def get_data(image_set='train',
path='~/.fastai/data/stanford-cars',
path_devkit='~/.fastai/data/devkit',
normalization_type='default'):
"""
Returns an ImageDataBunch for the specified image_set
Parameters
----------
image_set: str
One of {'train', 'test'}
path: PosixPath or str
Image directory path
path_devkit: PosixPath or str
Devkit directory path
normalization_type: str
One of {'default', 'imagenet'}
Returns
-------
data: ImageDataBunch obj
"""
if type(path) == str:
path = PosixPath(os.path.expanduser(path))
if type(path_devkit) == str:
path_devkit = PosixPath(os.path.expanduser(path_devkit))
path_images = path / 'cars_{}_p'.format(image_set)
path_annos = path_devkit / 'cars_{}_annos.mat'.format(image_set)
path_labels_names = path_devkit / 'cars_meta.mat'
annotations = scipy.io.loadmat(path_annos)['annotations'][0]
label_names = scipy.io.loadmat(path_labels_names)['class_names'][0]
images = [path_images / x[5][0] for x in annotations]
labels = [label_names[int(x[4][0]) - 1][0] for x in annotations]
if normalization_type == 'default':
normalization_stats = [
torch.tensor([0.5, 0.5, 0.5]),
torch.tensor([0.5, 0.5, 0.5])
]
elif normalization_type == 'imagenet':
normalization_stats = imagenet_stats
else:
raise ValueError("invalid normalization_type provided")
data = ImageDataBunch.from_lists(path_images,
fnames=images,
labels=labels,
ds_tfms=get_transforms(),
size=331,
bs=8).normalize(normalization_stats)
return data
def fastai_image_classifier(image_dir, filenames, labels, output_dir):
"""
This script provides FastAI-compatible training for the input labeled images
:param image_dir: directory with images
:param filenames: image filenames
:param labels: image labels
:param output_dir: output directory where results will be exported
:return: fastai.basic_train.Learner object
"""
tfms = get_transforms()
data = ImageDataBunch.from_lists(Path(image_dir),
filenames,
labels=labels,
ds_tfms=tfms,
size=224,
bs=4)
learn = cnn_learner(data,
models.resnet18,
metrics=accuracy,
path=output_dir)
learn.fit_one_cycle(10)
return learn
def train_script(input_data,
output_dir,
image_dir,
batch_size=4,
num_iter=10,
**kwargs):
"""
This script provides FastAI-compatible training for the input labeled images
:param image_dir: directory with images
:param filenames: image filenames
:param labels: image labels
:param output_dir: output directory where results will be exported
:return: fastai.basic_train.Learner object
"""
filenames, labels = [], []
for item in input_data:
if item['output'] is None:
continue
image_url = item['input'][0]
image_path = download(image_url, image_dir)
filenames.append(image_path)
labels.append(item['output'][0])
tfms = get_transforms()
data = ImageDataBunch.from_lists(Path(image_dir),
filenames,
labels=labels,
ds_tfms=tfms,
size=224,
bs=batch_size)
learn = cnn_learner(data,
models.resnet18,
metrics=accuracy,
path=output_dir)
learn.fit_one_cycle(num_iter)
learn.export()
return {'model_path': output_dir, 'image_dir': image_dir}