CODEWITHSUNDEEP
pythontensorflowkeras
AaronDT
AaronDT

Reputation: 4060

Keras: Combining data generators to handle image + text

I am working on a multilabel classification model where I am trying to combine two models, a CNN and a text-classifier into one model using Keras and train them together, like so:

#cnn_model is a vgg16 model

#text_model looks as follows:
### takes the vectorized text as input
text_model = Sequential()
text_model .add(Dense(vec_size, input_shape=(vec_size,), name='aux_input'))

## merging both models
merged = Merge([cnn_model, text_model], mode='concat')

### final_model takes the combined models and adds a sofmax classifier to it
final_model = Sequential()
final_model.add(merged)
final_model.add(Dense(n_classes, activation='softmax'))

As such, I am working with an ImageDataGenerator to process the images and the respective labels.

For the images I am using a custom helper function that reads images into the model via paths provided by pandas dataframes - one for training (df_train) and one for validation (df_validation). The dataframes also provide the final labels for the model in the "label_vec" column:

# From https://github.com/keras-team/keras/issues/5152
def flow_from_dataframe(img_data_gen, in_df, path_col, y_col, **dflow_args):
    base_dir = os.path.dirname(in_df[path_col].values[0])
    print('## Ignore next message from keras, values are replaced anyways')
    df_gen = img_data_gen.flow_from_directory(base_dir, class_mode = 'sparse', **dflow_args)
    df_gen.filenames = in_df[path_col].values
    df_gen.classes = numpy.stack(in_df[y_col].values)
    df_gen.samples = in_df.shape[0]
    df_gen.n = in_df.shape[0]
    df_gen._set_index_array()
    df_gen.directory = '' # since we have the full path
    print('Reinserting dataframe: {} images'.format(in_df.shape[0]))
    return df_gen 

from keras.applications.vgg16 import preprocess_input

train_datagen = keras.preprocessing.image.ImageDataGenerator(preprocessing_function=preprocess_input)                                                  horizontal_flip=True)
validation_datagen = keras.preprocessing.image.ImageDataGenerator(preprocessing_function=preprocess_input)#rescale=1./255)

train_generator = flow_from_dataframe(train_datagen, df_train,
                                                     path_col = 'filename',
                                                     y_col = 'label_vec', 

                                                    target_size=(224, 224), batch_size=128, shuffle=False)
validation_generator = flow_from_dataframe(validation_datagen, df_validation,
                                                     path_col = 'filename',
                                                     y_col = 'label_vec', 
                                                         target_size=(224, 224), batch_size=64, shuffle=False)

Now I am trying to provide my one-hot-encoded text vectors (i.e. [0,0,0,1,0,0]) to the model, which are also stored in a pandas dataframe.

Since my train_generator provides me with the image and label data, I am now looking for a solution to combine this generator with a generator which allows me to additionally feed the respective text-vector

Upvotes: 1

Views: 2759

Answers (1)

sdcbr
sdcbr

Reputation: 7129

You might want to consider writing your own generator (making use of Keras' Sequence object to allow for multiprocessing) instead of modifying the ImageDataGenerator code. From the Keras docs:

class CIFAR10Sequence(Sequence):

    def __init__(self, x_set, y_set, batch_size):
        self.x, self.y = x_set, y_set
        self.batch_size = batch_size

    def __len__(self):
        return int(np.ceil(len(self.x) / float(self.batch_size)))

    def __getitem__(self, idx):
        batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]
        batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]

        return np.array([
            resize(imread(file_name), (200, 200))
               for file_name in batch_x]), np.array(batch_y)

You could have your labels, paths to the images, and paths to the text files in a single pandas dataframe and modify the __getitem__ method from above to have your generator yield all three of them simultaneously: one list of numpy arraysX which contains all the inputs, one numpy array Y which contains the outputs.

Upvotes: 2

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