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import torch
import torch.nn as nn
from unidepth.models.backbones import ConvNeXt, ConvNeXtV2, _make_dinov2_model
class ModelWrap(nn.Module):
def __init__(self, model) -> None:
super().__init__()
self.backbone = model
def forward(self, x, *args, **kwargs):
features = []
for layer in self.backbone.features:
x = layer(x)
features.append(x)
return features
def convnextv2_base(config, **kwargs):
model = ConvNeXtV2(
depths=[3, 3, 27, 3],
dims=[128, 256, 512, 1024],
output_idx=config.get("output_idx", [3, 6, 33, 36]),
use_checkpoint=config.get("use_checkpoint", False),
**kwargs,
)
url = "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt"
state_dict = torch.hub.load_state_dict_from_url(
url, map_location="cpu", progress=False
)["model"]
info = model.load_state_dict(state_dict, strict=False)
print(info)
return model
def convnextv2_large(config, **kwargs):
model = ConvNeXtV2(
depths=[3, 3, 27, 3],
dims=[192, 384, 768, 1536],
output_idx=config.get("output_idx", [3, 6, 33, 36]),
use_checkpoint=config.get("use_checkpoint", False),
**kwargs,
)
url = "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt"
state_dict = torch.hub.load_state_dict_from_url(
url, map_location="cpu", progress=False
)["model"]
info = model.load_state_dict(state_dict, strict=False)
print(info)
return model
def convnextv2_large_mae(config, **kwargs):
model = ConvNeXtV2(
depths=[3, 3, 27, 3],
dims=[192, 384, 768, 1536],
output_idx=config.get("output_idx", [3, 6, 33, 36]),
use_checkpoint=config.get("use_checkpoint", False),
**kwargs,
)
url = "https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_large_1k_224_fcmae.pt"
state_dict = torch.hub.load_state_dict_from_url(
url, map_location="cpu", progress=False
)["model"]
info = model.load_state_dict(state_dict, strict=False)
print(info)
return model
def convnextv2_huge(config, **kwargs):
model = ConvNeXtV2(
depths=[3, 3, 27, 3],
dims=[352, 704, 1408, 2816],
output_idx=config.get("output_idx", [3, 6, 33, 36]),
use_checkpoint=config.get("use_checkpoint", False),
**kwargs,
)
url = "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt"
state_dict = torch.hub.load_state_dict_from_url(
url, map_location="cpu", progress=False
)["model"]
info = model.load_state_dict(state_dict, strict=False)
print(info)
return model
def convnextv2_huge_mae(config, **kwargs):
model = ConvNeXtV2(
depths=[3, 3, 27, 3],
dims=[352, 704, 1408, 2816],
output_idx=config.get("output_idx", [3, 6, 33, 36]),
use_checkpoint=config.get("use_checkpoint", False),
**kwargs,
)
url = "https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_huge_1k_224_fcmae.pt"
state_dict = torch.hub.load_state_dict_from_url(
url, map_location="cpu", progress=False
)["model"]
info = model.load_state_dict(state_dict, strict=False)
print(info)
return model
def convnext_large_pt(config, **kwargs):
model = ConvNeXt(
depths=[3, 3, 27, 3],
dims=[192, 384, 768, 1536],
output_idx=config.get("output_idx", [3, 6, 33, 36]),
use_checkpoint=config.get("use_checkpoint", False),
**kwargs,
)
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import disable_progress_bars
from unidepth.models.backbones.convnext import HF_URL, checkpoint_filter_fn
disable_progress_bars()
repo_id, filename = HF_URL["convnext_large_pt"]
state_dict = torch.load(hf_hub_download(repo_id=repo_id, filename=filename))
state_dict = checkpoint_filter_fn(state_dict, model)
info = model.load_state_dict(state_dict, strict=False)
print(info)
return model
def convnext_large(config, **kwargs):
model = ConvNeXt(
depths=[3, 3, 27, 3],
dims=[192, 384, 768, 1536],
output_idx=config.get("output_idx", [3, 6, 33, 36]),
use_checkpoint=config.get("use_checkpoint", False),
drop_path_rate=config.get("drop_path", 0.0),
**kwargs,
)
return model
def dinov2_vits14(config, pretrained: bool = True, **kwargs):
"""
DINOv2 ViT-S/14 model (optionally) pretrained on the LVD-142M dataset.
"""
vit = _make_dinov2_model(
arch_name="vit_small",
pretrained=config["pretrained"],
output_idx=config.get("output_idx", [3, 6, 9, 12]),
checkpoint=config.get("use_checkpoint", False),
drop_path_rate=config.get("drop_path", 0.0),
num_register_tokens=config.get("num_register_tokens", 0),
use_norm=config.get("use_norm", False),
export=config.get("export", False),
interpolate_offset=config.get("interpolate_offset", 0.0),
**kwargs,
)
return vit
def dinov2_vitb14(config, pretrained: bool = True, **kwargs):
"""
DINOv2 ViT-B/14 model (optionally) pretrained on the LVD-142M dataset.
"""
vit = _make_dinov2_model(
arch_name="vit_base",
pretrained=config["pretrained"],
output_idx=config.get("output_idx", [3, 6, 9, 12]),
checkpoint=config.get("use_checkpoint", False),
drop_path_rate=config.get("drop_path", 0.0),
num_register_tokens=config.get("num_register_tokens", 0),
use_norm=config.get("use_norm", False),
export=config.get("export", False),
interpolate_offset=config.get("interpolate_offset", 0.0),
**kwargs,
)
return vit
def dinov2_vitl14(config, pretrained: str = "", **kwargs):
"""
DINOv2 ViT-L/14 model (optionally) pretrained on the LVD-142M dataset.
"""
vit = _make_dinov2_model(
arch_name="vit_large",
pretrained=config["pretrained"],
output_idx=config.get("output_idx", [5, 12, 18, 24]),
checkpoint=config.get("use_checkpoint", False),
drop_path_rate=config.get("drop_path", 0.0),
num_register_tokens=config.get("num_register_tokens", 0),
use_norm=config.get("use_norm", False),
export=config.get("export", False),
interpolate_offset=config.get("interpolate_offset", 0.0),
**kwargs,
)
return vit
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