def build(self, config, dataset_type):
download_folder = os.path.join(get_multimodelity_root(),
config.data_dir, config.data_folder)
file_name = CLEVR_DOWNLOAD_URL.split("/")[-1]
local_filename = os.path.join(download_folder, file_name)
extraction_folder = os.path.join(download_folder,
".".join(file_name.split(".")[:-1]))
self.data_folder = extraction_folder
# Either if the zip file is already present or if there are some
# files inside the folder we don't continue download process
if os.path.exists(local_filename):
logger.info("CLEVR dataset is already present. Skipping download.")
return
if (os.path.exists(extraction_folder)
and len(os.listdir(extraction_folder)) != 0):
return
logger.info("Downloading the CLEVR dataset now")
download(CLEVR_DOWNLOAD_URL, download_folder,
CLEVR_DOWNLOAD_URL.split("/")[-1])
logger.info("Downloaded. Extracting now. This can take time.")
with zipfile.ZipFile(local_filename, "r") as zip_ref:
zip_ref.extractall(download_folder)
def resolve_cache_dir(env_variable="multimodelity_CACHE_DIR",
default="multimodelity"):
# Some of this follow what "transformers" does for there cache resolving
try:
from torch.hub import _get_torch_home
torch_cache_home = _get_torch_home()
except ImportError:
torch_cache_home = os.path.expanduser(
os.getenv(
"TORCH_HOME",
os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"),
))
default_cache_path = os.path.join(torch_cache_home, default)
cache_path = os.getenv(env_variable, default_cache_path)
if not PathManager.exists(cache_path):
try:
PathManager.mkdirs(cache_path)
except PermissionError:
cache_path = os.path.join(get_multimodelity_root(),
".multimodelity_cache")
PathManager.mkdirs(cache_path)
return cache_path
def get_multimodelity_cache_dir():
config = get_global_config()
cache_dir = config.env.cache_dir
# If cache_dir path exists do not join to multimodelity root
if not os.path.exists(cache_dir):
cache_dir = os.path.join(get_multimodelity_root(), cache_dir)
return cache_dir
def __init__(self,
config,
dataset_type,
data_folder=None,
*args,
**kwargs):
super().__init__(_CONSTANTS["dataset_key"], config, dataset_type)
self._data_folder = data_folder
self._data_dir = os.path.join(get_multimodelity_root(),
config.data_dir)
if not self._data_folder:
self._data_folder = os.path.join(self._data_dir,
config.data_folder)
if not os.path.exists(self._data_folder):
raise RuntimeError(_TEMPLATES["data_folder_missing_error"].format(
self._data_folder))
# Check if the folder was actually extracted in the subfolder
if config.data_folder in os.listdir(self._data_folder):
self._data_folder = os.path.join(self._data_folder,
config.data_folder)
if len(os.listdir(self._data_folder)) == 0:
raise FileNotFoundError(_CONSTANTS["empty_folder_error"])
self.load()
def load_yaml(f):
# Convert to absolute path for loading includes
abs_f = get_absolute_path(f)
try:
mapping = OmegaConf.load(abs_f)
f = abs_f
except FileNotFoundError as e:
# Check if this file might be relative to root?
# TODO: Later test if this can be removed
relative = os.path.abspath(os.path.join(get_multimodelity_root(), f))
if not PathManager.isfile(relative):
raise e
else:
f = relative
mapping = OmegaConf.load(f)
if mapping is None:
mapping = OmegaConf.create()
includes = mapping.get("includes", [])
if not isinstance(includes, collections.abc.Sequence):
raise AttributeError("Includes must be a list, {} provided".format(
type(includes)))
include_mapping = OmegaConf.create()
multimodelity_root_dir = get_multimodelity_root()
for include in includes:
original_include_path = include
include = os.path.join(multimodelity_root_dir, include)
# If path doesn't exist relative to multimodelity root, try relative to current file
if not PathManager.exists(include):
include = os.path.join(os.path.dirname(f), original_include_path)
current_include_mapping = load_yaml(include)
include_mapping = OmegaConf.merge(include_mapping,
current_include_mapping)
mapping.pop("includes", None)
mapping = OmegaConf.merge(include_mapping, mapping)
return mapping
def build(self, config, dataset_type):
self._dataset_type = dataset_type
self._config = config
data_folder = os.path.join(get_multimodelity_root(),
self._config.data_dir)
# Since the imdb tar file contains all of the sets, we won't download them
# except in case of train
if self._dataset_type != "train":
return
self._download_and_extract_imdb(data_folder)
self._download_and_extract_features(data_folder)
def build(self, config, dataset_type):
self._dataset_type = dataset_type
self._config = config
data_folder = os.path.join(get_multimodelity_root(),
self._config.data_dir)
self._download_and_extract_imdb(data_folder)
if self._dataset_type != "train":
return
self._download_and_extract("vocabs", VISUAL_DIALOG_CONSTS["vocabs"],
data_folder)
self._download_and_extract_features(data_folder)
def setUp(self):
setup_imports()
torch.manual_seed(1234)
config_path = os.path.join(
get_multimodelity_root(),
"..",
"projects",
"butd",
"configs",
"coco",
"beam_search.yaml",
)
config_path = os.path.abspath(config_path)
args = dummy_args(model="butd", dataset="coco")
args.opts.append(f"config={config_path}")
configuration = Configuration(args)
configuration.config.datasets = "coco"
configuration.freeze()
self.config = configuration.config
registry.register("config", self.config)
def setUp(self):
setup_imports()
torch.manual_seed(1234)
config_path = os.path.join(
get_multimodelity_root(),
"..",
"projects",
"butd",
"configs",
"coco",
"nucleus_sampling.yaml",
)
config_path = os.path.abspath(config_path)
args = dummy_args(model="butd", dataset="coco")
args.opts.append(f"config={config_path}")
configuration = Configuration(args)
configuration.config.datasets = "coco"
configuration.config.model_config.butd.inference.params.sum_threshold = 0.5
configuration.freeze()
self.config = configuration.config
registry.register("config", self.config)
def setUp(self):
torch.manual_seed(1234)
registry.register("clevr_text_vocab_size", 80)
registry.register("clevr_num_final_outputs", 32)
config_path = os.path.join(
get_multimodelity_root(),
"..",
"projects",
"others",
"cnn_lstm",
"clevr",
"defaults.yaml",
)
config_path = os.path.abspath(config_path)
args = dummy_args(model="cnn_lstm", dataset="clevr")
args.opts.append(f"config={config_path}")
configuration = Configuration(args)
configuration.config.datasets = "clevr"
configuration.freeze()
self.config = configuration.config
registry.register("config", self.config)
def test_config_overrides(self):
config_path = os.path.join(
get_multimodelity_root(),
"..",
"projects",
"m4c",
"configs",
"textvqa",
"defaults.yaml",
)
config_path = os.path.abspath(config_path)
args = dummy_args(model="m4c", dataset="textvqa")
args.opts += [
f"config={config_path}",
"training.lr_steps[1]=10000",
'dataset_config.textvqa.zoo_requirements[0]="test"',
]
configuration = Configuration(args)
configuration.freeze()
config = configuration.get_config()
self.assertEqual(config.training.lr_steps[1], 10000)
self.assertEqual(config.dataset_config.textvqa.zoo_requirements[0],
"test")
def _test_quality_check(self, fn):
fn(get_multimodelity_root())
fn(os.path.join(get_multimodelity_root(), "..", "multimodelity_cli"))
fn(os.path.join(get_multimodelity_root(), "..", "tests"))