def make_randoms(source_dir, number_of_images_per_folder,
number_of_random_folders):
logging.basicConfig(filename=source_dir + '/logger.log',
level=logging.INFO)
# Run script to download data to source_dir
if not gfile.exists(source_dir):
gfile.makedirs(source_dir)
if not gfile.exists(os.path.join(
source_dir, 'broden1_224/')) or not gfile.exists(
os.path.join(source_dir, 'inception5h')):
subprocess.call(['bash', 'FetchDataAndModels.sh', source_dir])
# make targets from imagenet
imagenet_dataframe = fetcher.make_imagenet_dataframe(
"/home/tomohiro/code/tcav/tcav/tcav_examples/image_models/imagenet/imagenet_url_map.csv"
)
# Make random folders. If we want to run N random experiments with tcav, we need N+1 folders.
fetcher.generate_random_folders(
working_directory=source_dir,
random_folder_prefix="random50",
number_of_random_folders=number_of_random_folders + 1,
number_of_examples_per_folder=number_of_images_per_folder,
imagenet_dataframe=imagenet_dataframe)
def make_concepts_targets_and_randoms(source_dir, number_of_images_per_folder,
number_of_random_folders):
logging.basicConfig(filename=source_dir + '/logger.log',
level=logging.INFO)
# Run script to download data to source_dir
if not gfile.exists(source_dir):
gfile.makedirs(source_dir)
if not gfile.exists(os.path.join(
source_dir, 'broden1_224/')) or not gfile.exists(
os.path.join(source_dir, 'inception5h')):
subprocess.call(['bash', 'FetchDataAndModels.sh', source_dir])
# make targets from imagenet
imagenet_dataframe = fetcher.make_imagenet_dataframe(
"/home/tomohiro/code/tcav/tcav/tcav_examples/image_models/imagenet/imagenet_url_map.csv"
)
all_class = imagenet_dataframe["class_name"].values.tolist()
# Determine classes that we will fetch
imagenet_classes = ['fire engine']
broden_concepts = ['striped', 'dotted', 'zigzagged']
random_except_concepts = ['zebra', 'fire engine']
except_words = [
'cat', 'shark', 'apron', 'dogsled', 'dumbbell', 'ball', 'bus'
]
for e_word in except_words:
random_except_concepts.extend([
element for element in all_class
if e_word == str(element)[-len(e_word):]
])
tf.logging.info('imagenet_classe %s' % imagenet_classes)
tf.logging.info('concepts %s' % broden_concepts)
tf.logging.info('random_except_concepts %s' % random_except_concepts)
for image in imagenet_classes:
fetcher.fetch_imagenet_class(source_dir, image,
number_of_images_per_folder,
imagenet_dataframe)
# Make concepts from broden
for concept in broden_concepts:
fetcher.download_texture_to_working_folder(
broden_path=os.path.join(source_dir, 'broden1_224'),
saving_path=source_dir,
texture_name=concept,
number_of_images=number_of_images_per_folder)
# Make random folders. If we want to run N random experiments with tcav, we need N+1 folders.
# (変更) 除外するクラスを指定
fetcher.generate_random_folders(
working_directory=source_dir,
random_folder_prefix="random500",
number_of_random_folders=number_of_random_folders + 1,
number_of_examples_per_folder=number_of_images_per_folder,
imagenet_dataframe=imagenet_dataframe,
random_except_concepts=random_except_concepts)
def make_imagent_color_concept(source_dir, number_of_images_per_folder):
# make targets from imagenet
imagenet_dataframe = fetcher.make_imagenet_dataframe(
"/home/tomohiro/code/tcav/tcav/tcav_examples/image_models/imagenet/imagenet_url_map.csv"
)
color_lst = ['red', 'yellow', 'blue', 'green']
fetcher.fetch_imagenet_class_color(source_dir,
number_of_images_per_folder,
imagenet_dataframe,
folder_prefix="imagenet",
color_lst=color_lst)
def make_targets(source_dir, number_of_images_per_folder):
# make targets from imagenet
imagenet_dataframe = fetcher.make_imagenet_dataframe(
"/home/tomohiro/code/tcav/tcav/tcav_examples/image_models/imagenet/imagenet_url_map.csv"
)
all_class = imagenet_dataframe["class_name"].values.tolist()
# Determine classes that we will fetch
imagenet_classes = ['soccer ball']
for image in imagenet_classes:
fetcher.fetch_imagenet_class(source_dir, image,
number_of_images_per_folder,
imagenet_dataframe)
def make_concepts_targets_and_randoms(source_dir, number_of_images_per_folder,
number_of_random_folders):
# Run script to download data to source_dir
if not gfile.exists(source_dir):
gfile.makedirs(source_dir)
if not gfile.exists(os.path.join(
source_dir, 'broden1_224/')) or not gfile.exists(
os.path.join(source_dir, 'inception5h')):
subprocess.call(['bash', 'FetchDataAndModels.sh', source_dir])
# Determine classes that we will fetch
imagenet_classes = ['zebra']
broden_concepts = ['striped', 'dotted', 'zigzagged']
# make targets from imagenet
imagenet_dataframe = fetcher.make_imagenet_dataframe(
"./imagenet_url_map.csv")
for image in imagenet_classes:
fetcher.fetch_imagenet_class(source_dir, image,
number_of_images_per_folder,
imagenet_dataframe)
# Make concepts from broden
for concept in broden_concepts:
fetcher.download_texture_to_working_folder(
broden_path=os.path.join(source_dir, 'broden1_224'),
saving_path=source_dir,
texture_name=concept,
number_of_images=number_of_images_per_folder)
# Make random folders. If we want to run N random experiments with tcav, we need N+1 folders.
fetcher.generate_random_folders(
working_directory=source_dir,
random_folder_prefix="random500",
number_of_random_folders=number_of_random_folders + 1,
number_of_examples_per_folder=number_of_images_per_folder,
imagenet_dataframe=imagenet_dataframe)