""" ConvNeXt
Paper: `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
Original code and weights from https://github.com/facebookresearch/ConvNeXt, original copyright below
Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman
"""
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the MIT license
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import named_apply, build_model_with_cfg, checkpoint_seq
from .layers import trunc_normal_, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp, LayerNorm2d, create_conv2d
from .registry import register_model
__all__ = ['ConvNeXt'] # model_registry will add each entrypoint fn to this
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.0', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = dict(
convnext_tiny=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth"),
convnext_small=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth"),
convnext_base=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth"),
convnext_large=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth"),
convnext_nano_hnf=_cfg(url=''),
convnext_nano_ols=_cfg(url=''),
convnext_tiny_hnf=_cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_tiny_hnf_a2h-ab7e9df2.pth',
crop_pct=0.95),
convnext_tiny_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_224.pth'),
convnext_small_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_224.pth'),
convnext_base_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth'),
convnext_large_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth'),
convnext_xlarge_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth'),
convnext_tiny_384_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_384.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
convnext_small_384_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_384.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
convnext_base_384_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
convnext_large_384_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
convnext_xlarge_384_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
convnext_tiny_in22k=_cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth", num_classes=21841),
convnext_small_in22k=_cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth", num_classes=21841),
convnext_base_in22k=_cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth", num_classes=21841),
convnext_large_in22k=_cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth", num_classes=21841),
convnext_xlarge_in22k=_cfg(
url="https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth", num_classes=21841),
)
class ConvNeXtBlock(nn.Module):
""" ConvNeXt Block
There are two equivalent implementations:
(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
Unlike the official impl, this one allows choice of 1 or 2, 1x1 conv can be faster with appropriate
choice of LayerNorm impl, however as model size increases the tradeoffs appear to change and nn.Linear
is a better choice. This was observed with PyTorch 1.10 on 3090 GPU, it could change over time & w/ different HW.
Args:
dim (int): Number of input channels.
drop_path (float): Stochastic depth rate. Default: 0.0
ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
"""
def __init__(
self,
dim,
dim_out=None,
stride=1,
dilation=1,
mlp_ratio=4,
conv_mlp=False,
conv_bias=True,
ls_init_value=1e-6,
norm_layer=None,
act_layer=nn.GELU,
drop_path=0.,
):
super().__init__()
dim_out = dim_out or dim
if not norm_layer:
norm_layer = partial(LayerNorm2d, eps=1e-6) if conv_mlp else partial(nn.LayerNorm, eps=1e-6)
mlp_layer = ConvMlp if conv_mlp else Mlp
self.use_conv_mlp = conv_mlp
self.conv_dw = create_conv2d(
dim, dim_out, kernel_size=7, stride=stride, dilation=dilation, depthwise=True, bias=conv_bias)
self.norm = norm_layer(dim_out)
self.mlp = mlp_layer(dim_out, int(mlp_ratio * dim_out), act_layer=act_layer)
self.gamma = nn.Parameter(ls_init_value * torch.ones(dim_out)) if ls_init_value > 0 else None
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = x
x = self.conv_dw(x)
if self.use_conv_mlp:
x = self.norm(x)
x = self.mlp(x)
else:
x = x.permute(0, 2, 3, 1)
x = self.norm(x)
x = self.mlp(x)
x = x.permute(0, 3, 1, 2)
if self.gamma is not None:
x = x.mul(self.gamma.reshape(1, -1, 1, 1))
x = self.drop_path(x) + shortcut
return x
class ConvNeXtStage(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride=2,
depth=2,
dilation=(1, 1),
drop_path_rates=None,
ls_init_value=1.0,
conv_mlp=False,
conv_bias=True,
norm_layer=None,
norm_layer_cl=None
):
super().__init__()
self.grad_checkpointing = False
if in_chs != out_chs or stride > 1 or dilation[0] != dilation[1]:
ds_ks = 2 if stride > 1 or dilation[0] != dilation[1] else 1
pad = 'same' if dilation[1] > 1 else 0 # same padding needed if dilation used
self.downsample = nn.Sequential(
norm_layer(in_chs),
create_conv2d(
in_chs, out_chs, kernel_size=ds_ks, stride=stride,
dilation=dilation[0], padding=pad, bias=conv_bias),
)
in_chs = out_chs
else:
self.downsample = nn.Identity()
drop_path_rates = drop_path_rates or [0.] * depth
stage_blocks = []
for i in range(depth):
stage_blocks.append(ConvNeXtBlock(
dim=in_chs,
dim_out=out_chs,
dilation=dilation[1],
drop_path=drop_path_rates[i],
ls_init_value=ls_init_value,
conv_mlp=conv_mlp,
conv_bias=conv_bias,
norm_layer=norm_layer if conv_mlp else norm_layer_cl
))
in_chs = out_chs
self.blocks = nn.Sequential(*stage_blocks)
def forward(self, x):
x = self.downsample(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
class ConvNeXt(nn.Module):
r""" ConvNeXt
A PyTorch impl of : `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for classification head. Default: 1000
depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
dims (tuple(int)): Feature dimension at each stage. Default: [96, 192, 384, 768]
drop_rate (float): Head dropout rate
drop_path_rate (float): Stochastic depth rate. Default: 0.
ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
"""
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
output_stride=32,
depths=(3, 3, 9, 3),
dims=(96, 192, 384, 768),
ls_init_value=1e-6,
stem_type='patch',
stem_kernel_size=4,
stem_stride=4,
head_init_scale=1.,
head_norm_first=False,
conv_mlp=False,
conv_bias=True,
norm_layer=None,
drop_rate=0.,
drop_path_rate=0.,
):
super().__init__()
assert output_stride in (8, 16, 32)
if norm_layer is None:
norm_layer = partial(LayerNorm2d, eps=1e-6)
norm_layer_cl = norm_layer if conv_mlp else partial(nn.LayerNorm, eps=1e-6)
else:
assert conv_mlp,\
'If a norm_layer is specified, conv MLP must be used so all norm expect rank-4, channels-first input'
norm_layer_cl = norm_layer
self.num_classes = num_classes
self.drop_rate = drop_rate
self.feature_info = []
assert stem_type in ('patch', 'overlap')
if stem_type == 'patch':
assert stem_kernel_size == stem_stride
# NOTE: this stem is a minimal form of ViT PatchEmbed, as used in SwinTransformer w/ patch_size = 4
self.stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=stem_kernel_size, stride=stem_stride, bias=conv_bias),
norm_layer(dims[0])
)
else:
self.stem = nn.Sequential(
nn.Conv2d(
in_chans, dims[0], kernel_size=stem_kernel_size, stride=stem_stride,
padding=stem_kernel_size // 2, bias=conv_bias),
norm_layer(dims[0]),
)
self.stages = nn.Sequential()
dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
stages = []
prev_chs = dims[0]
curr_stride = stem_stride
dilation = 1
# 4 feature resolution stages, each consisting of multiple residual blocks
for i in range(4):
stride = 2 if curr_stride == 2 or i > 0 else 1
if curr_stride >= output_stride and stride > 1:
dilation *= stride
stride = 1
curr_stride *= stride
first_dilation = 1 if dilation in (1, 2) else 2
out_chs = dims[i]
stages.append(ConvNeXtStage(
prev_chs,
out_chs,
stride=stride,
dilation=(first_dilation, dilation),
depth=depths[i],
drop_path_rates=dp_rates[i],
ls_init_value=ls_init_value,
conv_mlp=conv_mlp,
conv_bias=conv_bias,
norm_layer=norm_layer,
norm_layer_cl=norm_layer_cl
))
prev_chs = out_chs
# NOTE feature_info use currently assumes stage 0 == stride 1, rest are stride 2
self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{i}')]
self.stages = nn.Sequential(*stages)
self.num_features = prev_chs
# 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.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)),
('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())]))
named_apply(partial(_init_weights, head_init_scale=head_init_scale), self)
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+)\.downsample', (0,)), # blocks
(r'^stages\.(\d+)\.blocks\.(\d+)', None),
(r'^norm_pre', (99999,))
]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes=0, global_pool=None):
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()
def forward_features(self, x):
x = self.stem(x)
x = self.stages(x)
x = self.norm_pre(x)
return x
def forward_head(self, x, pre_logits: bool = False):
# NOTE nn.Sequential in head broken down since can't call head[:-1](x) in torchscript :(
x = self.head.global_pool(x)
x = self.head.norm(x)
x = self.head.flatten(x)
x = self.head.drop(x)
return x if pre_logits else self.head.fc(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _init_weights(module, name=None, head_init_scale=1.0):
if isinstance(module, nn.Conv2d):
trunc_normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Linear):
trunc_normal_(module.weight, std=.02)
nn.init.zeros_(module.bias)
if name and 'head.' in name:
module.weight.data.mul_(head_init_scale)
module.bias.data.mul_(head_init_scale)
def checkpoint_filter_fn(state_dict, model):
""" Remap FB checkpoints -> timm """
if 'head.norm.weight' in state_dict or 'norm_pre.weight' in state_dict:
return state_dict # non-FB checkpoint
if 'model' in state_dict:
state_dict = state_dict['model']
out_dict = {}
import re
for k, v in state_dict.items():
k = k.replace('downsample_layers.0.', 'stem.')
k = re.sub(r'stages.([0-9]+).([0-9]+)', r'stages.\1.blocks.\2', k)
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')
k = k.replace('head.', 'head.fc.')
if k.startswith('norm.'):
k = k.replace('norm', 'head.norm')
if v.ndim == 2 and 'head' not in k:
model_shape = model.state_dict()[k].shape
v = v.reshape(model_shape)
out_dict[k] = v
return out_dict
def _create_convnext(variant, pretrained=False, **kwargs):
model = build_model_with_cfg(
ConvNeXt, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
**kwargs)
return model
@register_model
def convnext_nano_hnf(pretrained=False, **kwargs):
model_args = dict(
depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), head_norm_first=True, conv_mlp=True, **kwargs)
model = _create_convnext('convnext_nano_hnf', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_nano_ols(pretrained=False, **kwargs):
model_args = dict(
depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), head_norm_first=True, conv_mlp=True,
conv_bias=False, stem_type='overlap', stem_kernel_size=9, **kwargs)
model = _create_convnext('convnext_nano_ols', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_tiny_hnf(pretrained=False, **kwargs):
model_args = dict(
depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, conv_mlp=True, **kwargs)
model = _create_convnext('convnext_tiny_hnf', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_tiny_hnfd(pretrained=False, **kwargs):
model_args = dict(
depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, conv_mlp=True, **kwargs)
model = _create_convnext('convnext_tiny_hnf', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_tiny(pretrained=False, **kwargs):
model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), **kwargs)
model = _create_convnext('convnext_tiny', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_small(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
model = _create_convnext('convnext_small', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_base(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
model = _create_convnext('convnext_base', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_large(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
model = _create_convnext('convnext_large', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_tiny_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), **kwargs)
model = _create_convnext('convnext_tiny_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_small_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
model = _create_convnext('convnext_small_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_base_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
model = _create_convnext('convnext_base_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_large_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
model = _create_convnext('convnext_large_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_xlarge_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
model = _create_convnext('convnext_xlarge_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_tiny_384_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), **kwargs)
model = _create_convnext('convnext_tiny_384_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_small_384_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
model = _create_convnext('convnext_small_384_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_base_384_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
model = _create_convnext('convnext_base_384_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_large_384_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
model = _create_convnext('convnext_large_384_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_xlarge_384_in22ft1k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
model = _create_convnext('convnext_xlarge_384_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_tiny_in22k(pretrained=False, **kwargs):
model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), **kwargs)
model = _create_convnext('convnext_tiny_in22k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_small_in22k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
model = _create_convnext('convnext_small_in22k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_base_in22k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
model = _create_convnext('convnext_base_in22k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_large_in22k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
model = _create_convnext('convnext_large_in22k', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_xlarge_in22k(pretrained=False, **kwargs):
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
model = _create_convnext('convnext_xlarge_in22k', pretrained=pretrained, **model_args)
return model