def get_train_length(wordnet_ids):
mode = get_mode('train')
n = 0
logger.info('Getting number of train examples...')
bar = IncrementalBar(max=len(wordnet_ids))
for wordnet_id in wordnet_ids:
n += get_tar_length(get_tar_path(mode, wordnet_id))
bar.next()
bar.finish()
return n
def get_test_records(delete_tar):
mode = get_mode('test')
with tarfile.TarFile(get_tar_path(mode)) as tar:
for member in tar.getmembers():
filename = member.name
image = tar.extractfile(member)
yield image, filename
image.close()
maybe_delete(mode, None, delete_tar)
def get_train_records(wordnet_ids, delete_tar=False):
class_indices = meta.get_wordnet_indices(zero_based=ZERO_BASED)
mode = get_mode('train')
for wordnet_id in wordnet_ids:
target = class_indices[wordnet_id]
with tarfile.TarFile(get_tar_path(mode, wordnet_id)) as tar:
for member in tar.getmembers():
filename = member.name
image = tar.extractfile(member)
yield image, filename, target
image.close()
maybe_delete(mode, wordnet_id, delete_tar)
def get_val_records(delete_tar=False):
from imagenet.meta import load_val_labels
mode = get_mode('val')
labels = load_val_labels(zero_based=ZERO_BASED)
with tarfile.TarFile(get_tar_path(mode)) as tar:
for member in tar.getmembers():
filename = member.name
index = filename.split('_')[-1][:-5]
target = labels[int(index)-1]
image = tar.extractfile(member)
yield image, filename, target
image.close()
maybe_delete(mode, None, delete_tar)
def convert_other(mode, delete_tar, overwrite=False):
mode = get_mode(mode)
check_overwrite(mode, overwrite)
n_examples = get_tar_length(get_tar_path(mode))
with get_file(mode, 'w') as fp:
if mode == 'val':
write_examples(
fp, n_examples, include_targets=True,
records=get_val_records(delete_tar), shuffle=False)
elif mode == 'test':
write_examples(
fp, n_examples, include_targets=False,
records=get_test_records(delete_tar), shuffle=False)
else:
raise ValueError('Invalid mode: "%s"' % mode)
def main(_):
mode = FLAGS.mode
with get_file(get_mode(mode), 'r') as fp:
images = fp['encoded_images']
filenames = fp['filenames']
targets = fp['targets']
if 'bounding_boxes' in fp:
bbs = fp['bounding_boxes']
else:
bbs = None
n = len(images)
print('number of examples: %d' % n)
indices = list(range(n))
print('shuffling...')
random.shuffle(indices)
print('Done!')
for i in indices:
vis(images[i], filenames[i], targets[i],
None if bbs is None else bbs[i])
def main(_):
add_bbox(get_mode(FLAGS.mode), FLAGS.overwrite)
def main(_):
compute_bbox(get_mode(FLAGS.mode), FLAGS.overwrite)
def _main(mode, delete_tar, overwrite):
mode = get_mode(mode)
if mode == 'train':
convert_train(delete_tar=delete_tar, overwrite=overwrite)
else:
convert_other(mode, delete_tar=delete_tar, overwrite=overwrite)