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Add ConvNeXt-V2 support (model additions and weights)

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pull/1614/head
Ross Wightman 2 years ago
parent 960f5f92e6
commit 1bfcb9ce43
4 changed files with 380 additions and 22 deletions
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2
timm/layers/__init__.py
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@ -26,7 +26,7 @@ from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible,
from .inplace_abn import InplaceAbn
from .linear import Linear
from .mixed_conv2d import MixedConv2d
from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp
from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp, GlobalResponseNormMlp
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d
from .norm_act import BatchNormAct2d, GroupNormAct, convert_sync_batchnorm

39
timm/layers/grn.py
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@ -0,0 +1,39 @@
""" Global Response Normalization Module
Based on the GRN layer presented in
`ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
This implementation
* works for both NCHW and NHWC tensor layouts
* uses affine param names matching existing torch norm layers
* slightly improves eager mode performance via fused addcmul
Hacked together by / Copyright 2023 Ross Wightman
"""
import torch
from torch import nn as nn
class GlobalResponseNorm(nn.Module):
""" Global Response Normalization layer
"""
def __init__(self, dim, eps=1e-6, channels_last=True):
super().__init__()
self.eps = eps
if channels_last:
self.spatial_dim = (1, 2)
self.channel_dim = -1
self.wb_shape = (1, 1, 1, -1)
else:
self.spatial_dim = (2, 3)
self.channel_dim = 1
self.wb_shape = (1, -1, 1, 1)
self.weight = nn.Parameter(torch.zeros(dim))
self.bias = nn.Parameter(torch.zeros(dim))
def forward(self, x):
x_g = x.norm(p=2, dim=self.spatial_dim, keepdim=True)
x_n = x_g / (x_g.mean(dim=self.channel_dim, keepdim=True) + self.eps)
return x + torch.addcmul(self.bias.view(self.wb_shape), self.weight.view(self.wb_shape), x * x_n)

97
timm/layers/mlp.py
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@ -2,25 +2,38 @@
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partial
from torch import nn as nn
from .grn import GlobalResponseNorm
from .helpers import to_2tuple
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
bias=True,
drop=0.,
use_conv=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0])
self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
@ -36,18 +49,29 @@ class GluMlp(nn.Module):
""" MLP w/ GLU style gating
See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202
"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.Sigmoid, bias=True, drop=0.):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.Sigmoid,
bias=True,
drop=0.,
use_conv=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
assert hidden_features % 2 == 0
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.chunk_dim = 1 if use_conv else -1
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0])
self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.fc2 = nn.Linear(hidden_features // 2, out_features, bias=bias[1])
self.fc2 = linear_layer(hidden_features // 2, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def init_weights(self):
@ -58,7 +82,7 @@ class GluMlp(nn.Module):
def forward(self, x):
x = self.fc1(x)
x, gates = x.chunk(2, dim=-1)
x, gates = x.chunk(2, dim=self.chunk_dim)
x = x * self.act(gates)
x = self.drop1(x)
x = self.fc2(x)
@ -70,8 +94,15 @@ class GatedMlp(nn.Module):
""" MLP as used in gMLP
"""
def __init__(
self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU,
gate_layer=None, bias=True, drop=0.):
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
gate_layer=None,
bias=True,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
@ -104,8 +135,15 @@ class ConvMlp(nn.Module):
""" MLP using 1x1 convs that keeps spatial dims
"""
def __init__(
self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU,
norm_layer=None, bias=True, drop=0.):
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.ReLU,
norm_layer=None,
bias=True,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
@ -124,3 +162,40 @@ class ConvMlp(nn.Module):
x = self.drop(x)
x = self.fc2(x)
return x
class GlobalResponseNormMlp(nn.Module):
""" MLP w/ Global Response Norm (see grn.py), nn.Linear or 1x1 Conv2d
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
bias=True,
drop=0.,
use_conv=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.grn = GlobalResponseNorm(hidden_features, channels_last=not use_conv)
self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.grn(x)
x = self.fc2(x)
x = self.drop2(x)
return x

264
timm/models/convnext.py
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@ -1,16 +1,29 @@
""" ConvNeXt
Paper: `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
Papers:
* `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
* `ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
Original code and weights from https://github.com/facebookresearch/ConvNeXt, original copyright below
Original code and weights from:
* https://github.com/facebookresearch/ConvNeXt, original copyright below
* https://github.com/facebookresearch/ConvNeXt-V2, original copyright below
Model defs atto, femto, pico, nano and _ols / _hnf variants are timm specific.
Model defs atto, femto, pico, nano and _ols / _hnf variants are timm originals.
Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman
"""
# ConvNeXt
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the MIT license
# ConvNeXt-V2
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree (Attribution-NonCommercial 4.0 International (CC BY-NC 4.0))
# No code was used directly from ConvNeXt-V2, however the weights are CC BY-NC 4.0 so beware if using commercially.
from collections import OrderedDict
from functools import partial
@ -18,8 +31,8 @@ import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import trunc_normal_, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp, LayerNorm2d, LayerNorm, \
create_conv2d, get_act_layer, make_divisible, to_ntuple
from timm.layers import trunc_normal_, SelectAdaptivePool2d, DropPath, Mlp, GlobalResponseNormMlp, \
LayerNorm2d, LayerNorm, create_conv2d, get_act_layer, make_divisible, to_ntuple
from ._builder import build_model_with_cfg
from ._manipulate import named_apply, checkpoint_seq
from ._pretrained import generate_default_cfgs
@ -54,6 +67,7 @@ class ConvNeXtBlock(nn.Module):
mlp_ratio=4,
conv_mlp=False,
conv_bias=True,
use_grn=False,
ls_init_value=1e-6,
act_layer='gelu',
norm_layer=None,
@ -64,14 +78,13 @@ class ConvNeXtBlock(nn.Module):
act_layer = get_act_layer(act_layer)
if not norm_layer:
norm_layer = LayerNorm2d if conv_mlp else LayerNorm
mlp_layer = ConvMlp if conv_mlp else Mlp
mlp_layer = partial(GlobalResponseNormMlp if use_grn else Mlp, use_conv=conv_mlp)
self.use_conv_mlp = conv_mlp
self.conv_dw = create_conv2d(
in_chs, out_chs, kernel_size=kernel_size, stride=stride, dilation=dilation, depthwise=True, bias=conv_bias)
self.norm = norm_layer(out_chs)
self.mlp = mlp_layer(out_chs, int(mlp_ratio * out_chs), act_layer=act_layer)
self.gamma = nn.Parameter(ls_init_value * torch.ones(out_chs)) if ls_init_value > 0 else None
self.gamma = nn.Parameter(ls_init_value * torch.ones(out_chs)) if ls_init_value is not None else None
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
@ -106,6 +119,7 @@ class ConvNeXtStage(nn.Module):
ls_init_value=1.0,
conv_mlp=False,
conv_bias=True,
use_grn=False,
act_layer='gelu',
norm_layer=None,
norm_layer_cl=None
@ -138,6 +152,7 @@ class ConvNeXtStage(nn.Module):
ls_init_value=ls_init_value,
conv_mlp=conv_mlp,
conv_bias=conv_bias,
use_grn=use_grn,
act_layer=act_layer,
norm_layer=norm_layer if conv_mlp else norm_layer_cl
))
@ -184,6 +199,7 @@ class ConvNeXt(nn.Module):
head_norm_first=False,
conv_mlp=False,
conv_bias=True,
use_grn=False,
act_layer='gelu',
norm_layer=None,
drop_rate=0.,
@ -247,6 +263,7 @@ class ConvNeXt(nn.Module):
ls_init_value=ls_init_value,
conv_mlp=conv_mlp,
conv_bias=conv_bias,
use_grn=use_grn,
act_layer=act_layer,
norm_layer=norm_layer,
norm_layer_cl=norm_layer_cl
@ -259,10 +276,11 @@ class ConvNeXt(nn.Module):
# if head_norm_first == true, norm -> global pool -> fc ordering, like most other nets
# otherwise pool -> norm -> fc, the default ConvNeXt ordering (pretrained FB weights)
self.head_norm_first = head_norm_first
self.norm_pre = norm_layer(self.num_features) if head_norm_first else nn.Identity()
self.head = nn.Sequential(OrderedDict([
('global_pool', SelectAdaptivePool2d(pool_type=global_pool)),
('norm', nn.Identity() if head_norm_first else norm_layer(self.num_features)),
('norm', nn.Identity() if head_norm_first or num_classes == 0 else norm_layer(self.num_features)),
('flatten', nn.Flatten(1) if global_pool else nn.Identity()),
('drop', nn.Dropout(self.drop_rate)),
('fc', nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity())]))
@ -293,7 +311,14 @@ class ConvNeXt(nn.Module):
if global_pool is not None:
self.head.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.head.flatten = nn.Flatten(1) if global_pool else nn.Identity()
self.head.fc = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
if num_classes == 0:
self.head.norm = nn.Identity()
self.head.fc = nn.Identity()
else:
if not self.head_norm_first:
norm_layer = type(self.stem[-1]) # obtain type from stem norm
self.head.norm = norm_layer(self.num_features)
self.head.fc = nn.Linear(self.num_features, num_classes)
def forward_features(self, x):
x = self.stem(x)
@ -342,6 +367,10 @@ def checkpoint_filter_fn(state_dict, model):
k = re.sub(r'downsample_layers.([0-9]+).([0-9]+)', r'stages.\1.downsample.\2', k)
k = k.replace('dwconv', 'conv_dw')
k = k.replace('pwconv', 'mlp.fc')
if 'grn' in k:
k = k.replace('grn.beta', 'mlp.grn.bias')
k = k.replace('grn.gamma', 'mlp.grn.weight')
v = v.reshape(v.shape[-1])
k = k.replace('head.', 'head.fc.')
if k.startswith('norm.'):
k = k.replace('norm', 'head.norm')
@ -499,6 +528,149 @@ default_cfgs = generate_default_cfgs({
url="https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth",
hf_hub_id='timm/',
num_classes=21841),
'convnextv2_nano.fcmae_ft_in22k_in1k': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt',
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_nano.fcmae_ft_in22k_in1k_384': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt',
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
crop_pct=0.95, input_size=(3, 384, 384), test_input_size=(3, 416, 416), test_crop_pct=1.0, crop_mode='squash'),
'convnextv2_tiny.fcmae_ft_in22k_in1k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt",
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_tiny.fcmae_ft_in22k_in1k_384': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt",
# hf_hub_id='timm/',
license='cc-by-nc-4.0',
crop_pct=0.95, input_size=(3, 384, 384), test_input_size=(3, 416, 416), test_crop_pct=1.0, crop_mode='squash'),
'convnextv2_base.fcmae_ft_in22k_in1k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt",
#hf_hub_id='timm/'
license='cc-by-nc-4.0',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_base.fcmae_ft_in22k_in1k_384': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt",
#hf_hub_id='timm/'
license='cc-by-nc-4.0',
crop_pct=0.95, input_size=(3, 384, 384), test_input_size=(3, 416, 416), test_crop_pct=1.0, crop_mode='squash'),
'convnextv2_large.fcmae_ft_in22k_in1k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt",
#hf_hub_id='timm/'
license='cc-by-nc-4.0',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_large.fcmae_ft_in22k_in1k_384': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt",
#hf_hub_id='timm/'
license='cc-by-nc-4.0',
crop_pct=0.95, input_size=(3, 384, 384), test_input_size=(3, 416, 416), test_crop_pct=1.0, crop_mode='squash'),
'convnextv2_huge.fcmae_ft_in22k_in1k_384': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt",
#hf_hub_id='timm/'
license='cc-by-nc-4.0',
crop_pct=0.95, input_size=(3, 384, 384), test_input_size=(3, 416, 416), test_crop_pct=1.0, crop_mode='squash'),
'convnextv2_huge.fcmae_ft_in22k_in1k_512': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt",
#hf_hub_id='timm/'
license='cc-by-nc-4.0',
crop_pct=0.95, input_size=(3, 512, 512), test_input_size=(3, 576, 576), test_crop_pct=1.0, crop_mode='squash'),
'convnextv2_atto.fcmae_ft_in1k': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'convnextv2_femto.fcmae_ft_in1k': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt',
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'convnextv2_pico.fcmae_ft_in1k': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt',
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
test_input_size=(3, 288, 288), test_crop_pct=0.95),
'convnextv2_nano.fcmae_ft_in1k': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt',
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_tiny.fcmae_ft_in1k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt",
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_base.fcmae_ft_in1k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt",
#hf_hub_id='timm/'
license='cc-by-nc-4.0',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_large.fcmae_ft_in1k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt",
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_huge.fcmae_ft_in1k': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt",
# hf_hub_id='timm/',
license='cc-by-nc-4.0',
test_input_size=(3, 288, 288), test_crop_pct=1.0),
'convnextv2_atto.fcmae': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_atto_1k_224_fcmae.pt',
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
num_classes=0,
),
'convnextv2_femto.fcmae': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_femto_1k_224_fcmae.pt',
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
num_classes=0,
),
'convnextv2_pico.fcmae': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_pico_1k_224_fcmae.pt',
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
num_classes=0,
),
'convnextv2_nano.fcmae': _cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_nano_1k_224_fcmae.pt',
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
num_classes=0,
),
'convnextv2_tiny.fcmae': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_tiny_1k_224_fcmae.pt",
#hf_hub_id='timm/',
license='cc-by-nc-4.0',
num_classes=0,
),
'convnextv2_base.fcmae': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_base_1k_224_fcmae.pt",
#hf_hub_id='timm/'
license='cc-by-nc-4.0',
num_classes=0,
),
'convnextv2_large.fcmae': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_large_1k_224_fcmae.pt",
#hf_hub_id='timm/'
license='cc-by-nc-4.0',
num_classes=0,
),
'convnextv2_huge.fcmae': _cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_huge_1k_224_fcmae.pt",
#hf_hub_id='timm/'
license='cc-by-nc-4.0',
num_classes=0,
),
'convnextv2_small.untrained': _cfg(),
})
@ -623,3 +795,75 @@ def convnext_xxlarge(pretrained=False, **kwargs):
model_args = dict(depths=[3, 4, 30, 3], dims=[384, 768, 1536, 3072], **kwargs)
model = _create_convnext('convnext_xxlarge', pretrained=pretrained, **model_args)
return model
@register_model
def convnextv2_atto(pretrained=False, **kwargs):
# timm femto variant (NOTE: still tweaking depths, will vary between 3-4M param, current is 3.7M
model_args = dict(
depths=(2, 2, 6, 2), dims=(40, 80, 160, 320), use_grn=True, ls_init_value=None, conv_mlp=True, **kwargs)
model = _create_convnext('convnextv2_atto', pretrained=pretrained, **model_args)
return model
@register_model
def convnextv2_femto(pretrained=False, **kwargs):
# timm femto variant
model_args = dict(
depths=(2, 2, 6, 2), dims=(48, 96, 192, 384), use_grn=True, ls_init_value=None, conv_mlp=True, **kwargs)
model = _create_convnext('convnextv2_femto', pretrained=pretrained, **model_args)
return model
@register_model
def convnextv2_pico(pretrained=False, **kwargs):
# timm pico variant
model_args = dict(
depths=(2, 2, 6, 2), dims=(64, 128, 256, 512), use_grn=True, ls_init_value=None, conv_mlp=True, **kwargs)
model = _create_convnext('convnextv2_pico', pretrained=pretrained, **model_args)
return model
@register_model
def convnextv2_nano(pretrained=False, **kwargs):
# timm nano variant with standard stem and head
model_args = dict(
depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), use_grn=True, ls_init_value=None, conv_mlp=True, **kwargs)
model = _create_convnext('convnextv2_nano', pretrained=pretrained, **model_args)
return model
@register_model
def convnextv2_tiny(pretrained=False, **kwargs):
model_args = dict(
depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), use_grn=True, ls_init_value=None, **kwargs)
model = _create_convnext('convnextv2_tiny', pretrained=pretrained, **model_args)
return model
@register_model
def convnextv2_small(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], use_grn=True, ls_init_value=None, **kwargs)
model = _create_convnext('convnextv2_small', pretrained=pretrained, **model_args)
return model
@register_model
def convnextv2_base(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], use_grn=True, ls_init_value=None, **kwargs)
model = _create_convnext('convnextv2_base', pretrained=pretrained, **model_args)
return model
@register_model
def convnextv2_large(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], use_grn=True, ls_init_value=None, **kwargs)
model = _create_convnext('convnextv2_large', pretrained=pretrained, **model_args)
return model
@register_model
def convnextv2_huge(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[352, 704, 1408, 2816], use_grn=True, ls_init_value=None, **kwargs)
model = _create_convnext('convnextv2_huge', pretrained=pretrained, **model_args)
return model
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