Error when training with XML/Pascal format Parsing

Hello. I’m also having trouble using the newly updated version of the project with the xml generator

The error:

> ValueError                                Traceback (most recent call last)
> <ipython-input-9-480a1c690cb2> in <module>()
>       7                               epochs=epochs,
>       8                               callbacks=callbacks,
> ----> 9                               verbose=0)
> 
> /home/rodsnjr/miniconda3/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
>      89                 warnings.warn('Update your `' + object_name +
>      90                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
> ---> 91             return func(*args, **kwargs)
>      92         wrapper._original_function = func
>      93         return wrapper
> 
> /home/rodsnjr/miniconda3/lib/python3.6/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
>    2175                     outs = self.train_on_batch(x, y,
>    2176                                                sample_weight=sample_weight,
> -> 2177                                                class_weight=class_weight)
>    2178 
>    2179                     if not isinstance(outs, list):
> 
> /home/rodsnjr/miniconda3/lib/python3.6/site-packages/keras/engine/training.py in train_on_batch(self, x, y, sample_weight, class_weight)
>    1841             sample_weight=sample_weight,
>    1842             class_weight=class_weight,
> -> 1843             check_batch_axis=True)
>    1844         if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
>    1845             ins = x + y + sample_weights + [1.]
> 
> /home/rodsnjr/miniconda3/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
>    1424                                     self._feed_input_shapes,
>    1425                                     check_batch_axis=False,
> -> 1426                                     exception_prefix='input')
>    1427         y = _standardize_input_data(y, self._feed_output_names,
>    1428                                     output_shapes,
> 
> /home/rodsnjr/miniconda3/lib/python3.6/site-packages/keras/engine/training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
>     108                         ': expected ' + names[i] + ' to have ' +
>     109                         str(len(shape)) + ' dimensions, but got array '
> --> 110                         'with shape ' + str(data_shape))
>     111                 if not check_batch_axis:
>     112                     data_shape = data_shape[1:]
> 
> ValueError: Error when checking input: expected input_1 to have 4 dimensions, but got array with shape (16, 1)

My generator funciton:

# 1: Instantiate to `BatchGenerator` objects: One for training, one for validation.

train_dataset = DataGenerator()
val_dataset = DataGenerator()

# Dataset dirs
train_images_dir = '/media/rodsnjr/Files/Datasets/gvc_dataset_full/training/dataset/'
train_ids_dir = '/media/rodsnjr/Files/Datasets/gvc_dataset_full/training/ids'
train_labels_files_dir = '/media/rodsnjr/Files/Datasets/gvc_dataset_full/training/annotations/'

# Validation dataset dirs
validation_images_dir = '/media/rodsnjr/Files/Datasets/gvc_dataset_full/validation/dataset/'
validation_ids_dir = '/media/rodsnjr/Files/Datasets/gvc_dataset_full/validation/ids'
validation_labels_files_dir = '/media/rodsnjr/Files/Datasets/gvc_dataset_full/validation/annotations/'

train_annotations_dirs = sorted(list_path(train_labels_files_dir))

validation_annotations_dirs = sorted(list_path(validation_labels_files_dir))

train_images_set_filenames = sorted(list_dir(train_ids_dir, 'txt'))
train_images_file_paths = sorted(list_path(train_images_dir))

validation_images_set_filenames = sorted(list_dir(validation_ids_dir, 'txt'))
validation_images_file_paths = sorted(list_path(validation_images_dir))

right_classes = [
        'ascending_stair',
        'descending_stair',
        'door',
        'double_door',
        'elevator_door',
        'half_opened_door',
        'opened_door'
]

filenames, labels, image_ids = train_dataset.parse_xml(
    images_dirs=train_images_file_paths,
    image_set_filenames=train_images_set_filenames,
    annotations_dirs=train_annotations_dirs,
    classes = right_classes,
    ret=True
)

v_filenames, v_labels, v_image_ids = val_dataset.parse_xml(
    images_dirs=validation_images_file_paths,
    image_set_filenames=validation_images_set_filenames,
    annotations_dirs=validation_annotations_dirs,
    classes = right_classes,
    ret=True
)

train_dataset_size = train_dataset.get_dataset_size()
val_dataset_size   = val_dataset.get_dataset_size()

print("Number of images in the training dataset:\t{:>6}".format(train_dataset_size))
print("Number of images in the validation dataset:\t{:>6}".format(val_dataset_size))

Again using the ssd_7 training notebook.

The parser correctly finds all images and etc:

set_filename_ascending_stairs.txt: 100%|██████████| 735/735 [00:01<00:00, 561.38it/s] set_filename_descending_stairs.txt: 100%|██████████| 198/198 [00:00<00:00, 673.99it/s] set_filename_doors.txt: 100%|██████████| 1869/1869 [00:02<00:00, 792.73it/s] set_filename_double_doors.txt: 100%|██████████| 291/291 [00:00<00:00, 795.94it/s] set_filename_elevator_doors.txt: 100%|██████████| 390/390 [00:00<00:00, 750.01it/s] set_filename_half_opened_doors.txt: 100%|██████████| 227/227 [00:00<00:00, 786.35it/s] set_filename_opened_doors.txt: 100%|██████████| 465/465 [00:00<00:00, 783.15it/s] set_filename_ascending_stairs.txt: 100%|██████████| 211/211 [00:00<00:00, 512.35it/s] set_filename_descending_stairs.txt: 100%|██████████| 45/45 [00:00<00:00, 659.41it/s] set_filename_doors.txt: 100%|██████████| 445/445 [00:00<00:00, 784.51it/s] set_filename_double_doors.txt: 100%|██████████| 68/68 [00:00<00:00, 680.39it/s] set_filename_elevator_doors.txt: 100%|██████████| 108/108 [00:00<00:00, 762.51it/s] set_filename_half_opened_doors.txt: 100%|██████████| 53/53 [00:00<00:00, 728.35it/s] set_filename_opened_doors.txt: 100%|██████████| 114/114 [00:00<00:00, 697.97it/s] Number of images in the training dataset: 4175 Number of images in the validation dataset: 1044

[ParmeetSingh]

I did have the same error but turns out I was passing images of different width and height. I resized the all images to same width and height and it worked.

[ashah03]

I’m having the same issue that @rodsnjr was having:

Epoch 1/20
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-18-dc621119a80e> in <module>()
     11                               validation_data=val_generator,
     12                               validation_steps=ceil(val_dataset_size/batch_size),
---> 13                               initial_epoch=initial_epoch)

/usr/local/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name +
     90                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

/usr/local/lib/python3.6/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   2175                     outs = self.train_on_batch(x, y,
   2176                                                sample_weight=sample_weight,
-> 2177                                                class_weight=class_weight)
   2178 
   2179                     if not isinstance(outs, list):

/usr/local/lib/python3.6/site-packages/keras/engine/training.py in train_on_batch(self, x, y, sample_weight, class_weight)
   1841             sample_weight=sample_weight,
   1842             class_weight=class_weight,
-> 1843             check_batch_axis=True)
   1844         if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
   1845             ins = x + y + sample_weights + [1.]

/usr/local/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
   1424                                     self._feed_input_shapes,
   1425                                     check_batch_axis=False,
-> 1426                                     exception_prefix='input')
   1427         y = _standardize_input_data(y, self._feed_output_names,
   1428                                     output_shapes,

/usr/local/lib/python3.6/site-packages/keras/engine/training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    108                         ': expected ' + names[i] + ' to have ' +
    109                         str(len(shape)) + ' dimensions, but got array '
--> 110                         'with shape ' + str(data_shape))
    111                 if not check_batch_axis:
    112                     data_shape = data_shape[1:]

ValueError: Error when checking input: expected input_1 to have 4 dimensions, but got array with shape (16, 1)

I’m using xml parsing from your data generator object.