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Extend ImageGenerator to multilabel case #6128

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Extend ImageGenerator to multilabel case #6128

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4b689ec
extend DirectoryIterator to multilabel case. A file is still stored i…
Apr 3, 2017
ce97a26
update whitespacing to pass PEP8-check
Apr 4, 2017
d51ba93
add newline at end of file to pass PEP8-check
Apr 4, 2017
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29 changes: 22 additions & 7 deletions keras/preprocessing/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -461,7 +461,8 @@ def flow_from_directory(self, directory,
save_to_dir=None,
save_prefix='',
save_format='jpeg',
follow_links=False):
follow_links=False,
multilabel_classes=None):
return DirectoryIterator(
directory, self,
target_size=target_size, color_mode=color_mode,
Expand All @@ -471,7 +472,8 @@ def flow_from_directory(self, directory,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format,
follow_links=follow_links)
follow_links=follow_links,
multilabel_classes=multilabel_classes)

def standardize(self, x):
"""Apply the normalization configuration to a batch of inputs.
Expand Down Expand Up @@ -838,6 +840,7 @@ class DirectoryIterator(Iterator):
`"binary"`: binary targets (if there are only two classes),
`"categorical"`: categorical targets,
`"sparse"`: integer targets,
`"multilabel"`: multiple categorical 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.
Expand All @@ -851,6 +854,7 @@ class DirectoryIterator(Iterator):
images (if `save_to_dir` is set).
save_format: Format to use for saving sample images
(if `save_to_dir` is set).
multilabel_classes: Dictionary, mapping filepaths to numpy array of (multiple) classes (e.g. {'folder/some_image.jpg': np.array([ 0., 1., 0., 1.]),...})
"""

def __init__(self, directory, image_data_generator,
Expand All @@ -859,7 +863,7 @@ def __init__(self, directory, image_data_generator,
batch_size=32, shuffle=True, seed=None,
data_format=None,
save_to_dir=None, save_prefix='', save_format='jpeg',
follow_links=False):
follow_links=False, multilabel_classes=None):
if data_format is None:
data_format = K.image_data_format()
self.directory = directory
Expand All @@ -881,11 +885,12 @@ def __init__(self, directory, image_data_generator,
else:
self.image_shape = (1,) + self.target_size
self.classes = classes
if class_mode not in {'categorical', 'binary', 'sparse', None}:
if class_mode not in {'categorical', 'binary', 'sparse', 'multilabel', None}:
raise ValueError('Invalid class_mode:', class_mode,
'; expected one of "categorical", '
'"binary", "sparse", or None.')
'"binary", "sparse","multilabel" or None.')
self.class_mode = class_mode
self.multilabel_classes = multilabel_classes
self.save_to_dir = save_to_dir
self.save_prefix = save_prefix
self.save_format = save_format
Expand Down Expand Up @@ -921,7 +926,11 @@ def _recursive_list(subpath):

# second, build an index of the images in the different class subfolders
self.filenames = []
self.classes = np.zeros((self.samples,), dtype='int32')
# in multilabel case, we store multiple labels (binary format) instead of one single integer
if(self.class_mode == 'multilabel'):
self.classes = np.zeros((self.samples, self.num_class), dtype='int32')
else:
self.classes = np.zeros((self.samples,), dtype='int32')
i = 0
for subdir in classes:
subpath = os.path.join(directory, subdir)
Expand All @@ -933,7 +942,11 @@ def _recursive_list(subpath):
is_valid = True
break
if is_valid:
self.classes[i] = self.class_indices[subdir]
if(self.class_mode == 'multilabel'):
fileid = os.path.join(subdir, fname)
self.classes[i] = self.multilabel_classes[fileid]
else:
self.classes[i] = self.class_indices[subdir]
i += 1
# add filename relative to directory
absolute_path = os.path.join(root, fname)
Expand Down Expand Up @@ -980,6 +993,8 @@ def next(self):
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.
elif self.class_mode == 'multilabel':
batch_y = self.classes[index_array]
else:
return batch_x
return batch_x, batch_y