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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the Apache License, Version 2.0
# found in the LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
dependencies = ["torch"]
_DINOV2_BASE_URL = "https://dl.fbaipublicfiles.com/dinov2"
def _make_dinov2_model_name(arch_name: str, patch_size: int) -> str:
compact_arch_name = arch_name.replace("_", "")[:4]
return f"dinov2_{compact_arch_name}{patch_size}"
def _make_dinov2_model(
*,
arch_name: str = "vit_large",
img_size: int = 518,
patch_size: int = 14,
init_values: float = 1.0,
ffn_layer: str = "mlp",
block_chunks: int = 0,
pretrained: bool = True,
**kwargs,
):
from dinov2.models import vision_transformer as vits
model_name = _make_dinov2_model_name(arch_name, patch_size)
vit_kwargs = dict(
img_size=img_size,
patch_size=patch_size,
init_values=init_values,
ffn_layer=ffn_layer,
block_chunks=block_chunks,
)
vit_kwargs.update(**kwargs)
model = vits.__dict__[arch_name](**vit_kwargs)
if pretrained:
url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_pretrain.pth"
state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu")
model.load_state_dict(state_dict, strict=False)
return model
def dinov2_vits14(*, pretrained: bool = True, **kwargs):
"""
DINOv2 ViT-S/14 model (optionally) pretrained on the LVD-142M dataset.
"""
return _make_dinov2_model(arch_name="vit_small", pretrained=pretrained, **kwargs)
def dinov2_vitb14(*, pretrained: bool = True, **kwargs):
"""
DINOv2 ViT-B/14 model pretrained on the LVD-142M dataset.
"""
return _make_dinov2_model(arch_name="vit_base", pretrained=pretrained, **kwargs)
def dinov2_vitl14(*, pretrained: bool = True, **kwargs):
"""
DINOv2 ViT-L/14 model (optionally) pretrained on the LVD-142M dataset.
"""
return _make_dinov2_model(arch_name="vit_large", pretrained=pretrained, **kwargs)
def dinov2_vitg14(*, pretrained: bool = True, **kwargs):
"""
DINOv2 ViT-g/14 model (optionally) pretrained on the LVD-142M dataset.
"""
return _make_dinov2_model(arch_name="vit_giant2", ffn_layer="swiglufused", pretrained=pretrained, **kwargs)
def _make_dinov2_linear_head(
*,
model_name: str = "dinov2_vitl14",
embed_dim: int = 1024,
layers: int = 4,
pretrained: bool = True,
**kwargs,
):
assert layers in (1, 4), f"Unsupported number of layers: {layers}"
linear_head = nn.Linear((1 + layers) * embed_dim, 1_000)
if pretrained:
layers_str = str(layers) if layers == 4 else ""
url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_linear{layers_str}_head.pth"
state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu")
linear_head.load_state_dict(state_dict, strict=False)
return linear_head
class _LinearClassifierWrapper(nn.Module):
def __init__(self, *, backbone: nn.Module, linear_head: nn.Module, layers: int = 4):
super().__init__()
self.backbone = backbone
self.linear_head = linear_head
self.layers = layers
def forward(self, x):
if self.layers == 1:
x = self.backbone.forward_features(x)
cls_token = x["x_norm_clstoken"]
patch_tokens = x["x_norm_patchtokens"]
# fmt: off
linear_input = torch.cat([
cls_token,
patch_tokens.mean(dim=1),
], dim=1)
# fmt: on
elif self.layers == 4:
x = self.backbone.get_intermediate_layers(x, n=4, return_class_token=True)
# fmt: off
linear_input = torch.cat([
x[0][1],
x[1][1],
x[2][1],
x[3][1],
x[3][0].mean(dim=1),
], dim=1)
# fmt: on
else:
assert False, f"Unsupported number of layers: {self.layers}"
return self.linear_head(linear_input)
def _make_dinov2_linear_classifier(
*,
arch_name: str = "vit_large",
layers: int = 4,
pretrained: bool = True,
**kwargs,
):
backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)
embed_dim = backbone.embed_dim
patch_size = backbone.patch_size
model_name = _make_dinov2_model_name(arch_name, patch_size)
linear_head = _make_dinov2_linear_head(
model_name=model_name, embed_dim=embed_dim, layers=layers, pretrained=pretrained
)
return _LinearClassifierWrapper(backbone=backbone, linear_head=linear_head, layers=layers)
def dinov2_vits14_lc(*, layers: int = 4, pretrained: bool = True, **kwargs):
"""
Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-S/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
"""
return _make_dinov2_linear_classifier(arch_name="vit_small", layers=layers, pretrained=pretrained, **kwargs)
def dinov2_vitb14_lc(*, layers: int = 4, pretrained: bool = True, **kwargs):
"""
Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-B/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
"""
return _make_dinov2_linear_classifier(arch_name="vit_base", layers=layers, pretrained=pretrained, **kwargs)
def dinov2_vitl14_lc(*, layers: int = 4, pretrained: bool = True, **kwargs):
"""
Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-L/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
"""
return _make_dinov2_linear_classifier(arch_name="vit_large", layers=layers, pretrained=pretrained, **kwargs)
def dinov2_vitg14_lc(*, layers: int = 4, pretrained: bool = True, **kwargs):
"""
Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-g/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
"""
return _make_dinov2_linear_classifier(
arch_name="vit_giant2", layers=layers, ffn_layer="swiglufused", pretrained=pretrained, **kwargs
)
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