'Dataloder' object has no attribute 'shape'

[Dhia Moumeni]

I Tried to run the collab for segmenting my own data and run into this error , loaded the data into generator all went smoothly until model.fit

here is my code

import segmentation_models as sm
sm.set_framework('tf.keras')
BACKBONE = 'efficientnetb3'
BATCH_SIZE = 8
CLASSES = ['trailer']
LR = 0.0001
EPOCHS = 40

preprocess_input = sm.get_preprocessing(BACKBONE)


n_classes = 1 if len(CLASSES) == 1 else (len(CLASSES) + 1)  # case for binary and multiclass segmentation
activation = 'sigmoid' if n_classes == 1 else 'softmax'

#create model
model = sm.Unet(BACKBONE, classes=n_classes, activation=activation)

import tensorflow
# Segmentation models losses can be combined together by '+' and scaled by integer or float factor
dice_loss = sm.losses.DiceLoss()
focal_loss = sm.losses.BinaryFocalLoss() if n_classes == 1 else sm.losses.CategoricalFocalLoss()
total_loss = dice_loss + (1 * focal_loss)

# actulally total_loss can be imported directly from library, above example just show you how to manipulate with losses
# total_loss = sm.losses.binary_focal_dice_loss # or sm.losses.categorical_focal_dice_loss 

metrics = [sm.metrics.IOUScore(threshold=0.5), sm.metrics.FScore(threshold=0.5)]

# compile keras model with defined optimozer, loss and metrics
model.compile(optimizer= tensorflow.keras.optimizers.Adam(LR), loss=total_loss, metrics=metrics)

# Dataset for train images
train_dataset = Dataset(
    x_train_dir, 
    y_train_dir, 
    classes=CLASSES, 
    augmentation=get_training_augmentation(),
    preprocessing=get_preprocessing(preprocess_input),
)

# Dataset for validation images
valid_dataset = Dataset(
    x_valid_dir, 
    y_valid_dir, 
    classes=CLASSES, 
    augmentation=get_validation_augmentation(),
    preprocessing=get_preprocessing(preprocess_input),
)

train_dataloader = Dataloder(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
valid_dataloader = Dataloder(valid_dataset, batch_size=1, shuffle=False)

# check shapes for errors
assert train_dataloader[0][0].shape == (BATCH_SIZE, 320, 320, 3)
assert train_dataloader[0][1].shape == (BATCH_SIZE, 320, 320, 2)

# define callbacks for learning rate scheduling and best checkpoints saving
callbacks = [
    keras.callbacks.ModelCheckpoint('./best_model.h5', save_weights_only=True, save_best_only=True, mode='min'),
    keras.callbacks.ReduceLROnPlateau(),
]



history = model.fit_generator(
    train_dataloader, 
    steps_per_epoch=len(train_dataloader), 
    epochs=EPOCHS, 
    callbacks=callbacks, 
    validation_data=valid_dataloader, 
    validation_steps=len(valid_dataloader),
)


the error happens with model.fit i get 

---

AttributeError Traceback (most recent call last)
in
5 callbacks=callbacks,
6 validation_data=valid_dataloader,
----> 7 validation_steps=len(valid_dataloader),
8 )

~/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1431 shuffle=shuffle,
1432 initial_epoch=initial_epoch,
-> 1433 steps_name='steps_per_epoch')
1434
1435 def evaluate_generator(self,

~/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_generator.py in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)
142 batch_size=batch_size,
143 epochs=epochs - initial_epoch,
--> 144 shuffle=shuffle)
145
146 do_validation = validation_data is not None

~/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_generator.py in convert_to_generator_like(data, batch_size, steps_per_epoch, epochs, shuffle)
478
479 # Create generator from NumPy or EagerTensor Input.
--> 480 num_samples = int(nest.flatten(data)[0].shape[0])
481 if batch_size is None:
482 raise ValueError('You must specify batch_size')

AttributeError: 'Dataloder' object has no attribute 'shape'