"""
InceptionNeXt implementation, paper: https://arxiv.org/abs/2303.16900
Some code is borrowed from timm: https://github.com/huggingface/pytorch-image-models
"""
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helpers import checkpoint_seq
from timm.models.layers import trunc_normal_, DropPath
from timm.models.registry import register_model
from timm.models.layers.helpers import to_2tuple
class InceptionDWConv2d(nn.Module):
""" Inception depthweise convolution
"""
def __init__(self, in_channels, square_kernel_size=3, band_kernel_size=11, branch_ratio=0.125):
super().__init__()
gc = int(in_channels * branch_ratio)
self.dwconv_hw = nn.Conv2d(gc, gc, square_kernel_size, padding=square_kernel_size // 2, groups=gc)
self.dwconv_w = nn.Conv2d(gc, gc, kernel_size=(1, band_kernel_size), padding=(0, band_kernel_size // 2),
groups=gc)
self.dwconv_h = nn.Conv2d(gc, gc, kernel_size=(band_kernel_size, 1), padding=(band_kernel_size // 2, 0),
groups=gc)
self.split_indexes = (in_channels - 3 * gc, gc, gc, gc)
def forward(self, x):
x_id, x_hw, x_w, x_h = torch.split(x, self.split_indexes, dim=1)
return torch.cat(
(x_id, self.dwconv_hw(x_hw), self.dwconv_w(x_w), self.dwconv_h(x_h)),
dim=1,
)
class ConvMlp(nn.Module):
""" MLP using 1x1 convs that keeps spatial dims
copied from timm: https://github.com/huggingface/pytorch-image-models/blob/v0.6.11/timm/models/layers/mlp.py
"""
def __init__(
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
bias = to_2tuple(bias)
self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0])
self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
self.act = act_layer()
self.drop = nn.Dropout(drop)
self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1])
def forward(self, x):
x = self.fc1(x)
x = self.norm(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
return x
class MlpHead(nn.Module):
""" MLP classification head
"""
def __init__(self, dim, num_classes=1000, mlp_ratio=3, act_layer=nn.GELU,
norm_layer=partial(nn.LayerNorm, eps=1e-6), drop=0., bias=True):
super().__init__()
hidden_features = int(mlp_ratio * dim)
self.fc1 = nn.Linear(dim, hidden_features, bias=bias)
self.act = act_layer()
self.norm = norm_layer(hidden_features)
self.fc2 = nn.Linear(hidden_features, num_classes, bias=bias)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = x.mean((2, 3))
x = self.fc1(x)
x = self.act(x)
x = self.norm(x)
x = self.drop(x)
x = self.fc2(x)
return x
class MetaNeXtBlock(nn.Module):
""" MetaNeXtBlock Block
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,
token_mixer=InceptionDWConv2d,
norm_layer=nn.BatchNorm2d,
mlp_layer=ConvMlp,
mlp_ratio=4,
act_layer=nn.GELU,
ls_init_value=1e-6,
drop_path=0.,
):
super().__init__()
self.token_mixer = token_mixer(dim)
self.norm = norm_layer(dim)
self.mlp = mlp_layer(dim, int(mlp_ratio * dim), act_layer=act_layer)
self.gamma = nn.Parameter(ls_init_value * torch.ones(dim)) if ls_init_value else None
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = x
x = self.token_mixer(x)
x = self.norm(x)
x = self.mlp(x)
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 MetaNeXtStage(nn.Module):
def __init__(
self,
in_chs,
out_chs,
ds_stride=2,
depth=2,
drop_path_rates=None,
ls_init_value=1.0,
act_layer=nn.GELU,
norm_layer=None,
mlp_ratio=4,
):
super().__init__()
self.grad_checkpointing = False
if ds_stride > 1:
self.downsample = nn.Sequential(
norm_layer(in_chs),
nn.Conv2d(in_chs, out_chs, kernel_size=ds_stride, stride=ds_stride),
)
else:
self.downsample = nn.Identity()
drop_path_rates = drop_path_rates or [0.] * depth
stage_blocks = []
for i in range(depth):
stage_blocks.append(MetaNeXtBlock(
dim=out_chs,
drop_path=drop_path_rates[i],
ls_init_value=ls_init_value,
act_layer=act_layer,
norm_layer=norm_layer,
mlp_ratio=mlp_ratio,
))
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 MetaNeXt(nn.Module):
r""" MetaNeXt
A PyTorch impl of : `InceptionNeXt: When Inception Meets ConvNeXt` - https://arxiv.org/pdf/2203.xxxxx.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)
token_mixers: Token mixer function. Default: nn.Identity
norm_layer: Normalziation layer. Default: nn.BatchNorm2d
act_layer: Activation function for MLP. Default: nn.GELU
mlp_ratios (int or tuple(int)): MLP ratios. Default: (4, 4, 4, 3)
head_fn: classifier head
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.
"""
def __init__(
self,
in_chans=3,
num_classes=1000,
depths=(3, 3, 9, 3),
dims=(96, 192, 384, 768),
token_mixers=nn.Identity,
norm_layer=nn.BatchNorm2d,
act_layer=nn.GELU,
mlp_ratios=(4, 4, 4, 3),
head_fn=MlpHead,
drop_rate=0.,
drop_path_rate=0.,
ls_init_value=1e-6,
**kwargs,
):
super().__init__()
num_stage = len(depths)
if not isinstance(token_mixers, (list, tuple)):
token_mixers = [token_mixers] * num_stage
if not isinstance(mlp_ratios, (list, tuple)):
mlp_ratios = [mlp_ratios] * num_stage
self.num_classes = num_classes
self.drop_rate = drop_rate
self.stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
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]
for i in range(num_stage):
out_chs = dims[i]
stages.append(MetaNeXtStage(
prev_chs,
out_chs,
ds_stride=2 if i > 0 else 1,
depth=depths[i],
drop_path_rates=dp_rates[i],
ls_init_value=ls_init_value,
act_layer=act_layer,
norm_layer=norm_layer,
mlp_ratio=mlp_ratios[i],
))
prev_chs = out_chs
self.stages = nn.Sequential(*stages)
self.num_features = prev_chs
self.head = head_fn(self.num_features, num_classes, drop=drop_rate)
self.apply(self._init_weights)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
@torch.jit.ignore
def no_weight_decay(self):
return {'norm'}
def forward_features(self, x):
x = self.stem(x)
x = self.stages(x)
return x
def forward_head(self, x):
x = self.head(x)
return x
def forward(self, x):
x = self.forward_features(x)
return x
def _init_weights(self, m):
if isinstance(m, (nn.Conv2d, nn.Linear)):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
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(
inceptionnext_tiny=_cfg(
url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_tiny.pth',
),
inceptionnext_small=_cfg(
url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_small.pth',
),
inceptionnext_base=_cfg(
url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base.pth',
),
inceptionnext_base_384=_cfg(
url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base_384.pth',
input_size=(3, 384, 384), crop_pct=1.0,
),
)
@register_model
def inceptionnext_tiny(pretrained=True, **kwargs):
model = MetaNeXt(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768),
token_mixers=InceptionDWConv2d,
**kwargs
)
model.default_cfg = default_cfgs['inceptionnext_tiny']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url=model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def inceptionnext_small(pretrained=True, **kwargs):
model = MetaNeXt(depths=(3, 3, 27, 3), dims=(96, 192, 384, 768),
token_mixers=InceptionDWConv2d,
**kwargs
)
model.default_cfg = default_cfgs['inceptionnext_small']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url=model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def inceptionnext_base(pretrained=False, **kwargs):
model = MetaNeXt(depths=(3, 3, 27, 3), dims=(128, 256, 512, 1024),
token_mixers=InceptionDWConv2d,
**kwargs
)
model.default_cfg = default_cfgs['inceptionnext_base']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url=model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def inceptionnext_base_384(pretrained=False, **kwargs):
model = MetaNeXt(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024],
mlp_ratios=[4, 4, 4, 3],
token_mixers=InceptionDWConv2d,
**kwargs
)
model.default_cfg = default_cfgs['inceptionnext_base_384']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url=model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
class InceptionNext_small(nn.Module):
def __init__(self, c2, Layers=0 ):
super().__init__()
models = inceptionnext_tiny(pretrained=False)
modules = list(models.stages)
modules = modules[Layers]
self.model = nn.Sequential(modules)
def forward(self, x):
return self.model(x)
if __name__ == '__main__':
input=torch.randn(1,96,224,224)
model = inceptionnext_tiny()
modules = list(model.stages)
for i in range(4):
moduless = nn.Sequential(modules[i])
out = moduless(input)
input = out
print(out.shape)
