# Path: datasets/Dataset.py
# class AbstractDataset(ABC):
# class FileListDataset(AbstractDataset):
#   def __init__(self, config, subset, num_classes):
#   def n_examples_per_epoch(self):
#   def create_input_tensors_dict(self, batch_size):
#   def num_classes(self):
#   def load_example(self, input_filenames):
#   def process_raw_example(self, example):
#   def load_raw_example(self, img_filename, label_filename=None, *args):
#   def load_image(self, img_filename):
#   def load_annotation(self, img, img_filename, annotation_filename):
#   def postproc_annotation(self, ann_filename, ann):
#   def resize_example(self, tensors):
#   def jointly_resize_examples(self, tensors_batch):
#   def augment_example_before_resize(self, tensors):
#   def augment_example_after_resize(self, tensors):
#   def jointly_augment_examples_before_resize(self, tensors_batch):
#   def jointly_augment_examples_after_resize(self, tensors_batch):
#   def postproc_example_initial(self, tensors):
#   def postproc_example_before_assembly(self, tensors):
#   def postproc_example_before_resize(self, tensors):
#   def assemble_example(self, tensors):
#   def create_summaries(self, data):
#   def __init__(self, config, dataset_name, subset, default_path, num_classes):
#   def _load_inputfile_lists(self):
#   def n_examples_per_epoch(self):
#   def create_input_tensors_dict(self, batch_size):
#     def _load_example(*input_filenames):
#     def _filter_example(tensors):
#   def _batch(self, tfdata, batch_size):
#   def get_extraction_keys(self):
#   def read_inputfile_lists(self):


from abc import abstractmethod
from datasets.Dataset import AbstractDataset, DataKeys
import tensorflow as tf

  def get_batch_size(self):
    return self._batch_size

  def _create_placeholders(self):
    dtypes_and_shapes = {
      DataKeys.IMAGES: (tf.float32, (None, None, 3)),
      DataKeys.SEGMENTATION_LABELS: (tf.uint8, (None, None, 1)),
      DataKeys.BBOX_GUIDANCE: (tf.uint8, (None, None, 1)),
      DataKeys.IMAGE_FILENAMES: (tf.string, ()),
      DataKeys.OBJ_TAGS: (tf.string, ()),
      DataKeys.LASER_GUIDANCE: (tf.float32, (None, None, 1)),
      DataKeys.BBOXES_y0x0y1x1: (tf.float32, (4,)),
      DataKeys.IDS: (tf.int32, ()),
      DataKeys.N_VALID_IDS: (tf.int32, (2,))
    }
    placeholders = {}
    for key in self._data_keys_to_use:
      dtype, shape = dtypes_and_shapes[key]
      key_placeholders = [tf.placeholder(dtype, shape, name=key + "_placeholder_{}".format(idx))
                          for idx in range(self._batch_size)]
      placeholders[key] = key_placeholders
    return placeholders

  def n_examples_per_epoch(self):
    raise NotImplementedError()

  def create_input_tensors_dict(self, batch_size):
    examples = [self._create_input_tensors_dict_single_example(idx_in_minibatch=idx) for idx in range(batch_size)]
    keys = examples[0].keys()

# Path: datasets/Dataset.py
# class AbstractDataset(ABC):
# class FileListDataset(AbstractDataset):
#   def __init__(self, config, subset, num_classes):
#   def n_examples_per_epoch(self):
#   def create_input_tensors_dict(self, batch_size):
#   def num_classes(self):
#   def load_example(self, input_filenames):
#   def process_raw_example(self, example):
#   def load_raw_example(self, img_filename, label_filename=None, *args):
#   def load_image(self, img_filename):
#   def load_annotation(self, img, img_filename, annotation_filename):
#   def postproc_annotation(self, ann_filename, ann):
#   def resize_example(self, tensors):
#   def jointly_resize_examples(self, tensors_batch):
#   def augment_example_before_resize(self, tensors):
#   def augment_example_after_resize(self, tensors):
#   def jointly_augment_examples_before_resize(self, tensors_batch):
#   def jointly_augment_examples_after_resize(self, tensors_batch):
#   def postproc_example_initial(self, tensors):
#   def postproc_example_before_assembly(self, tensors):
#   def postproc_example_before_resize(self, tensors):
#   def assemble_example(self, tensors):
#   def create_summaries(self, data):
#   def __init__(self, config, dataset_name, subset, default_path, num_classes):
#   def _load_inputfile_lists(self):
#   def n_examples_per_epoch(self):
#   def create_input_tensors_dict(self, batch_size):
#     def _load_example(*input_filenames):
#     def _filter_example(tensors):
#   def _batch(self, tfdata, batch_size):
#   def get_extraction_keys(self):
#   def read_inputfile_lists(self):


# Path: datasets/FeedDataset.py
from abc import abstractmethod
from datasets.Dataset import AbstractDataset, DataKeys
import tensorflow as tf


  def get_batch_size(self):
    return self._batch_size

  def _create_placeholders(self):
    dtypes_and_shapes = {
      DataKeys.IMAGES: (tf.float32, (None, None, 3)),
      DataKeys.SEGMENTATION_LABELS: (tf.uint8, (None, None, 1)),
      DataKeys.BBOX_GUIDANCE: (tf.uint8, (None, None, 1)),
      DataKeys.IMAGE_FILENAMES: (tf.string, ()),
      DataKeys.OBJ_TAGS: (tf.string, ()),
      DataKeys.LASER_GUIDANCE: (tf.float32, (None, None, 1)),
      DataKeys.BBOXES_y0x0y1x1: (tf.float32, (4,)),
      DataKeys.IDS: (tf.int32, ()),
      DataKeys.N_VALID_IDS: (tf.int32, (2,))
    }
    placeholders = {}
    for key in self._data_keys_to_use:
      dtype, shape = dtypes_and_shapes[key]
      key_placeholders = [tf.placeholder(dtype, shape, name=key + "_placeholder_{}".format(idx))
                          for idx in range(self._batch_size)]
      placeholders[key] = key_placeholders
    return placeholders

  def n_examples_per_epoch(self):
    raise NotImplementedError()

  def create_input_tensors_dict(self, batch_size):
    examples = [self._create_input_tensors_dict_single_example(idx_in_minibatch=idx) for idx in range(batch_size)]
    keys = examples[0].keys()

    data = {}