匯出模型

訓練完成後得到一些checkpoint檔案在ssd_mobilenet_train_logs中，如：

• graph.pbtxt
• model.ckpt-200000.data-00000-of-00001
• model.ckpt-200000.info
• model.ckpt-200000.meta

其中meta儲存了graph和metadata，ckpt儲存了網路的weights。

而進行預測時只需模型和權重，不需要metadata，故可使用官方提供的指令碼生成推導圖。

python object_detection/export_inference_graph.py \
    --input_type image_tensor \
    --pipeline_config_path object_detection/VOC2012/ssd_mobilenet_v1_voc2012.config \
    --trained_checkpoint_prefix object_detection/VOC2012/ssd_mobilenet_train_logs/model.ckpt-200000
 
 \
    --output_directory object_detection/VOC2012

測試圖片

• 執行object_detection_tutorial.ipynb並修改其中的各種路徑即可。

• 或自寫編譯inference指令碼，如tensorflow/models/object_detection/infer.py

  import sys
  sys.path.append('..')
  import os
  import time
  import tensorflow as tf
  import numpy as np
  from PIL import Image
  from matplotlib import
   
   pyplot as plt
  
  from utils import label_map_util
  from utils import visualization_utils as vis_util
  
  PATH_TEST_IMAGE = sys.argv[1]
  PATH_TO_CKPT = 'VOC2012/frozen_inference_graph.pb'
  PATH_TO_LABELS = 'VOC2012/pascal_label_map.pbtxt'
  NUM_CLASSES = 21
  IMAGE_SIZE = (18, 12)
  
  label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes=NUM_CLASSES, use_display_name=True
   
  )
  category_index = label_map_util.create_category_index(categories)
  
  detection_graph = tf.Graph()
  with detection_graph.as_default():
      od_graph_def = tf.GraphDef()
      with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
          serialized_graph = fid.read()
          od_graph_def.ParseFromString(serialized_graph)
          tf.import_graph_def(od_graph_def, name='')
  
  config = tf.ConfigProto()
  config.gpu_options.allow_growth = True
  
  with detection_graph.as_default():
      with tf.Session(graph=detection_graph, config=config) as sess:
          start_time = time.time()
          print(time.ctime())
          image = Image.open(PATH_TEST_IMAGE)
          image_np = np.array(image).astype(np.uint8)
          image_np_expanded = np.expand_dims(image_np, axis=0)
          image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
          boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
          scores = detection_graph.get_tensor_by_name('detection_scores:0')
          classes = detection_graph.get_tensor_by_name('detection_classes:0')
          num_detections = detection_graph.get_tensor_by_name('num_detections:0')
          (boxes, scores, classes, num_detections) = sess.run(
              [boxes, scores, classes, num_detections],
              feed_dict={image_tensor: image_np_expanded})
          print('{} elapsed time: {:.3f}s'.format(time.ctime(), time.time() - start_time))
          vis_util.visualize_boxes_and_labels_on_image_array(
              image_np, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores),
              category_index, use_normalized_coordinates=True, line_thickness=8)
          plt.figure(figsize=IMAGE_SIZE)
          plt.imshow(image_np)

  執行infer.py test_images/image1.jpg即可