexpr int kTensorArenaSize = 110 * 1024;
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//uint8_t *tensor_arena = nullptr;
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uint8_t tensor_arena[kTensorArenaSize];
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}
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extern "C" void mnist_app() {
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tflite::MicroErrorReporter micro_error_reporter;
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error_reporter = µ_error_reporter;
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model = tflite::GetModel(mnist_model);
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if (model->version() != TFLITE_SCHEMA_VERSION) {
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TF_LITE_REPORT_ERROR(error_reporter,
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"Model provided is schema version %d not equal "
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"to supported version %d.",
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model->version(), TFLITE_SCHEMA_VERSION);
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return;
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}
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/*
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tensor_arena = (uint8_t *)rt_malloc(kTensorArenaSize);
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if (tensor_arena == nullptr) {
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TF_LITE_REPORT_ERROR(error_reporter, "malloc for tensor_arena failed");
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return;
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}
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*/
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tflite::AllOpsResolver resolver;
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tflite::MicroInterpreter static_interpreter(
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model, resolver, tensor_arena, kTensorArenaSize, error_reporter);
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interpreter = &static_interpreter;
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// Allocate memory from the tensor_arena for the model's tensors.
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TfLiteStatus allocate_status = interpreter->AllocateTensors();
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if (allocate_status != kTfLiteOk) {
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TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
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return;
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}
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input = interpreter->input(0);
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output = interpreter->output(0);
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KPrintf("\n------- Input Digit -------\n");
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for (int i = 0; i < 28; i++) {
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for (int j = 0; j < 28; j++) {
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if (mnist_digit[i*28+j] > 0.3)
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KPrintf("#");
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else
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KPrintf(".");
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}
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KPrintf("\n");
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}
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for (int i = 0; i < 28*28; i++) {
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input->data.f[i] = mnist_digit[i];
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}
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TfLiteStatus invoke_status = interpreter->Invoke();
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if (invoke_status != kTfLiteOk) {
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TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed on x_val\n");
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return;
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}
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// Read the predicted y value from the model's output tensor
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float max = 0.0;
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int index;
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for (int i = 0; i < 10; i++) {
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if(output->data.f[i]>max){
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max = output->data.f[i];
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index = i;
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}
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}
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KPrintf("\n------- Output Result -------\n");
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KPrintf("result is %d\n\n", index);
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}
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@@ -0,0 +1,20 @@
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/*
|
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* Copyright (c) 2020 AIIT XUOS Lab
|
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* XiOS is licensed under Mulan PSL v2.
|
||||
* You can use this software according to the terms and conditions of the Mulan PSL v2.
|
||||
* You may obtain a copy of Mulan PSL v2 at:
|
||||
* http://license.coscl.org.cn/MulanPSL2
|
||||
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
|
||||
* EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
|
||||
* MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
|
||||
* See the Mulan PSL v2 for more details.
|
||||
*/
|
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#include <xiuos.h>
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void mnist_app(void);
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int tfmnist(void) {
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mnist_app();
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}
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SHELL_EXPORT_CMD(SHELL_CMD_PERMISSION(0)|SHELL_CMD_TYPE(SHELL_TYPE_CMD_FUNC)|SHELL_CMD_PARAM_NUM(0), tfmnist, tfmnist, run mnist demo of tflite);
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File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,47 @@
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# ==========================================================================================
|
||||
# Copyright (c) 2020 AIIT XUOS Lab
|
||||
# XiOS is licensed under Mulan PSL v2.
|
||||
# You can use this software according to the terms and conditions of the Mulan PSL v2.
|
||||
# You may obtain a copy of Mulan PSL v2 at:
|
||||
# http://license.coscl.org.cn/MulanPSL2
|
||||
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
|
||||
# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
|
||||
# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
|
||||
# See the Mulan PSL v2 for more details.
|
||||
# ==========================================================================================
|
||||
#!/usr/bin/env python3
|
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|
||||
import tensorflow as tf
|
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|
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print("TensorFlow version %s" % (tf.__version__))
|
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def show(image):
|
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for i in range(28):
|
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for j in range(28):
|
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if image[i][j] > 0.3:
|
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print('#', end = '')
|
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else:
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print('.', end = '')
|
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print()
|
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digit_file_path = 'digit.h'
|
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digit_content = '''const float mnist_digit[] = {
|
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%s
|
||||
};
|
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const int mnist_label = %d;
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'''
|
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if __name__ == '__main__':
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mnist = tf.keras.datasets.mnist
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(_, _), (test_images, test_labels) = mnist.load_data()
|
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index = 0
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shape = 28
|
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image = test_images[index].astype('float32')/255
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label = test_labels[index]
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print('label: %d' % label)
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#show(image)
|
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digit_data = (',\n ').join([ (', ').join([ '%.2f' % image[row][col] for col in range(shape)]) for row in range(shape)])
|
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digit_file = open(digit_file_path, 'w')
|
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digit_file.write(digit_content % (digit_data, label))
|
||||
digit_file.close()
|
||||
|
||||
@@ -0,0 +1,34 @@
|
||||
# ==========================================================================================
|
||||
# Copyright (c) 2020 AIIT XUOS Lab
|
||||
# XiOS is licensed under Mulan PSL v2.
|
||||
# You can use this software according to the terms and conditions of the Mulan PSL v2.
|
||||
# You may obtain a copy of Mulan PSL v2 at:
|
||||
# http://license.coscl.org.cn/MulanPSL2
|
||||
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
|
||||
# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
|
||||
# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
|
||||
# See the Mulan PSL v2 for more details.
|
||||
# ==========================================================================================
|
||||
#!/usr/bin/env python3
|
||||
|
||||
#tflite_file_path = 'mnist-default-quan.tflite'
|
||||
tflite_file_path = 'mnist.tflite'
|
||||
model_file_path = 'model.h'
|
||||
|
||||
tflite_file = open(tflite_file_path, 'rb')
|
||||
tflite_data = tflite_file.read()
|
||||
tflite_file.close()
|
||||
tflite_array = [ '0x%02x' % byte for byte in tflite_data ]
|
||||
|
||||
model_content = '''unsigned char mnist_model[] = {
|
||||
%s
|
||||
};
|
||||
unsigned int mnist_model_len = %d;
|
||||
'''
|
||||
# 12 bytes in a line, the same with xxd
|
||||
bytes_of_line = 12
|
||||
model_data = (',\n ').join([ (', ').join(tflite_array[i:i+bytes_of_line]) for i in range(0, len(tflite_array), bytes_of_line) ])
|
||||
model_file = open(model_file_path, 'w')
|
||||
model_file.write(model_content % (model_data, len(tflite_array)))
|
||||
model_file.close()
|
||||
|
||||
@@ -0,0 +1,51 @@
|
||||
# ==========================================================================================
|
||||
# Copyright (c) 2020 AIIT XUOS Lab
|
||||
# XiOS is licensed under Mulan PSL v2.
|
||||
# You can use this software according to the terms and conditions of the Mulan PSL v2.
|
||||
# You may obtain a copy of Mulan PSL v2 at:
|
||||
# http://license.coscl.org.cn/MulanPSL2
|
||||
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
|
||||
# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
|
||||
# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
|
||||
# See the Mulan PSL v2 for more details.
|
||||
# ==========================================================================================
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import tensorflow as tf
|
||||
|
||||
print("TensorFlow version %s" % (tf.__version__))
|
||||
|
||||
MODEL_NAME_H5 = 'mnist.h5'
|
||||
MODEL_NAME_TFLITE = 'mnist.tflite'
|
||||
DEFAULT_QUAN_MODEL_NAME_TFLITE = 'mnist-default-quan.tflite'
|
||||
FULL_QUAN_MODEL_NAME_TFLITE = 'mnist-full-quan.tflite'
|
||||
|
||||
|
||||
def show(image):
|
||||
for i in range(28):
|
||||
for j in range(28):
|
||||
if image[i][j][0] > 0.3:
|
||||
print('#', end = '')
|
||||
else:
|
||||
print(' ', end = '')
|
||||
print()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
mnist = tf.keras.datasets.mnist
|
||||
(_, _), (test_images, test_labels) = mnist.load_data()
|
||||
test_images = test_images.reshape(10000, 28, 28, 1)
|
||||
index = 0
|
||||
input_image = test_images[index].astype('float32')/255
|
||||
target_label = test_labels[index]
|
||||
|
||||
interpreter = tf.lite.Interpreter(model_path = DEFAULT_QUAN_MODEL_NAME_TFLITE)
|
||||
interpreter.allocate_tensors()
|
||||
input_details = interpreter.get_input_details()[0]
|
||||
output_details = interpreter.get_output_details()[0]
|
||||
interpreter.set_tensor(input_details['index'], [input_image])
|
||||
interpreter.invoke()
|
||||
output = interpreter.get_tensor(output_details['index'])[0]
|
||||
|
||||
show(input_image)
|
||||
print('target label: %d, predict label: %d' % (target_label, output.argmax()))
|
||||
@@ -0,0 +1,120 @@
|
||||
# ==========================================================================================
|
||||
# Copyright (c) 2020 AIIT XUOS Lab
|
||||
# XiOS is licensed under Mulan PSL v2.
|
||||
# You can use this software according to the terms and conditions of the Mulan PSL v2.
|
||||
# You may obtain a copy of Mulan PSL v2 at:
|
||||
# http://license.coscl.org.cn/MulanPSL2
|
||||
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
|
||||
# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
|
||||
# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
|
||||
# See the Mulan PSL v2 for more details.
|
||||
# ==========================================================================================
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import os
|
||||
import tensorflow as tf
|
||||
|
||||
print("TensorFlow version %s" % (tf.__version__))
|
||||
|
||||
MODEL_NAME_H5 = 'mnist.h5'
|
||||
MODEL_NAME_TFLITE = 'mnist.tflite'
|
||||
DEFAULT_QUAN_MODEL_NAME_TFLITE = 'mnist-default-quan.tflite'
|
||||
FULL_QUAN_MODEL_NAME_TFLITE = 'mnist-full-quan.tflite'
|
||||
|
||||
def build_model(model_name):
|
||||
print('\n>>> load mnist dataset')
|
||||
mnist = tf.keras.datasets.mnist
|
||||
(train_images, train_labels),(test_images, test_labels) = mnist.load_data()
|
||||
print("train images shape: ", train_images.shape)
|
||||
print("train labels shape: ", train_labels.shape)
|
||||
print("test images shape: ", test_images.shape)
|
||||
print("test labels shape: ", test_labels.shape)
|
||||
|
||||
# transform label to categorical, like: 2 -> [0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
|
||||
print('\n>>> transform label to categorical')
|
||||
train_labels = tf.keras.utils.to_categorical(train_labels)
|
||||
test_labels = tf.keras.utils.to_categorical(test_labels)
|
||||
print("train labels shape: ", train_labels.shape)
|
||||
print("test labels shape: ", test_labels.shape)
|
||||
|
||||
# transform color like: [0, 255] -> 0.xxx
|
||||
print('\n>>> transform image color into float32')
|
||||
train_images = train_images.astype('float32') / 255
|
||||
test_images = test_images.astype('float32') / 255
|
||||
|
||||
# reshape image like: (60000, 28, 28) -> (60000, 28, 28, 1)
|
||||
print('\n>>> reshape image with color channel')
|
||||
train_images = train_images.reshape((60000, 28, 28, 1))
|
||||
test_images = test_images.reshape((10000, 28, 28, 1))
|
||||
print("train images shape: ", train_images.shape)
|
||||
print("test images shape: ", test_images.shape)
|
||||
|
||||
print('\n>>> build model')
|
||||
model = tf.keras.models.Sequential([
|
||||
tf.keras.layers.Conv2D(32, (3, 3), activation=tf.nn.relu, input_shape=(28, 28, 1)),
|
||||
tf.keras.layers.MaxPooling2D((2, 2)),
|
||||
tf.keras.layers.Conv2D(64, (3, 3), activation=tf.nn.relu),
|
||||
tf.keras.layers.MaxPooling2D((2, 2)),
|
||||
tf.keras.layers.Conv2D(64, (3, 3), activation=tf.nn.relu),
|
||||
tf.keras.layers.Flatten(),
|
||||
tf.keras.layers.Dense(64, activation=tf.nn.relu),
|
||||
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
|
||||
])
|
||||
model.compile(optimizer='rmsprop',
|
||||
loss='categorical_crossentropy',
|
||||
metrics=['accuracy'])
|
||||
model.summary()
|
||||
|
||||
print('\n>>> train the model')
|
||||
early_stopping = tf.keras.callbacks.EarlyStopping(
|
||||
monitor='loss', min_delta=0.0005, patience=3, verbose=1, mode='auto',
|
||||
baseline=None, restore_best_weights=True
|
||||
)
|
||||
model.fit(train_images, train_labels, epochs=100, batch_size=64, callbacks=[early_stopping])
|
||||
|
||||
print('\n>>> evaluate the model')
|
||||
test_loss, test_acc = model.evaluate(test_images, test_labels)
|
||||
print("lost: %f, accuracy: %f" % (test_loss, test_acc))
|
||||
|
||||
print('\n>>> save the keras model as %s' % model_name)
|
||||
model.save(model_name)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
if not os.path.exists(MODEL_NAME_H5):
|
||||
build_model(MODEL_NAME_H5)
|
||||
|
||||
if not os.path.exists(MODEL_NAME_TFLITE):
|
||||
print('\n>>> save the tflite model as %s' % MODEL_NAME_TFLITE)
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model(tf.keras.models.load_model(MODEL_NAME_H5))
|
||||
tflite_model = converter.convert()
|
||||
with open(MODEL_NAME_TFLITE, "wb") as f:
|
||||
f.write(tflite_model)
|
||||
|
||||
if not os.path.exists(DEFAULT_QUAN_MODEL_NAME_TFLITE):
|
||||
print('\n>>> save the default quantized model as %s' % DEFAULT_QUAN_MODEL_NAME_TFLITE)
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model(tf.keras.models.load_model(MODEL_NAME_H5))
|
||||
converter.optimizations = [tf.lite.Optimize.DEFAULT]
|
||||
tflite_model = converter.convert()
|
||||
with open(DEFAULT_QUAN_MODEL_NAME_TFLITE, "wb") as f:
|
||||
f.write(tflite_model)
|
||||
|
||||
if not os.path.exists(FULL_QUAN_MODEL_NAME_TFLITE):
|
||||
mnist = tf.keras.datasets.mnist
|
||||
(train_images, _), (_, _) = mnist.load_data()
|
||||
train_images = train_images.astype('float32') / 255
|
||||
train_images = train_images.reshape((60000, 28, 28, 1))
|
||||
def representative_data_gen():
|
||||
for input_value in tf.data.Dataset.from_tensor_slices(train_images).batch(1).take(100):
|
||||
yield [input_value]
|
||||
print('\n>>> save the full quantized model as %s' % DEFAULT_QUAN_MODEL_NAME_TFLITE)
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model(tf.keras.models.load_model(MODEL_NAME_H5))
|
||||
converter.optimizations = [tf.lite.Optimize.DEFAULT]
|
||||
converter.representative_dataset = representative_data_gen
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
|
||||
converter.inference_input_type = tf.uint8
|
||||
converter.inference_output_type = tf.uint8
|
||||
tflite_model = converter.convert()
|
||||
with open(FULL_QUAN_MODEL_NAME_TFLITE, "wb") as f:
|
||||
f.write(tflite_model)
|
||||
Reference in New Issue
Block a user