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496 lines
21 KiB
496 lines
21 KiB
""" 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 Optional, Tuple
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import torch
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import torch.nn as nn
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from torch.utils.checkpoint import checkpoint
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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
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from ._builder import build_model_with_cfg
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from ._pretrained import generate_default_cfgs
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from ._registry import register_model
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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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def __init__(self, dim, num_heads=8, qkv_bias=False, rel_pos_cls=None, attn_drop=0., proj_drop=0.):
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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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head_dim = dim // num_heads
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self.scale = head_dim ** -0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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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, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
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attn = (q @ k.transpose(-2, -1)) * self.scale
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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).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, dim, num_heads, mlp_ratio=4., qkv_bias=False, rel_pos_cls=None, init_values=None,
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drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
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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, num_heads, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls, attn_drop=attn_drop, proj_drop=drop)
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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(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
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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, dim, num_heads, mlp_ratio=4., qkv_bias=False, rel_pos_cls=None, init_values=None,
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drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
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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, num_heads, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls, attn_drop=attn_drop, proj_drop=drop)
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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(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
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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=224,
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patch_size=16,
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in_chans=3,
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num_classes=1000,
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global_pool='avg',
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embed_dim=768,
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depth=12,
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num_heads=12,
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mlp_ratio=4.,
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qkv_bias=True,
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init_values=1e-6,
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class_token=False,
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fc_norm=False,
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rel_pos_type='mlp',
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rel_pos_dim=None,
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shared_rel_pos=False,
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drop_rate=0.,
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attn_drop_rate=0.,
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drop_path_rate=0.,
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weight_init='skip',
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embed_layer=PatchEmbed,
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norm_layer=None,
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act_layer=None,
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block_fn=RelPosBlock
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):
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"""
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Args:
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img_size (int, tuple): input image size
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patch_size (int, tuple): patch size
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in_chans (int): number of input channels
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num_classes (int): number of classes for classification head
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global_pool (str): type of global pooling for final sequence (default: 'avg')
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embed_dim (int): embedding dimension
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depth (int): depth of transformer
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num_heads (int): number of attention heads
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mlp_ratio (int): ratio of mlp hidden dim to embedding dim
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qkv_bias (bool): enable bias for qkv if True
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init_values: (float): layer-scale init values
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class_token (bool): use class token (default: False)
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fc_norm (bool): use pre classifier norm instead of pre-pool
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rel_pos_ty pe (str): type of relative position
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shared_rel_pos (bool): share relative pos across all blocks
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drop_rate (float): dropout rate
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attn_drop_rate (float): attention dropout rate
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drop_path_rate (float): stochastic depth rate
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weight_init (str): weight init scheme
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embed_layer (nn.Module): patch embedding layer
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norm_layer: (nn.Module): normalization layer
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act_layer: (nn.Module): 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.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
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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, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
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feat_size = self.patch_embed.grid_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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# FIXME experimenting with different relpos log coord configs
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if 'swin' in rel_pos_type:
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rel_pos_args['mode'] = 'swin'
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elif 'rw' in rel_pos_type:
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rel_pos_args['mode'] = 'rw'
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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, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls,
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init_values=init_values, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i],
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norm_layer=norm_layer, act_layer=act_layer)
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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 = 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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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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# FIXME weight init scheme using PyTorch defaults curently
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#named_apply(get_init_weights_vit(mode, head_bias), self)
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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):
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return self.head
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def reset_classifier(self, num_classes: int, global_pool=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_features(self, x):
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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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for blk in self.blocks:
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if self.grad_checkpointing and not torch.jit.is_scripting():
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x = checkpoint(blk, x, shared_rel_pos=shared_rel_pos)
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else:
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x = blk(x, shared_rel_pos=shared_rel_pos)
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x = self.norm(x)
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return x
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def forward_head(self, x, pre_logits: bool = False):
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if self.global_pool:
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x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
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x = self.fc_norm(x)
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return x if pre_logits else self.head(x)
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def forward(self, x):
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x = self.forward_features(x)
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x = self.forward_head(x)
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return x
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def _create_vision_transformer_relpos(variant, pretrained=False, **kwargs):
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if kwargs.get('features_only', None):
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raise RuntimeError('features_only not implemented for Vision Transformer models.')
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model = build_model_with_cfg(VisionTransformerRelPos, variant, pretrained, **kwargs)
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return model
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'patch_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = generate_default_cfgs({
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'vit_relpos_base_patch32_plus_rpn_256.sw_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_replos_base_patch32_plus_rpn_256-sw-dd486f51.pth',
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hf_hub_id='timm/',
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input_size=(3, 256, 256)),
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'vit_relpos_base_patch16_plus_240.untrained': _cfg(url='', input_size=(3, 240, 240)),
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'vit_relpos_small_patch16_224.sw_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_small_patch16_224-sw-ec2778b4.pth',
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hf_hub_id='timm/'),
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'vit_relpos_medium_patch16_224.sw_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_224-sw-11c174af.pth',
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hf_hub_id='timm/'),
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'vit_relpos_base_patch16_224.sw_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_224-sw-49049aed.pth',
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hf_hub_id='timm/'),
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'vit_srelpos_small_patch16_224.sw_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_small_patch16_224-sw-6cdb8849.pth',
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hf_hub_id='timm/'),
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'vit_srelpos_medium_patch16_224.sw_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_medium_patch16_224-sw-ad702b8c.pth',
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hf_hub_id='timm/'),
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'vit_relpos_medium_patch16_cls_224.sw_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_cls_224-sw-cfe8e259.pth',
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hf_hub_id='timm/'),
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'vit_relpos_base_patch16_cls_224.untrained': _cfg(),
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'vit_relpos_base_patch16_clsgap_224.sw_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_gapcls_224-sw-1a341d6c.pth',
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hf_hub_id='timm/'),
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'vit_relpos_small_patch16_rpn_224.untrained': _cfg(),
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'vit_relpos_medium_patch16_rpn_224.sw_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_rpn_224-sw-5d2befd8.pth',
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hf_hub_id='timm/'),
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'vit_relpos_base_patch16_rpn_224.untrained': _cfg(),
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})
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@register_model
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def vit_relpos_base_patch32_plus_rpn_256(pretrained=False, **kwargs):
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""" ViT-Base (ViT-B/32+) w/ relative log-coord position and residual post-norm, no class token
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"""
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model_kwargs = dict(
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patch_size=32, embed_dim=896, depth=12, num_heads=14, block_fn=ResPostRelPosBlock, **kwargs)
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model = _create_vision_transformer_relpos(
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'vit_relpos_base_patch32_plus_rpn_256', pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_relpos_base_patch16_plus_240(pretrained=False, **kwargs):
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""" ViT-Base (ViT-B/16+) w/ relative log-coord position, no class token
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"""
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model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, **kwargs)
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model = _create_vision_transformer_relpos('vit_relpos_base_patch16_plus_240', pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_relpos_small_patch16_224(pretrained=False, **kwargs):
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""" ViT-Base (ViT-B/16) w/ relative log-coord position, no class token
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"""
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model_kwargs = dict(
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patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, fc_norm=True, **kwargs)
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model = _create_vision_transformer_relpos('vit_relpos_small_patch16_224', pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_relpos_medium_patch16_224(pretrained=False, **kwargs):
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""" ViT-Base (ViT-B/16) w/ relative log-coord position, no class token
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"""
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model_kwargs = dict(
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patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=True, **kwargs)
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model = _create_vision_transformer_relpos('vit_relpos_medium_patch16_224', pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_relpos_base_patch16_224(pretrained=False, **kwargs):
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""" ViT-Base (ViT-B/16) w/ relative log-coord position, no class token
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"""
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model_kwargs = dict(
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patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, fc_norm=True, **kwargs)
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model = _create_vision_transformer_relpos('vit_relpos_base_patch16_224', pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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|
def vit_srelpos_small_patch16_224(pretrained=False, **kwargs):
|
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""" ViT-Base (ViT-B/16) w/ shared relative log-coord position, no class token
|
|
"""
|
|
model_kwargs = dict(
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patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, fc_norm=False,
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rel_pos_dim=384, shared_rel_pos=True, **kwargs)
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|
model = _create_vision_transformer_relpos('vit_srelpos_small_patch16_224', pretrained=pretrained, **model_kwargs)
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return model
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|
|
|
|
|
@register_model
|
|
def vit_srelpos_medium_patch16_224(pretrained=False, **kwargs):
|
|
""" ViT-Base (ViT-B/16) w/ shared relative log-coord position, no class token
|
|
"""
|
|
model_kwargs = 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, **kwargs)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_srelpos_medium_patch16_224', pretrained=pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_medium_patch16_cls_224(pretrained=False, **kwargs):
|
|
""" ViT-Base (ViT-M/16) w/ relative log-coord position, class token present
|
|
"""
|
|
model_kwargs = 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', **kwargs)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_medium_patch16_cls_224', pretrained=pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_base_patch16_cls_224(pretrained=False, **kwargs):
|
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position, class token present
|
|
"""
|
|
model_kwargs = dict(
|
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False,
|
|
class_token=True, global_pool='token', **kwargs)
|
|
model = _create_vision_transformer_relpos('vit_relpos_base_patch16_cls_224', pretrained=pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_base_patch16_clsgap_224(pretrained=False, **kwargs):
|
|
""" 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_kwargs = dict(
|
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, fc_norm=True, class_token=True, **kwargs)
|
|
model = _create_vision_transformer_relpos('vit_relpos_base_patch16_clsgap_224', pretrained=pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_small_patch16_rpn_224(pretrained=False, **kwargs):
|
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token
|
|
"""
|
|
model_kwargs = dict(
|
|
patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, block_fn=ResPostRelPosBlock, **kwargs)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_small_patch16_rpn_224', pretrained=pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_medium_patch16_rpn_224(pretrained=False, **kwargs):
|
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token
|
|
"""
|
|
model_kwargs = dict(
|
|
patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, block_fn=ResPostRelPosBlock, **kwargs)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_medium_patch16_rpn_224', pretrained=pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def vit_relpos_base_patch16_rpn_224(pretrained=False, **kwargs):
|
|
""" ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token
|
|
"""
|
|
model_kwargs = dict(
|
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, block_fn=ResPostRelPosBlock, **kwargs)
|
|
model = _create_vision_transformer_relpos(
|
|
'vit_relpos_base_patch16_rpn_224', pretrained=pretrained, **model_kwargs)
|
|
return model
|