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pytorch-image-models/timm/models/inception_next.py

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"""
InceptionNeXt paper: https://arxiv.org/abs/2303.16900
Original implementation & weights from: https://github.com/sail-sg/inceptionnext
"""
from functools import partial
from typing import Optional
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import trunc_normal_, DropPath, to_2tuple, get_padding, SelectAdaptivePool2d
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
__all__ = ['MetaNeXt']
class InceptionDWConv2d(nn.Module):
""" Inception depthwise convolution
"""
def __init__(
self,
in_chs,
square_kernel_size=3,
band_kernel_size=11,
branch_ratio=0.125,
dilation=1,
):
super().__init__()
gc = int(in_chs * branch_ratio) # channel numbers of a convolution branch
square_padding = get_padding(square_kernel_size, dilation=dilation)
band_padding = get_padding(band_kernel_size, dilation=dilation)
self.dwconv_hw = nn.Conv2d(
gc, gc, square_kernel_size,
padding=square_padding, dilation=dilation, groups=gc)
self.dwconv_w = nn.Conv2d(
gc, gc, (1, band_kernel_size),
padding=(0, band_padding), dilation=(1, dilation), groups=gc)
self.dwconv_h = nn.Conv2d(
gc, gc, (band_kernel_size, 1),
padding=(band_padding, 0), dilation=(dilation, 1), groups=gc)
self.split_indexes = (in_chs - 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 MlpClassifierHead(nn.Module):
""" MLP classification head
"""
def __init__(
self,
in_features,
num_classes=1000,
pool_type='avg',
mlp_ratio=3,
act_layer=nn.GELU,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
drop=0.,
bias=True
):
super().__init__()
self.use_conv = False
self.in_features = in_features
self.num_features = hidden_features = int(mlp_ratio * in_features)
assert pool_type, 'Cannot disable pooling'
self.global_pool = SelectAdaptivePool2d(pool_type=pool_type, flatten=True)
self.fc1 = nn.Linear(in_features * self.global_pool.feat_mult(), 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 reset(self, num_classes: int, pool_type: Optional[str] = None):
if pool_type is not None:
assert pool_type, 'Cannot disable pooling'
self.global_pool = SelectAdaptivePool2d(pool_type=pool_type, flatten=True)
self.fc2 = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.fc1(x)
x = self.act(x)
x = self.norm(x)
x = self.drop(x)
return x if pre_logits else self.fc2(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,
dilation=1,
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, dilation=dilation)
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,
stride=2,
depth=2,
dilation=(1, 1),
drop_path_rates=None,
ls_init_value=1.0,
token_mixer=InceptionDWConv2d,
act_layer=nn.GELU,
norm_layer=None,
mlp_ratio=4,
):
super().__init__()
self.grad_checkpointing = False
if stride > 1 or dilation[0] != dilation[1]:
self.downsample = nn.Sequential(
norm_layer(in_chs),
nn.Conv2d(
in_chs,
out_chs,
kernel_size=2,
stride=stride,
dilation=dilation[0],
),
)
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,
dilation=dilation[1],
drop_path=drop_path_rates[i],
ls_init_value=ls_init_value,
token_mixer=token_mixer,
act_layer=act_layer,
norm_layer=norm_layer,
mlp_ratio=mlp_ratio,
))
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/abs/2303.16900
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: Normalization 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)
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,
global_pool='avg',
output_stride=32,
depths=(3, 3, 9, 3),
dims=(96, 192, 384, 768),
token_mixers=InceptionDWConv2d,
norm_layer=nn.BatchNorm2d,
act_layer=nn.GELU,
mlp_ratios=(4, 4, 4, 3),
drop_rate=0.,
drop_path_rate=0.,
ls_init_value=1e-6,
):
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.global_pool = global_pool
self.drop_rate = drop_rate
self.feature_info = []
self.stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
norm_layer(dims[0])
)
dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
prev_chs = dims[0]
curr_stride = 4
dilation = 1
# feature resolution stages, each consisting of multiple residual blocks
self.stages = nn.Sequential()
for i in range(num_stage):
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]
self.stages.append(MetaNeXtStage(
prev_chs,
out_chs,
stride=stride if i > 0 else 1,
dilation=(first_dilation, dilation),
depth=depths[i],
drop_path_rates=dp_rates[i],
ls_init_value=ls_init_value,
act_layer=act_layer,
token_mixer=token_mixers[i],
norm_layer=norm_layer,
mlp_ratio=mlp_ratios[i],
))
prev_chs = out_chs
self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{i}')]
self.num_features = prev_chs
self.head = MlpClassifierHead(self.num_features, num_classes, pool_type=self.global_pool, drop=drop_rate)
self.head_hidden_size = self.head.num_features
self.apply(self._init_weights)
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)
@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),
]
)
@torch.jit.ignore
def get_classifier(self) -> nn.Module:
return self.head.fc2
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
self.num_classes = num_classes
self.head.reset(num_classes, global_pool)
@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 set()
def forward_features(self, x):
x = self.stem(x)
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, '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.fc2',
**kwargs
}
default_cfgs = generate_default_cfgs({
'inception_next_tiny.sail_in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_tiny.pth',
),
'inception_next_small.sail_in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_small.pth',
),
'inception_next_base.sail_in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base.pth',
crop_pct=0.95,
),
'inception_next_base.sail_in1k_384': _cfg(
hf_hub_id='timm/',
# url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base_384.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
),
})
def _create_inception_next(variant, pretrained=False, **kwargs):
model = build_model_with_cfg(
MetaNeXt, variant, pretrained,
feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
**kwargs,
)
return model
@register_model
def inception_next_tiny(pretrained=False, **kwargs):
model_args = dict(
depths=(3, 3, 9, 3), dims=(96, 192, 384, 768),
token_mixers=InceptionDWConv2d,
)
return _create_inception_next('inception_next_tiny', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def inception_next_small(pretrained=False, **kwargs):
model_args = dict(
depths=(3, 3, 27, 3), dims=(96, 192, 384, 768),
token_mixers=InceptionDWConv2d,
)
return _create_inception_next('inception_next_small', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def inception_next_base(pretrained=False, **kwargs):
model_args = dict(
depths=(3, 3, 27, 3), dims=(128, 256, 512, 1024),
token_mixers=InceptionDWConv2d,
)
return _create_inception_next('inception_next_base', pretrained=pretrained, **dict(model_args, **kwargs))
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