While load images from image_dataset_from_directory, I’m afraid the image file and label is not matching

I’m using image_dataset_from_directory method to load images from file. And I’ve prepared an label.csv file for each image filename.

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However, after my first training, all of the prediction makes predict for same class. I’d checked up the directory folder and don’t know why.

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I want to check up the label and image pair to see if any bug, but I don’t find a simple way to do this, so I combine the other custom image_dataset_from_directory function that returns image_path, and compare with the order of given label:

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def image_dataset_from_directory(directory, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=None, validation_split=None, subset=None, interpolation='bilinear', follow_links=False): if labels != 'inferred': if not isinstance(labels, (list, tuple)): raise ValueError( '`labels` argument should be a list/tuple of integer labels, of ' 'the same size as the number of image files in the target ' 'directory. If you wish to infer the labels from the subdirectory ' 'names in the target directory, pass `labels="inferred"`. ' 'If you wish to get a dataset that only contains images ' '(no labels), pass `label_mode=None`.') if class_names: raise ValueError('You can only pass `class_names` if the labels are ' 'inferred from the subdirectory names in the target ' 'directory (`labels="inferred"`).') if label_mode not in {'int', 'categorical', 'binary', None}: raise ValueError( '`label_mode` argument must be one of "int", "categorical", "binary", ' 'or None. Received: %s' % (label_mode,)) if color_mode == 'rgb': num_channels = 3 elif color_mode == 'rgba': num_channels = 4 elif color_mode == 'grayscale': num_channels = 1 else: raise ValueError( '`color_mode` must be one of {"rbg", "rgba", "grayscale"}. ' 'Received: %s' % (color_mode,)) interpolation = image_preprocessing.get_interpolation(interpolation) dataset_utils.check_validation_split_arg( validation_split, subset, shuffle, seed) if seed is None: seed = np.random.randint(1e6) image_paths, labels, class_names = dataset_utils.index_directory( directory, labels, formats=WHITELIST_FORMATS, class_names=class_names, shuffle=shuffle, seed=seed, follow_links=follow_links) if label_mode == 'binary' and len(class_names) != 2: raise ValueError( 'When passing `label_mode="binary", there must exactly 2 classes. ' 'Found the following classes: %s' % (class_names,)) image_paths, labels = dataset_utils.get_training_or_validation_split( image_paths, labels, validation_split, subset) dataset = paths_and_labels_to_dataset( image_paths=image_paths, image_size=image_size, num_channels=num_channels, labels=labels, label_mode=label_mode, num_classes=len(class_names), interpolation=interpolation) if shuffle: # Shuffle locally at each iteration dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed) dataset = dataset.batch(batch_size) # Users may need to reference `class_names`. dataset.class_names = class_names return dataset, image_paths 

And this is data loader:

train_dataset, train_path = image_dataset_from_directory( img_path, labels = labels, label_mode = 'binary', validation_split = 0.2, color_mode = 'rgb', subset = 'training', image_size = (IMAGE_WIDTH, IMAGE_HEIGHT), batch_size = BATCH_SIZE, seed = 123 ) 

While I print the image and label pair, it’s not matching:

for i in range(len(train_path)): filename = train_path[i].split('/')[-1] print('File: {} label: {}'.format(filename, labels[i])) It returns: File: class_1_img.png label: 1 File: class_1_img.png label: 2 ... 

The unmatching image-label pair make the training meaningless.

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How could I load image from directory and give label list to make them match order?