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timm/models/__init__.py
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@ -61,6 +61,7 @@ from .selecsls import *
from .senet import *
from .sequencer import *
from .sknet import *
from .starnet import *
from .swiftformer import *
from .swin_transformer import *
from .swin_transformer_v2 import *

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timm/models/starnet.py Normal file
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@ -0,0 +1,344 @@
"""
Implementation of Prof-of-Concept Network: StarNet.
We make StarNet as simple as possible [to show the key contribution of element-wise multiplication]:
- like NO layer-scale in network design,
- and NO EMA during training,
- which would improve the performance further.
Created by: Xu Ma (Email: ma.xu1@northeastern.edu)
Modified Date: Mar/29/2024
"""
from typing import Any, Dict, List, Optional, Set, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, SelectAdaptivePool2d, Linear, LayerType, trunc_normal_
from ._builder import build_model_with_cfg
from ._features import feature_take_indices
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
__all__ = ['StarNet']
class ConvBN(nn.Sequential):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int = 1,
stride: int = 1,
padding: int = 0,
with_bn: bool = True,
**kwargs
):
super().__init__()
self.add_module('conv', nn.Conv2d(
in_channels, out_channels, kernel_size, stride=stride, padding=padding, **kwargs))
if with_bn:
self.add_module('bn', nn.BatchNorm2d(out_channels))
nn.init.constant_(self.bn.weight, 1)
nn.init.constant_(self.bn.bias, 0)
class Block(nn.Module):
def __init__(
self,
dim: int,
mlp_ratio: int = 3,
drop_path: float = 0.,
act_layer: LayerType = nn.ReLU6,
):
super().__init__()
self.dwconv = ConvBN(dim, dim, 7, 1, 3, groups=dim, with_bn=True)
self.f1 = ConvBN(dim, mlp_ratio * dim, 1, with_bn=False)
self.f2 = ConvBN(dim, mlp_ratio * dim, 1, with_bn=False)
self.g = ConvBN(mlp_ratio * dim, dim, 1, with_bn=True)
self.dwconv2 = ConvBN(dim, dim, 7, 1, 3, groups=dim, with_bn=False)
self.act = act_layer()
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
residual = x
x = self.dwconv(x)
x1, x2 = self.f1(x), self.f2(x)
x = self.act(x1) * x2
x = self.dwconv2(self.g(x))
x = residual + self.drop_path(x)
return x
class StarNet(nn.Module):
def __init__(
self,
base_dim: int = 32,
depths: List[int] = [3, 3, 12, 5],
mlp_ratio: int = 4,
drop_rate: float = 0.,
drop_path_rate: float = 0.,
act_layer: LayerType = nn.ReLU6,
num_classes: int = 1000,
in_chans: int = 3,
global_pool: str = 'avg',
output_stride: int = 32,
**kwargs,
):
super().__init__()
assert output_stride == 32
self.num_classes = num_classes
self.drop_rate = drop_rate
self.grad_checkpointing = False
self.feature_info = []
stem_chs = 32
# stem layer
self.stem = nn.Sequential(
ConvBN(in_chans, stem_chs, kernel_size=3, stride=2, padding=1),
act_layer(),
)
prev_chs = stem_chs
# build stages
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth
stages = []
cur = 0
for i_layer in range(len(depths)):
embed_dim = base_dim * 2 ** i_layer
down_sampler = ConvBN(prev_chs, embed_dim, 3, stride=2, padding=1)
blocks = [Block(embed_dim, mlp_ratio, dpr[cur + i], act_layer) for i in range(depths[i_layer])]
cur += depths[i_layer]
prev_chs = embed_dim
stages.append(nn.Sequential(down_sampler, *blocks))
self.feature_info.append(dict(
num_chs=prev_chs, reduction=2**(i_layer+2), module=f'stages.{i_layer}'))
self.stages = nn.Sequential(*stages)
# head
self.num_features = self.head_hidden_size = prev_chs
self.norm = nn.BatchNorm2d(self.num_features)
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
self.head = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self) -> Set:
return set()
@torch.jit.ignore
def group_matcher(self, coarse: bool = False) -> Dict[str, Any]:
matcher = dict(
stem=r'^stem\.\d+',
blocks=[(r'^stages\.(\d+)', None), (r'^norm', (99999,))]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable: bool = True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self) -> nn.Module:
return self.head
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
self.num_classes = num_classes
if global_pool is not None:
# NOTE: cannot meaningfully change pooling of efficient head after creation
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
self.head = Linear(self.head_hidden_size, num_classes) if num_classes > 0 else nn.Identity()
def forward_intermediates(
self,
x: torch.Tensor,
indices: Optional[Union[int, List[int]]] = None,
norm: bool = False,
stop_early: bool = False,
output_fmt: str = 'NCHW',
intermediates_only: bool = False,
) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
""" Forward features that returns intermediates.
Args:
x: Input image tensor
indices: Take last n blocks if int, all if None, select matching indices if sequence
norm: Apply norm layer to compatible intermediates
stop_early: Stop iterating over blocks when last desired intermediate hit
output_fmt: Shape of intermediate feature outputs
intermediates_only: Only return intermediate features
Returns:
"""
assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
intermediates = []
take_indices, max_index = feature_take_indices(len(self.stages), indices)
last_idx = len(self.stages) - 1
# forward pass
x = self.stem(x)
if torch.jit.is_scripting() or not stop_early: # can't slice blocks in torchscript
stages = self.stages
else:
stages = self.stages[:max_index + 1]
for feat_idx, stage in enumerate(stages):
x = stage(x)
if feat_idx in take_indices:
if norm and feat_idx == last_idx:
x_inter = self.norm(x) # applying final norm last intermediate
else:
x_inter = x
intermediates.append(x_inter)
if intermediates_only:
return intermediates
x = self.norm(x)
return x, intermediates
def prune_intermediate_layers(
self,
indices: Union[int, List[int]] = 1,
prune_norm: bool = False,
prune_head: bool = True,
):
""" Prune layers not required for specified intermediates.
"""
take_indices, max_index = feature_take_indices(len(self.stages), indices)
self.stages = self.stages[:max_index + 1] # truncate blocks w/ stem as idx 0
if prune_norm:
self.norm = nn.Identity()
if prune_head:
self.reset_classifier(0, '')
return take_indices
def forward_features(self, x: torch.Tensor) -> torch.Tensor:
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x, flatten=True)
else:
x = self.stages(x)
x = self.norm(x)
return x
def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
x = self.global_pool(x)
x = self.flatten(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
return x if pre_logits else self.head(x)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict: Dict[str, torch.Tensor], model: nn.Module) -> Dict[str, torch.Tensor]:
if 'state_dict' in state_dict:
state_dict = state_dict['state_dict']
out_dict = state_dict
return out_dict
def _cfg(url: str = '', **kwargs: Any) -> Dict[str, Any]:
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.conv', 'classifier': 'head',
'paper_ids': 'arXiv:2403.19967',
'paper_name': 'Rewrite the Stars',
'origin_url': 'https://github.com/ma-xu/Rewrite-the-Stars',
**kwargs
}
default_cfgs = generate_default_cfgs({
'starnet_s1.in1k': _cfg(
# hf_hub_id='timm/',
url='https://github.com/ma-xu/Rewrite-the-Stars/releases/download/checkpoints_v1/starnet_s1.pth.tar',
),
'starnet_s2.in1k': _cfg(
# hf_hub_id='timm/',
url='https://github.com/ma-xu/Rewrite-the-Stars/releases/download/checkpoints_v1/starnet_s2.pth.tar',
),
'starnet_s3.in1k': _cfg(
# hf_hub_id='timm/',
url='https://github.com/ma-xu/Rewrite-the-Stars/releases/download/checkpoints_v1/starnet_s3.pth.tar',
),
'starnet_s4.in1k': _cfg(
# hf_hub_id='timm/',
url='https://github.com/ma-xu/Rewrite-the-Stars/releases/download/checkpoints_v1/starnet_s4.pth.tar',
),
'starnet_s050.untrained': _cfg(),
'starnet_s100.untrained': _cfg(),
'starnet_s150.untrained': _cfg(),
})
def _create_starnet(variant: str, pretrained: bool = False, **kwargs: Any) -> StarNet:
model = build_model_with_cfg(
StarNet, 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 starnet_s1(pretrained: bool = False, **kwargs: Any) -> StarNet:
model_args = dict(base_dim=24, depths=[2, 2, 8, 3])
return _create_starnet('starnet_s1', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def starnet_s2(pretrained: bool = False, **kwargs: Any) -> StarNet:
model_args = dict(base_dim=32, depths=[1, 2, 6, 2])
return _create_starnet('starnet_s2', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def starnet_s3(pretrained: bool = False, **kwargs: Any) -> StarNet:
model_args = dict(base_dim=32, depths=[2, 2, 8, 4])
return _create_starnet('starnet_s3', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def starnet_s4(pretrained: bool = False, **kwargs: Any) -> StarNet:
model_args = dict(base_dim=32, depths=[3, 3, 12, 5])
return _create_starnet('starnet_s4', pretrained=pretrained, **dict(model_args, **kwargs))
# very small networks #
@register_model
def starnet_s050(pretrained: bool = False, **kwargs: Any) -> StarNet:
model_args = dict(base_dim=16, depths=[1, 1, 3, 1], mlp_ratio=3)
return _create_starnet('starnet_s050', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def starnet_s100(pretrained: bool = False, **kwargs: Any) -> StarNet:
model_args = dict(base_dim=20, depths=[1, 2, 4, 1], mlp_ratio=4)
return _create_starnet('starnet_s100', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def starnet_s150(pretrained: bool = False, **kwargs: Any) -> StarNet:
model_args = dict(base_dim=24, depths=[1, 2, 4, 2], mlp_ratio=3)
return _create_starnet('starnet_s150', pretrained=pretrained, **dict(model_args, **kwargs))