Ross Wightman
GitHub
commit
5874d1bfc7
|
|
@ -40,6 +40,7 @@ from .pit import *
|
|||
from .pnasnet import *
|
||||
from .pvt_v2 import *
|
||||
from .regnet import *
|
||||
from .repvit import *
|
||||
from .res2net import *
|
||||
from .resnest import *
|
||||
from .resnet import *
|
||||
|
|
|
|||
|
|
@ -0,0 +1,404 @@
|
|||
""" RepViT
|
||||
|
||||
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
|
||||
- https://arxiv.org/abs/2307.09283
|
||||
|
||||
@misc{wang2023repvit,
|
||||
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
|
||||
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
|
||||
year={2023},
|
||||
eprint={2307.09283},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
|
||||
Adapted from official impl at https://github.com/jameslahm/RepViT
|
||||
"""
|
||||
|
||||
__all__ = ['RepViT']
|
||||
|
||||
import torch.nn as nn
|
||||
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
|
||||
from ._registry import register_model, generate_default_cfgs
|
||||
from ._builder import build_model_with_cfg
|
||||
from timm.layers import SqueezeExcite, trunc_normal_, to_ntuple, to_2tuple
|
||||
from ._manipulate import checkpoint_seq
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
class ConvNorm(nn.Sequential):
|
||||
def __init__(self, in_dim, out_dim, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
|
||||
super().__init__()
|
||||
self.add_module('c', nn.Conv2d(in_dim, out_dim, ks, stride, pad, dilation, groups, bias=False))
|
||||
self.add_module('bn', nn.BatchNorm2d(out_dim))
|
||||
nn.init.constant_(self.bn.weight, bn_weight_init)
|
||||
nn.init.constant_(self.bn.bias, 0)
|
||||
|
||||
@torch.no_grad()
|
||||
def fuse(self):
|
||||
c, bn = self._modules.values()
|
||||
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
|
||||
w = c.weight * w[:, None, None, None]
|
||||
b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5
|
||||
m = nn.Conv2d(
|
||||
w.size(1) * self.c.groups,
|
||||
w.size(0),
|
||||
w.shape[2:],
|
||||
stride=self.c.stride,
|
||||
padding=self.c.padding,
|
||||
dilation=self.c.dilation,
|
||||
groups=self.c.groups,
|
||||
device=c.weight.device,
|
||||
)
|
||||
m.weight.data.copy_(w)
|
||||
m.bias.data.copy_(b)
|
||||
return m
|
||||
|
||||
|
||||
class NormLinear(nn.Sequential):
|
||||
def __init__(self, in_dim, out_dim, bias=True, std=0.02):
|
||||
super().__init__()
|
||||
self.add_module('bn', nn.BatchNorm1d(in_dim))
|
||||
self.add_module('l', nn.Linear(in_dim, out_dim, bias=bias))
|
||||
trunc_normal_(self.l.weight, std=std)
|
||||
if bias:
|
||||
nn.init.constant_(self.l.bias, 0)
|
||||
|
||||
@torch.no_grad()
|
||||
def fuse(self):
|
||||
bn, l = self._modules.values()
|
||||
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
|
||||
b = bn.bias - self.bn.running_mean * self.bn.weight / (bn.running_var + bn.eps) ** 0.5
|
||||
w = l.weight * w[None, :]
|
||||
if l.bias is None:
|
||||
b = b @ self.l.weight.T
|
||||
else:
|
||||
b = (l.weight @ b[:, None]).view(-1) + self.l.bias
|
||||
m = nn.Linear(w.size(1), w.size(0), device=l.weight.device)
|
||||
m.weight.data.copy_(w)
|
||||
m.bias.data.copy_(b)
|
||||
return m
|
||||
|
||||
|
||||
class RepVGGDW(nn.Module):
|
||||
def __init__(self, ed, kernel_size):
|
||||
super().__init__()
|
||||
self.conv = ConvNorm(ed, ed, kernel_size, 1, (kernel_size - 1) // 2, groups=ed)
|
||||
self.conv1 = ConvNorm(ed, ed, 1, 1, 0, groups=ed)
|
||||
self.dim = ed
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv(x) + self.conv1(x) + x
|
||||
|
||||
@torch.no_grad()
|
||||
def fuse(self):
|
||||
conv = self.conv.fuse()
|
||||
conv1 = self.conv1.fuse()
|
||||
|
||||
conv_w = conv.weight
|
||||
conv_b = conv.bias
|
||||
conv1_w = conv1.weight
|
||||
conv1_b = conv1.bias
|
||||
|
||||
conv1_w = nn.functional.pad(conv1_w, [1, 1, 1, 1])
|
||||
|
||||
identity = nn.functional.pad(
|
||||
torch.ones(conv1_w.shape[0], conv1_w.shape[1], 1, 1, device=conv1_w.device), [1, 1, 1, 1]
|
||||
)
|
||||
|
||||
final_conv_w = conv_w + conv1_w + identity
|
||||
final_conv_b = conv_b + conv1_b
|
||||
|
||||
conv.weight.data.copy_(final_conv_w)
|
||||
conv.bias.data.copy_(final_conv_b)
|
||||
return conv
|
||||
|
||||
|
||||
class RepViTMlp(nn.Module):
|
||||
def __init__(self, in_dim, hidden_dim, act_layer):
|
||||
super().__init__()
|
||||
self.conv1 = ConvNorm(in_dim, hidden_dim, 1, 1, 0)
|
||||
self.act = act_layer()
|
||||
self.conv2 = ConvNorm(hidden_dim, in_dim, 1, 1, 0, bn_weight_init=0)
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv2(self.act(self.conv1(x)))
|
||||
|
||||
|
||||
class RepViTBlock(nn.Module):
|
||||
def __init__(self, in_dim, mlp_ratio, kernel_size, use_se, act_layer):
|
||||
super(RepViTBlock, self).__init__()
|
||||
|
||||
self.token_mixer = RepVGGDW(in_dim, kernel_size)
|
||||
self.se = SqueezeExcite(in_dim, 0.25) if use_se else nn.Identity()
|
||||
self.channel_mixer = RepViTMlp(in_dim, in_dim * mlp_ratio, act_layer)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.token_mixer(x)
|
||||
x = self.se(x)
|
||||
identity = x
|
||||
x = self.channel_mixer(x)
|
||||
return identity + x
|
||||
|
||||
|
||||
class RepViTStem(nn.Module):
|
||||
def __init__(self, in_chs, out_chs, act_layer):
|
||||
super().__init__()
|
||||
self.conv1 = ConvNorm(in_chs, out_chs // 2, 3, 2, 1)
|
||||
self.act1 = act_layer()
|
||||
self.conv2 = ConvNorm(out_chs // 2, out_chs, 3, 2, 1)
|
||||
self.stride = 4
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv2(self.act1(self.conv1(x)))
|
||||
|
||||
|
||||
class RepViTDownsample(nn.Module):
|
||||
def __init__(self, in_dim, mlp_ratio, out_dim, kernel_size, act_layer):
|
||||
super().__init__()
|
||||
self.pre_block = RepViTBlock(in_dim, mlp_ratio, kernel_size, use_se=False, act_layer=act_layer)
|
||||
self.spatial_downsample = ConvNorm(in_dim, in_dim, kernel_size, 2, (kernel_size - 1) // 2, groups=in_dim)
|
||||
self.channel_downsample = ConvNorm(in_dim, out_dim, 1, 1)
|
||||
self.ffn = RepViTMlp(out_dim, out_dim * mlp_ratio, act_layer)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.pre_block(x)
|
||||
x = self.spatial_downsample(x)
|
||||
x = self.channel_downsample(x)
|
||||
identity = x
|
||||
x = self.ffn(x)
|
||||
return x + identity
|
||||
|
||||
|
||||
class RepViTClassifier(nn.Module):
|
||||
def __init__(self, dim, num_classes, distillation=False):
|
||||
super().__init__()
|
||||
self.head = NormLinear(dim, num_classes) if num_classes > 0 else nn.Identity()
|
||||
self.distillation = distillation
|
||||
if distillation:
|
||||
self.head_dist = NormLinear(dim, num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
if self.distillation:
|
||||
x1, x2 = self.head(x), self.head_dist(x)
|
||||
if (not self.training) or torch.jit.is_scripting():
|
||||
return (x1 + x2) / 2
|
||||
else:
|
||||
return x1, x2
|
||||
else:
|
||||
x = self.head(x)
|
||||
return x
|
||||
|
||||
@torch.no_grad()
|
||||
def fuse(self):
|
||||
if not self.num_classes > 0:
|
||||
return nn.Identity()
|
||||
head = self.head.fuse()
|
||||
if self.distillation:
|
||||
head_dist = self.head_dist.fuse()
|
||||
head.weight += head_dist.weight
|
||||
head.bias += head_dist.bias
|
||||
head.weight /= 2
|
||||
head.bias /= 2
|
||||
return head
|
||||
else:
|
||||
return head
|
||||
|
||||
|
||||
class RepViTStage(nn.Module):
|
||||
def __init__(self, in_dim, out_dim, depth, mlp_ratio, act_layer, kernel_size=3, downsample=True):
|
||||
super().__init__()
|
||||
if downsample:
|
||||
self.downsample = RepViTDownsample(in_dim, mlp_ratio, out_dim, kernel_size, act_layer)
|
||||
else:
|
||||
assert in_dim == out_dim
|
||||
self.downsample = nn.Identity()
|
||||
|
||||
blocks = []
|
||||
use_se = True
|
||||
for _ in range(depth):
|
||||
blocks.append(RepViTBlock(out_dim, mlp_ratio, kernel_size, use_se, act_layer))
|
||||
use_se = not use_se
|
||||
|
||||
self.blocks = nn.Sequential(*blocks)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.downsample(x)
|
||||
x = self.blocks(x)
|
||||
return x
|
||||
|
||||
|
||||
class RepViT(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_chans=3,
|
||||
img_size=224,
|
||||
embed_dim=(48,),
|
||||
depth=(2,),
|
||||
mlp_ratio=2,
|
||||
global_pool='avg',
|
||||
kernel_size=3,
|
||||
num_classes=1000,
|
||||
act_layer=nn.GELU,
|
||||
distillation=True,
|
||||
):
|
||||
super(RepViT, self).__init__()
|
||||
self.grad_checkpointing = False
|
||||
self.global_pool = global_pool
|
||||
self.embed_dim = embed_dim
|
||||
self.num_classes = num_classes
|
||||
|
||||
in_dim = embed_dim[0]
|
||||
self.stem = RepViTStem(in_chans, in_dim, act_layer)
|
||||
stride = self.stem.stride
|
||||
resolution = tuple([i // p for i, p in zip(to_2tuple(img_size), to_2tuple(stride))])
|
||||
|
||||
num_stages = len(embed_dim)
|
||||
mlp_ratios = to_ntuple(num_stages)(mlp_ratio)
|
||||
|
||||
self.feature_info = []
|
||||
stages = []
|
||||
for i in range(num_stages):
|
||||
downsample = True if i != 0 else False
|
||||
stages.append(
|
||||
RepViTStage(
|
||||
in_dim,
|
||||
embed_dim[i],
|
||||
depth[i],
|
||||
mlp_ratio=mlp_ratios[i],
|
||||
act_layer=act_layer,
|
||||
kernel_size=kernel_size,
|
||||
downsample=downsample,
|
||||
)
|
||||
)
|
||||
stage_stride = 2 if downsample else 1
|
||||
stride *= stage_stride
|
||||
resolution = tuple([(r - 1) // stage_stride + 1 for r in resolution])
|
||||
self.feature_info += [dict(num_chs=embed_dim[i], reduction=stride, module=f'stages.{i}')]
|
||||
in_dim = embed_dim[i]
|
||||
self.stages = nn.Sequential(*stages)
|
||||
|
||||
self.num_features = embed_dim[-1]
|
||||
self.head = RepViTClassifier(embed_dim[-1], num_classes, distillation)
|
||||
|
||||
@torch.jit.ignore
|
||||
def group_matcher(self, coarse=False):
|
||||
matcher = dict(
|
||||
stem=r'^stem', # stem and embed
|
||||
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
|
||||
)
|
||||
return matcher
|
||||
|
||||
@torch.jit.ignore
|
||||
def set_grad_checkpointing(self, enable=True):
|
||||
self.grad_checkpointing = enable
|
||||
|
||||
@torch.jit.ignore
|
||||
def get_classifier(self):
|
||||
return self.head
|
||||
|
||||
def reset_classifier(self, num_classes, global_pool=None, distillation=False):
|
||||
self.num_classes = num_classes
|
||||
if global_pool is not None:
|
||||
self.global_pool = global_pool
|
||||
self.head = (
|
||||
RepViTClassifier(self.embed_dim[-1], num_classes, distillation) if num_classes > 0 else nn.Identity()
|
||||
)
|
||||
|
||||
def forward_features(self, x):
|
||||
x = self.stem(x)
|
||||
if self.grad_checkpointing and not torch.jit.is_scripting():
|
||||
x = checkpoint_seq(self.stages, x)
|
||||
else:
|
||||
x = self.stages(x)
|
||||
return x
|
||||
|
||||
def forward_head(self, x, pre_logits: bool = False):
|
||||
if self.global_pool == 'avg':
|
||||
x = nn.functional.adaptive_avg_pool2d(x, 1).flatten(1)
|
||||
return x if pre_logits else self.head(x)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
x = self.forward_head(x)
|
||||
return x
|
||||
|
||||
@torch.no_grad()
|
||||
def fuse(self):
|
||||
def fuse_children(net):
|
||||
for child_name, child in net.named_children():
|
||||
if hasattr(child, 'fuse'):
|
||||
fused = child.fuse()
|
||||
setattr(net, child_name, fused)
|
||||
fuse_children(fused)
|
||||
else:
|
||||
fuse_children(child)
|
||||
|
||||
fuse_children(self)
|
||||
|
||||
|
||||
def _cfg(url='', **kwargs):
|
||||
return {
|
||||
'url': url,
|
||||
'num_classes': 1000,
|
||||
'input_size': (3, 224, 224),
|
||||
'pool_size': (7, 7),
|
||||
'crop_pct': 0.95,
|
||||
'interpolation': 'bicubic',
|
||||
'mean': IMAGENET_DEFAULT_MEAN,
|
||||
'std': IMAGENET_DEFAULT_STD,
|
||||
'first_conv': 'stem.conv1.c',
|
||||
'classifier': ('head.head.l', 'head.head_dist.l'),
|
||||
**kwargs,
|
||||
}
|
||||
|
||||
|
||||
default_cfgs = generate_default_cfgs(
|
||||
{
|
||||
'repvit_m1.dist_in1k': _cfg(
|
||||
url='https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_distill_300_timm.pth'
|
||||
),
|
||||
'repvit_m2.dist_in1k': _cfg(
|
||||
url='https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m2_distill_300_timm.pth'
|
||||
),
|
||||
'repvit_m3.dist_in1k': _cfg(
|
||||
url='https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m3_distill_300_timm.pth'
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def _create_repvit(variant, pretrained=False, **kwargs):
|
||||
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3))
|
||||
model = build_model_with_cfg(
|
||||
RepViT, variant, pretrained, feature_cfg=dict(flatten_sequential=True, out_indices=out_indices), **kwargs
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def repvit_m1(pretrained=False, **kwargs):
|
||||
"""
|
||||
Constructs a RepViT-M1 model
|
||||
"""
|
||||
model_args = dict(embed_dim=(48, 96, 192, 384), depth=(2, 2, 14, 2))
|
||||
return _create_repvit('repvit_m1', pretrained=pretrained, **dict(model_args, **kwargs))
|
||||
|
||||
|
||||
@register_model
|
||||
def repvit_m2(pretrained=False, **kwargs):
|
||||
"""
|
||||
Constructs a RepViT-M2 model
|
||||
"""
|
||||
model_args = dict(embed_dim=(64, 128, 256, 512), depth=(2, 2, 12, 2))
|
||||
return _create_repvit('repvit_m2', pretrained=pretrained, **dict(model_args, **kwargs))
|
||||
|
||||
|
||||
@register_model
|
||||
def repvit_m3(pretrained=False, **kwargs):
|
||||
"""
|
||||
Constructs a RepViT-M3 model
|
||||
"""
|
||||
model_args = dict(embed_dim=(64, 128, 256, 512), depth=(4, 4, 18, 2))
|
||||
return _create_repvit('repvit_m3', pretrained=pretrained, **dict(model_args, **kwargs))
|
||||
Loading…
Reference in New Issue