RUN ENV

  • tf-1.12.3: import tensorflow as tf
  • tf-1.15.2: import tensorflow as tf
  • tf-2.1.0: import tensorflow.compat.v1 as tf

Functions

  • 加载ckpt模型
  • ckpt->SavedModel
  • ckpt->Frozen Grapha
  1. with tf.Graph.as_default(), tf.Session() as sess:
  2.     input_shape = [1, height, width, 3]
  3.     inputs = tf.placeholder(name='input', dtype=tf.float32, shape=input_shape)
  4.     outputs = build_model(inputs, is_training=False)
  5.     checkpoint = tf.train.latest_checkpoint(ckpt_path)
  6.     ## TODO
  7.     checkpoint = 'xxx'
  8.     print('Loading checkpoint: %s', checkpoint)
  9.     saver.restore(sess, checkpoint)
  10.     output_node_names = [node.name.split(':')[0] for node in outputs]
  12.     ## export SavedModel
  13.     saved_model_name = './tmp/saved_model'
  14.     tf.saved_model.simple_save(sess, saved_model_name,
  15.                                inputs={'input': inputs},
  16.                                outputs={output_node_names[0]: outputs[0],
  17.                                         output_node_names[1]: outputs[1],
  18.                                         output_node_names[2]: outputs[2],
  19.                                         output_node_names[3]: outputs[3],
  20.                                         output_node_names[4]: outputs[4],
  21.                                         output_node_names[5]: outputs[5],
  22.                                         output_node_names[6]: outputs[6],
  23.                                         output_node_names[7]: outputs[7],
  24.                                         output_node_names[8]: outputs[8],
  25.                                         output_node_names[9]: outputs[9]})
  29.     ## export frozen graph
  30.     pbtxt_name = './tmp/pb_model/efficientdet_d0.pbtxt'
  31.     pb_model_name = './tmp/pb_model/efficientdet_d0.pb'
  32.     tf.train.write_graph(sess.graph_def, '', pbtxt_name, as_text=True)
  33.     input_graph_def = tf.get_default_graph().as_graph_def()
  34.     output_graph_def = tf.graph_util.convert_variables_to_constants(sess, 
  35.                                                                     sess.graph_def,
  36.                                                                     # input_graph_def,
  37.                                                                     output_node_names)
  38.        with tf.io.gfile.GFile(pb_model_name, "wb") as f:
  39.         f.write(output_graph_def.SerializeToString())

RUN ENV

  • tf-1.12.3: import tensorflow as tf
  • tf-1.15.2: import tensorflow as tf
  • tf-2.1.0: import tensorflow.compat.v1 as tf

Functions

  • SavedModel->float32 tflite
  • SavedModel->int8 tflite
  1. # 以来模型文件是基于哪个环境构建的，高版本中有些ops低版本不支持
  2. import sys
  3. if sys.version_info.major >= 3:
  4.     import pathlib
  5. else:
  6.     import pathlib2 as pathlib  
  8. tflite_model_dir = pathlib.Path('./tmp/tflite/efficentdet_d0/')
  9. tflite_model_dir.mkdir(exist_ok=True, parents=True)
  10. tflite_model_name = 'efficentdet_d0_flot32.tflite'
  11. tflite_model_file = tflite_model_dir/tflite_model_name
  12. converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
  13. converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
  14. tflite_model = converter.convert()
  15. tflite_model_file.write_bytes(tflite_model)
  19. ## export tflite: SavedModel to tflite_int8
  20. # tf_1.15.2: import tensorflow as tf
  21. # tf_2.1: import tensorflow as tf
  22. # tf_2.1: import tensorflow.compat.v1 as tf
  23. # 以来模型文件是基于哪个环境构建的，高版本中有些ops低版本不支持
  24. import sys
  25. if sys.version_info.major >= 3:
  26.     import pathlib
  27. else:
  28.     import pathlib2 as pathlib  
  30. tflite_model_dir = pathlib.Path('./tmp/tflite/efficentdet_d0/')
  31. tflite_model_dir.mkdir(exist_ok=True, parents=True)
  32. tflite_model_name = 'efficentdet_d0_int8.tflite'
  33. tflite_model_file = tflite_model_dir/tflite_model_name
  34. converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
  36. data_path = './sample_imgs/*'
  37. data = glob.glob(data_path)
  38. print('Convert using full integer quantization ...')
  39. print("Calibration data size : ", len(data))
  41. def aspect_preserving_resize(img, _network_w, _network_h):
  42.     orig_height, orig_width, _ = img.shape
  43.     if orig_width > orig_height:
  44.         resize_ratio = 1.0 * _network_w / orig_width
  45.         target_size = (_network_w, int(resize_ratio * orig_height))
  46.     else:
  47.         resize_ratio = 1.0 * _network_h / orig_height
  48.         target_size = (int(resize_ratio * orig_width), _network_h)
  49.     img = cv2.resize(img, target_size, interpolation=cv2.INTER_AREA)
  51.     ## Pad image and label to have dimensions >= [crop_height, crop_width]
  52.     image_height = target_size[1]
  53.     image_width  = target_size[0]
  55.     bottom = max(_network_h - image_height, 0)
  56.     right  = max(_network_w - image_width, 0)
  58.     # Pad image with mean pixel value.
  59.     color = [127.5,127.5,127.5]#[0.0,0.0,0.0]#
  60.     img  = cv2.copyMakeBorder(img, 0, bottom, 0, right, cv2.BORDER_CONSTANT,value=color)
  61.     return img
  63. def representative_data_gen():
  64.     for idx in range(len(data)):
  65.         img = cv2.cvtColor(cv2.imread(data[idx]), cv2.COLOR_BGR2RGB)            
  66.         img = aspect_preserving_resize(img, 512, 512)
  67.         img = np.float32(img - 127.5) * 2.0 / 255.0
  68.         input_value = np.expand_dims(img, axis=0)            
  69.         yield [input_value]
  71. image_size = [512, 512]
  72. def representative_dataset_gen():
  73.     for idx in range(len(data)):
  74.         image = cv2.imread(data[idx])
  75.         image_ori = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
  76.         image = np.float32((image_ori /255.0  - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225])
  77.         _crop_offset_y = 0
  78.         _crop_offset_x = 0
  79.         height, width = image.shape[0], image.shape[1]
  80.         image_scale_y = 512 * 1.0 / height
  81.         image_scale_x = 512 * 1.0 / width
  82.         image_scale = np.minimum(image_scale_x, image_scale_y)
  83.         scaled_height = int(height * image_scale)
  84.         scaled_width = int(width * image_scale)
  86.         scaled_image = cv2.resize(image, (scaled_width, scaled_height), cv2.INTER_LINEAR)
  87.         padding_value = (0.0, 0.0, 0.0)
  88.         bottom = max(512 - scaled_height, 0)
  89.         right  = max(512 - scaled_width, 0)
  90.         padding_img = cv2.copyMakeBorder(scaled_image, 
  91.                                           0, bottom, 0, right, 
  92.                                           cv2.BORDER_CONSTANT, value=padding_value)
  93.         # padding_img = np.float32(padding_img / 255.0)
  94.         # padding_img = np.float32((padding_img - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225])
  95.         input_value = np.expand_dims(padding_img, 0)
  96.         yield [input_value]
  98. converter.representative_dataset = representative_dataset_gen
  99. converter.optimizations = [tf.lite.Optimize.DEFAULT]
  100. # converter.inference_input_type = tf.uint8
  101. # converter.inference_output_type = tf.uint8
  102. # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
  103. tflite_model = converter.convert()
  104. tflite_model_file.write_bytes(tflite_model)
  108. ## export tflite: pb model to tflite_flot32
  109. # tf_1.15.2: import tensorflow as tf
  110. # tf_2.1: import tensorflow as tf
  111. # tf_2.1: import tensorflow.compat.v1 as tf
  112. import tensorflow.compat.v1 as tf
  113. pb_model_name = './tmp/pb_model/efficientdet_d0.pb'
  114. tflite_model_name = './tmp/tflite/efficientdet_d0_flot32.tflite'      
  115. inputs=["input"]               
  116. classes=["class_net/class-predict/BiasAdd", 'class_net/class-predict_1/BiasAdd', 
  117.          'class_net/class-predict_2/BiasAdd', 'class_net/class-predict_3/BiasAdd', 
  118.          'class_net/class-predict_4/BiasAdd', 'box_net/box-predict/BiasAdd',
  119.          'box_net/box-predict_1/BiasAdd', 'box_net/box-predict_2/BiasAdd',
  120.          'box_net/box-predict_3/BiasAdd', 'box_net/box-predict_4/BiasAdd']  #模型文件的输出节点名称
  121. converter = tf.contrib.lite.TocoConverter.from_frozen_graph(pb_model_name, 
  122.                                                             inputs, 
  123.                                                             classes)
  124. tflite_model=converter.convert()
  125. open(tflite_model_name, 'wb').write(tflite_model)