def deit_base_patch16_384(pretrained=False, **kwargs):
if "num_landmarks" not in kwargs:
model = VisionTransformer(patch_size=16,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs)
else:
model = Nystromformer(patch_size=16,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs)
model.default_cfg = _cfg()
if pretrained:
checkpoint = torch.hub.load_state_dict_from_url(
url=
"https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth",
map_location="cpu",
check_hash=True)
model.load_state_dict(checkpoint["model"])
return model
def deit_tiny_colab_patch16_224(pretrained=False, all_key_dim=None, **kwargs):
model = VisionTransformer(
patch_size=16,
embed_dim=192,
depth=12,
num_heads=3,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs,
)
model.default_cfg = _cfg()
if pretrained:
checkpoint = torch.hub.load_state_dict_from_url(
url="https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth",
map_location="cpu",
check_hash=True,
)
model.load_state_dict(checkpoint["model"])
model.cuda()
collaborate_attention.swap(model, all_key_dim)
model.cpu()
return model
def deit_small_patch16_224(pretrained=False, **kwargs):
model = VisionTransformer(
patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
model.default_cfg = _cfg()
if pretrained:
checkpoint = torch.hub.load_state_dict_from_url(
url="https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth",
map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint["model"])
return model
def load_pretrained(self):
model = VisionTransformer(
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
norm_layer=nn.LayerNorm)
model.default_cfg = _cfg()
checkpoint = torch.hub.load_state_dict_from_url(
url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',
map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint['model'])
self.pos_embed = model.pos_embed
self.patch_embed = model.patch_embed
self.blocks = model.blocks
self.norm = model.norm
def deit_base_patch16_384(pretrained=False, **kwargs):
model = VisionTransformer(img_size=384,
patch_size=16,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs)
model.default_cfg = _cfg()
if pretrained:
checkpoint = torch.hub.load_state_dict_from_url(url="",
map_location="cpu",
check_hash=True)
model.load_state_dict(checkpoint["model"])
return model
def deit_base3_patch16_224_collab96(pretrained=False, models_directory=None, **kwargs):
model = VisionTransformer(
patch_size=16,
embed_dim=768,
depth=3,
num_heads=12,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs,
)
collaborate_attention.swap(model, compressed_key_dim=96, reparametrize=False)
model.default_cfg = _cfg()
if pretrained:
checkpoint_path = pathlib.Path(models_directory) / "deit_base3_patch16_224_collab96.pth"
print(f"Load model from '{checkpoint_path}'")
checkpoint = torch.load(checkpoint_path, map_location="cpu")
model.load_state_dict(checkpoint["model"])
return model