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vision_transformer_relpos.py
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vision_transformer_relpos.py
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""" Relative Position Vision Transformer (ViT) in PyTorch
NOTE: these models are experimental / WIP, expect changes
Hacked together by / Copyright 2022, Ross Wightman
"""
import logging
import math
from functools import partial
from typing import List, Optional, Tuple, Type, Union
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
import torch
import torch.nn as nn
from torch.jit import Final
from torch.utils.checkpoint import checkpoint
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import PatchEmbed, Mlp, DropPath, RelPosMlp, RelPosBias, use_fused_attn, LayerType
from ._builder import build_model_with_cfg
from ._features import feature_take_indices
from ._manipulate import named_apply
from ._registry import generate_default_cfgs, register_model
from .vision_transformer import get_init_weights_vit
__all__ = ['VisionTransformerRelPos'] # model_registry will add each entrypoint fn to this
_logger = logging.getLogger(__name__)
class RelPosAttention(nn.Module):
fused_attn: Final[bool]
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
qk_norm=False,
rel_pos_cls=None,
attn_drop=0.,
proj_drop=0.,
norm_layer=nn.LayerNorm,
):
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.fused_attn = use_fused_attn()
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.rel_pos = rel_pos_cls(num_heads=num_heads) if rel_pos_cls else None
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
q = self.q_norm(q)
k = self.k_norm(k)
if self.fused_attn:
if self.rel_pos is not None:
attn_bias = self.rel_pos.get_bias()
elif shared_rel_pos is not None:
attn_bias = shared_rel_pos
else:
attn_bias = None
x = torch.nn.functional.scaled_dot_product_attention(
q, k, v,
attn_mask=attn_bias,
dropout_p=self.attn_drop.p if self.training else 0.,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
if self.rel_pos is not None:
attn = self.rel_pos(attn, shared_rel_pos=shared_rel_pos)
elif shared_rel_pos is not None:
attn = attn + shared_rel_pos
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class LayerScale(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
return x.mul_(self.gamma) if self.inplace else x * self.gamma
class RelPosBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
qk_norm=False,
rel_pos_cls=None,
init_values=None,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = RelPosAttention(
dim,
num_heads,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
rel_pos_cls=rel_pos_cls,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x), shared_rel_pos=shared_rel_pos)))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
class ResPostRelPosBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
qk_norm=False,
rel_pos_cls=None,
init_values=None,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.init_values = init_values
self.attn = RelPosAttention(
dim,
num_heads,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
rel_pos_cls=rel_pos_cls,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.norm1 = norm_layer(dim)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.norm2 = norm_layer(dim)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.init_weights()
def init_weights(self):
# NOTE this init overrides that base model init with specific changes for the block type
if self.init_values is not None:
nn.init.constant_(self.norm1.weight, self.init_values)
nn.init.constant_(self.norm2.weight, self.init_values)
def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
x = x + self.drop_path1(self.norm1(self.attn(x, shared_rel_pos=shared_rel_pos)))
x = x + self.drop_path2(self.norm2(self.mlp(x)))
return x
class VisionTransformerRelPos(nn.Module):
""" Vision Transformer w/ Relative Position Bias
Differing from classic vit, this impl
* uses relative position index (swin v1 / beit) or relative log coord + mlp (swin v2) pos embed
* defaults to no class token (can be enabled)
* defaults to global avg pool for head (can be changed)
* layer-scale (residual branch gain) enabled
"""
def __init__(
self,
img_size: Union[int, Tuple[int, int]] = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: Literal['', 'avg', 'token', 'map'] = 'avg',
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
qk_norm: bool = False,
init_values: Optional[float] = 1e-6,
class_token: bool = False,
fc_norm: bool = False,
rel_pos_type: str = 'mlp',
rel_pos_dim: Optional[int] = None,
shared_rel_pos: bool = False,
drop_rate: float = 0.,
proj_drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
weight_init: Literal['skip', 'jax', 'moco', ''] = 'skip',
fix_init: bool = False,
embed_layer: Type[nn.Module] = PatchEmbed,
norm_layer: Optional[LayerType] = None,
act_layer: Optional[LayerType] = None,
block_fn: Type[nn.Module] = RelPosBlock
):
"""
Args:
img_size: input image size
patch_size: patch size
in_chans: number of input channels
num_classes: number of classes for classification head
global_pool: type of global pooling for final sequence (default: 'avg')
embed_dim: embedding dimension
depth: depth of transformer
num_heads: number of attention heads
mlp_ratio: ratio of mlp hidden dim to embedding dim
qkv_bias: enable bias for qkv if True
qk_norm: Enable normalization of query and key in attention
init_values: layer-scale init values
class_token: use class token (default: False)
fc_norm: use pre classifier norm instead of pre-pool
rel_pos_type: type of relative position
shared_rel_pos: share relative pos across all blocks
drop_rate: dropout rate
proj_drop_rate: projection dropout rate
attn_drop_rate: attention dropout rate
drop_path_rate: stochastic depth rate
weight_init: weight init scheme
fix_init: apply weight initialization fix (scaling w/ layer index)
embed_layer: patch embedding layer
norm_layer: normalization layer
act_layer: MLP activation layer
"""
super().__init__()
assert global_pool in ('', 'avg', 'token')
assert class_token or global_pool != 'token'
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
act_layer = act_layer or nn.GELU
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.head_hidden_size = self.embed_dim = embed_dim # for consistency with other models
self.num_prefix_tokens = 1 if class_token else 0
self.grad_checkpointing = False
self.patch_embed = embed_layer(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
)
feat_size = self.patch_embed.grid_size
r = self.patch_embed.feat_ratio() if hasattr(self.patch_embed, 'feat_ratio') else patch_size
rel_pos_args = dict(window_size=feat_size, prefix_tokens=self.num_prefix_tokens)
if rel_pos_type.startswith('mlp'):
if rel_pos_dim:
rel_pos_args['hidden_dim'] = rel_pos_dim
if 'swin' in rel_pos_type:
rel_pos_args['mode'] = 'swin'
rel_pos_cls = partial(RelPosMlp, **rel_pos_args)
else:
rel_pos_cls = partial(RelPosBias, **rel_pos_args)
self.shared_rel_pos = None
if shared_rel_pos:
self.shared_rel_pos = rel_pos_cls(num_heads=num_heads)
# NOTE shared rel pos currently mutually exclusive w/ per-block, but could support both...
rel_pos_cls = None
self.cls_token = nn.Parameter(torch.zeros(1, self.num_prefix_tokens, embed_dim)) if class_token else None
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
block_fn(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
rel_pos_cls=rel_pos_cls,
init_values=init_values,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
act_layer=act_layer,
)
for i in range(depth)])
self.feature_info = [
dict(module=f'blocks.{i}', num_chs=embed_dim, reduction=r) for i in range(depth)]
self.norm = norm_layer(embed_dim) if not fc_norm else nn.Identity()
# Classifier Head
self.fc_norm = norm_layer(embed_dim) if fc_norm else nn.Identity()
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
if weight_init != 'skip':
self.init_weights(weight_init)
if fix_init:
self.fix_init_weight()
def init_weights(self, mode=''):
assert mode in ('jax', 'moco', '')
if self.cls_token is not None:
nn.init.normal_(self.cls_token, std=1e-6)
named_apply(get_init_weights_vit(mode), self)
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)
@torch.jit.ignore
def no_weight_decay(self):
return {'cls_token'}
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^cls_token|patch_embed', # stem and embed
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self) -> nn.Module:
return self.head
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('', 'avg', 'token')
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_intermediates(
self,
x: torch.Tensor,
indices: Optional[Union[int, List[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 int, all if None, select matching indices if sequence
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
Returns:
"""
assert output_fmt in ('NCHW', 'NLC'), 'Output format 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)
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
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, shared_rel_pos=shared_rel_pos)
if i in take_indices:
# normalize intermediates with final norm layer if enabled
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]] = 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)
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