Shape (None, None) of ground truth labels when using custom Keras data generator and loss function

Question

I'm trying to implement my own detection model that tries to find objects in a grayscale image by its coordinates, for this I created a custom dataset, defined by a custom data generator:

class DataGenerator(tf.compat.v2.keras.utils.Sequence):

    def __init__(self, X_data , y_data, batch_size, shuffle = True):
        self.batch_size = batch_size
        self.X_data = X_data
        self.labels = y_data
        self.y_data = y_data
        self.shuffle = shuffle
        self.n = 0
        self.dim = (480, 848)
        self.list_IDs = np.arange(len(self.X_data))
        self.on_epoch_end()

    def __next__(self):
        # Get one batch of data
        data = self.__getitem__(self.n)
        # Batch index
        self.n += 1

        # If we have processed the entire dataset then
        if self.n >= self.__len__():
            self.on_epoch_end
            self.n = 0

        return data
    def __len__(self):
        # Return the number of batches of the dataset
        return math.ceil(len(self.indexes)/self.batch_size)

    def __getitem__(self, index):
        # Generate indexes of the batch
        indexes = self.indexes[index*self.batch_size:
            (index+1)*self.batch_size]
        # Find list of IDs
        list_IDs_temp = [self.list_IDs[k] for k in indexes]

        X = self._generate_x(list_IDs_temp)
        y = self._generate_y(list_IDs_temp)
        return X, y


    def on_epoch_end(self):

        self.indexes = np.arange(len(self.X_data))

        if self.shuffle: 
            np.random.shuffle(self.indexes)

    def _generate_x(self, list_IDs_temp):

        X = np.empty((self.batch_size, *self.dim))
        for i, ID in enumerate(list_IDs_temp):
            X[i,] = cv2.imread(self.X_data[ID],0)
            X = (X/255).astype('float32') # Normalize data  
        return X[:,:,:, np.newaxis]

    def _generate_y(self, list_IDs_temp):

        y = np.empty((self.batch_size, 2))
        for i, ID in enumerate(list_IDs_temp):
            y[i] = self.y_data[ID]
        return y

When called it gives the following output:

val_generator  = DataGenerator(x_test, y_test, batch_size=4, shuffle=False)
images, labels = next(val_generator)
print(labels.shape)
>>>> (4, 2)

Which is what you would expect for a batch size of 4 with an x and an y as coordinates in the image.

The model looks as follows:

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                activation='relu',
                batch_input_shape=(4, 480, 848, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(15, activation="relu"))

model.compile(loss=simple_loss, optimizer=keras.optimizers.Adam(lr=0.001))

Calling the model:

steps_per_epoch = len(train_generator)
validation_steps = len(val_generator)

model.fit(train_generator,
        steps_per_epoch=steps_per_epoch,
        epochs=10,
        validation_data=val_generator,
        validation_steps=validation_steps)

My custom but simple loss function to test if the model is running gave some errors:

def simple_loss(yTrue, yPred):
    probs_logs, coords = yPred[:,:5], yPred[:,5:]
    coords_2d = tf.reshape(coords, [4, 10]) # 4 batch size and 10 coords flatten out
    _abs = tf.math.abs(yTrue, coords_2d )
    return tf.sqrt(_abs)

I first was thinking yPred contained errors, but that produced the following tensor: Tensor("simple_loss/Reshape:0", shape=(4, 10), dtype=float32)

Then I looked at yTrue and found the shape was (None, None): Tensor("IteratorGetNext:1", shape=(None, None), dtype=float32)

So I guess there is something wrong with my generator, I have no clue what, so I was wondering if any of you could help me out?

Thanks

Isam Abdullah Isam Abdullah · Accepted Answer · 2020-07-11T21:45:27

The shape for ground truths is supposed to be None since they have not been evaluated yet when the code is first run. Moreover, the "yTrue" and "yPred" are purely tensors, so be sure to use tensorflow functions for each and every operation on them.

I faced same error just because I inferred the length of tensor by using tensor.shape which gave me float not Tensor. Fixing that fixed the problem, which was using tf.size() function. This returns length of tensor as Tensor object.

Shape (None, None) of ground truth labels when using custom Keras data generator and loss function

1 Answers