First commit XiUOS

2021-04-28 17:49:18 +08:00
commit 6001051eb7
1331 changed files with 433955 additions and 0 deletions
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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.
+*/
+#include <xiuos.h>
+
+#include "tensorflow/lite/micro/all_ops_resolver.h"
+#include "tensorflow/lite/micro/micro_error_reporter.h"
+#include "tensorflow/lite/micro/micro_interpreter.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+
+#include "digit.h"
+#include "model.h"
+
+namespace {
+tflite::ErrorReporter* error_reporter = nullptr;
+const tflite::Model* model = nullptr;
+tflite::MicroInterpreter* interpreter = nullptr;
+TfLiteTensor* input = nullptr;
+TfLiteTensor* output = nullptr;
+constexpr int kTensorArenaSize = 110 * 1024;
+//uint8_t *tensor_arena = nullptr;
+uint8_t tensor_arena[kTensorArenaSize];
+}
+
+extern "C" void mnist_app() {
+  tflite::MicroErrorReporter micro_error_reporter;
+  error_reporter = &micro_error_reporter;
+
+  model = tflite::GetModel(mnist_model);
+  if (model->version() != TFLITE_SCHEMA_VERSION) {
+    TF_LITE_REPORT_ERROR(error_reporter,
+                         "Model provided is schema version %d not equal "
+                         "to supported version %d.",
+                         model->version(), TFLITE_SCHEMA_VERSION);
+    return;
+  }
+  /*
+  tensor_arena = (uint8_t *)rt_malloc(kTensorArenaSize);
+  if (tensor_arena == nullptr) {
+    TF_LITE_REPORT_ERROR(error_reporter, "malloc for tensor_arena failed");
+    return;
+  }
+  */
+
+  tflite::AllOpsResolver resolver;
+  tflite::MicroInterpreter static_interpreter(
+      model, resolver, tensor_arena, kTensorArenaSize, error_reporter);
+  interpreter = &static_interpreter;
+
+  // Allocate memory from the tensor_arena for the model's tensors.
+  TfLiteStatus allocate_status = interpreter->AllocateTensors();
+  if (allocate_status != kTfLiteOk) {
+    TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
+    return;
+  }
+
+  input = interpreter->input(0);
+  output = interpreter->output(0);
+
+  KPrintf("\n------- Input Digit -------\n");
+  for (int i = 0; i < 28; i++) {
+    for (int j = 0; j < 28; j++) {
+      if (mnist_digit[i*28+j] > 0.3)
+        KPrintf("#");
+      else
+        KPrintf(".");
+    }
+    KPrintf("\n");
+  }
+
+  for (int i = 0; i < 28*28; i++) {
+        input->data.f[i] = mnist_digit[i];
+    }
+
+  TfLiteStatus invoke_status = interpreter->Invoke();
+  if (invoke_status != kTfLiteOk) {
+    TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed on x_val\n");
+    return;
+  }
+
+  // Read the predicted y value from the model's output tensor
+  float max = 0.0;
+  int index;
+  for (int i = 0; i < 10; i++) {
+         if(output->data.f[i]>max){
+           max = output->data.f[i];
+           index = i;
+         }
+  }
+
+  KPrintf("\n------- Output Result -------\n");
+  KPrintf("result is %d\n\n", index);
+
+}