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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.
from enum import Enum
from typing import Union
import torch
import torch.nn as nn
from .backbones import _make_dinov2_model
from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name
class Weights(Enum):
IMAGENET1K = "IMAGENET1K"
def _make_dinov2_linear_classification_head(
*,
arch_name: str = "vit_large",
patch_size: int = 14,
embed_dim: int = 1024,
layers: int = 4,
pretrained: bool = True,
weights: Union[Weights, str] = Weights.IMAGENET1K,
num_register_tokens: int = 0,
**kwargs,
):
if layers not in (1, 4):
raise AssertionError(f"Unsupported number of layers: {layers}")
if isinstance(weights, str):
try:
weights = Weights[weights]
except KeyError:
raise AssertionError(f"Unsupported weights: {weights}")
linear_head = nn.Linear((1 + layers) * embed_dim, 1_000)
if pretrained:
model_base_name = _make_dinov2_model_name(arch_name, patch_size)
model_full_name = _make_dinov2_model_name(arch_name, patch_size, num_register_tokens)
layers_str = str(layers) if layers == 4 else ""
url = _DINOV2_BASE_URL + f"/{model_base_name}/{model_full_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=True)
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,
weights: Union[Weights, str] = Weights.IMAGENET1K,
num_register_tokens: int = 0,
interpolate_antialias: bool = False,
interpolate_offset: float = 0.1,
**kwargs,
):
backbone = _make_dinov2_model(
arch_name=arch_name,
pretrained=pretrained,
num_register_tokens=num_register_tokens,
interpolate_antialias=interpolate_antialias,
interpolate_offset=interpolate_offset,
**kwargs,
)
embed_dim = backbone.embed_dim
patch_size = backbone.patch_size
linear_head = _make_dinov2_linear_classification_head(
arch_name=arch_name,
patch_size=patch_size,
embed_dim=embed_dim,
layers=layers,
pretrained=pretrained,
weights=weights,
num_register_tokens=num_register_tokens,
)
return _LinearClassifierWrapper(backbone=backbone, linear_head=linear_head, layers=layers)
def dinov2_vits14_lc(
*,
layers: int = 4,
pretrained: bool = True,
weights: Union[Weights, str] = Weights.IMAGENET1K,
**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,
weights=weights,
**kwargs,
)
def dinov2_vitb14_lc(
*,
layers: int = 4,
pretrained: bool = True,
weights: Union[Weights, str] = Weights.IMAGENET1K,
**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,
weights=weights,
**kwargs,
)
def dinov2_vitl14_lc(
*,
layers: int = 4,
pretrained: bool = True,
weights: Union[Weights, str] = Weights.IMAGENET1K,
**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,
weights=weights,
**kwargs,
)
def dinov2_vitg14_lc(
*,
layers: int = 4,
pretrained: bool = True,
weights: Union[Weights, str] = Weights.IMAGENET1K,
**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,
weights=weights,
**kwargs,
)
def dinov2_vits14_reg_lc(
*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
):
"""
Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-S/14 backbone with registers (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,
weights=weights,
num_register_tokens=4,
interpolate_antialias=True,
interpolate_offset=0.0,
**kwargs,
)
def dinov2_vitb14_reg_lc(
*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
):
"""
Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-B/14 backbone with registers (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,
weights=weights,
num_register_tokens=4,
interpolate_antialias=True,
interpolate_offset=0.0,
**kwargs,
)
def dinov2_vitl14_reg_lc(
*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
):
"""
Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-L/14 backbone with registers (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,
weights=weights,
num_register_tokens=4,
interpolate_antialias=True,
interpolate_offset=0.0,
**kwargs,
)
def dinov2_vitg14_reg_lc(
*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
):
"""
Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-g/14 backbone with registers (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,
weights=weights,
num_register_tokens=4,
interpolate_antialias=True,
interpolate_offset=0.0,
**kwargs,
)
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