1323 lines
48 KiB
Python
1323 lines
48 KiB
Python
""" EVA
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EVA from https://github.com/baaivision/EVA , paper: https://arxiv.org/abs/2211.07636
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@article{EVA,
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title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale},
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author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang,
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Tiejun and Wang, Xinlong and Cao, Yue},
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journal={arXiv preprint arXiv:2211.07636},
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year={2022}
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}
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EVA-02: A Visual Representation for Neon Genesis - https://arxiv.org/abs/2303.11331
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@article{EVA02,
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title={EVA-02: A Visual Representation for Neon Genesis},
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author={Fang, Yuxin and Sun, Quan and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue},
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journal={arXiv preprint arXiv:2303.11331},
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year={2023}
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}
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This file contains EVA & EVA02 model implementations evolved from BEiT, additional models in vision_transformer.py.
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Modifications by / Copyright 2023 Ross Wightman, original copyrights below
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"""
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# EVA models Copyright (c) 2022 BAAI-Vision
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# EVA02 models Copyright (c) 2023 BAAI-Vision
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import math
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from typing import Callable, List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.utils.checkpoint import checkpoint
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
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from timm.layers import PatchEmbed, Mlp, GluMlp, SwiGLU, LayerNorm, DropPath, PatchDropout, RotaryEmbeddingCat, \
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apply_rot_embed_cat, apply_keep_indices_nlc, trunc_normal_, resample_patch_embed, resample_abs_pos_embed, \
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to_2tuple, use_fused_attn
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from ._builder import build_model_with_cfg
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from ._features import feature_take_indices
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from ._registry import generate_default_cfgs, register_model
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__all__ = ['Eva']
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class EvaAttention(nn.Module):
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fused_attn: torch.jit.Final[bool]
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def __init__(
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self,
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dim: int,
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num_heads: int = 8,
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qkv_bias: bool = True,
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qkv_fused: bool = True,
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num_prefix_tokens: int = 1,
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attn_drop: float = 0.,
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proj_drop: float = 0.,
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attn_head_dim: Optional[int] = None,
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norm_layer: Optional[Callable] = None,
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):
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"""
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Args:
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dim:
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num_heads:
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qkv_bias:
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qkv_fused:
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attn_drop:
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proj_drop:
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attn_head_dim:
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norm_layer:
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"""
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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if attn_head_dim is not None:
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head_dim = attn_head_dim
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all_head_dim = head_dim * self.num_heads
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self.scale = head_dim ** -0.5
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self.num_prefix_tokens = num_prefix_tokens
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self.fused_attn = use_fused_attn()
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if qkv_fused:
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self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
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self.q_proj = self.k_proj = self.v_proj = None
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if qkv_bias:
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self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
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self.register_buffer('k_bias', torch.zeros(all_head_dim), persistent=False)
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self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
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else:
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self.q_bias = self.k_bias = self.v_bias = None
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else:
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self.q_proj = nn.Linear(dim, all_head_dim, bias=qkv_bias)
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self.k_proj = nn.Linear(dim, all_head_dim, bias=False)
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self.v_proj = nn.Linear(dim, all_head_dim, bias=qkv_bias)
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self.qkv = None
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self.q_bias = self.k_bias = self.v_bias = None
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self.attn_drop = nn.Dropout(attn_drop)
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self.norm = norm_layer(all_head_dim) if norm_layer is not None else nn.Identity()
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self.proj = nn.Linear(all_head_dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(
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self,
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x,
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rope: Optional[torch.Tensor] = None,
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attn_mask: Optional[torch.Tensor] = None,
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):
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B, N, C = x.shape
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if self.qkv is not None:
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qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias)) if self.q_bias is not None else None
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qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
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qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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q, k, v = qkv.unbind(0) # B, num_heads, N, head_dim
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else:
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q = self.q_proj(x).reshape(B, N, self.num_heads, -1).transpose(1, 2) # B, num_heads, N, C
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k = self.k_proj(x).reshape(B, N, self.num_heads, -1).transpose(1, 2)
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v = self.v_proj(x).reshape(B, N, self.num_heads, -1).transpose(1, 2)
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if rope is not None:
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npt = self.num_prefix_tokens
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q = torch.cat([q[:, :, :npt, :], apply_rot_embed_cat(q[:, :, npt:, :], rope)], dim=2).type_as(v)
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k = torch.cat([k[:, :, :npt, :], apply_rot_embed_cat(k[:, :, npt:, :], rope)], dim=2).type_as(v)
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if self.fused_attn:
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x = F.scaled_dot_product_attention(
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q, k, v,
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attn_mask=attn_mask,
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dropout_p=self.attn_drop.p if self.training else 0.,
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)
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else:
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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attn = attn.softmax(dim=-1)
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if attn_mask is not None:
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attn_mask = attn_mask.to(torch.bool)
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attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf"))
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attn = self.attn_drop(attn)
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x = attn @ v
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x = x.transpose(1, 2).reshape(B, N, C)
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x = self.norm(x)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class EvaBlock(nn.Module):
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def __init__(
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self,
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dim: int,
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num_heads: int,
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qkv_bias: bool = True,
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qkv_fused: bool = True,
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mlp_ratio: float = 4.,
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swiglu_mlp: bool = False,
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scale_mlp: bool = False,
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scale_attn_inner: bool = False,
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num_prefix_tokens: int = 1,
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proj_drop: float = 0.,
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attn_drop: float = 0.,
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drop_path: float = 0.,
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init_values: Optional[float] = None,
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act_layer: Callable = nn.GELU,
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norm_layer: Callable = LayerNorm,
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attn_head_dim: Optional[int] = None,
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):
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"""
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Args:
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dim:
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num_heads:
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qkv_bias:
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qkv_fused:
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mlp_ratio:
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swiglu_mlp:
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scale_mlp:
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scale_attn_inner:
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proj_drop:
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attn_drop:
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drop_path:
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init_values:
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act_layer:
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norm_layer:
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attn_head_dim:
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"""
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = EvaAttention(
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dim,
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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qkv_fused=qkv_fused,
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num_prefix_tokens=num_prefix_tokens,
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attn_drop=attn_drop,
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proj_drop=proj_drop,
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attn_head_dim=attn_head_dim,
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norm_layer=norm_layer if scale_attn_inner else None,
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)
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self.gamma_1 = nn.Parameter(init_values * torch.ones(dim)) if init_values is not None else None
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self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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hidden_features = int(dim * mlp_ratio)
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if swiglu_mlp:
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if scale_mlp:
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# when norm in SwiGLU used, an impl with separate fc for gate & x is used
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self.mlp = SwiGLU(
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in_features=dim,
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hidden_features=hidden_features,
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norm_layer=norm_layer if scale_mlp else None,
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drop=proj_drop,
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)
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else:
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# w/o any extra norm, an impl with packed weights is used, matches existing GluMLP
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self.mlp = GluMlp(
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in_features=dim,
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hidden_features=hidden_features * 2,
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norm_layer=norm_layer if scale_mlp else None,
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act_layer=nn.SiLU,
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gate_last=False,
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drop=proj_drop,
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)
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else:
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self.mlp = Mlp(
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in_features=dim,
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hidden_features=hidden_features,
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act_layer=act_layer,
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norm_layer=norm_layer if scale_mlp else None,
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drop=proj_drop,
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)
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self.gamma_2 = nn.Parameter(init_values * torch.ones(dim)) if init_values is not None else None
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self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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def forward(self, x, rope: Optional[torch.Tensor] = None, attn_mask: Optional[torch.Tensor] = None):
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if self.gamma_1 is None:
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x = x + self.drop_path1(self.attn(self.norm1(x), rope=rope, attn_mask=attn_mask))
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x = x + self.drop_path2(self.mlp(self.norm2(x)))
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else:
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x = x + self.drop_path1(self.gamma_1 * self.attn(self.norm1(x), rope=rope, attn_mask=attn_mask))
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x = x + self.drop_path2(self.gamma_2 * self.mlp(self.norm2(x)))
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return x
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class EvaBlockPostNorm(nn.Module):
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""" EVA block w/ post-norm and support for swiglu, MLP norm scale, ROPE. """
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def __init__(
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self,
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dim: int,
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num_heads: int,
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qkv_bias: bool = True,
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qkv_fused: bool = True,
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mlp_ratio: float = 4.,
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swiglu_mlp: bool = False,
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scale_mlp: bool = False,
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scale_attn_inner: bool = False,
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num_prefix_tokens: int = 1,
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proj_drop: float = 0.,
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attn_drop: float = 0.,
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drop_path: float = 0.,
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init_values: Optional[float] = None, # ignore for post-norm
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act_layer: Callable = nn.GELU,
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norm_layer: Callable = nn.LayerNorm,
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attn_head_dim: Optional[int] = None,
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):
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"""
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Args:
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dim:
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num_heads:
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qkv_bias:
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qkv_fused:
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mlp_ratio:
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swiglu_mlp:
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scale_mlp:
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scale_attn_inner:
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proj_drop:
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attn_drop:
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drop_path:
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init_values:
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act_layer:
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norm_layer:
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attn_head_dim:
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"""
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super().__init__()
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self.attn = EvaAttention(
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dim,
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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qkv_fused=qkv_fused,
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num_prefix_tokens=num_prefix_tokens,
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attn_drop=attn_drop,
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proj_drop=proj_drop,
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attn_head_dim=attn_head_dim,
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norm_layer=norm_layer if scale_attn_inner else None,
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)
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self.norm1 = norm_layer(dim)
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self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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hidden_features = int(dim * mlp_ratio)
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if swiglu_mlp:
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if scale_mlp:
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# when norm in SwiGLU used, an impl with separate fc for gate & x is used
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self.mlp = SwiGLU(
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in_features=dim,
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hidden_features=hidden_features,
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norm_layer=norm_layer if scale_mlp else None,
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drop=proj_drop,
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)
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else:
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# w/o any extra norm, an impl with packed fc1 weights is used, matches existing GluMLP
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self.mlp = GluMlp(
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in_features=dim,
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hidden_features=hidden_features * 2,
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norm_layer=norm_layer if scale_mlp else None,
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act_layer=nn.SiLU,
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gate_last=False,
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drop=proj_drop,
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)
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else:
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self.mlp = Mlp(
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in_features=dim,
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hidden_features=hidden_features,
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act_layer=act_layer,
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norm_layer=norm_layer if scale_mlp else None,
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drop=proj_drop,
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)
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self.norm2 = norm_layer(dim)
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self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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def forward(self, x, rope: Optional[torch.Tensor] = None, attn_mask: Optional[torch.Tensor] = None):
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x = x + self.drop_path1(self.norm1(self.attn(x, rope=rope, attn_mask=attn_mask)))
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x = x + self.drop_path2(self.norm2(self.mlp(x)))
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return x
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class Eva(nn.Module):
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""" Eva Vision Transformer w/ Abs & Rotary Pos Embed
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This class implements the EVA and EVA02 models that were based on the BEiT ViT variant
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* EVA - abs pos embed, global avg pool
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* EVA02 - abs + rope pos embed, global avg pool, SwiGLU, scale Norm in MLP (ala normformer)
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"""
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def __init__(
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self,
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img_size: Union[int, Tuple[int, int]] = 224,
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patch_size: Union[int, Tuple[int, int]] = 16,
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in_chans: int = 3,
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num_classes: int = 1000,
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global_pool: str = 'avg',
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embed_dim: int = 768,
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depth: int = 12,
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num_heads: int = 12,
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qkv_bias: bool = True,
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qkv_fused: bool = True,
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mlp_ratio: float = 4.,
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swiglu_mlp: bool = False,
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scale_mlp: bool = False,
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scale_attn_inner: bool = False,
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drop_rate: float = 0.,
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pos_drop_rate: float = 0.,
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patch_drop_rate: float = 0.,
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proj_drop_rate: float = 0.,
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attn_drop_rate: float = 0.,
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drop_path_rate: float = 0.,
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norm_layer: Callable = LayerNorm,
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init_values: Optional[float] = None,
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class_token: bool = True,
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num_reg_tokens: int = 0,
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use_abs_pos_emb: bool = True,
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use_rot_pos_emb: bool = False,
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use_post_norm: bool = False,
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dynamic_img_size: bool = False,
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dynamic_img_pad: bool = False,
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ref_feat_shape: Optional[Union[Tuple[int, int], int]] = None,
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head_init_scale: float = 0.001,
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):
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"""
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Args:
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img_size:
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patch_size:
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in_chans:
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num_classes:
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global_pool:
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embed_dim:
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depth:
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num_heads:
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qkv_bias:
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qkv_fused:
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mlp_ratio:
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swiglu_mlp:
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scale_mlp:
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scale_attn_inner:
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drop_rate:
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pos_drop_rate:
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proj_drop_rate:
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attn_drop_rate:
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drop_path_rate:
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norm_layer:
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init_values:
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class_token:
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use_abs_pos_emb:
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use_rot_pos_emb:
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use_post_norm:
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ref_feat_shape:
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head_init_scale:
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"""
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super().__init__()
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self.num_classes = num_classes
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self.global_pool = global_pool
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self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
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self.num_prefix_tokens = (1 if class_token else 0) + num_reg_tokens
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self.dynamic_img_size = dynamic_img_size
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self.grad_checkpointing = False
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embed_args = {}
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if dynamic_img_size:
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# flatten deferred until after pos embed
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embed_args.update(dict(strict_img_size=False, output_fmt='NHWC'))
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self.patch_embed = PatchEmbed(
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img_size=img_size,
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patch_size=patch_size,
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in_chans=in_chans,
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embed_dim=embed_dim,
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dynamic_img_pad=dynamic_img_pad,
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**embed_args,
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)
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num_patches = self.patch_embed.num_patches
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r = self.patch_embed.feat_ratio() if hasattr(self.patch_embed, 'feat_ratio') else patch_size
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
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self.reg_token = nn.Parameter(torch.zeros(1, num_reg_tokens, embed_dim)) if num_reg_tokens else None
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self.cls_embed = class_token and self.reg_token is None
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self.pos_embed = nn.Parameter(
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torch.zeros(1, num_patches + self.num_prefix_tokens, embed_dim)) if use_abs_pos_emb else None
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self.pos_drop = nn.Dropout(p=pos_drop_rate)
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if patch_drop_rate > 0:
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self.patch_drop = PatchDropout(
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patch_drop_rate,
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num_prefix_tokens=self.num_prefix_tokens,
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return_indices=True,
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)
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else:
|
|
self.patch_drop = None
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|
|
if use_rot_pos_emb:
|
|
ref_feat_shape = to_2tuple(ref_feat_shape) if ref_feat_shape is not None else None
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|
self.rope = RotaryEmbeddingCat(
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embed_dim // num_heads,
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|
in_pixels=False,
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feat_shape=None if dynamic_img_size else self.patch_embed.grid_size,
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ref_feat_shape=ref_feat_shape,
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)
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else:
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self.rope = None
|
|
|
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
|
block_fn = EvaBlockPostNorm if use_post_norm else EvaBlock
|
|
self.blocks = nn.ModuleList([
|
|
block_fn(
|
|
dim=embed_dim,
|
|
num_heads=num_heads,
|
|
qkv_bias=qkv_bias,
|
|
qkv_fused=qkv_fused,
|
|
mlp_ratio=mlp_ratio,
|
|
swiglu_mlp=swiglu_mlp,
|
|
scale_mlp=scale_mlp,
|
|
scale_attn_inner=scale_attn_inner,
|
|
num_prefix_tokens=self.num_prefix_tokens,
|
|
proj_drop=proj_drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[i],
|
|
norm_layer=norm_layer,
|
|
init_values=init_values,
|
|
)
|
|
for i in range(depth)])
|
|
self.feature_info = [
|
|
dict(module=f'blocks.{i}', num_chs=embed_dim, reduction=r) for i in range(depth)]
|
|
|
|
use_fc_norm = self.global_pool == 'avg'
|
|
self.norm = nn.Identity() if use_fc_norm else norm_layer(embed_dim)
|
|
self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
|
|
self.head_drop = nn.Dropout(drop_rate)
|
|
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
self.apply(self._init_weights)
|
|
if self.pos_embed is not None:
|
|
trunc_normal_(self.pos_embed, std=.02)
|
|
if self.cls_token is not None:
|
|
trunc_normal_(self.cls_token, std=.02)
|
|
if self.reg_token is not None:
|
|
trunc_normal_(self.reg_token, std=.02)
|
|
|
|
self.fix_init_weight()
|
|
if isinstance(self.head, nn.Linear):
|
|
trunc_normal_(self.head.weight, std=.02)
|
|
self.head.weight.data.mul_(head_init_scale)
|
|
self.head.bias.data.mul_(head_init_scale)
|
|
|
|
def fix_init_weight(self):
|
|
def rescale(param, layer_id):
|
|
param.div_(math.sqrt(2.0 * layer_id))
|
|
|
|
for layer_id, layer in enumerate(self.blocks):
|
|
rescale(layer.attn.proj.weight.data, layer_id + 1)
|
|
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
|
|
|
|
def _init_weights(self, m):
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight, std=.02)
|
|
if m.bias is not None:
|
|
nn.init.zeros_(m.bias)
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay(self):
|
|
nwd = {'pos_embed', 'cls_token'}
|
|
return nwd
|
|
|
|
@torch.jit.ignore
|
|
def set_grad_checkpointing(self, enable=True):
|
|
self.grad_checkpointing = enable
|
|
|
|
@torch.jit.ignore
|
|
def group_matcher(self, coarse=False):
|
|
matcher = dict(
|
|
stem=r'^cls_token|pos_embed|patch_embed', # stem and embed
|
|
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))],
|
|
)
|
|
return matcher
|
|
|
|
@torch.jit.ignore
|
|
def get_classifier(self):
|
|
return self.head
|
|
|
|
def reset_classifier(self, num_classes, global_pool=None):
|
|
self.num_classes = num_classes
|
|
if global_pool is not None:
|
|
self.global_pool = global_pool
|
|
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
def _pos_embed(self, x) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
|
|
if self.dynamic_img_size:
|
|
B, H, W, C = x.shape
|
|
if self.pos_embed is not None:
|
|
pos_embed = resample_abs_pos_embed(
|
|
self.pos_embed,
|
|
(H, W),
|
|
num_prefix_tokens=self.num_prefix_tokens,
|
|
)
|
|
else:
|
|
pos_embed = None
|
|
x = x.view(B, -1, C)
|
|
rot_pos_embed = self.rope.get_embed(shape=(H, W)) if self.rope is not None else None
|
|
else:
|
|
pos_embed = self.pos_embed
|
|
rot_pos_embed = self.rope.get_embed() if self.rope is not None else None
|
|
|
|
if self.cls_token is not None:
|
|
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
|
|
|
|
if pos_embed is not None:
|
|
x = x + pos_embed
|
|
|
|
if self.reg_token is not None:
|
|
to_cat = []
|
|
if self.cls_token is not None:
|
|
to_cat.append(self.cls_token.expand(x.shape[0], -1, -1))
|
|
to_cat.append(self.reg_token.expand(x.shape[0], -1, -1))
|
|
x = torch.cat(to_cat + [x], dim=1)
|
|
|
|
x = self.pos_drop(x)
|
|
|
|
# obtain shared rotary position embedding and apply patch dropout
|
|
if self.patch_drop is not None:
|
|
x, keep_indices = self.patch_drop(x)
|
|
if rot_pos_embed is not None and keep_indices is not None:
|
|
rot_pos_embed = apply_keep_indices_nlc(x, rot_pos_embed, keep_indices)
|
|
return x, rot_pos_embed
|
|
|
|
def forward_intermediates(
|
|
self,
|
|
x: torch.Tensor,
|
|
indices: Optional[Union[int, List[int], Tuple[int]]] = None,
|
|
return_prefix_tokens: bool = False,
|
|
norm: bool = False,
|
|
stop_early: bool = False,
|
|
output_fmt: str = 'NCHW',
|
|
intermediates_only: bool = False,
|
|
) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
|
|
""" Forward features that returns intermediates.
|
|
Args:
|
|
x: Input image tensor
|
|
indices: Take last n blocks if an int, if is a sequence, select by matching indices
|
|
return_prefix_tokens: Return both prefix and spatial intermediate tokens
|
|
norm: Apply norm layer to all intermediates
|
|
stop_early: Stop iterating over blocks when last desired intermediate hit
|
|
output_fmt: Shape of intermediate feature outputs
|
|
intermediates_only: Only return intermediate features
|
|
"""
|
|
assert output_fmt in ('NCHW', 'NLC'), 'Output format for EVA-ViT features must be one of NCHW or NLC.'
|
|
reshape = output_fmt == 'NCHW'
|
|
intermediates = []
|
|
take_indices, max_index = feature_take_indices(len(self.blocks), indices)
|
|
|
|
# forward pass
|
|
B, _, height, width = x.shape
|
|
x = self.patch_embed(x)
|
|
x, rot_pos_embed = self._pos_embed(x)
|
|
if torch.jit.is_scripting() or not stop_early: # can't slice blocks in torchscript
|
|
blocks = self.blocks
|
|
else:
|
|
blocks = self.blocks[:max_index + 1]
|
|
for i, blk in enumerate(blocks):
|
|
x = blk(x, rope=rot_pos_embed)
|
|
if i in take_indices:
|
|
intermediates.append(self.norm(x) if norm else x)
|
|
|
|
# process intermediates
|
|
if self.num_prefix_tokens:
|
|
# split prefix (e.g. class, distill) and spatial feature tokens
|
|
prefix_tokens = [y[:, 0:self.num_prefix_tokens] for y in intermediates]
|
|
intermediates = [y[:, self.num_prefix_tokens:] for y in intermediates]
|
|
if reshape:
|
|
# reshape to BCHW output format
|
|
H, W = self.patch_embed.dynamic_feat_size((height, width))
|
|
intermediates = [y.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() for y in intermediates]
|
|
if not torch.jit.is_scripting() and return_prefix_tokens:
|
|
# return_prefix not support in torchscript due to poor type handling
|
|
intermediates = list(zip(intermediates, prefix_tokens))
|
|
|
|
if intermediates_only:
|
|
return intermediates
|
|
|
|
x = self.norm(x)
|
|
|
|
return x, intermediates
|
|
|
|
def prune_intermediate_layers(
|
|
self,
|
|
indices: Union[int, List[int], Tuple[int]] = 1,
|
|
prune_norm: bool = False,
|
|
prune_head: bool = True,
|
|
):
|
|
""" Prune layers not required for specified intermediates.
|
|
"""
|
|
take_indices, max_index = feature_take_indices(len(self.blocks), indices)
|
|
self.blocks = self.blocks[:max_index + 1] # truncate blocks
|
|
if prune_norm:
|
|
self.norm = nn.Identity()
|
|
if prune_head:
|
|
self.fc_norm = nn.Identity()
|
|
self.reset_classifier(0, '')
|
|
return take_indices
|
|
|
|
def forward_features(self, x):
|
|
x = self.patch_embed(x)
|
|
x, rot_pos_embed = self._pos_embed(x)
|
|
for blk in self.blocks:
|
|
if self.grad_checkpointing and not torch.jit.is_scripting():
|
|
x = checkpoint(blk, x, rope=rot_pos_embed)
|
|
else:
|
|
x = blk(x, rope=rot_pos_embed)
|
|
x = self.norm(x)
|
|
return x
|
|
|
|
def forward_head(self, x, pre_logits: bool = False):
|
|
if self.global_pool:
|
|
x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
|
|
x = self.fc_norm(x)
|
|
x = self.head_drop(x)
|
|
return x if pre_logits else self.head(x)
|
|
|
|
def forward(self, x):
|
|
x = self.forward_features(x)
|
|
x = self.forward_head(x)
|
|
return x
|
|
|
|
|
|
def checkpoint_filter_fn(
|
|
state_dict,
|
|
model,
|
|
interpolation='bicubic',
|
|
antialias=True,
|
|
):
|
|
""" convert patch embedding weight from manual patchify + linear proj to conv"""
|
|
out_dict = {}
|
|
state_dict = state_dict.get('model_ema', state_dict)
|
|
state_dict = state_dict.get('model', state_dict)
|
|
state_dict = state_dict.get('module', state_dict)
|
|
state_dict = state_dict.get('state_dict', state_dict)
|
|
# prefix for loading OpenCLIP compatible weights
|
|
if 'visual.trunk.pos_embed' in state_dict:
|
|
prefix = 'visual.trunk.'
|
|
elif 'visual.pos_embed' in state_dict:
|
|
prefix = 'visual.'
|
|
else:
|
|
prefix = ''
|
|
mim_weights = prefix + 'mask_token' in state_dict
|
|
no_qkv = prefix + 'blocks.0.attn.q_proj.weight' in state_dict
|
|
|
|
len_prefix = len(prefix)
|
|
for k, v in state_dict.items():
|
|
if prefix:
|
|
if k.startswith(prefix):
|
|
k = k[len_prefix:]
|
|
else:
|
|
continue
|
|
|
|
if 'rope' in k:
|
|
# fixed embedding no need to load buffer from checkpoint
|
|
continue
|
|
|
|
# FIXME here while import new weights, to remove
|
|
# if k == 'cls_token':
|
|
# print('DEBUG: cls token -> reg')
|
|
# k = 'reg_token'
|
|
# #v = v + state_dict['pos_embed'][0, :]
|
|
|
|
if 'patch_embed.proj.weight' in k:
|
|
_, _, H, W = model.patch_embed.proj.weight.shape
|
|
if v.shape[-1] != W or v.shape[-2] != H:
|
|
v = resample_patch_embed(
|
|
v,
|
|
(H, W),
|
|
interpolation=interpolation,
|
|
antialias=antialias,
|
|
verbose=True,
|
|
)
|
|
elif k == 'pos_embed' and v.shape[1] != model.pos_embed.shape[1]:
|
|
# To resize pos embedding when using model at different size from pretrained weights
|
|
num_prefix_tokens = 0 if getattr(model, 'no_embed_class', False) else getattr(model, 'num_prefix_tokens', 1)
|
|
v = resample_abs_pos_embed(
|
|
v,
|
|
new_size=model.patch_embed.grid_size,
|
|
num_prefix_tokens=num_prefix_tokens,
|
|
interpolation=interpolation,
|
|
antialias=antialias,
|
|
verbose=True,
|
|
)
|
|
|
|
k = k.replace('mlp.ffn_ln', 'mlp.norm')
|
|
k = k.replace('attn.inner_attn_ln', 'attn.norm')
|
|
k = k.replace('mlp.w12', 'mlp.fc1')
|
|
k = k.replace('mlp.w1', 'mlp.fc1_g')
|
|
k = k.replace('mlp.w2', 'mlp.fc1_x')
|
|
k = k.replace('mlp.w3', 'mlp.fc2')
|
|
if no_qkv:
|
|
k = k.replace('q_bias', 'q_proj.bias')
|
|
k = k.replace('v_bias', 'v_proj.bias')
|
|
|
|
if mim_weights and k in ('mask_token', 'lm_head.weight', 'lm_head.bias', 'norm.weight', 'norm.bias'):
|
|
if k == 'norm.weight' or k == 'norm.bias':
|
|
# try moving norm -> fc norm on fine-tune, probably a better starting point than new init
|
|
k = k.replace('norm', 'fc_norm')
|
|
else:
|
|
# skip pretrain mask token & head weights
|
|
continue
|
|
|
|
out_dict[k] = v
|
|
|
|
return out_dict
|
|
|
|
|
|
def _create_eva(variant, pretrained=False, **kwargs):
|
|
out_indices = kwargs.pop('out_indices', 3)
|
|
model = build_model_with_cfg(
|
|
Eva, variant, pretrained,
|
|
pretrained_filter_fn=checkpoint_filter_fn,
|
|
feature_cfg=dict(out_indices=out_indices, feature_cls='getter'),
|
|
**kwargs,
|
|
)
|
|
return model
|
|
|
|
|
|
def _cfg(url='', **kwargs):
|
|
return {
|
|
'url': url,
|
|
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
|
|
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
|
|
'mean': OPENAI_CLIP_MEAN, 'std': OPENAI_CLIP_STD,
|
|
'first_conv': 'patch_embed.proj', 'classifier': 'head',
|
|
'license': 'mit', **kwargs
|
|
}
|
|
|
|
|
|
default_cfgs = generate_default_cfgs({
|
|
|
|
# EVA 01 CLIP fine-tuned on imagenet-1k
|
|
'eva_giant_patch14_224.clip_ft_in1k': _cfg(
|
|
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_clip_vis_enc_sz224_ftcls_89p1.pt',
|
|
hf_hub_id='timm/',
|
|
),
|
|
'eva_giant_patch14_336.clip_ft_in1k': _cfg(
|
|
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_clip_vis_enc_sz336_ftcls_89p4.pt',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'),
|
|
|
|
# MIM EVA 01 pretrain, ft on in22k -> in1k
|
|
'eva_giant_patch14_336.m30m_ft_in22k_in1k': _cfg(
|
|
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_21k_1k_336px_psz14_ema_89p6.pt',
|
|
hf_hub_id='timm/',
|
|
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD,
|
|
input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'),
|
|
'eva_giant_patch14_560.m30m_ft_in22k_in1k': _cfg(
|
|
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_21k_1k_560px_psz14_ema_89p7.pt',
|
|
hf_hub_id='timm/',
|
|
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD,
|
|
input_size=(3, 560, 560), crop_pct=1.0, crop_mode='squash'),
|
|
|
|
# in22k or m38m MIM pretrain w/ intermediate in22k fine-tune and final in1k fine-tune
|
|
'eva02_base_patch14_448.mim_in22k_ft_in22k_in1k': _cfg(
|
|
# hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k_to_in1k/eva02_B_pt_in21k_medft_in21k_ft_in1k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash',
|
|
),
|
|
'eva02_large_patch14_448.mim_in22k_ft_in22k_in1k': _cfg(
|
|
# hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k_to_in1k/eva02_L_pt_in21k_medft_in21k_ft_in1k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash',
|
|
),
|
|
'eva02_large_patch14_448.mim_m38m_ft_in22k_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k_to_in1k/eva02_L_pt_m38m_medft_in21k_ft_in1k_p14.pt',
|
|
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash',
|
|
),
|
|
|
|
# in22k or m3m MIM pretrain w/ in1k fine-tune
|
|
'eva02_tiny_patch14_336.mim_in22k_ft_in1k': _cfg(
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_Ti_pt_in21k_ft_in1k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 336, 336), crop_pct=1.0,
|
|
),
|
|
'eva02_small_patch14_336.mim_in22k_ft_in1k': _cfg(
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_S_pt_in21k_ft_in1k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 336, 336), crop_pct=1.0,
|
|
),
|
|
'eva02_base_patch14_448.mim_in22k_ft_in1k': _cfg(
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_B_pt_in21k_ft_in1k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 448, 448), crop_pct=1.0,
|
|
),
|
|
'eva02_large_patch14_448.mim_in22k_ft_in1k': _cfg(
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_L_pt_in21k_ft_in1k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 448, 448), crop_pct=1.0,
|
|
),
|
|
'eva02_large_patch14_448.mim_m38m_ft_in1k': _cfg(
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_L_pt_m38m_ft_in1k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 448, 448), crop_pct=1.0,
|
|
),
|
|
|
|
# in22k or m3m MIM pretrain w/ in22k fine-tune
|
|
'eva02_base_patch14_448.mim_in22k_ft_in22k': _cfg(
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k/eva02_B_pt_in21k_medft_in21k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash', num_classes=21841,
|
|
),
|
|
'eva02_large_patch14_448.mim_in22k_ft_in22k': _cfg(
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k/eva02_L_pt_in21k_medft_in21k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash', num_classes=21841,
|
|
),
|
|
'eva02_large_patch14_448.mim_m38m_ft_in22k': _cfg(
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k/eva02_L_pt_m38m_medft_in21k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash', num_classes=21841,
|
|
),
|
|
|
|
# in22k or m38m MIM pretrain
|
|
'eva02_tiny_patch14_224.mim_in22k': _cfg(
|
|
# hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_Ti_pt_in21k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
num_classes=0,
|
|
),
|
|
'eva02_small_patch14_224.mim_in22k': _cfg(
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_S_pt_in21k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
num_classes=0,
|
|
),
|
|
'eva02_base_patch14_224.mim_in22k': _cfg(
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_B_pt_in21k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
num_classes=0,
|
|
),
|
|
'eva02_large_patch14_224.mim_in22k': _cfg(
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_L_pt_in21k_p14.pt',
|
|
hf_hub_id='timm/',
|
|
num_classes=0,
|
|
),
|
|
'eva02_large_patch14_224.mim_m38m': _cfg(
|
|
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_L_pt_m38m_p14.pt',
|
|
hf_hub_id='timm/',
|
|
num_classes=0,
|
|
),
|
|
|
|
# EVA01 and EVA02 CLIP image towers
|
|
'eva_giant_patch14_clip_224.laion400m': _cfg(
|
|
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA01_CLIP_g_14_plus_psz14_s11B.pt',
|
|
hf_hub_id='timm/eva_giant_patch14_clip_224.laion400m_s11b_b41k', # float16 weights
|
|
hf_hub_filename='open_clip_pytorch_model.bin',
|
|
num_classes=1024,
|
|
),
|
|
'eva_giant_patch14_clip_224.merged2b': _cfg(
|
|
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA01_CLIP_g_14_plus_psz14_s11B.pt',
|
|
hf_hub_id='timm/eva_giant_patch14_plus_clip_224.merged2b_s11b_b114k', # float16 weights
|
|
hf_hub_filename='open_clip_pytorch_model.bin',
|
|
num_classes=1024,
|
|
),
|
|
'eva02_base_patch16_clip_224.merged2b': _cfg(
|
|
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
|
|
hf_hub_id='timm/eva02_base_patch16_clip_224.merged2b_s8b_b131k', # float16 weights
|
|
hf_hub_filename='open_clip_pytorch_model.bin',
|
|
num_classes=512,
|
|
),
|
|
'eva02_large_patch14_clip_224.merged2b': _cfg(
|
|
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
|
|
hf_hub_id='timm/eva02_large_patch14_clip_224.merged2b_s4b_b131k', # float16 weights
|
|
hf_hub_filename='open_clip_pytorch_model.bin',
|
|
num_classes=768,
|
|
),
|
|
'eva02_large_patch14_clip_336.merged2b': _cfg(
|
|
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
|
|
hf_hub_id='timm/eva02_large_patch14_clip_336.merged2b_s6b_b61k', # float16 weights
|
|
hf_hub_filename='open_clip_pytorch_model.bin',
|
|
input_size=(3, 336, 336), crop_pct=1.0,
|
|
num_classes=768,
|
|
),
|
|
'eva02_enormous_patch14_clip_224.laion2b': _cfg(
|
|
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_E_psz14_plus_s9B.pt',
|
|
hf_hub_id='timm/eva02_enormous_patch14_clip_224.laion2b_s4b_b115k', # float16 weights
|
|
hf_hub_filename='open_clip_pytorch_model.bin',
|
|
num_classes=1024,
|
|
),
|
|
'eva02_enormous_patch14_clip_224.laion2b_plus': _cfg(
|
|
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_E_psz14_plus_s9B.pt',
|
|
hf_hub_id='timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k', # bfloat16 weights
|
|
hf_hub_filename='open_clip_pytorch_model.bin',
|
|
num_classes=1024,
|
|
),
|
|
'eva02_enormous_patch14_clip_224.pretrain': _cfg(
|
|
# hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_E_psz14.pt',
|
|
num_classes=0,
|
|
),
|
|
|
|
'vit_medium_patch16_rope_reg1_gap_256.in1k': _cfg(
|
|
#hf_hub_id='timm/',
|
|
#file='vit_medium_gap1_rope-in1k-20230920-5.pth',
|
|
input_size=(3, 256, 256), crop_pct=0.95,
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)
|
|
),
|
|
'vit_mediumd_patch16_rope_reg1_gap_256.in1k': _cfg(
|
|
#hf_hub_id='timm/',
|
|
#file='vit_mediumd_gap1_rope-in1k-20230926-5.pth',
|
|
input_size=(3, 256, 256), crop_pct=0.95,
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)
|
|
),
|
|
'vit_betwixt_patch16_rope_reg4_gap_256.in1k': _cfg(
|
|
#hf_hub_id='timm/',
|
|
#file='vit_betwixt_gap4_rope-in1k-20231005-5.pth',
|
|
input_size=(3, 256, 256), crop_pct=0.95,
|
|
),
|
|
'vit_base_patch16_rope_reg1_gap_256.in1k': _cfg(
|
|
#hf_hub_id='timm/',
|
|
#file='vit_base_gap1_rope-in1k-20230930-5.pth',
|
|
input_size=(3, 256, 256), crop_pct=0.95,
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)
|
|
),
|
|
})
|
|
|
|
|
|
@register_model
|
|
def eva_giant_patch14_224(pretrained=False, **kwargs) -> Eva:
|
|
""" EVA-g model https://arxiv.org/abs/2211.07636 """
|
|
model_args = dict(patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=6144 / 1408)
|
|
model = _create_eva('eva_giant_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva_giant_patch14_336(pretrained=False, **kwargs) -> Eva:
|
|
""" EVA-g model https://arxiv.org/abs/2211.07636 """
|
|
model_args = dict(patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=6144 / 1408)
|
|
model = _create_eva('eva_giant_patch14_336', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva_giant_patch14_560(pretrained=False, **kwargs) -> Eva:
|
|
""" EVA-g model https://arxiv.org/abs/2211.07636 """
|
|
model_args = dict(patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=6144 / 1408)
|
|
model = _create_eva('eva_giant_patch14_560', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva02_tiny_patch14_224(pretrained=False, **kwargs) -> Eva:
|
|
model_args = dict(
|
|
img_size=224,
|
|
patch_size=14,
|
|
embed_dim=192,
|
|
depth=12,
|
|
num_heads=3,
|
|
mlp_ratio=4 * 2 / 3,
|
|
swiglu_mlp=True,
|
|
use_rot_pos_emb=True,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
)
|
|
model = _create_eva('eva02_tiny_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva02_small_patch14_224(pretrained=False, **kwargs) -> Eva:
|
|
model_args = dict(
|
|
img_size=224,
|
|
patch_size=14,
|
|
embed_dim=384,
|
|
depth=12,
|
|
num_heads=6,
|
|
mlp_ratio=4 * 2 / 3,
|
|
swiglu_mlp=True,
|
|
use_rot_pos_emb=True,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
)
|
|
model = _create_eva('eva02_small_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva02_base_patch14_224(pretrained=False, **kwargs) -> Eva:
|
|
model_args = dict(
|
|
img_size=224,
|
|
patch_size=14,
|
|
embed_dim=768,
|
|
depth=12,
|
|
num_heads=12,
|
|
qkv_fused=False,
|
|
mlp_ratio=4 * 2 / 3,
|
|
swiglu_mlp=True,
|
|
scale_mlp=True,
|
|
use_rot_pos_emb=True,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
)
|
|
model = _create_eva('eva02_base_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva02_large_patch14_224(pretrained=False, **kwargs) -> Eva:
|
|
model_args = dict(
|
|
img_size=224,
|
|
patch_size=14,
|
|
embed_dim=1024,
|
|
depth=24,
|
|
num_heads=16,
|
|
mlp_ratio=4 * 2 / 3,
|
|
qkv_fused=False,
|
|
swiglu_mlp=True,
|
|
scale_mlp=True,
|
|
use_rot_pos_emb=True,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
)
|
|
model = _create_eva('eva02_large_patch14_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva02_tiny_patch14_336(pretrained=False, **kwargs) -> Eva:
|
|
model_args = dict(
|
|
img_size=336,
|
|
patch_size=14,
|
|
embed_dim=192,
|
|
depth=12,
|
|
num_heads=3,
|
|
mlp_ratio=4 * 2 / 3,
|
|
swiglu_mlp=True,
|
|
use_rot_pos_emb=True,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
)
|
|
model = _create_eva('eva02_tiny_patch14_336', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva02_small_patch14_336(pretrained=False, **kwargs) -> Eva:
|
|
model_args = dict(
|
|
img_size=336,
|
|
patch_size=14,
|
|
embed_dim=384,
|
|
depth=12,
|
|
num_heads=6,
|
|
mlp_ratio=4 * 2 / 3,
|
|
swiglu_mlp=True,
|
|
use_rot_pos_emb=True,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
)
|
|
model = _create_eva('eva02_small_patch14_336', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva02_base_patch14_448(pretrained=False, **kwargs) -> Eva:
|
|
model_args = dict(
|
|
img_size=448,
|
|
patch_size=14,
|
|
embed_dim=768,
|
|
depth=12,
|
|
num_heads=12,
|
|
qkv_fused=False,
|
|
mlp_ratio=4 * 2 / 3,
|
|
swiglu_mlp=True,
|
|
scale_mlp=True,
|
|
use_rot_pos_emb=True,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
)
|
|
model = _create_eva('eva02_base_patch14_448', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva02_large_patch14_448(pretrained=False, **kwargs) -> Eva:
|
|
model_args = dict(
|
|
img_size=448,
|
|
patch_size=14,
|
|
embed_dim=1024,
|
|
depth=24,
|
|
num_heads=16,
|
|
mlp_ratio=4 * 2 / 3,
|
|
qkv_fused=False,
|
|
swiglu_mlp=True,
|
|
scale_mlp=True,
|
|
use_rot_pos_emb=True,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
)
|
|
model = _create_eva('eva02_large_patch14_448', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva_giant_patch14_clip_224(pretrained=False, **kwargs) -> Eva:
|
|
""" EVA-g CLIP model (only difference from non-CLIP is the pooling) """
|
|
model_args = dict(
|
|
patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=6144 / 1408,
|
|
global_pool=kwargs.pop('global_pool', 'token'))
|
|
model = _create_eva('eva_giant_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva02_base_patch16_clip_224(pretrained=False, **kwargs) -> Eva:
|
|
""" A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_base """
|
|
model_args = dict(
|
|
img_size=224,
|
|
patch_size=16,
|
|
embed_dim=768,
|
|
depth=12,
|
|
num_heads=12,
|
|
qkv_fused=False,
|
|
mlp_ratio=4 * 2 / 3,
|
|
swiglu_mlp=True,
|
|
scale_mlp=True,
|
|
scale_attn_inner=True,
|
|
use_rot_pos_emb=True,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
global_pool=kwargs.pop('global_pool', 'token'),
|
|
)
|
|
model = _create_eva('eva02_base_patch16_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva02_large_patch14_clip_224(pretrained=False, **kwargs) -> Eva:
|
|
""" A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_large """
|
|
model_args = dict(
|
|
img_size=224,
|
|
patch_size=14,
|
|
embed_dim=1024,
|
|
depth=24,
|
|
num_heads=16,
|
|
mlp_ratio=4 * 2 / 3,
|
|
qkv_fused=False,
|
|
swiglu_mlp=True,
|
|
scale_mlp=True,
|
|
scale_attn_inner=True,
|
|
use_rot_pos_emb=True,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
global_pool=kwargs.pop('global_pool', 'token'),
|
|
)
|
|
model = _create_eva('eva02_large_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva02_large_patch14_clip_336(pretrained=False, **kwargs) -> Eva:
|
|
""" A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_large """
|
|
model_args = dict(
|
|
img_size=336,
|
|
patch_size=14,
|
|
embed_dim=1024,
|
|
depth=24,
|
|
num_heads=16,
|
|
mlp_ratio=4 * 2 / 3,
|
|
qkv_fused=False,
|
|
swiglu_mlp=True,
|
|
scale_mlp=True,
|
|
scale_attn_inner=True,
|
|
use_rot_pos_emb=True,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
global_pool=kwargs.pop('global_pool', 'token'),
|
|
)
|
|
model = _create_eva('eva02_large_patch14_clip_336', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def eva02_enormous_patch14_clip_224(pretrained=False, **kwargs) -> Eva:
|
|
""" A EVA-CLIP specific variant that uses residual post-norm in blocks """
|
|
model_args = dict(
|
|
img_size=224,
|
|
patch_size=14,
|
|
embed_dim=1792,
|
|
depth=64,
|
|
num_heads=16,
|
|
mlp_ratio=15360 / 1792,
|
|
use_post_norm=True,
|
|
global_pool=kwargs.pop('global_pool', 'token'),
|
|
)
|
|
model = _create_eva('eva02_enormous_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_medium_patch16_rope_reg1_gap_256(pretrained=False, **kwargs) -> Eva:
|
|
model_args = dict(
|
|
img_size=256,
|
|
patch_size=16,
|
|
embed_dim=512,
|
|
depth=12,
|
|
num_heads=8,
|
|
qkv_fused=True,
|
|
qkv_bias=True,
|
|
init_values=1e-5,
|
|
class_token=False,
|
|
num_reg_tokens=1,
|
|
use_rot_pos_emb=True,
|
|
use_abs_pos_emb=False,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
)
|
|
model = _create_eva('vit_medium_patch16_rope_reg1_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_mediumd_patch16_rope_reg1_gap_256(pretrained=False, **kwargs) -> Eva:
|
|
model_args = dict(
|
|
img_size=256,
|
|
patch_size=16,
|
|
embed_dim=512,
|
|
depth=20,
|
|
num_heads=8,
|
|
qkv_fused=True,
|
|
qkv_bias=False,
|
|
init_values=1e-5,
|
|
class_token=False,
|
|
num_reg_tokens=1,
|
|
use_rot_pos_emb=True,
|
|
use_abs_pos_emb=False,
|
|
ref_feat_shape=(16, 16), # 224/14
|
|
)
|
|
model = _create_eva('vit_mediumd_patch16_rope_reg1_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
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@register_model
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def vit_betwixt_patch16_rope_reg4_gap_256(pretrained=False, **kwargs) -> Eva:
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model_args = dict(
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img_size=256,
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patch_size=16,
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embed_dim=640,
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depth=12,
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num_heads=10,
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qkv_fused=True,
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qkv_bias=True,
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init_values=1e-5,
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class_token=False,
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num_reg_tokens=4,
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use_rot_pos_emb=True,
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use_abs_pos_emb=False,
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ref_feat_shape=(16, 16), # 224/14
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)
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model = _create_eva('vit_betwixt_patch16_rope_reg4_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
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return model
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@register_model
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def vit_base_patch16_rope_reg1_gap_256(pretrained=False, **kwargs) -> Eva:
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|
model_args = dict(
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|
img_size=256,
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|
patch_size=16,
|
|
embed_dim=768,
|
|
depth=12,
|
|
num_heads=12,
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|
qkv_fused=True,
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|
qkv_bias=True,
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|
init_values=1e-5,
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|
class_token=False,
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|
num_reg_tokens=1,
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|
use_rot_pos_emb=True,
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|
use_abs_pos_emb=False,
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|
ref_feat_shape=(16, 16), # 224/14
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|
)
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|
model = _create_eva('vit_base_patch16_rope_reg1_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
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|
return model
|