TensorFlow对象检测API扩充

Question

我很好奇TensorFlow对象检测API中调整大小和扩充的顺序。例如，我正在使用配置文件ssd_mobilenet_v2_oid_v4.config。这使用fixed_shape_resizer和ssd_random_crop。那么这两个模块之间的相互作用是什么？

ssd_random_crop是否采取了fixed_shape_resizer中定义的大小？如果首先调整大小，那么调整大小后的作物大小是多少？我认为它们都需要具有相同的尺寸才能创建合适的批次？

Answer 1

数据增强在调整大小之前发生。所有预处理步骤都在文件transform_input_data中的函数inputs.py中指定，该文件包含create_train_input_fn，create_eval_input_fn和create_predict_input_fn等函数，它们将在训练，评估和预测期间将输入图像张量提供给模型。在create_train_input_fn中，使用以下变换函数。

def transform_input_data(tensor_dict,
                         model_preprocess_fn,
                         image_resizer_fn,
                         num_classes,
                         data_augmentation_fn=None,
                         merge_multiple_boxes=False,
                         retain_original_image=False,
                         use_multiclass_scores=False,
                         use_bfloat16=False):
  """A single function that is responsible for all input data transformations.
  Data transformation functions are applied in the following order.
  1. If key fields.InputDataFields.image_additional_channels is present in
     tensor_dict, the additional channels will be merged into
     fields.InputDataFields.image.
  2. data_augmentation_fn (optional): applied on tensor_dict.
  3. model_preprocess_fn: applied only on image tensor in tensor_dict.
  4. image_resizer_fn: applied on original image and instance mask tensor in
     tensor_dict.
  5. one_hot_encoding: applied to classes tensor in tensor_dict.
  6. merge_multiple_boxes (optional): when groundtruth boxes are exactly the
     same they can be merged into a single box with an associated k-hot class
     label.
  Args:
    tensor_dict: dictionary containing input tensors keyed by
      fields.InputDataFields.
    model_preprocess_fn: model's preprocess function to apply on image tensor.
      This function must take in a 4-D float tensor and return a 4-D preprocess
      float tensor and a tensor containing the true image shape.
    image_resizer_fn: image resizer function to apply on groundtruth instance
      `masks. This function must take a 3-D float tensor of an image and a 3-D
      tensor of instance masks and return a resized version of these along with
      the true shapes.
    num_classes: number of max classes to one-hot (or k-hot) encode the class
      labels.
    data_augmentation_fn: (optional) data augmentation function to apply on
      input `tensor_dict`.
    merge_multiple_boxes: (optional) whether to merge multiple groundtruth boxes
      and classes for a given image if the boxes are exactly the same.
    retain_original_image: (optional) whether to retain original image in the
      output dictionary.
    use_multiclass_scores: whether to use multiclass scores as
      class targets instead of one-hot encoding of `groundtruth_classes`.
    use_bfloat16: (optional) a bool, whether to use bfloat16 in training.
  Returns:
    A dictionary keyed by fields.InputDataFields containing the tensors obtained
    after applying all the transformations.
  """

在步骤2（如果有的话）执行数据增加，并且在步骤4上执行调整大小。