def test_download(self) -> None:
download(
"https://www.facebook.com",
".",
filename=self._filename,
progress=False,
)
self.assertTrue(os.path.isfile(self._filename))
def _get_local_path(self, path: str, **kwargs: Any) -> str:
"""
This implementation downloads the remote resource and caches it locally.
The resource will only be downloaded if not previously requested.
"""
self._check_kwargs(kwargs)
if path not in self.cache_map or not os.path.exists(
self.cache_map[path]
):
logger = logging.getLogger(__name__)
parsed_url = urlparse(path)
dirname = os.path.join(
get_cache_dir(), os.path.dirname(parsed_url.path.lstrip("/"))
)
filename = path.split("/")[-1]
cached = os.path.join(dirname, filename)
print()
print("Path in PM:")
print(path)
print()
print("dirname in PM")
print(dirname)
dirname = "/content"
with file_lock(cached):
if not os.path.isfile(cached):
logger.info("Downloading {} ...".format(path))
cached = download(path, dirname, filename=filename)
logger.info("URL {} cached in {}".format(path, cached))
self.cache_map[path] = cached
return self.cache_map[path]
def prepare_panoptic_coco():
dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"),
"coco")
for s in ["val2017", "train2017"]:
separate_coco_semantic_from_panoptic(
os.path.join(dataset_dir,
"annotations/panoptic_{}.json".format(s)),
os.path.join(dataset_dir, "panoptic_{}".format(s)),
os.path.join(dataset_dir, "panoptic_stuff_{}".format(s)),
COCO_CATEGORIES,
)
# Prepare val2017_100 for quick testing:
dest_dir = os.path.join(dataset_dir, "annotations/")
URL_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/"
download(URL_PREFIX + "annotations/coco/panoptic_val2017_100.json",
dest_dir)
with open(os.path.join(dest_dir, "panoptic_val2017_100.json")) as f:
obj = json.load(f)
def link_val100(dir_full, dir_100):
print("Creating " + dir_100 + " ...")
os.makedirs(dir_100, exist_ok=True)
for img in obj["images"]:
basename = os.path.splitext(img["file_name"])[0]
src = os.path.join(dir_full, basename + ".png")
dst = os.path.join(dir_100, basename + ".png")
src = os.path.relpath(src, start=dir_100)
os.symlink(src, dst)
link_val100(
os.path.join(dataset_dir, "panoptic_val2017"),
os.path.join(dataset_dir, "panoptic_val2017_100"),
)
link_val100(
os.path.join(dataset_dir, "panoptic_stuff_val2017"),
os.path.join(dataset_dir, "panoptic_stuff_val2017_100"),
)
def cache_url(url: str, cache_dir: str) -> str:
"""
This implementation downloads the remote resource and caches it locally.
The resource will only be downloaded if not previously requested.
"""
parsed_url = urlparse(url)
dirname = os.path.join(cache_dir,
os.path.dirname(parsed_url.path.lstrip("/")))
makedir(dirname)
filename = url.split("/")[-1]
cached = os.path.join(dirname, filename)
with file_lock(cached):
if not os.path.isfile(cached):
logging.info(f"Downloading {url} to {cached} ...")
cached = download(url, dirname, filename=filename)
logging.info(f"URL {url} cached in {cached}")
return cached
def get(config_path: str, pretrained: bool = False):
r"""
Get a model specified by relative path under Detectron2's official
``configs/`` directory.
Args:
config_path: Name of config file relative to ``configs/`` directory
under project root. (E.g. ``width_ablations/bicaptioning_R_50_L1_H2048.yaml``)
pretrained: If ``True``, will initialize the model with the pretrained
weights. If ``False``, the weights will be initialized randomly.
"""
# Get the original path to config file (shipped with inside the package).
_pkg_config_path = pkg_resources.resource_filename(
"virtex.model_zoo", os.path.join("configs", config_path)
)
if not os.path.exists(_pkg_config_path):
raise RuntimeError("{} not available in Model Zoo!".format(config_path))
_C = Config(_pkg_config_path)
model = PretrainingModelFactory.from_config(_C)
if pretrained:
# Get URL for the checkpoint for this config path.
if config_path in _ModelZooUrls.CONFIG_PATH_TO_DB_ID:
dropbox_id = _ModelZooUrls.CONFIG_PATH_TO_DB_ID[config_path]
filename = os.path.basename(config_path).replace(".yaml", ".pth")
checkpoint_url = f"{_ModelZooUrls.URL_PREFIX}/{dropbox_id}/{filename}?dl=1"
else:
raise RuntimeError("{} not available in Model Zoo!".format(config_path))
# Download the pretrained model weights and save with a sensible name.
# This will be downloaded only if it does not exist.
checkpoint_path = download(
checkpoint_url,
dir=os.path.expanduser("~/.torch/virtex_cache"),
filename=os.path.basename(config_path).replace(".yaml", ".pth")
)
CheckpointManager(model=model).load(checkpoint_path)
return model
if __name__ == "__main__":
dataset_dir = os.path.join(os.path.dirname(__file__), "coco")
for s in ["val2017", "train2017"]:
separate_coco_semantic_from_panoptic(
os.path.join(dataset_dir,
"annotations/panoptic_{}.json".format(s)),
os.path.join(dataset_dir, "panoptic_{}".format(s)),
os.path.join(dataset_dir, "panoptic_stuff_{}".format(s)),
COCO_CATEGORIES,
)
# Prepare val2017_100 for quick testing:
dest_dir = os.path.join(dataset_dir, "annotations/")
URL_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/"
download(URL_PREFIX + "annotations/coco/panoptic_val2017_100.json",
dest_dir)
with open(os.path.join(dest_dir, "panoptic_val2017_100.json")) as f:
obj = json.load(f)
def link_val100(dir_full, dir_100):
print("Creating " + dir_100 + " ...")
os.makedirs(dir_100, exist_ok=True)
for img in obj["images"]:
basename = os.path.splitext(img["file_name"])[0]
src = os.path.join(dir_full, basename + ".png")
dst = os.path.join(dir_100, basename + ".png")
src = os.path.relpath(src, start=dir_100)
os.symlink(src, dst)
link_val100(
os.path.join(dataset_dir, "panoptic_val2017"),
