def vit_clone(key: str):
src = timm.create_model(key, pretrained='True')
dst = AutoModel.from_name(key)
dst.embedding.positions.data.copy_(src.pos_embed.data.squeeze(0))
dst.embedding.cls_token.data.copy_(src.cls_token.data)
cfg = AutoConfig.from_name(key)
return clone_model(src, dst,
torch.randn((1, 3, cfg.input_size, cfg.input_size)))
def vit_clone(key: str):
src = timm.create_model(key, pretrained="True")
dst = AutoModel.from_name(key)
cfg = AutoTransform.from_name(key)
dst = clone_model(
src,
dst,
torch.randn((1, 3, cfg.input_size, cfg.input_size)),
dest_skip=[ViTTokens],
)
dst.embedding.positions.data.copy_(src.pos_embed.data.squeeze(0))
dst.embedding.tokens.cls.data.copy_(src.cls_token.data)
return dst
def deit_clone(key: str):
k_split = key.split('_')
hub_key = "_".join(k_split[:2]) + '_distilled_' + "_".join(k_split[2:])
src = torch.hub.load('facebookresearch/deit:main',
hub_key, pretrained=True)
dst = AutoModel.from_name(key)
cfg = AutoConfig.from_name(f"vit_{'_'.join(key.split('_')[1:])}")
dst = clone_model(src, dst, torch.randn(
(1, 3, cfg.input_size, cfg.input_size)), dest_skip=[DeiTTokens])
dst.embedding.positions.data.copy_(src.pos_embed.data.squeeze(0))
dst.embedding.tokens.cls.data.copy_(src.cls_token.data)
dst.embedding.tokens.dist.data.copy_(src.dist_token.data)
return dst
with open("pretrained_models.txt", "w") as f:
f.write(",".join(list(zoo_source.keys())))
if args.o is not None:
save_dir = args.o
save_dir.mkdir(exist_ok=True)
storages = {"local": LocalStorage, "hf": HuggingFaceStorage}
storage = storages[args.storage]()
if args.storage == "local":
logging.info(f"Store root={storage.root}")
override = True
bar = tqdm(zoo_source.items())
uploading_bar = tqdm()
for key, src_def in bar:
bar.set_description(key)
if src_def is None:
# it means I was lazy and I meant to use timm
src_def = partial(timm.create_model, key, pretrained=True)
if key not in storage or override:
if type(src_def) is tuple:
# I have a custom clone func -> not the most elegant way, but it works!
clone_func, flag = src_def
cloned = clone_func(key)
else:
src, dst = src_def(), AutoModel.from_name(key)
cloned = clone_model(src, dst)
storage.put(key, cloned)