Keras split train test set when using ImageDataGenerator

Keras has now added Train / validation split from a single directory using ImageDataGenerator:

train_datagen = ImageDataGenerator(rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    validation_split=0.2) # set validation split

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='binary',
    subset='training') # set as training data

validation_generator = train_datagen.flow_from_directory(
    train_data_dir, # same directory as training data
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='binary',
    subset='validation') # set as validation data

model.fit_generator(
    train_generator,
    steps_per_epoch = train_generator.samples // batch_size,
    validation_data = validation_generator, 
    validation_steps = validation_generator.samples // batch_size,
    epochs = nb_epochs)

https://keras.io/preprocessing/image/

For example, you have folder like this

full_dataset
|--horse (40 images)
|--donkey (30 images)
|--cow ((50 images)
|--zebra (70 images)

FIRST WAY

image_generator = ImageDataGenerator(rescale=1/255, validation_split=0.2)    

train_dataset = image_generator.flow_from_directory(batch_size=32,
                                                 directory='full_dataset',
                                                 shuffle=True,
                                                 target_size=(280, 280), 
                                                 subset="training",
                                                 class_mode='categorical')

validation_dataset = image_generator.flow_from_directory(batch_size=32,
                                                 directory='full_dataset',
                                                 shuffle=True,
                                                 target_size=(280, 280), 
                                                 subset="validation",
                                                 class_mode='categorical')

SECOND WAY

import glob
horse = glob.glob('full_dataset/horse/*.*')
donkey = glob.glob('full_dataset/donkey/*.*')
cow = glob.glob('full_dataset/cow/*.*')
zebra = glob.glob('full_dataset/zebra/*.*')

data = []
labels = []

for i in horse:   
    image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
    target_size= (280,280))
    image=np.array(image)
    data.append(image)
    labels.append(0)
for i in donkey:   
    image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
    target_size= (280,280))
    image=np.array(image)
    data.append(image)
    labels.append(1)
for i in cow:   
    image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
    target_size= (280,280))
    image=np.array(image)
    data.append(image)
    labels.append(2)
for i in zebra:   
    image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
    target_size= (280,280))
    image=np.array(image)
    data.append(image)
    labels.append(3)

data = np.array(data)
labels = np.array(labels)

from sklearn.model_selection import train_test_split
X_train, X_test, ytrain, ytest = train_test_split(data, labels, test_size=0.2,
                                                random_state=42)

Main drawback from First way, you can't use for display a picture. It will error if you write validation_dataset[1]. But it worked if I use first way : X_test[1]

With reference to this question https://github.com/keras-team/keras/issues/597 , you can use the following code to split the whole set into train and val:

train_datagen = ImageDataGenerator(rescale=1./255,
                                   rotation_range=20,
                                   width_shift_range=0.2,
                                   height_shift_range=0.2,
                                   horizontal_flip=True
                                   validation_split=0.2)  # val 20%

val_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)


train_data = train_datagen.flow_from_directory(train_path, 
                                               target_size=(224, 224), 
                                               color_mode='rgb',
                                               batch_size=BS, 
                                               class_mode='categorical',
                                               shuffle=True,
                                               subset = 'training') 

val_data = val_datagen.flow_from_directory(train_path, 
                                           target_size=(224, 224), 
                                           color_mode='rgb',
                                           batch_size=BS, 
                                           class_mode='categorical',
                                           shuffle=False,
                                           subset = 'validation')

If we use subset in ImageDataGenerator then same augmentation will be applied to both training and validation. If you want to apply augmentation only on training set, you can split the folders using split-folders package which can be installed directly using pip.

https://pypi.org/project/split-folders/

This will separate the dataset into train, val and test directory and then you can create separate generator for each of them.

I have a PR for it. One way is to hash the filenames and do a variant assignment.

Example:

# -*- coding: utf-8 -*-
"""Train model using transfer learning."""
import os
import re
import glob
import hashlib
import argparse
import warnings

import six
import numpy as np
import tensorflow as tf
from tensorflow.python.platform import gfile
from keras.models import Model
from keras import backend as K
from keras.optimizers import SGD
from keras.layers import Dense, GlobalAveragePooling2D, Input
from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing.image import (ImageDataGenerator, Iterator,
                                       array_to_img, img_to_array, load_img)
from keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping

RANDOM_SEED = 0
MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1  # ~134M
VALID_IMAGE_FORMATS = frozenset(['jpg', 'jpeg', 'JPG', 'JPEG'])
# we chose to train the top 2 inception blocks
BATCH_SIZE = 100
TRAINABLE_LAYERS = 172
INCEPTIONV3_BASE_LAYERS = len(InceptionV3(weights=None, include_top=False).layers)

STEPS_PER_EPOCH = 625
VALIDATION_STEPS = 100
MODEL_INPUT_WIDTH = 299
MODEL_INPUT_HEIGHT = 299
MODEL_INPUT_DEPTH = 3
FC_LAYER_SIZE = 1024

# Helper: Save the model.
checkpointer = ModelCheckpoint(
    filepath='./output/checkpoints/inception.{epoch:03d}-{val_loss:.2f}.hdf5',
    verbose=1,
    save_best_only=True)

# Helper: Stop when we stop learning.
early_stopper = EarlyStopping(patience=10)

# Helper: TensorBoard
tensorboard = TensorBoard(log_dir='./output/')


def as_bytes(bytes_or_text, encoding='utf-8'):
    """Converts bytes or unicode to `bytes`, using utf-8 encoding for text.

    # Arguments
        bytes_or_text: A `bytes`, `str`, or `unicode` object.
        encoding: A string indicating the charset for encoding unicode.

    # Returns
        A `bytes` object.

    # Raises
        TypeError: If `bytes_or_text` is not a binary or unicode string.
    """
    if isinstance(bytes_or_text, six.text_type):
        return bytes_or_text.encode(encoding)
    elif isinstance(bytes_or_text, bytes):
        return bytes_or_text
    else:
        raise TypeError('Expected binary or unicode string, got %r' %
                        (bytes_or_text,))


class CustomImageDataGenerator(ImageDataGenerator):
    def flow_from_image_lists(self, image_lists,
                              category, image_dir,
                              target_size=(256, 256), color_mode='rgb',
                              class_mode='categorical',
                              batch_size=32, shuffle=True, seed=None,
                              save_to_dir=None,
                              save_prefix='',
                              save_format='jpeg'):
        return ImageListIterator(
            image_lists, self,
            category, image_dir,
            target_size=target_size, color_mode=color_mode,
            class_mode=class_mode,
            data_format=self.data_format,
            batch_size=batch_size, shuffle=shuffle, seed=seed,
            save_to_dir=save_to_dir,
            save_prefix=save_prefix,
            save_format=save_format)


class ImageListIterator(Iterator):
    """Iterator capable of reading images from a directory on disk.

    # Arguments
        image_lists: Dictionary of training images for each label.
        image_data_generator: Instance of `ImageDataGenerator`
            to use for random transformations and normalization.
        target_size: tuple of integers, dimensions to resize input images to.
        color_mode: One of `"rgb"`, `"grayscale"`. Color mode to read images.
        classes: Optional list of strings, names of sudirectories
            containing images from each class (e.g. `["dogs", "cats"]`).
            It will be computed automatically if not set.
        class_mode: Mode for yielding the targets:
            `"binary"`: binary targets (if there are only two classes),
            `"categorical"`: categorical targets,
            `"sparse"`: integer targets,
            `None`: no targets get yielded (only input images are yielded).
        batch_size: Integer, size of a batch.
        shuffle: Boolean, whether to shuffle the data between epochs.
        seed: Random seed for data shuffling.
        data_format: String, one of `channels_first`, `channels_last`.
        save_to_dir: Optional directory where to save the pictures
            being yielded, in a viewable format. This is useful
            for visualizing the random transformations being
            applied, for debugging purposes.
        save_prefix: String prefix to use for saving sample
            images (if `save_to_dir` is set).
        save_format: Format to use for saving sample images
            (if `save_to_dir` is set).
    """

    def __init__(self, image_lists, image_data_generator,
                 category, image_dir,
                 target_size=(256, 256), color_mode='rgb',
                 class_mode='categorical',
                 batch_size=32, shuffle=True, seed=None,
                 data_format=None,
                 save_to_dir=None, save_prefix='', save_format='jpeg'):
        if data_format is None:
            data_format = K.image_data_format()

        classes = list(image_lists.keys())
        self.category = category
        self.num_class = len(classes)
        self.image_lists = image_lists
        self.image_dir = image_dir

        how_many_files = 0
        for label_name in classes:
            for _ in self.image_lists[label_name][category]:
                how_many_files += 1

        self.samples = how_many_files
        self.class2id = dict(zip(classes, range(len(classes))))
        self.id2class = dict((v, k) for k, v in self.class2id.items())
        self.classes = np.zeros((self.samples,), dtype='int32')

        self.image_data_generator = image_data_generator
        self.target_size = tuple(target_size)
        if color_mode not in {'rgb', 'grayscale'}:
            raise ValueError('Invalid color mode:', color_mode,
                             '; expected "rgb" or "grayscale".')
        self.color_mode = color_mode
        self.data_format = data_format
        if self.color_mode == 'rgb':
            if self.data_format == 'channels_last':
                self.image_shape = self.target_size + (3,)
            else:
                self.image_shape = (3,) + self.target_size
        else:
            if self.data_format == 'channels_last':
                self.image_shape = self.target_size + (1,)
            else:
                self.image_shape = (1,) + self.target_size

        if class_mode not in {'categorical', 'binary', 'sparse', None}:
            raise ValueError('Invalid class_mode:', class_mode,
                             '; expected one of "categorical", '
                             '"binary", "sparse", or None.')
        self.class_mode = class_mode
        self.save_to_dir = save_to_dir
        self.save_prefix = save_prefix
        self.save_format = save_format

        i = 0
        self.filenames = []
        for label_name in classes:
            for j, _ in enumerate(self.image_lists[label_name][category]):
                self.classes[i] = self.class2id[label_name]
                img_path = get_image_path(self.image_lists,
                                          label_name,
                                          j,
                                          self.image_dir,
                                          self.category)
                self.filenames.append(img_path)
                i += 1

        print("Found {} {} files".format(len(self.filenames), category))
        super(ImageListIterator, self).__init__(self.samples, batch_size, shuffle,
                                                seed)

    def next(self):
        """For python 2.x.

        # Returns
            The next batch.
        """
        with self.lock:
            index_array, current_index, current_batch_size = next(
                self.index_generator)
        # The transformation of images is not under thread lock
        # so it can be done in parallel
        batch_x = np.zeros((current_batch_size,) + self.image_shape,
                           dtype=K.floatx())
        grayscale = self.color_mode == 'grayscale'
        # build batch of image data
        for i, j in enumerate(index_array):
            img = load_img(self.filenames[j],
                           grayscale=grayscale,
                           target_size=self.target_size)
            x = img_to_array(img, data_format=self.data_format)
            x = self.image_data_generator.random_transform(x)
            x = self.image_data_generator.standardize(x)
            batch_x[i] = x
        # optionally save augmented images to disk for debugging purposes
        if self.save_to_dir:
            for i in range(current_batch_size):
                img = array_to_img(batch_x[i], self.data_format, scale=True)
                fname = '{prefix}_{index}_{hash}.{format}'.format(
                    prefix=self.save_prefix,
                    index=current_index + i,
                    hash=np.random.randint(10000),
                    format=self.save_format)
                img.save(os.path.join(self.save_to_dir, fname))
        # build batch of labels
        if self.class_mode == 'sparse':
            batch_y = self.classes[index_array]
        elif self.class_mode == 'binary':
            batch_y = self.classes[index_array].astype(K.floatx())
        elif self.class_mode == 'categorical':
            batch_y = np.zeros((len(batch_x), self.num_class),
                               dtype=K.floatx())
            for i, label in enumerate(self.classes[index_array]):
                batch_y[i, label] = 1.
        else:
            return batch_x
        return batch_x, batch_y


# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py
def create_image_lists(image_dir, validation_pct=10):
    """Builds a list of training images from the file system.

    Analyzes the sub folders in the image directory, splits them into stable
    training, testing, and validation sets, and returns a data structure
    describing the lists of images for each label and their paths.

    # Arguments
        image_dir: string path to a folder containing subfolders of images.
        validation_pct: integer percentage of images reserved for validation.

    # Returns
        dictionary of label subfolder, with images split into training
        and validation sets within each label.
    """
    if not os.path.isdir(image_dir):
        raise ValueError("Image directory {} not found.".format(image_dir))
    image_lists = {}
    sub_dirs = [x[0] for x in os.walk(image_dir)]
    sub_dirs_without_root = sub_dirs[1:]  # first element is root directory
    for sub_dir in sub_dirs_without_root:
        file_list = []
        dir_name = os.path.basename(sub_dir)
        if dir_name == image_dir:
            continue
        print("Looking for images in '{}'".format(dir_name))
        for extension in VALID_IMAGE_FORMATS:
            file_glob = os.path.join(image_dir, dir_name, '*.' + extension)
            file_list.extend(glob.glob(file_glob))
        if not file_list:
            warnings.warn('No files found')
            continue
        if len(file_list) < 20:
            warnings.warn('Folder has less than 20 images, which may cause '
                          'issues.')
        elif len(file_list) > MAX_NUM_IMAGES_PER_CLASS:
            warnings.warn('WARNING: Folder {} has more than {} images. Some '
                          'images will never be selected.'
                          .format(dir_name, MAX_NUM_IMAGES_PER_CLASS))
        label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower())
        training_images = []
        validation_images = []
        for file_name in file_list:
            base_name = os.path.basename(file_name)
            # Get the hash of the file name and perform variant assignment.
            hash_name = hashlib.sha1(as_bytes(base_name)).hexdigest()
            hash_pct = ((int(hash_name, 16) % (MAX_NUM_IMAGES_PER_CLASS + 1)) *
                        (100.0 / MAX_NUM_IMAGES_PER_CLASS))
            if hash_pct < validation_pct:
                validation_images.append(base_name)
            else:
                training_images.append(base_name)
        image_lists[label_name] = {
            'dir': dir_name,
            'training': training_images,
            'validation': validation_images,
        }
    return image_lists


# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py
def get_image_path(image_lists, label_name, index, image_dir, category):
    """"Returns a path to an image for a label at the given index.

    # Arguments
      image_lists: Dictionary of training images for each label.
      label_name: Label string we want to get an image for.
      index: Int offset of the image we want. This will be moduloed by the
      available number of images for the label, so it can be arbitrarily large.
      image_dir: Root folder string of the subfolders containing the training
      images.
      category: Name string of set to pull images from - training, testing, or
      validation.

    # Returns
      File system path string to an image that meets the requested parameters.
    """
    if label_name not in image_lists:
        raise ValueError('Label does not exist ', label_name)
    label_lists = image_lists[label_name]
    if category not in label_lists:
        raise ValueError('Category does not exist ', category)
    category_list = label_lists[category]
    if not category_list:
        raise ValueError('Label %s has no images in the category %s.',
                         label_name, category)
    mod_index = index % len(category_list)
    base_name = category_list[mod_index]
    sub_dir = label_lists['dir']
    full_path = os.path.join(image_dir, sub_dir, base_name)
    return full_path


def get_generators(image_lists, image_dir):
    train_datagen = CustomImageDataGenerator(rescale=1. / 255,
                                             horizontal_flip=True)

    test_datagen = CustomImageDataGenerator(rescale=1. / 255)

    train_generator = train_datagen.flow_from_image_lists(
        image_lists=image_lists,
        category='training',
        image_dir=image_dir,
        target_size=(MODEL_INPUT_HEIGHT, MODEL_INPUT_WIDTH),
        batch_size=BATCH_SIZE,
        class_mode='categorical',
        seed=RANDOM_SEED)

    validation_generator = test_datagen.flow_from_image_lists(
        image_lists=image_lists,
        category='validation',
        image_dir=image_dir,
        target_size=(MODEL_INPUT_HEIGHT, MODEL_INPUT_WIDTH),
        batch_size=BATCH_SIZE,
        class_mode='categorical',
        seed=RANDOM_SEED)

    return train_generator, validation_generator


def get_model(num_classes, weights='imagenet'):
    # create the base pre-trained model
    # , input_tensor=input_tensor
    base_model = InceptionV3(weights=weights, include_top=False)

    # add a global spatial average pooling layer
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    # let's add a fully-connected layer
    x = Dense(FC_LAYER_SIZE, activation='relu')(x)
    # and a logistic layer -- let's say we have 2 classes
    predictions = Dense(num_classes, activation='softmax')(x)

    # this is the model we will train
    model = Model(inputs=[base_model.input], outputs=[predictions])
    return model


def get_top_layer_model(model):
    """Used to train just the top layers of the model."""
    # first: train only the top layers (which were randomly initialized)
    # i.e. freeze all convolutional InceptionV3 layers
    for layer in model.layers[:INCEPTIONV3_BASE_LAYERS]:
        layer.trainable = False
    for layer in model.layers[INCEPTIONV3_BASE_LAYERS:]:
        layer.trainable = True

    # compile the model (should be done after setting layers to non-trainable)
    model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
                  metrics=['accuracy'])

    return model


def get_mid_layer_model(model):
    """After we fine-tune the dense layers, train deeper."""
    # freeze the first TRAINABLE_LAYER_INDEX layers and unfreeze the rest
    for layer in model.layers[:TRAINABLE_LAYERS]:
        layer.trainable = False
    for layer in model.layers[TRAINABLE_LAYERS:]:
        layer.trainable = True

    # we need to recompile the model for these modifications to take effect
    # we use SGD with a low learning rate
    model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])

    return model


def train_model(model, epochs, generators, callbacks=None):
    train_generator, validation_generator = generators
    model.fit_generator(
        train_generator,
        steps_per_epoch=STEPS_PER_EPOCH,
        validation_data=validation_generator,
        validation_steps=VALIDATION_STEPS,
        epochs=epochs,
        callbacks=callbacks)
    return model


def main(image_dir, validation_pct):
    sub_dirs = [x[0] for x in gfile.Walk(image_dir)]
    num_classes = len(sub_dirs) - 1
    print("Number of classes found: {}".format(num_classes))

    model = get_model(num_classes)

    print("Using validation percent of %{}".format(validation_pct))
    image_lists = create_image_lists(image_dir, validation_pct)

    generators = get_generators(image_lists, image_dir)

    # Get and train the top layers.
    model = get_top_layer_model(model)
    model = train_model(model, epochs=10, generators=generators)

    # Get and train the mid layers.
    model = get_mid_layer_model(model)
    _ = train_model(model, epochs=100, generators=generators,
                    callbacks=[checkpointer, early_stopper, tensorboard])

    # save model
    model.save('./output/model.hdf5', overwrite=True)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--image-dir', required=True, help='data directory')
    parser.add_argument('--validation-pct', default=10, help='validation percentage')
    args = parser.parse_args()

    os.makedirs('./output/checkpoints/', exist_ok=True)

    main(**vars(args))

If you simply want to divide the dataset for training and validation (without any augmentation, etc.)

from tensorflow.keras.applications.xception import preprocess_input
from tensorflow.keras.preprocessing.image import ImageDataGenerator

ds_gen = ImageDataGenerator(
    preprocessing_function=preprocess_input,
    validation_split=0.2 
)

train_ds = ds_gen.flow_from_directory(
  "/path/to/dataset", 
  seed=1,
  target_size=(150, 150), #adjust to your needs
  batch_size=32,#adjust to your needs
  class_mode='categorical',
  subset='training' 
)

val_ds = ds_gen.flow_from_directory(
  "/path/to/dataset",
  seed=1,
  target_size=(150, 150),
  batch_size=32,
  class_mode='categorical',
  subset='validation'
)

This is a simple tensorflow 2.0 code

from tensorflow.keras.preprocessing.image import ImageDataGenerator

def image_data_generator(data_dir,
                       data_augment=False,
                       batch_size=BATCH_SIZE,
                       target_size=(100, 100),
                       color_mode='rgb',
                       class_mode='binary',
                       shuffle=True):
  if data_augment:
      datagen = ImageDataGenerator(rescale=1./255,
                                   rotation_range=20,
                                   width_shift_range=0.2,
                                   height_shift_range=0.2,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   validation_split=0.2,#this is the trick
                                   horizontal_flip=True)
  else:
      datagen = ImageDataGenerator(rescale=1./255)

  generator = datagen.flow_from_directory(data_dir,
                                          target_size=target_size,
                                          color_mode=color_mode,
                                          batch_size=batch_size,
                                          shuffle=shuffle,
                                          class_mode=class_mode)
  return generator

train_generator = image_data_generator('Your_DataBase_Path',data_augment=True)

if you want to use pre processing units of VGG16 model and split your dataset into 70% training and 30% validation just follow this approach:

train_path = 'your dataset path'
train_batch= 
ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input, validation_split=0.3) \
.flow_from_directory(directory=train_path, target_size=(,), classes=['',''], batch_size= ,class_mode='categorical', subset='training')
val_batch= 
ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input, validation_split=0.3) \
.flow_from_directory(directory=train_path, target_size=(,), classes=['',''], batch_size=, class_mode='categorical', subset='validation')

Here is the answer:

data_path = 'path/to/dir'
data_gen = ImageDataGenerator(rescale=1./255,  validation_split=0.3)

train_data = data_gen.flow_from_directory(directory=data_path,target_size=img_size, batch_size=batch_size, subset='training', seed=42, class_mode='binary'  ) 
test_data = data_gen.flow_from_directory(directory=data_path,target_size=img_size, batch_size=batch_size, subset='validation', seed=42, class_mode='binary' ) 

Note that, starting from TF 2.9, the ImageDataGenerator() has been deprecated in favour of tf.keras.utils.image_dataset_from_directory() which achieves the same functionality.

It is highly likely that the former will be removed altogether in the upcoming TF versions.

Deprecated: tf.keras.preprocessing.image.ImageDataGenerator is not recommended for new code. Prefer loading images with tf.keras.utils.image_dataset_from_directory and transforming the output tf.data.Dataset with preprocessing layers. For more information, see the tutorials for loading images and augmenting images, as well as the preprocessing layer guide.

If you are using TensorFlow 2.x, you can use the same fit() function and use the parameter validation_split also for Image ImageDataGenerator

In case you are looking for train/validation split in generators for segmentation task, you can use the following snippet:

from tensorflow.keras.preprocessing.image import ImageDataGenerator

BATCH_SIZE = 16
val_fraction = 0.1
image_generator = ImageDataGenerator(rescale=1/255,
                                     brightness_range=[0.75, 1.75],
                                     validation_split=val_fraction)

mask_generator = ImageDataGenerator(validation_split=val_fraction)

train_image_generator = image_generator.flow_from_dataframe(df_img,
                                                            directory=image_dir,
                                                            x_col='image',
                                                            class_mode=None,
                                                            color_mode='rgb',
                                                            target_size=(INPUT_SIZE, INPUT_SIZE),
                                                            batch_size=BATCH_SIZE,
                                                            shuffle=True,
                                                            subset='training',
                                                            seed=1)

train_mask_generator = mask_generator.flow_from_dataframe(df_gt,
                                                          directory=gt_dir,
                                                          x_col='mask',
                                                          color_mode='grayscale',
                                                          class_mode=None,
                                                          target_size=(INPUT_SIZE, INPUT_SIZE),
                                                          batch_size=BATCH_SIZE,
                                                          shuffle=True,
                                                          subset='training',
                                                          seed=1)

validation_image_generator = image_generator.flow_from_dataframe(df_img,
                                                            directory=image_dir,
                                                            x_col='image',
                                                            class_mode=None,
                                                            color_mode='rgb',
                                                            target_size=(INPUT_SIZE, INPUT_SIZE),
                                                            batch_size=BATCH_SIZE,
                                                            subset='validation',
                                                            seed=1)

validation_mask_generator = mask_generator.flow_from_dataframe(df_gt,
                                                          directory=gt_dir,
                                                          x_col='mask',
                                                          color_mode='grayscale',
                                                          class_mode=None,
                                                          target_size=(INPUT_SIZE, INPUT_SIZE),
                                                          batch_size=BATCH_SIZE,
                                                          subset='validation',
                                                          seed=1)

train_generator = zip(train_image_generator, train_mask_generator)
validation_generator = zip(validation_image_generator, validation_mask_generator)

I don't know if you are still interested, but I found the following workaround. The most important function is GetTrainValidTestGeneratorFromDir, the other ones are just used by it. The basic idea is that you first divide the ImageDataGenerator by two using validation_split. By means of this you will get two iterators. You can use the second one as the test iterator. You will further divide the first one in the following way: First use flow_from_directory using training subset (so you can be sure that test data are excluded). Now you can use the same generator to get two divided dataframes and then you can use flow_from_dataframe function. You will get three ImageDataIterators without changing the folders

# -*- coding: utf-8 -*-
"""
Created on Thu Apr 15 10:15:18 2021

@author: Alessandro
"""
import pandas as pd
from keras.preprocessing.image import ImageDataGenerator


def ShuffleDataframe(thedataframe):
    thedataframe = thedataframe.sample(n=len(thedataframe), random_state=42)
    thedataframe = thedataframe.reset_index()
    thedataframe.drop('index', axis='columns', inplace=True)
    return(thedataframe)


def TransformGeneratorClassNumberToLabels(theGenerator, theLabelsNumbers):
    labelnames = theGenerator.class_indices
    labelnames =  list(labelnames.keys())
    theLabelsString = [labelnames[i] for i in theLabelsNumbers]
    return(theLabelsString)


def GetGeneratorDataframe(theGenerator):
    training_filenames = theGenerator.filenames    
    theLabelsNumbers = theGenerator.classes
    
    thelabelsString =  TransformGeneratorClassNumberToLabels(theGenerator, 
                                                             theLabelsNumbers)

    
    
    
    thedataframe = pd.DataFrame({'File': training_filenames,
                                'Label': thelabelsString})

    thedataframe = ShuffleDataframe(thedataframe)
    return(thedataframe)   

def GetTrainValidTestGeneratorFromDir(thedirectory,
                                      input_shape= (256, 256, 3),
                                      validation_split=0.1,
                                      rescaling = 1./255):
    
    train_datagen = ImageDataGenerator(rescale=1./255,
                                       validation_split=0.2)   
    
    
    train_and_valid_generator = train_datagen.flow_from_directory(thedirectory,
                                                                  target_size=input_shape[0:2],
                                                                  batch_size=20,
                                                                  class_mode="categorical",
                                                                  subset = 'training',
                                                                  save_to_dir ='checkdir')
    
    test_generator = train_datagen.flow_from_directory(thedirectory,
                                                       target_size=input_shape[0:2],
                                                       batch_size=20,
                                                       class_mode="categorical",
                                                       subset = 'validation')
    
    thedataframe = GetGeneratorDataframe(train_and_valid_generator)
    

    class_mode = "categorical"
        
    training_generator = train_datagen.flow_from_dataframe(dataframe = thedataframe,
                                                           directory = thedirectory,
                                                           target_size=input_shape[0:2],
                                                           x_col="File",
                                                           y_col = "Label",
                                                           subset= "training",
                                                           class_mode=class_mode)    


    validation_generator = train_datagen.flow_from_dataframe(dataframe = thedataframe,
                                                             directory = thedirectory,
                                                             target_size=input_shape[0:2],
                                                             x_col="File",
                                                             y_col = "Label",
                                                             subset= "validation",
                                                             class_mode=class_mode)
    return training_generator, validation_generator, test_generator 


input_shape = (450, 450, 3)
myDir      = "MyFolder"

(training_generator, 
 validation_generator, 
 test_generator) = GetTrainValidTestGeneratorFromDir(myDir)

# next part is just to verify whhat you got

training_dataframe = GetGeneratorDataframe(training_generator)
valid_dataframe = GetGeneratorDataframe(validation_generator)
test_dataframe = GetGeneratorDataframe(test_generator)