""" EfficientViT (by MIT Song Han's Lab)
Paper: `Efficientvit: Enhanced linear attention for high-resolution low-computation visual recognition`
- https://arxiv.org/abs/2205.14756
Adapted from official impl at https://github.com/mit-han-lab/efficientvit
"""
__all__ = ['EfficientVit']
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.batchnorm import _BatchNorm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import SelectAdaptivePool2d, create_conv2d
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_module
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
def val2list(x: list or tuple or any, repeat_time=1):
if isinstance(x, (list, tuple)):
return list(x)
return [x for _ in range(repeat_time)]
def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1):
# repeat elements if necessary
x = val2list(x)
if len(x) > 0:
x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))]
return tuple(x)
def get_same_padding(kernel_size: int or tuple[int, ...]) -> int or tuple[int, ...]:
if isinstance(kernel_size, tuple):
return tuple([get_same_padding(ks) for ks in kernel_size])
else:
assert kernel_size % 2 > 0, "kernel size should be odd number"
return kernel_size // 2
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size=3,
stride=1,
dilation=1,
groups=1,
bias=False,
dropout=0.,
norm_layer=nn.BatchNorm2d,
act_layer=nn.ReLU,
):
super(ConvNormAct, self).__init__()
self.dropout = nn.Dropout(dropout, inplace=False)
self.conv = create_conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
groups=groups,
bias=bias,
)
self.norm = norm_layer(num_features=out_channels) if norm_layer else nn.Identity()
if act_layer is not None:
self.act = act_layer(inplace=True) if act_layer is not nn.GELU else act_layer(approximate="tanh")
else:
self.act = nn.Identity()
def forward(self, x):
x = self.dropout(x)
x = self.conv(x)
x = self.norm(x)
x = self.act(x)
return x
class DSConv(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size=3,
stride=1,
use_bias=False,
norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d),
act_layer=(nn.ReLU6, None),
):
super(DSConv, self).__init__()
use_bias = val2tuple(use_bias, 2)
norm_layer = val2tuple(norm_layer, 2)
act_layer = val2tuple(act_layer, 2)
self.depth_conv = ConvNormAct(
in_channels,
in_channels,
kernel_size,
stride,
groups=in_channels,
norm_layer=norm_layer[0],
act_layer=act_layer[0],
bias=use_bias[0],
)
self.point_conv = ConvNormAct(
in_channels,
out_channels,
1,
norm_layer=norm_layer[1],
act_layer=act_layer[1],
bias=use_bias[1],
)
def forward(self, x):
x = self.depth_conv(x)
x = self.point_conv(x)
return x
class ConvBlock(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size=3,
stride=1,
mid_channels=None,
expand_ratio=1,
use_bias=False,
norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d),
act_layer=(nn.ReLU6, None),
):
super(ConvBlock, self).__init__()
use_bias = val2tuple(use_bias, 2)
norm_layer = val2tuple(norm_layer, 2)
act_layer = val2tuple(act_layer, 2)
mid_channels = mid_channels or round(in_channels * expand_ratio)
self.conv1 = ConvNormAct(
in_channels,
mid_channels,
kernel_size,
stride,
norm_layer=norm_layer[0],
act_layer=act_layer[0],
bias=use_bias[0],
)
self.conv2 = ConvNormAct(
mid_channels,
out_channels,
kernel_size,
1,
norm_layer=norm_layer[1],
act_layer=act_layer[1],
bias=use_bias[1],
)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
return x
class MBConv(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size=3,
stride=1,
mid_channels=None,
expand_ratio=6,
use_bias=False,
norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d, nn.BatchNorm2d),
act_layer=(nn.ReLU6, nn.ReLU6, None),
):
super(MBConv, self).__init__()
use_bias = val2tuple(use_bias, 3)
norm_layer = val2tuple(norm_layer, 3)
act_layer = val2tuple(act_layer, 3)
mid_channels = mid_channels or round(in_channels * expand_ratio)
self.inverted_conv = ConvNormAct(
in_channels,
mid_channels,
1,
stride=1,
norm_layer=norm_layer[0],
act_layer=act_layer[0],
bias=use_bias[0],
)
self.depth_conv = ConvNormAct(
mid_channels,
mid_channels,
kernel_size,
stride=stride,
groups=mid_channels,
norm_layer=norm_layer[1],
act_layer=act_layer[1],
bias=use_bias[1],
)
self.point_conv = ConvNormAct(
mid_channels,
out_channels,
1,
norm_layer=norm_layer[2],
act_layer=act_layer[2],
bias=use_bias[2],
)
def forward(self, x):
x = self.inverted_conv(x)
x = self.depth_conv(x)
x = self.point_conv(x)
return x
class FusedMBConv(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size=3,
stride=1,
mid_channels=None,
expand_ratio=6,
groups=1,
use_bias=False,
norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d),
act_layer=(nn.ReLU6, None),
):
super(FusedMBConv, self).__init__()
use_bias = val2tuple(use_bias, 2)
norm_layer = val2tuple(norm_layer, 2)
act_layer = val2tuple(act_layer, 2)
mid_channels = mid_channels or round(in_channels * expand_ratio)
self.spatial_conv = ConvNormAct(
in_channels,
mid_channels,
kernel_size,
stride=stride,
groups=groups,
norm_layer=norm_layer[0],
act_layer=act_layer[0],
bias=use_bias[0],
)
self.point_conv = ConvNormAct(
mid_channels,
out_channels,
1,
norm_layer=norm_layer[1],
act_layer=act_layer[1],
bias=use_bias[1],
)
def forward(self, x):
x = self.spatial_conv(x)
x = self.point_conv(x)
return x
class LiteMLA(nn.Module):
"""Lightweight multi-scale linear attention"""
def __init__(
self,
in_channels: int,
out_channels: int,
heads: int or None = None,
heads_ratio: float = 1.0,
dim=8,
use_bias=False,
norm_layer=(None, nn.BatchNorm2d),
act_layer=(None, None),
kernel_func=nn.ReLU,
scales=(5,),
eps=1e-5,
):
super(LiteMLA, self).__init__()
self.eps = eps
heads = heads or int(in_channels // dim * heads_ratio)
total_dim = heads * dim
use_bias = val2tuple(use_bias, 2)
norm_layer = val2tuple(norm_layer, 2)
act_layer = val2tuple(act_layer, 2)
self.dim = dim
self.qkv = ConvNormAct(
in_channels,
3 * total_dim,
1,
bias=use_bias[0],
norm_layer=norm_layer[0],
act_layer=act_layer[0],
)
self.aggreg = nn.ModuleList([
nn.Sequential(
nn.Conv2d(
3 * total_dim,
3 * total_dim,
scale,
padding=get_same_padding(scale),
groups=3 * total_dim,
bias=use_bias[0],
),
nn.Conv2d(3 * total_dim, 3 * total_dim, 1, groups=3 * heads, bias=use_bias[0]),
)
for scale in scales
])
self.kernel_func = kernel_func(inplace=False)
self.proj = ConvNormAct(
total_dim * (1 + len(scales)),
out_channels,
1,
bias=use_bias[1],
norm_layer=norm_layer[1],
act_layer=act_layer[1],
)
def _attn(self, q, k, v):
dtype = v.dtype
q, k, v = q.float(), k.float(), v.float()
kv = k.transpose(-1, -2) @ v
out = q @ kv
out = out[..., :-1] / (out[..., -1:] + self.eps)
return out.to(dtype)
def forward(self, x):
B, _, H, W = x.shape
# generate multi-scale q, k, v
qkv = self.qkv(x)
multi_scale_qkv = [qkv]
for op in self.aggreg:
multi_scale_qkv.append(op(qkv))
multi_scale_qkv = torch.cat(multi_scale_qkv, dim=1)
multi_scale_qkv = multi_scale_qkv.reshape(B, -1, 3 * self.dim, H * W).transpose(-1, -2)
q, k, v = multi_scale_qkv.chunk(3, dim=-1)
# lightweight global attention
q = self.kernel_func(q)
k = self.kernel_func(k)
v = F.pad(v, (0, 1), mode="constant", value=1.)
if not torch.jit.is_scripting():
with torch.autocast(device_type=v.device.type, enabled=False):
out = self._attn(q, k, v)
else:
out = self._attn(q, k, v)
# final projection
out = out.transpose(-1, -2).reshape(B, -1, H, W)
out = self.proj(out)
return out
register_notrace_module(LiteMLA)
class EfficientVitBlock(nn.Module):
def __init__(
self,
in_channels,
heads_ratio=1.0,
head_dim=32,
expand_ratio=4,
norm_layer=nn.BatchNorm2d,
act_layer=nn.Hardswish,
):
super(EfficientVitBlock, self).__init__()
self.context_module = ResidualBlock(
LiteMLA(
in_channels=in_channels,
out_channels=in_channels,
heads_ratio=heads_ratio,
dim=head_dim,
norm_layer=(None, norm_layer),
),
nn.Identity(),
)
self.local_module = ResidualBlock(
MBConv(
in_channels=in_channels,
out_channels=in_channels,
expand_ratio=expand_ratio,
use_bias=(True, True, False),
norm_layer=(None, None, norm_layer),
act_layer=(act_layer, act_layer, None),
),
nn.Identity(),
)
def forward(self, x):
x = self.context_module(x)
x = self.local_module(x)
return x
class ResidualBlock(nn.Module):
def __init__(
self,
main: Optional[nn.Module],
shortcut: Optional[nn.Module] = None,
pre_norm: Optional[nn.Module] = None,
):
super(ResidualBlock, self).__init__()
self.pre_norm = pre_norm if pre_norm is not None else nn.Identity()
self.main = main
self.shortcut = shortcut
def forward(self, x):
res = self.main(self.pre_norm(x))
if self.shortcut is not None:
res = res + self.shortcut(x)
return res
def build_local_block(
in_channels: int,
out_channels: int,
stride: int,
expand_ratio: float,
norm_layer: str,
act_layer: str,
fewer_norm: bool = False,
block_type: str = "default",
):
assert block_type in ["default", "large", "fused"]
if expand_ratio == 1:
if block_type == "default":
block = DSConv(
in_channels=in_channels,
out_channels=out_channels,
stride=stride,
use_bias=(True, False) if fewer_norm else False,
norm_layer=(None, norm_layer) if fewer_norm else norm_layer,
act_layer=(act_layer, None),
)
else:
block = ConvBlock(
in_channels=in_channels,
out_channels=out_channels,
stride=stride,
use_bias=(True, False) if fewer_norm else False,
norm_layer=(None, norm_layer) if fewer_norm else norm_layer,
act_layer=(act_layer, None),
)
else:
if block_type == "default":
block = MBConv(
in_channels=in_channels,
out_channels=out_channels,
stride=stride,
expand_ratio=expand_ratio,
use_bias=(True, True, False) if fewer_norm else False,
norm_layer=(None, None, norm_layer) if fewer_norm else norm_layer,
act_layer=(act_layer, act_layer, None),
)
else:
block = FusedMBConv(
in_channels=in_channels,
out_channels=out_channels,
stride=stride,
expand_ratio=expand_ratio,
use_bias=(True, False) if fewer_norm else False,
norm_layer=(None, norm_layer) if fewer_norm else norm_layer,
act_layer=(act_layer, None),
)
return block
class Stem(nn.Sequential):
def __init__(self, in_chs, out_chs, depth, norm_layer, act_layer, block_type='default'):
super().__init__()
self.stride = 2
self.add_module(
'in_conv',
ConvNormAct(
in_chs, out_chs,
kernel_size=3, stride=2, norm_layer=norm_layer, act_layer=act_layer,
)
)
stem_block = 0
for _ in range(depth):
self.add_module(f'res{stem_block}', ResidualBlock(
build_local_block(
in_channels=out_chs,
out_channels=out_chs,
stride=1,
expand_ratio=1,
norm_layer=norm_layer,
act_layer=act_layer,
block_type=block_type,
),
nn.Identity(),
))
stem_block += 1
class EfficientVitStage(nn.Module):
def __init__(
self,
in_chs,
out_chs,
depth,
norm_layer,
act_layer,
expand_ratio,
head_dim,
vit_stage=False,
):
super(EfficientVitStage, self).__init__()
blocks = [ResidualBlock(
build_local_block(
in_channels=in_chs,
out_channels=out_chs,
stride=2,
expand_ratio=expand_ratio,
norm_layer=norm_layer,
act_layer=act_layer,
fewer_norm=vit_stage,
),
None,
)]
in_chs = out_chs
if vit_stage:
# for stage 3, 4
for _ in range(depth):
blocks.append(
EfficientVitBlock(
in_channels=in_chs,
head_dim=head_dim,
expand_ratio=expand_ratio,
norm_layer=norm_layer,
act_layer=act_layer,
)
)
else:
# for stage 1, 2
for i in range(1, depth):
blocks.append(ResidualBlock(
build_local_block(
in_channels=in_chs,
out_channels=out_chs,
stride=1,
expand_ratio=expand_ratio,
norm_layer=norm_layer,
act_layer=act_layer
),
nn.Identity(),
))
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
return self.blocks(x)
class EfficientVitLargeStage(nn.Module):
def __init__(
self,
in_chs,
out_chs,
depth,
norm_layer,
act_layer,
head_dim,
vit_stage=False,
fewer_norm=False,
):
super(EfficientVitLargeStage, self).__init__()
blocks = [ResidualBlock(
build_local_block(
in_channels=in_chs,
out_channels=out_chs,
stride=2,
expand_ratio=24 if vit_stage else 16,
norm_layer=norm_layer,
act_layer=act_layer,
fewer_norm=vit_stage or fewer_norm,
block_type='default' if fewer_norm else 'fused',
),
None,
)]
in_chs = out_chs
if vit_stage:
# for stage 4
for _ in range(depth):
blocks.append(
EfficientVitBlock(
in_channels=in_chs,
head_dim=head_dim,
expand_ratio=6,
norm_layer=norm_layer,
act_layer=act_layer,
)
)
else:
# for stage 1, 2, 3
for i in range(depth):
blocks.append(ResidualBlock(
build_local_block(
in_channels=in_chs,
out_channels=out_chs,
stride=1,
expand_ratio=4,
norm_layer=norm_layer,
act_layer=act_layer,
fewer_norm=fewer_norm,
block_type='default' if fewer_norm else 'fused',
),
nn.Identity(),
))
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
return self.blocks(x)
class ClassifierHead(nn.Module):
def __init__(
self,
in_channels,
widths,
n_classes=1000,
dropout=0.,
norm_layer=nn.BatchNorm2d,
act_layer=nn.Hardswish,
global_pool='avg',
):
super(ClassifierHead, self).__init__()
self.in_conv = ConvNormAct(in_channels, widths[0], 1, norm_layer=norm_layer, act_layer=act_layer)
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool, flatten=True, input_fmt='NCHW')
self.classifier = nn.Sequential(
nn.Linear(widths[0], widths[1], bias=False),
nn.LayerNorm(widths[1]),
act_layer(inplace=True) if act_layer is not nn.GELU else act_layer(approximate="tanh"),
nn.Dropout(dropout, inplace=False),
nn.Linear(widths[1], n_classes, bias=True),
)
def forward(self, x, pre_logits: bool = False):
x = self.in_conv(x)
x = self.global_pool(x)
if pre_logits:
return x
x = self.classifier(x)
return x
class EfficientVit(nn.Module):
def __init__(
self,
in_chans=3,
widths=(),
depths=(),
head_dim=32,
expand_ratio=4,
norm_layer=nn.BatchNorm2d,
act_layer=nn.Hardswish,
global_pool='avg',
head_widths=(),
drop_rate=0.0,
num_classes=1000,
):
super(EfficientVit, self).__init__()
self.grad_checkpointing = False
self.global_pool = global_pool
self.num_classes = num_classes
# input stem
self.stem = Stem(in_chans, widths[0], depths[0], norm_layer, act_layer)
stride = self.stem.stride
# stages
self.feature_info = []
self.stages = nn.Sequential()
in_channels = widths[0]
for i, (w, d) in enumerate(zip(widths[1:], depths[1:])):
self.stages.append(EfficientVitStage(
in_channels,
w,
depth=d,
norm_layer=norm_layer,
act_layer=act_layer,
expand_ratio=expand_ratio,
head_dim=head_dim,
vit_stage=i >= 2,
))
stride *= 2
in_channels = w
self.feature_info += [dict(num_chs=in_channels, reduction=stride, module=f'stages.{i}')]
self.num_features = in_channels
self.head_widths = head_widths
self.head_dropout = drop_rate
if num_classes > 0:
self.head = ClassifierHead(
self.num_features,
self.head_widths,
n_classes=num_classes,
dropout=self.head_dropout,
global_pool=self.global_pool,
)
else:
if self.global_pool == 'avg':
self.head = SelectAdaptivePool2d(pool_type=global_pool, flatten=True)
else:
self.head = nn.Identity()
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+).downsample', (0,)),
(r'^stages\.(\d+)\.\w+\.(\d+)', None),
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.classifier[-1]
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
if num_classes > 0:
self.head = ClassifierHead(
self.num_features,
self.head_widths,
n_classes=num_classes,
dropout=self.head_dropout,
global_pool=self.global_pool,
)
else:
if self.global_pool == 'avg':
self.head = SelectAdaptivePool2d(pool_type=self.global_pool, flatten=True)
else:
self.head = nn.Identity()
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x)
else:
x = self.stages(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits) if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
class EfficientVitLarge(nn.Module):
def __init__(
self,
in_chans=3,
widths=(),
depths=(),
head_dim=32,
norm_layer=nn.BatchNorm2d,
act_layer=nn.GELU,
global_pool='avg',
head_widths=(),
drop_rate=0.0,
num_classes=1000,
eps=1e-7,
):
super(EfficientVitLarge, self).__init__()
self.grad_checkpointing = False
self.global_pool = global_pool
self.num_classes = num_classes
# input stem
self.stem = Stem(in_chans, widths[0], depths[0], norm_layer, act_layer, block_type='large')
stride = self.stem.stride
# stages
self.feature_info = []
self.stages = nn.Sequential()
in_channels = widths[0]
for i, (w, d) in enumerate(zip(widths[1:], depths[1:])):
self.stages.append(EfficientVitLargeStage(
in_channels,
w,
depth=d,
norm_layer=norm_layer,
act_layer=act_layer,
head_dim=head_dim,
vit_stage=i >= 3,
fewer_norm=i >= 2,
))
stride *= 2
in_channels = w
self.feature_info += [dict(num_chs=in_channels, reduction=stride, module=f'stages.{i}')]
self.num_features = in_channels
self.head_widths = head_widths
self.head_dropout = drop_rate
if num_classes > 0:
self.head = ClassifierHead(
self.num_features,
self.head_widths,
n_classes=num_classes,
dropout=self.head_dropout,
global_pool=self.global_pool,
act_layer=act_layer,
)
else:
if self.global_pool == 'avg':
self.head = SelectAdaptivePool2d(pool_type=global_pool, flatten=True)
else:
self.head = nn.Identity()
self.set_norm_eps(eps)
@torch.jit.ignore
def set_norm_eps(self, eps):
for m in self.modules():
if isinstance(m, (nn.GroupNorm, nn.LayerNorm, _BatchNorm)):
if eps is not None:
m.eps = eps
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+).downsample', (0,)),
(r'^stages\.(\d+)\.\w+\.(\d+)', None),
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.classifier[-1]
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
if num_classes > 0:
self.head = ClassifierHead(
self.num_features,
self.head_widths,
n_classes=num_classes,
dropout=self.head_dropout,
global_pool=self.global_pool,
)
else:
if self.global_pool == 'avg':
self.head = SelectAdaptivePool2d(pool_type=self.global_pool, flatten=True)
else:
self.head = nn.Identity()
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x)
else:
x = self.stages(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits) if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'mean': IMAGENET_DEFAULT_MEAN,
'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.in_conv.conv',
'classifier': 'head.classifier.4',
'crop_pct': 0.95,
'input_size': (3, 224, 224),
'pool_size': (7, 7),
**kwargs,
}
default_cfgs = generate_default_cfgs({
'efficientvit_b0.r224_in1k': _cfg(
hf_hub_id='timm/',
),
'efficientvit_b1.r224_in1k': _cfg(
hf_hub_id='timm/',
),
'efficientvit_b1.r256_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0,
),
'efficientvit_b1.r288_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0,
),
'efficientvit_b2.r224_in1k': _cfg(
hf_hub_id='timm/',
),
'efficientvit_b2.r256_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0,
),
'efficientvit_b2.r288_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0,
),
'efficientvit_b3.r224_in1k': _cfg(
hf_hub_id='timm/',
),
'efficientvit_b3.r256_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0,
),
'efficientvit_b3.r288_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0,
),
'efficientvit_l1.r224_in1k': _cfg(
# hf_hub_id='timm/',
),
'efficientvit_l2.r224_in1k': _cfg(
# hf_hub_id='timm/',
),
'efficientvit_l2.r384_in1k': _cfg(
# hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
),
'efficientvit_l3.r224_in1k': _cfg(
# hf_hub_id='timm/',
),
'efficientvit_l3.r384_in1k': _cfg(
# hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
),
'efficientvit_l0_sam.sam': _cfg(
# hf_hub_id='timm/',
),
'efficientvit_l1_sam.sam': _cfg(
# hf_hub_id='timm/',
),
'efficientvit_l2_sam.sam': _cfg(
# hf_hub_id='timm/',
),
})
def _create_efficientvit(variant, pretrained=False, **kwargs):
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3))
model = build_model_with_cfg(
EfficientVit,
variant,
pretrained,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs
)
return model
def _create_efficientvit_large(variant, pretrained=False, **kwargs):
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3))
model = build_model_with_cfg(
EfficientVitLarge,
variant,
pretrained,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs
)
return model
@register_model
def efficientvit_b0(pretrained=False, **kwargs):
model_args = dict(
widths=(8, 16, 32, 64, 128), depths=(1, 2, 2, 2, 2), head_dim=16, head_widths=(1024, 1280))
return _create_efficientvit('efficientvit_b0', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_b1(pretrained=False, **kwargs):
model_args = dict(
widths=(16, 32, 64, 128, 256), depths=(1, 2, 3, 3, 4), head_dim=16, head_widths=(1536, 1600))
return _create_efficientvit('efficientvit_b1', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_b2(pretrained=False, **kwargs):
model_args = dict(
widths=(24, 48, 96, 192, 384), depths=(1, 3, 4, 4, 6), head_dim=32, head_widths=(2304, 2560))
return _create_efficientvit('efficientvit_b2', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_b3(pretrained=False, **kwargs):
model_args = dict(
widths=(32, 64, 128, 256, 512), depths=(1, 4, 6, 6, 9), head_dim=32, head_widths=(2304, 2560))
return _create_efficientvit('efficientvit_b3', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_l1(pretrained=False, **kwargs):
model_args = dict(
widths=(32, 64, 128, 256, 512), depths=(1, 1, 1, 6, 6), head_dim=32, head_widths=(3072, 3200))
return _create_efficientvit_large('efficientvit_l1', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_l2(pretrained=False, **kwargs):
model_args = dict(
widths=(32, 64, 128, 256, 512), depths=(1, 2, 2, 8, 8), head_dim=32, head_widths=(3072, 3200))
return _create_efficientvit_large('efficientvit_l2', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_l3(pretrained=False, **kwargs):
model_args = dict(
widths=(64, 128, 256, 512, 1024), depths=(1, 2, 2, 8, 8), head_dim=32, head_widths=(6144, 6400))
return _create_efficientvit_large('efficientvit_l3', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_l0_sam(pretrained=False, **kwargs):
model_args = dict(
widths=(32, 64, 128, 256, 512), depths=(1, 1, 1, 4, 4), head_dim=32, num_classes=0) # only backbone for segment-anything-model weights
return _create_efficientvit_large('efficientvit_l0_sam', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_l1_sam(pretrained=False, **kwargs):
model_args = dict(
widths=(32, 64, 128, 256, 512), depths=(1, 1, 1, 6, 6), head_dim=32, num_classes=0) # only backbone for segment-anything-model weights
return _create_efficientvit_large('efficientvit_l1_sam', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def efficientvit_l2_sam(pretrained=False, **kwargs):
model_args = dict(
widths=(32, 64, 128, 256, 512), depths=(1, 2, 2, 8, 8), head_dim=32, num_classes=0) # only backbone for segment-anything-model weights
return _create_efficientvit_large('efficientvit_l2_sam', pretrained=pretrained, **dict(model_args, **kwargs))