703 lines
28 KiB
Python
703 lines
28 KiB
Python
""" Relative Position Vision Transformer (ViT) in PyTorch
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NOTE: these models are experimental / WIP, expect changes
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Hacked together by / Copyright 2022, Ross Wightman
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"""
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import logging
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import math
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from functools import partial
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from typing import List, Optional, Tuple, Type, Union
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try:
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from typing import Literal
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except ImportError:
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from typing_extensions import Literal
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import torch
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import torch.nn as nn
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from torch.jit import Final
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from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from timm.layers import PatchEmbed, Mlp, DropPath, RelPosMlp, RelPosBias, use_fused_attn, LayerType
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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 ._manipulate import named_apply, checkpoint
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from ._registry import generate_default_cfgs, register_model
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from .vision_transformer import get_init_weights_vit
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__all__ = ['VisionTransformerRelPos'] # model_registry will add each entrypoint fn to this
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_logger = logging.getLogger(__name__)
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class RelPosAttention(nn.Module):
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fused_attn: Final[bool]
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def __init__(
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self,
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dim,
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num_heads=8,
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qkv_bias=False,
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qk_norm=False,
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rel_pos_cls=None,
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attn_drop=0.,
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proj_drop=0.,
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norm_layer=nn.LayerNorm,
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):
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super().__init__()
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assert dim % num_heads == 0, 'dim should be divisible by num_heads'
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self.num_heads = num_heads
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self.head_dim = dim // num_heads
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self.scale = self.head_dim ** -0.5
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self.fused_attn = use_fused_attn()
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
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self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
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self.rel_pos = rel_pos_cls(num_heads=num_heads) if rel_pos_cls else None
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
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q, k, v = qkv.unbind(0)
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q = self.q_norm(q)
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k = self.k_norm(k)
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if self.fused_attn:
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if self.rel_pos is not None:
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attn_bias = self.rel_pos.get_bias()
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elif shared_rel_pos is not None:
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attn_bias = shared_rel_pos
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else:
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attn_bias = None
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x = torch.nn.functional.scaled_dot_product_attention(
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q, k, v,
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attn_mask=attn_bias,
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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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if self.rel_pos is not None:
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attn = self.rel_pos(attn, shared_rel_pos=shared_rel_pos)
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elif shared_rel_pos is not None:
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attn = attn + shared_rel_pos
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attn = attn.softmax(dim=-1)
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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.proj(x)
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x = self.proj_drop(x)
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return x
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class LayerScale(nn.Module):
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def __init__(self, dim, init_values=1e-5, inplace=False):
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super().__init__()
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self.inplace = inplace
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self.gamma = nn.Parameter(init_values * torch.ones(dim))
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def forward(self, x):
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return x.mul_(self.gamma) if self.inplace else x * self.gamma
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class RelPosBlock(nn.Module):
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def __init__(
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self,
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dim,
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num_heads,
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mlp_ratio=4.,
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qkv_bias=False,
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qk_norm=False,
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rel_pos_cls=None,
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init_values=None,
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proj_drop=0.,
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attn_drop=0.,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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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 = RelPosAttention(
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dim,
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num_heads,
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qkv_bias=qkv_bias,
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qk_norm=qk_norm,
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rel_pos_cls=rel_pos_cls,
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attn_drop=attn_drop,
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proj_drop=proj_drop,
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)
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self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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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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self.mlp = Mlp(
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in_features=dim,
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hidden_features=int(dim * mlp_ratio),
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act_layer=act_layer,
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drop=proj_drop,
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)
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self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
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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, shared_rel_pos: Optional[torch.Tensor] = None):
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x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x), shared_rel_pos=shared_rel_pos)))
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x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
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return x
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class ResPostRelPosBlock(nn.Module):
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def __init__(
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self,
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dim,
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num_heads,
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mlp_ratio=4.,
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qkv_bias=False,
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qk_norm=False,
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rel_pos_cls=None,
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init_values=None,
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proj_drop=0.,
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attn_drop=0.,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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):
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super().__init__()
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self.init_values = init_values
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self.attn = RelPosAttention(
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dim,
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num_heads,
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qkv_bias=qkv_bias,
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qk_norm=qk_norm,
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rel_pos_cls=rel_pos_cls,
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attn_drop=attn_drop,
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proj_drop=proj_drop,
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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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self.mlp = Mlp(
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in_features=dim,
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hidden_features=int(dim * mlp_ratio),
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act_layer=act_layer,
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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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self.init_weights()
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def init_weights(self):
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# NOTE this init overrides that base model init with specific changes for the block type
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if self.init_values is not None:
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nn.init.constant_(self.norm1.weight, self.init_values)
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nn.init.constant_(self.norm2.weight, self.init_values)
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def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
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x = x + self.drop_path1(self.norm1(self.attn(x, shared_rel_pos=shared_rel_pos)))
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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 VisionTransformerRelPos(nn.Module):
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""" Vision Transformer w/ Relative Position Bias
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Differing from classic vit, this impl
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* uses relative position index (swin v1 / beit) or relative log coord + mlp (swin v2) pos embed
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* defaults to no class token (can be enabled)
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* defaults to global avg pool for head (can be changed)
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* layer-scale (residual branch gain) enabled
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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: Literal['', 'avg', 'token', 'map'] = '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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mlp_ratio: float = 4.,
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qkv_bias: bool = True,
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qk_norm: bool = False,
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init_values: Optional[float] = 1e-6,
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class_token: bool = False,
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fc_norm: bool = False,
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rel_pos_type: str = 'mlp',
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rel_pos_dim: Optional[int] = None,
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shared_rel_pos: bool = False,
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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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weight_init: Literal['skip', 'jax', 'moco', ''] = 'skip',
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fix_init: bool = False,
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embed_layer: Type[nn.Module] = PatchEmbed,
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norm_layer: Optional[LayerType] = None,
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act_layer: Optional[LayerType] = None,
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block_fn: Type[nn.Module] = RelPosBlock
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):
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"""
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Args:
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img_size: input image size
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patch_size: patch size
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in_chans: number of input channels
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num_classes: number of classes for classification head
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global_pool: type of global pooling for final sequence (default: 'avg')
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embed_dim: embedding dimension
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depth: depth of transformer
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num_heads: number of attention heads
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mlp_ratio: ratio of mlp hidden dim to embedding dim
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qkv_bias: enable bias for qkv if True
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qk_norm: Enable normalization of query and key in attention
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init_values: layer-scale init values
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class_token: use class token (default: False)
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fc_norm: use pre classifier norm instead of pre-pool
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rel_pos_type: type of relative position
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shared_rel_pos: share relative pos across all blocks
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drop_rate: dropout rate
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proj_drop_rate: projection dropout rate
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attn_drop_rate: attention dropout rate
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drop_path_rate: stochastic depth rate
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weight_init: weight init scheme
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fix_init: apply weight initialization fix (scaling w/ layer index)
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embed_layer: patch embedding layer
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norm_layer: normalization layer
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act_layer: MLP activation layer
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"""
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super().__init__()
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assert global_pool in ('', 'avg', 'token')
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assert class_token or global_pool != 'token'
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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act_layer = act_layer or nn.GELU
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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.head_hidden_size = self.embed_dim = embed_dim # for consistency with other models
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self.num_prefix_tokens = 1 if class_token else 0
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self.grad_checkpointing = False
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self.patch_embed = embed_layer(
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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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)
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feat_size = self.patch_embed.grid_size
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r = self.patch_embed.feat_ratio() if hasattr(self.patch_embed, 'feat_ratio') else patch_size
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rel_pos_args = dict(window_size=feat_size, prefix_tokens=self.num_prefix_tokens)
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if rel_pos_type.startswith('mlp'):
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if rel_pos_dim:
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rel_pos_args['hidden_dim'] = rel_pos_dim
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if 'swin' in rel_pos_type:
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rel_pos_args['mode'] = 'swin'
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rel_pos_cls = partial(RelPosMlp, **rel_pos_args)
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else:
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rel_pos_cls = partial(RelPosBias, **rel_pos_args)
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self.shared_rel_pos = None
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if shared_rel_pos:
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self.shared_rel_pos = rel_pos_cls(num_heads=num_heads)
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# NOTE shared rel pos currently mutually exclusive w/ per-block, but could support both...
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rel_pos_cls = None
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self.cls_token = nn.Parameter(torch.zeros(1, self.num_prefix_tokens, embed_dim)) if class_token else None
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
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self.blocks = nn.ModuleList([
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block_fn(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_norm=qk_norm,
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rel_pos_cls=rel_pos_cls,
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init_values=init_values,
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proj_drop=proj_drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i],
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norm_layer=norm_layer,
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act_layer=act_layer,
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)
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for i in range(depth)])
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self.feature_info = [
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dict(module=f'blocks.{i}', num_chs=embed_dim, reduction=r) for i in range(depth)]
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self.norm = norm_layer(embed_dim) if not fc_norm else nn.Identity()
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# Classifier Head
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self.fc_norm = norm_layer(embed_dim) if fc_norm else nn.Identity()
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self.head_drop = nn.Dropout(drop_rate)
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self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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if weight_init != 'skip':
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self.init_weights(weight_init)
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if fix_init:
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self.fix_init_weight()
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def init_weights(self, mode=''):
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assert mode in ('jax', 'moco', '')
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if self.cls_token is not None:
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nn.init.normal_(self.cls_token, std=1e-6)
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named_apply(get_init_weights_vit(mode), self)
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def fix_init_weight(self):
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def rescale(param, _layer_id):
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param.div_(math.sqrt(2.0 * _layer_id))
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for layer_id, layer in enumerate(self.blocks):
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rescale(layer.attn.proj.weight.data, layer_id + 1)
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rescale(layer.mlp.fc2.weight.data, layer_id + 1)
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@torch.jit.ignore
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def no_weight_decay(self):
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return {'cls_token'}
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@torch.jit.ignore
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def group_matcher(self, coarse=False):
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return dict(
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stem=r'^cls_token|patch_embed', # stem and embed
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blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
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)
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@torch.jit.ignore
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def set_grad_checkpointing(self, enable=True):
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self.grad_checkpointing = enable
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@torch.jit.ignore
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def get_classifier(self) -> nn.Module:
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return self.head
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def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
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self.num_classes = num_classes
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if global_pool is not None:
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assert global_pool in ('', 'avg', 'token')
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self.global_pool = global_pool
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self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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def forward_intermediates(
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self,
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x: torch.Tensor,
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indices: Optional[Union[int, List[int]]] = None,
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return_prefix_tokens: bool = False,
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norm: bool = False,
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stop_early: bool = False,
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output_fmt: str = 'NCHW',
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intermediates_only: bool = False,
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) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
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""" Forward features that returns intermediates.
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Args:
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x: Input image tensor
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indices: Take last n blocks if int, all if None, select matching indices if sequence
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return_prefix_tokens: Return both prefix and spatial intermediate tokens
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norm: Apply norm layer to all intermediates
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stop_early: Stop iterating over blocks when last desired intermediate hit
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output_fmt: Shape of intermediate feature outputs
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intermediates_only: Only return intermediate features
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Returns:
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"""
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assert output_fmt in ('NCHW', 'NLC'), 'Output format must be one of NCHW or NLC.'
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reshape = output_fmt == 'NCHW'
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intermediates = []
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take_indices, max_index = feature_take_indices(len(self.blocks), indices)
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# forward pass
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B, _, height, width = x.shape
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x = self.patch_embed(x)
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if self.cls_token is not None:
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x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
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shared_rel_pos = self.shared_rel_pos.get_bias() if self.shared_rel_pos is not None else None
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if torch.jit.is_scripting() or not stop_early: # can't slice blocks in torchscript
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blocks = self.blocks
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else:
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blocks = self.blocks[:max_index + 1]
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for i, blk in enumerate(blocks):
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x = blk(x, shared_rel_pos=shared_rel_pos)
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if i in take_indices:
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# normalize intermediates with final norm layer if enabled
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intermediates.append(self.norm(x) if norm else x)
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# process intermediates
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if self.num_prefix_tokens:
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# split prefix (e.g. class, distill) and spatial feature tokens
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prefix_tokens = [y[:, 0:self.num_prefix_tokens] for y in intermediates]
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intermediates = [y[:, self.num_prefix_tokens:] for y in intermediates]
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if reshape:
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# reshape to BCHW output format
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H, W = self.patch_embed.dynamic_feat_size((height, width))
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intermediates = [y.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() for y in intermediates]
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if not torch.jit.is_scripting() and return_prefix_tokens:
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# return_prefix not support in torchscript due to poor type handling
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intermediates = list(zip(intermediates, prefix_tokens))
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if intermediates_only:
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return intermediates
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x = self.norm(x)
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return x, intermediates
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def prune_intermediate_layers(
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self,
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indices: Union[int, List[int]] = 1,
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prune_norm: bool = False,
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prune_head: bool = True,
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):
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""" Prune layers not required for specified intermediates.
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"""
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take_indices, max_index = feature_take_indices(len(self.blocks), indices)
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self.blocks = self.blocks[:max_index + 1] # truncate blocks
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if prune_norm:
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self.norm = nn.Identity()
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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)
|
|
if self.cls_token is not None:
|
|
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
|
|
|
|
shared_rel_pos = self.shared_rel_pos.get_bias() if self.shared_rel_pos is not None else None
|
|
for blk in self.blocks:
|
|
if self.grad_checkpointing and not torch.jit.is_scripting():
|
|
x = checkpoint(blk, x, shared_rel_pos=shared_rel_pos)
|
|
else:
|
|
x = blk(x, shared_rel_pos=shared_rel_pos)
|
|
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 _create_vision_transformer_relpos(variant, pretrained=False, **kwargs):
|
|
out_indices = kwargs.pop('out_indices', 3)
|
|
model = build_model_with_cfg(
|
|
VisionTransformerRelPos, variant, pretrained,
|
|
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': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
|
|
'first_conv': 'patch_embed.proj', 'classifier': 'head',
|
|
**kwargs
|
|
}
|
|
|
|
|
|
default_cfgs = generate_default_cfgs({
|
|
'vit_relpos_base_patch32_plus_rpn_256.sw_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_replos_base_patch32_plus_rpn_256-sw-dd486f51.pth',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 256, 256)),
|
|
'vit_relpos_base_patch16_plus_240.untrained': _cfg(url='', input_size=(3, 240, 240)),
|
|
|
|
'vit_relpos_small_patch16_224.sw_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_small_patch16_224-sw-ec2778b4.pth',
|
|
hf_hub_id='timm/'),
|
|
'vit_relpos_medium_patch16_224.sw_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_224-sw-11c174af.pth',
|
|
hf_hub_id='timm/'),
|
|
'vit_relpos_base_patch16_224.sw_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_224-sw-49049aed.pth',
|
|
hf_hub_id='timm/'),
|
|
|
|
'vit_srelpos_small_patch16_224.sw_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_small_patch16_224-sw-6cdb8849.pth',
|
|
hf_hub_id='timm/'),
|
|
'vit_srelpos_medium_patch16_224.sw_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_medium_patch16_224-sw-ad702b8c.pth',
|
|
hf_hub_id='timm/'),
|
|
|
|
'vit_relpos_medium_patch16_cls_224.sw_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_cls_224-sw-cfe8e259.pth',
|
|
hf_hub_id='timm/'),
|
|
'vit_relpos_base_patch16_cls_224.untrained': _cfg(),
|
|
'vit_relpos_base_patch16_clsgap_224.sw_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_gapcls_224-sw-1a341d6c.pth',
|
|
hf_hub_id='timm/'),
|
|
|
|
'vit_relpos_small_patch16_rpn_224.untrained': _cfg(),
|
|
'vit_relpos_medium_patch16_rpn_224.sw_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_rpn_224-sw-5d2befd8.pth',
|
|
hf_hub_id='timm/'),
|
|
'vit_relpos_base_patch16_rpn_224.untrained': _cfg(),
|
|
})
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_base_patch32_plus_rpn_256(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-B/32+) w/ relative log-coord position and residual post-norm, no class token
|
|
"""
|
|
model_args = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, block_fn=ResPostRelPosBlock)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_base_patch32_plus_rpn_256', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_base_patch16_plus_240(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-B/16+) w/ relative log-coord position, no class token
|
|
"""
|
|
model_args = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_base_patch16_plus_240', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_small_patch16_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position, no class token
|
|
"""
|
|
model_args = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, fc_norm=True)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_small_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_medium_patch16_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position, no class token
|
|
"""
|
|
model_args = dict(
|
|
patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=True)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_medium_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_base_patch16_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position, no class token
|
|
"""
|
|
model_args = dict(
|
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, fc_norm=True)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_base_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_srelpos_small_patch16_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-B/16) w/ shared relative log-coord position, no class token
|
|
"""
|
|
model_args = dict(
|
|
patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, fc_norm=False,
|
|
rel_pos_dim=384, shared_rel_pos=True)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_srelpos_small_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_srelpos_medium_patch16_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-B/16) w/ shared relative log-coord position, no class token
|
|
"""
|
|
model_args = dict(
|
|
patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=False,
|
|
rel_pos_dim=512, shared_rel_pos=True)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_srelpos_medium_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_medium_patch16_cls_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-M/16) w/ relative log-coord position, class token present
|
|
"""
|
|
model_args = dict(
|
|
patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=False,
|
|
rel_pos_dim=256, class_token=True, global_pool='token')
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_medium_patch16_cls_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_base_patch16_cls_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position, class token present
|
|
"""
|
|
model_args = dict(
|
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, class_token=True, global_pool='token')
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_base_patch16_cls_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_base_patch16_clsgap_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position, class token present
|
|
NOTE this config is a bit of a mistake, class token was enabled but global avg-pool w/ fc-norm was not disabled
|
|
Leaving here for comparisons w/ a future re-train as it performs quite well.
|
|
"""
|
|
model_args = dict(
|
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, fc_norm=True, class_token=True)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_base_patch16_clsgap_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_small_patch16_rpn_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token
|
|
"""
|
|
model_args = dict(
|
|
patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, block_fn=ResPostRelPosBlock)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_small_patch16_rpn_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_medium_patch16_rpn_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token
|
|
"""
|
|
model_args = dict(
|
|
patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, block_fn=ResPostRelPosBlock)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_medium_patch16_rpn_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_base_patch16_rpn_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
|
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token
|
|
"""
|
|
model_args = dict(
|
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, block_fn=ResPostRelPosBlock)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_base_patch16_rpn_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|