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(doc/foundationals) add DINOv2
, related docstrings
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@ -7,11 +7,30 @@ from refiners.foundationals.dinov2.vit import ViT
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class DINOv2_small(ViT):
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"""DINOv2 small model.
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See [[arXiv:2304.07193] DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193)
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for more details.
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Attributes:
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embedding_dim (int): 384
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patch_size (int): 14
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image_size (int): 518
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num_layers (int): 12
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num_heads (int): 6
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"""
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def __init__(
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self,
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device: torch.device | str | None = None,
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dtype: torch.dtype | None = None,
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) -> None:
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"""Initialize DINOv2 small model.
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Args:
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device: The PyTorch device to use.
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dtype: The PyTorch data type to use.
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"""
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super().__init__(
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embedding_dim=384,
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patch_size=14,
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@ -24,11 +43,30 @@ class DINOv2_small(ViT):
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class DINOv2_base(ViT):
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"""DINOv2 base model.
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See [[arXiv:2304.07193] DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193)
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for more details.
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Attributes:
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embedding_dim (int): 768
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patch_size (int): 14
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image_size (int): 518
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num_layers (int): 12
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num_heads (int): 12
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"""
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def __init__(
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self,
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device: torch.device | str | None = None,
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dtype: torch.dtype | None = None,
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) -> None:
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"""Initialize DINOv2 base model.
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Args:
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device: The PyTorch device to use.
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dtype: The PyTorch data type to use.
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"""
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super().__init__(
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embedding_dim=768,
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patch_size=14,
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@ -41,11 +79,30 @@ class DINOv2_base(ViT):
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class DINOv2_large(ViT):
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"""DINOv2 large model.
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See [[arXiv:2304.07193] DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193)
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for more details.
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Attributes:
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embedding_dim (int): 1024
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patch_size (int): 14
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image_size (int): 518
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num_layers (int): 24
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num_heads (int): 16
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"""
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def __init__(
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self,
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device: torch.device | str | None = None,
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dtype: torch.dtype | None = None,
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) -> None:
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"""Initialize DINOv2 large model.
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Args:
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device: The PyTorch device to use.
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dtype: The PyTorch data type to use.
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"""
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super().__init__(
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embedding_dim=1024,
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patch_size=14,
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@ -76,11 +133,32 @@ class DINOv2_large(ViT):
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class DINOv2_small_reg(ViT):
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"""DINOv2 small model with register.
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See [[arXiv:2304.07193] DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193)
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and [[arXiv:2309.16588] Vision Transformers Need Registers](https://arxiv.org/abs/2309.16588)
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for more details.
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Attributes:
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embedding_dim (int): 384
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patch_size (int): 14
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image_size (int): 518
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num_layers (int): 12
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num_heads (int): 6
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num_registers (int): 4
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"""
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def __init__(
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self,
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device: torch.device | str | None = None,
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dtype: torch.dtype | None = None,
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) -> None:
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"""Initialize DINOv2 small model with register.
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Args:
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device (torch.device | str | None): The PyTorch device to use.
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dtype (torch.dtype | None): The PyTorch data type to use.
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"""
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super().__init__(
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embedding_dim=384,
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patch_size=14,
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@ -94,11 +172,32 @@ class DINOv2_small_reg(ViT):
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class DINOv2_base_reg(ViT):
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"""DINOv2 base model with register.
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See [[arXiv:2304.07193] DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193)
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and [[arXiv:2309.16588] Vision Transformers Need Registers](https://arxiv.org/abs/2309.16588)
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for more details.
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Attributes:
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embedding_dim (int): 768
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patch_size (int): 14
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image_size (int): 518
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num_layers (int): 12
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num_heads (int): 12
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num_registers (int): 4
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"""
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def __init__(
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self,
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device: torch.device | str | None = None,
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dtype: torch.dtype | None = None,
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) -> None:
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"""Initialize DINOv2 base model with register.
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Args:
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device (torch.device | str | None): The PyTorch device to use.
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dtype (torch.dtype | None): The PyTorch data type to use.
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"""
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super().__init__(
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embedding_dim=768,
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patch_size=14,
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@ -112,11 +211,32 @@ class DINOv2_base_reg(ViT):
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class DINOv2_large_reg(ViT):
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"""DINOv2 large model with register.
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See [[arXiv:2304.07193] DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193)
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and [[arXiv:2309.16588] Vision Transformers Need Registers](https://arxiv.org/abs/2309.16588)
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for more details.
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Attributes:
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embedding_dim (int): 1024
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patch_size (int): 14
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image_size (int): 518
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num_layers (int): 24
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num_heads (int): 16
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num_registers (int): 4
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"""
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def __init__(
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self,
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device: torch.device | str | None = None,
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dtype: torch.dtype | None = None,
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) -> None:
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"""Initialize DINOv2 large model with register.
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Args:
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device (torch.device | str | None): The PyTorch device to use.
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dtype (torch.dtype | None): The PyTorch data type to use.
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"""
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super().__init__(
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embedding_dim=1024,
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patch_size=14,
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@ -227,9 +227,10 @@ class Registers(fl.Concatenate):
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class ViT(fl.Chain):
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"""Vision Transformer (ViT).
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"""Vision Transformer (ViT) model.
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see https://arxiv.org/abs/2010.11929v2
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See [[arXiv:2010.11929] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929)
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for more details.
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"""
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def __init__(
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@ -245,6 +246,20 @@ class ViT(fl.Chain):
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device: torch.device | str | None = None,
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dtype: torch.dtype | None = None,
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) -> None:
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"""Initialize a Vision Transformer (ViT) model.
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Args:
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embedding_dim: The dimension of the embedding.
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patch_size: The size of the patches.
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image_size: The size of the input image.
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num_layers: The number of layers.
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num_heads: The number of heads.
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norm_eps: The epsilon value for normalization.
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mlp_ratio: The ratio for the multi-layer perceptron (MLP).
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num_registers: The number of registers.
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device: The PyTorch device to use.
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dtype: The PyTorch data type to use.
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"""
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num_patches = image_size // patch_size
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self.embedding_dim = embedding_dim
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self.patch_size = patch_size
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