c28a63fa9d9fb9ba3cced7052ea243e8884f9bf1,tensorflow_datasets/image/deep_weeds.py,DeepWeeds,_generate_examples,#DeepWeeds#Any#Any#,123

Before Change

  def _generate_examples(self, data_dir_path, label_dir_path):
    Generate images and labels for splits.
    // parse the csv-label data
    csv = np.loadtxt(label_dir_path,
            dtype={"names": ("Filename", "Label", "Species"), "formats": ("S21", "i4", "S1")},
            skiprows=1,
            delimiter=",")
    
    label_dict = {}
    for entry in csv:
        label_dict[entry[0].decode("UTF-8")] = int(entry[1])
    
    for file_name in tf.io.gfile.listdir(data_dir_path):
      image = os.path.join(data_dir_path, file_name)
      label = _NAMES[label_dict[file_name]]
      yield file_name, {"image": image, "label": label}

After Change

      // Extract the mapping int -> str and save the label name string to the feature
      label_id_to_name = {
          row["Label"]: row["Species"] for row in reader
      }
      self.info.features["label"].names = [v for k, v in sorted(label_id_to_name.items())]

    filename_to_label = {
        row["Filename"]: row["Species"] for row in reader

Instances