使用 TensorFlow2 訓練目標檢測模型

因為我的項目是計劃在飛騰派上實現一個目標檢測跟蹤算法，通過算法輸出控制信號控制電機跟隨目標運行。在第一章完成了Ubuntu系統的構建和燒寫，這幾天就在研究如何訓練目標檢測模型和部署，經過一段時間的資料搜集和測試，目前已經順利使用 TensorFlow2 完成了模型的訓練的測試，首先描述下我測試的 PC 配置。

單個 step 實際測試大概2s+，為了加快測試，我設置了訓練的 step 為 300 ，實際測試15分鐘左右完成了模型訓練，這個在后續配置文件中可以看到。

PC端關鍵的軟件配置

內核 ：Linux fedora 6.6.4-100.fc38.x86_64 #1 SMP PREEMPT_DYNAMIC Sun Dec 3 18:11:27 UTC 2023 x86_64 GNU/Linux

Python ：Python 3.8.18 (default, Aug 28 2023, 00:00:00)

參考內容

• [How to train your own Object Detector with TensorFlow’s Object Detector API]
• [How to Train Your Own Object Detector Using TensorFlow Object Detection API]

環境準備

為了訓練的方便，建議安裝一個虛擬的python環境，首先創建一個新的文件夾demo，然后 進入到 demo 目錄 。

1. 首先接著使用到 python 的 venv 模塊創建一個虛擬環境。

python -m venv tf2_api_env

2. 接著激活創建的虛擬環境

? source ../tf2_api_env/bin/activate
(tf2_api_env) ┏─?[red]?─?[17:01:44]?─?[0]
┗─?[~/Projects/ai_track_feiteng/demo2/workspace]
?

3. 接下來的操作都在這個虛擬環境中完成，下面開始安裝 tensorflow2：

pip install tensorflow==2.*

4. 下載，安裝編譯 models 下的 Protobuf

git clone https://github.com/tensorflow/models.git
cd models/research/
protoc models/research/object_detection/protos/*.proto --python_out=../../

5. 下載，安裝編譯 coco API

pip install cython
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
make
cp -r pycocotools ./models/research/

6. 對象檢測 API 安裝

cd models/research
cp object_detection/packages/tf2/setup.py .
python3.8 -m pip install .

7. 測試是否安裝正確

python3.8 object_detection/builders/model_builder_tf2_test.py
2023-12-14 18:30:03.462617: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-
off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-12-14 18:30:03.463746: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-12-14 18:30:03.489237: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-12-14 18:30:03.489587: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical
 operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-12-14 18:30:03.994817: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-12-14 18:30:04.975870: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there m
ust be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-12-14 18:30:04.976136: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above
are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your
platform.
Skipping registering GPU devices...
Running tests under Python 3.8.18: /home/red/Projects/ai_track_feiteng/demo2/tf2_api_env/bin/python3.8
[ RUN      ] ModelBuilderTF2Test.test_create_center_net_deepmac
WARNING:tensorflow:`tf.keras.layers.experimental.SyncBatchNormalization` endpoint is deprecated and will be removed in a future release. Please use `tf.keras.layers.BatchNorm
alization` with parameter `synchronized` set to True.
W1214 18:30:05.009487 140273879242560 batch_normalization.py:1531] `tf.keras.layers.experimental.SyncBatchNormalization` endpoint is deprecated and will be removed in a futur
e release. Please use `tf.keras.layers.BatchNormalization` with parameter `synchronized` set to True.
/home/red/Projects/ai_track_feiteng/demo2/tf2_api_env/lib64/python3.8/site-packages/object_detection/builders/model_builder.py:1112: DeprecationWarning: The 'warn' function i
s deprecated, use 'warning' instead
  logging.warn(('Building experimental DeepMAC meta-arch.'
...... 省略 ......
[ RUN      ] ModelBuilderTF2Test.test_session
[  SKIPPED ] ModelBuilderTF2Test.test_session
[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s
I1214 18:30:21.144221 140273879242560 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s
I1214 18:30:21.144374 140273879242560 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture
[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s
I1214 18:30:21.144848 140273879242560 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
----------------------------------------------------------------------
Ran 24 tests in 16.167s

OK (skipped=1)

8. 數據準備，這里為了重點關注模型訓練過程，我們這里從倉庫[raccoon_dataset]獲取已經標注好的數據集。
   然后放在對應的目錄 workspace/data 目錄，如下所示：

? ls workspace/data/
object-detection.pbtxt  raccoon_labels.csv  test_labels.csv  test.record  train_labels.csv  train.record

9. 模型選擇和訓練參數配置(重點！！！，這里為了演示不會詳細介紹每一個參數的意義，具體參數的意義可以查看)

• 模型選擇，現在有很多現成的模型可以加快我們的訓練，我們需要在此基礎上進行調參，TensorFlow2 對象檢測已有的算法模型在這里 [tf2_detection_zoo]，這里我們需要從中下載一個模型進行訓練，本章中我選擇的是 [efficientdet_d0_coco17_tpu-32.tar.gz]。將這個模型的壓縮包解壓到 demo/workspace/pre_trained_models 目錄下。

? tree -L 3 workspace/pre_trained_models/efficientdet_d0_coco17_tpu-32/
workspace/pre_trained_models/efficientdet_d0_coco17_tpu-32/
├── checkpoint
│   ├── checkpoint
│   ├── ckpt-0.data-00000-of-00001
│   └── ckpt-0.index
├── pipeline.config
└── saved_model
    ├── assets
    ├── saved_model.pb
    └── variables
        ├── variables.data-00000-of-00001
        └── variables.index

5 directories, 7 files

這里關鍵的是 chekpoint 目錄和 pipeline.config，checkpoint 包含了目標訓練的切入點，pipeline.config 是我們后續需要調整的模型訓練配置文件。

• 訓練參數微調，這里為了加快介紹模型訓練的過程，直接看下對該文件的 diff 文件可以更直觀看到做了哪些修改

--- workspace/pre_trained_models/efficientdet_d0_coco17_tpu-32/pipeline.config	2020-07-11 08:12:31.000000000 +0800
+++ workspace/models/efficientdet_d0/v2/pipeline.config	2023-12-14 14:10:58.998130084 +0800
@@ -1,6 +1,6 @@
 model {
   ssd {
-    num_classes: 90
+    num_classes: 1
     image_resizer {
       keep_aspect_ratio_resizer {
         min_dimension: 512
@@ -131,7 +131,7 @@
   }
 }
 train_config {
-  batch_size: 128
+  batch_size: 8
   data_augmentation_options {
     random_horizontal_flip {
     }
@@ -149,29 +149,29 @@
       learning_rate {
         cosine_decay_learning_rate {
           learning_rate_base: 0.07999999821186066
-          total_steps: 300000
+          total_steps: 300
           warmup_learning_rate: 0.0010000000474974513
-          warmup_steps: 2500
+          warmup_steps: 25
         }
       }
       momentum_optimizer_value: 0.8999999761581421
     }
     use_moving_average: false
   }
-  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED"
-  num_steps: 300000
+  fine_tune_checkpoint: "/home/red/Projects/ai_track_feiteng/demo2/workspace/pre_trained_models/efficientdet_d0_coco17_tpu-32/checkpoint/ckpt-0"
+  num_steps: 300
   startup_delay_steps: 0.0
   replicas_to_aggregate: 8
   max_number_of_boxes: 100
   unpad_groundtruth_tensors: false
-  fine_tune_checkpoint_type: "classification"
-  use_bfloat16: true
+  fine_tune_checkpoint_type: "detection"
+  use_bfloat16: false
   fine_tune_checkpoint_version: V2
 }
 train_input_reader: {
-  label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
+  label_map_path: "/home/red/Projects/ai_track_feiteng/demo2/workspace/data/object-detection.pbtxt"
   tf_record_input_reader {
-    input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
+    input_path: "/home/red/Projects/ai_track_feiteng/demo2/workspace/data/train.record"
   }
 }
 
@@ -182,10 +182,10 @@
 }
 
 eval_input_reader: {
-  label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
+  label_map_path: "/home/red/Projects/ai_track_feiteng/demo2/workspace/data/object-detection.pbtxt"
   shuffle: false
   num_epochs: 1
   tf_record_input_reader {
-    input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
+    input_path: "/home/red/Projects/ai_track_feiteng/demo2/workspace/data/test.record"
   }
 }

其中關鍵的修改點：

num_classes = 1  表示識別一類目標
batch_size = 8   表示這個參數會影響訓練時候消耗的內存
fine_tune_checkpoint_type: "detection" 表示進行目標檢測
use_bfloat16: false 不使用 TPU
fine_tune_checkpoint: "/home/red/Projects/ai_track_feiteng/demo2/workspace/pre_trained_models/efficientdet_d0_coco17_tpu-32/checkpoint/ckpt-0" 設置模型訓練的切入點
num_steps: 300 總的學習步數

10. 模型訓練和導出
    經過前面的鋪墊，目前已經具備了訓練條件，執行如下腳本開始訓練,我這邊訓練了大概15分鐘：

#!/bin/sh
python3.8 model_main_tf2.py 
  --pipeline_config_path=./models/efficientdet_d0/v2/pipeline.config 
  --model_dir=./models/efficientdet_d0/v2 
  --checkpoint_every_n=8 
  --num_workers=12 
  --alsologtostderr

訓練完成后，就可以將模型導出，使用如下命令：

python3.8 exporter_main_v2.py 
  --pipeline_config_path=./models/efficientdet_d0/v2/pipeline.config 
  --trained_checkpoint_dir=./models/efficientdet_d0/v2 
  --output_directory=./exported_models/efficientdet_d0 
  --input_type=image_tensor

上述命令會將模型導出到 ./exported_models/efficientdet_d0 目錄，導出成功后會看到如下內容：

? tree -L 3 workspace/exported_models/efficientdet_d0/
workspace/exported_models/efficientdet_d0/
├── checkpoint
│   ├── checkpoint
│   ├── ckpt-0.data-00000-of-00001
│   └── ckpt-0.index
├── pipeline.config
└── saved_model
    ├── assets
    ├── fingerprint.pb
    ├── saved_model.pb
    └── variables
        ├── variables.data-00000-of-00001
        └── variables.index

5 directories, 8 files

可以看到這是我們自己訓練出來的模型和前面提到的和網上下載的模型efficientdet_d0_coco17_tpu-32.tar.gz解壓之后的結構很像。
11. 最后就演示下訓練模型的精度，這里提供了網上的一個示例代碼，針對我的代碼結構，我做了下微調(代碼之前是在.ipynb格式文件中的，為此，我還改了一個 python 腳本用來提取其中的 python代碼)，該代碼會對測試圖像進行檢測，將識別出來的目標用框標注出來。首先看下測試的腳本：

#!/bin/python3.8

import os # importing OS in order to make GPU visible
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # do not change anything in here

# specify which device you want to work on.
# Use "-1" to work on a CPU. Default value "0" stands for the 1st GPU that will be used
os.environ["CUDA_VISIBLE_DEVICES"]="0" # TODO: specify your computational device
import tensorflow as tf # import tensorflow

# checking that GPU is found
if tf.test.gpu_device_name():
    print('GPU found')
else:
    print("No GPU found")
# other import
import numpy as np
from PIL import Image
import matplotlib
from matplotlib import pyplot as plt
from tqdm import tqdm
import sys # importyng sys in order to access scripts located in a different folder

print(matplotlib.get_backend())

path2scripts = ['../models/research/', '../models/'] # TODO: provide pass to the research folder
sys.path.insert(0, path2scripts[0]) # making scripts in models/research available for import
sys.path.insert(0, path2scripts[1]) # making scripts in models/research available for import
print(sys.path)
# importing all scripts that will be needed to export your model and use it for inference
from object_detection.utils import label_map_util
from object_detection.utils import config_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.builders import model_builder
# NOTE: your current working directory should be Tensorflow.

# TODO: specify two pathes: to the pipeline.config file and to the folder with trained model.
path2config ='exported_models/efficientdet_d0/pipeline.config'
path2model = 'exported_models/efficientdet_d0/'
# do not change anything in this cell
configs = config_util.get_configs_from_pipeline_file(path2config) # importing config
model_config = configs['model'] # recreating model config
detection_model = model_builder.build(model_config=model_config, is_training=False) # importing model
ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
ckpt.restore(os.path.join(path2model, 'checkpoint/ckpt-0')).expect_partial()
path2label_map = 'data/object-detection.pbtxt' # TODO: provide a path to the label map file
category_index = label_map_util.create_category_index_from_labelmap(path2label_map,use_display_name=True)
def detect_fn(image):
    """
    Detect objects in image.

    Args:
      image: (tf.tensor): 4D input image

    Returs:
      detections (dict): predictions that model made
    """

    image, shapes = detection_model.preprocess(image)
    prediction_dict = detection_model.predict(image, shapes)
    detections = detection_model.postprocess(prediction_dict, shapes)

    return detections
def load_image_into_numpy_array(path):
    """Load an image from file into a numpy array.

    Puts image into numpy array to feed into tensorflow graph.
    Note that by convention we put it into a numpy array with shape
    (height, width, channels), where channels=3 for RGB.

    Args:
      path: the file path to the image

    Returns:
      numpy array with shape (img_height, img_width, 3)
    """

    return np.array(Image.open(path))
def inference_with_plot(path2images, box_th=0.25):
    """
    Function that performs inference and plots resulting b-boxes

    Args:
      path2images: an array with pathes to images
      box_th: (float) value that defines threshold for model prediction.

    Returns:
      None
    """
    for image_path in path2images:

        print('Running inference for {}... '.format(image_path), end='')

        image_np = load_image_into_numpy_array(image_path)

        input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
        detections = detect_fn(input_tensor)

        # All outputs are batches tensors.
        # Convert to numpy arrays, and take index [0] to remove the batch dimension.
        # We're only interested in the first num_detections.
        num_detections = int(detections.pop('num_detections'))
        detections = {key: value[0, :num_detections].numpy()
                      for key, value in detections.items()}

        detections['num_detections'] = num_detections

        # detection_classes should be ints.
        detections['detection_classes'] = detections['detection_classes'].astype(np.int64)

        label_id_offset = 1
        image_np_with_detections = image_np.copy()

        viz_utils.visualize_boxes_and_labels_on_image_array(
                image_np_with_detections,
                detections['detection_boxes'],
                detections['detection_classes']+label_id_offset,
                detections['detection_scores'],
                category_index,
                use_normalized_coordinates=True,
                max_boxes_to_draw=200,
                min_score_thresh=box_th,
                agnostic_mode=False,
                line_thickness=5)

        plt.figure(figsize=(15,10))
        plt.imshow(image_np_with_detections)
        print('Done')
        marked_file_name="marked_"+image_path
        plt.savefig(marked_file_name)
        print('Saved {} Done'.format(marked_file_name))
    matplotlib.use('TkAgg')
    plt.show()
def nms(rects, thd=0.5):
    """
    Filter rectangles
    rects is array of oblects ([x1,y1,x2,y2], confidence, class)
    thd - intersection threshold (intersection divides min square of rectange)
    """
    out = []

    remove = [False] * len(rects)

    for i in range(0, len(rects) - 1):
        if remove[i]:
            continue
        inter = [0.0] * len(rects)
        for j in range(i, len(rects)):
            if remove[j]:
                continue
            inter[j] = intersection(rects[i][0], rects[j][0]) / min(square(rects[i][0]), square(rects[j][0]))

        max_prob = 0.0
        max_idx = 0
        for k in range(i, len(rects)):
            if inter[k] >= thd:
                if rects[k][1] > max_prob:
                    max_prob = rects[k][1]
                    max_idx = k

        for k in range(i, len(rects)):
            if (inter[k] >= thd) & (k != max_idx):
                remove[k] = True

    for k in range(0, len(rects)):
        if not remove[k]:
            out.append(rects[k])

    boxes = [box[0] for box in out]
    scores = [score[1] for score in out]
    classes = [cls[2] for cls in out]
    return boxes, scores, classes


def intersection(rect1, rect2):
    """
    Calculates square of intersection of two rectangles
    rect: list with coords of top-right and left-boom corners [x1,y1,x2,y2]
    return: square of intersection
    """
    x_overlap = max(0, min(rect1[2], rect2[2]) - max(rect1[0], rect2[0]));
    y_overlap = max(0, min(rect1[3], rect2[3]) - max(rect1[1], rect2[1]));
    overlapArea = x_overlap * y_overlap;
    return overlapArea


def square(rect):
    """
    Calculates square of rectangle
    """
    return abs(rect[2] - rect[0]) * abs(rect[3] - rect[1])
def inference_as_raw_output(path2images,
                            box_th = 0.25,
                            nms_th = 0.5,
                            to_file = False,
                            data = None,
                            path2dir = False):
    """
    Function that performs inference and return filtered predictions

    Args:
      path2images: an array with pathes to images
      box_th: (float) value that defines threshold for model prediction. Consider 0.25 as a value.
      nms_th: (float) value that defines threshold for non-maximum suppression. Consider 0.5 as a value.
      to_file: (boolean). When passed as True = > results are saved into a file. Writing format is
      path2image + (x1abs, y1abs, x2abs, y2abs, score, conf) for box in boxes
      data: (str) name of the dataset you passed in (e.g. test/validation)
      path2dir: (str). Should be passed if path2images has only basenames. If full pathes provided = > set False.

    Returs:
      detections (dict): filtered predictions that model made
    """
    print (f'Current data set is {data}')
    print (f'Ready to start inference on {len(path2images)} images!')

    for image_path in tqdm(path2images):

        if path2dir: # if a path to a directory where images are stored was passed in
            image_path = os.path.join(path2dir, image_path.strip())

        image_np = load_image_into_numpy_array(image_path)

        input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
        detections = detect_fn(input_tensor)

        # checking how many detections we got
        num_detections = int(detections.pop('num_detections'))

        # filtering out detection in order to get only the one that are indeed detections
        detections = {key: value[0, :num_detections].numpy() for key, value in detections.items()}

        # detection_classes should be ints.
        detections['detection_classes'] = detections['detection_classes'].astype(np.int64)

        # defining what we need from the resulting detection dict that we got from model output
        key_of_interest = ['detection_classes', 'detection_boxes', 'detection_scores']

        # filtering out detection dict in order to get only boxes, classes and scores
        detections = {key: value for key, value in detections.items() if key in key_of_interest}

        if box_th: # filtering detection if a confidence threshold for boxes was given as a parameter
            for key in key_of_interest:
                scores = detections['detection_scores']
                current_array = detections[key]
                filtered_current_array = current_array[scores > box_th]
                detections[key] = filtered_current_array

        if nms_th: # filtering rectangles if nms threshold was passed in as a parameter
            # creating a zip object that will contain model output info as
            output_info = list(zip(detections['detection_boxes'],
                                   detections['detection_scores'],
                                   detections['detection_classes']
                                  )
                              )
            boxes, scores, classes = nms(output_info)

            detections['detection_boxes'] = boxes # format: [y1, x1, y2, x2]
            detections['detection_scores'] = scores
            detections['detection_classes'] = classes

        if to_file and data: # if saving to txt file was requested

            image_h, image_w, _ = image_np.shape
            file_name = f'pred_result_{data}.txt'

            line2write = list()
            line2write.append(os.path.basename(image_path))

            with open(file_name, 'a+') as text_file:
                # iterating over boxes
                for b, s, c in zip(boxes, scores, classes):

                    y1abs, x1abs = b[0] * image_h, b[1] * image_w
                    y2abs, x2abs = b[2] * image_h, b[3] * image_w

                    list2append = [x1abs, y1abs, x2abs, y2abs, s, c]
                    line2append = ','.join([str(item) for item in list2append])

                    line2write.append(line2append)

                line2write = ' '.join(line2write)
                text_file.write(line2write + os.linesep)

        return detections
inference_with_plot(["1.jpg", "2.jpg"], 0.6)

這個腳本會檢測當前目錄下的 1.jpg 和 2.jpg 文件，然后將識別出來概率>0.5的目標用框框起來，并分別命名為marked_1.jpg和marked_2.jpg。原始圖像分別是：

執行腳本進行檢測處理：

? source ../t./b.py
2023-12-15 06:28:50.519691: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-12-15 06:28:50.520813: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-12-15 06:28:50.545707: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-12-15 06:28:50.546025: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-12-15 06:28:50.990588: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-12-15 06:28:51.480008: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error
2023-12-15 06:28:51.480053: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: fedora
2023-12-15 06:28:51.480057: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: fedora
2023-12-15 06:28:51.480104: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 535.146.2
2023-12-15 06:28:51.480114: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 535.146.2
2023-12-15 06:28:51.480117: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:309] kernel version seems to match DSO: 535.146.2
No GPU found
TkAgg
['../models/', '../models/research/', '/home/red/Projects/ai_track_feiteng/demo2/workspace', '/usr/lib64/python38.zip', '/usr/lib64/python3.8', '/usr/lib64/python3.8/lib-dynload', '/home/red/.local/lib/python3.8/site-packages', '/usr/lib64/python3.8/site-packages', '/usr/lib/python3.8/site-packages']
Running inference for 1.jpg... Done
Saved marked_1.jpg Done
Running inference for 2.jpg... Done
Saved marked_2.jpg Done

檢測后并處理的圖像是：

因為訓練的次數較少，導致識別的準確度并不是特別高，但是整個訓練和演示的流程的還是完整的。希望能對大家了解 TensorFlow2 進行目標檢測有所幫助。

這里再附下，提取.ipynb格式文件中python代碼的示例代碼：

#!/bin/python3.8

import json
import sys
import os
from pathlib import Path

out_file_name=Path(sys.argv[1]).stem+'.py'

with open(sys.argv[1],'r') as f:
    text=json.load(f)

if len(sys.argv) > 2:
    out_file_name = sys.argv[2]

print('args:{}nout_file:{}'.format(sys.argv[1:], out_file_name))
with open(out_file_name, 'w') as fp:
    fp.writelines("#!/bin/python3.8nn")
    for x in text['cells']:
        if x['cell_type'] == "code":
            fp.writelines([i.rstrip()+'n' for i in x['source']])

下一章，我會介紹如何獲取圖像數據， 標柱圖像 ，然后進行模型訓練，敬請期待。

審核編輯 黃宇