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Merge pull request #1923 from huggingface/yehuitang-Add-GhostNetV2 

ghostnetv2 cleanup
samvit_fix_and_rope^2
Ross Wightman 2023-08-20 02:27:58 -07:00 committed by GitHub
parent 7c2728c6fe be4e0d8f76
commit e6aeb91ac1
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1 changed files with 114 additions and 11 deletions
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125
timm/models/ghostnet.py
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@ -1,8 +1,11 @@
"""
An implementation of GhostNet Model as defined in:
An implementation of GhostNet & GhostNetV2 Models as defined in:
GhostNet: More Features from Cheap Operations. https://arxiv.org/abs/1911.11907
The train script of the model is similar to that of MobileNetV3
GhostNetV2: Enhance Cheap Operation with Long-Range Attention. https://proceedings.neurips.cc/paper_files/paper/2022/file/40b60852a4abdaa696b5a1a78da34635-Paper-Conference.pdf
The train script & code of models at:
Original model: https://github.com/huawei-noah/CV-backbones/tree/master/ghostnet_pytorch
Original model: https://github.com/huawei-noah/Efficient-AI-Backbones/blob/master/ghostnetv2_pytorch/model/ghostnetv2_torch.py
"""
import math
from functools import partial
@ -33,7 +36,8 @@ class GhostModule(nn.Module):
ratio=2,
dw_size=3,
stride=1,
relu=True,
use_act=True,
act_layer=nn.ReLU,
):
super(GhostModule, self).__init__()
self.out_chs = out_chs
@ -43,13 +47,13 @@ class GhostModule(nn.Module):
self.primary_conv = nn.Sequential(
nn.Conv2d(in_chs, init_chs, kernel_size, stride, kernel_size // 2, bias=False),
nn.BatchNorm2d(init_chs),
nn.ReLU(inplace=True) if relu else nn.Identity(),
act_layer(inplace=True) if use_act else nn.Identity(),
)
self.cheap_operation = nn.Sequential(
nn.Conv2d(init_chs, new_chs, dw_size, 1, dw_size//2, groups=init_chs, bias=False),
nn.BatchNorm2d(new_chs),
nn.ReLU(inplace=True) if relu else nn.Identity(),
act_layer(inplace=True) if use_act else nn.Identity(),
)
def forward(self, x):
@ -59,6 +63,51 @@ class GhostModule(nn.Module):
return out[:, :self.out_chs, :, :]
class GhostModuleV2(nn.Module):
def __init__(
self,
in_chs,
out_chs,
kernel_size=1,
ratio=2,
dw_size=3,
stride=1,
use_act=True,
act_layer=nn.ReLU,
):
super().__init__()
self.gate_fn = nn.Sigmoid()
self.out_chs = out_chs
init_chs = math.ceil(out_chs / ratio)
new_chs = init_chs * (ratio - 1)
self.primary_conv = nn.Sequential(
nn.Conv2d(in_chs, init_chs, kernel_size, stride, kernel_size // 2, bias=False),
nn.BatchNorm2d(init_chs),
act_layer(inplace=True) if use_act else nn.Identity(),
)
self.cheap_operation = nn.Sequential(
nn.Conv2d(init_chs, new_chs, dw_size, 1, dw_size // 2, groups=init_chs, bias=False),
nn.BatchNorm2d(new_chs),
act_layer(inplace=True) if use_act else nn.Identity(),
)
self.short_conv = nn.Sequential(
nn.Conv2d(in_chs, out_chs, kernel_size, stride, kernel_size // 2, bias=False),
nn.BatchNorm2d(out_chs),
nn.Conv2d(out_chs, out_chs, kernel_size=(1, 5), stride=1, padding=(0, 2), groups=out_chs, bias=False),
nn.BatchNorm2d(out_chs),
nn.Conv2d(out_chs, out_chs, kernel_size=(5, 1), stride=1, padding=(2, 0), groups=out_chs, bias=False),
nn.BatchNorm2d(out_chs),
)
def forward(self, x):
res = self.short_conv(F.avg_pool2d(x, kernel_size=2, stride=2))
x1 = self.primary_conv(x)
x2 = self.cheap_operation(x1)
out = torch.cat([x1, x2], dim=1)
return out[:, :self.out_chs, :, :] * F.interpolate(
self.gate_fn(res), size=(out.shape[-2], out.shape[-1]), mode='nearest')
class GhostBottleneck(nn.Module):
""" Ghost bottleneck w/ optional SE"""
@ -71,13 +120,17 @@ class GhostBottleneck(nn.Module):
stride=1,
act_layer=nn.ReLU,
se_ratio=0.,
mode='original',
):
super(GhostBottleneck, self).__init__()
has_se = se_ratio is not None and se_ratio > 0.
self.stride = stride
# Point-wise expansion
self.ghost1 = GhostModule(in_chs, mid_chs, relu=True)
if mode == 'original':
self.ghost1 = GhostModule(in_chs, mid_chs, use_act=True, act_layer=act_layer)
else:
self.ghost1 = GhostModuleV2(in_chs, mid_chs, use_act=True, act_layer=act_layer)
# Depth-wise convolution
if self.stride > 1:
@ -93,7 +146,7 @@ class GhostBottleneck(nn.Module):
self.se = _SE_LAYER(mid_chs, rd_ratio=se_ratio) if has_se else None
# Point-wise linear projection
self.ghost2 = GhostModule(mid_chs, out_chs, relu=False)
self.ghost2 = GhostModule(mid_chs, out_chs, use_act=False)
# shortcut
if in_chs == out_chs and self.stride == 1:
@ -140,6 +193,7 @@ class GhostNet(nn.Module):
output_stride=32,
global_pool='avg',
drop_rate=0.2,
version='v1',
):
super(GhostNet, self).__init__()
# setting of inverted residual blocks
@ -160,8 +214,8 @@ class GhostNet(nn.Module):
# building inverted residual blocks
stages = nn.ModuleList([])
block = GhostBottleneck
stage_idx = 0
layer_idx = 0
net_stride = 2
for cfg in self.cfgs:
layers = []
@ -169,8 +223,12 @@ class GhostNet(nn.Module):
for k, exp_size, c, se_ratio, s in cfg:
out_chs = make_divisible(c * width, 4)
mid_chs = make_divisible(exp_size * width, 4)
layers.append(block(prev_chs, mid_chs, out_chs, k, s, se_ratio=se_ratio))
layer_kwargs = {}
if version == 'v2' and layer_idx > 1:
layer_kwargs['mode'] = 'attn'
layers.append(GhostBottleneck(prev_chs, mid_chs, out_chs, k, s, se_ratio=se_ratio, **layer_kwargs))
prev_chs = out_chs
layer_idx += 1
if s > 1:
net_stride *= 2
self.feature_info.append(dict(
@ -246,6 +304,15 @@ class GhostNet(nn.Module):
return x
def checkpoint_filter_fn(state_dict, model: nn.Module):
out_dict = {}
for k, v in state_dict.items():
if 'total' in k:
continue
out_dict[k] = v
return out_dict
def _create_ghostnet(variant, width=1.0, pretrained=False, **kwargs):
"""
Constructs a GhostNet model
@ -285,6 +352,7 @@ def _create_ghostnet(variant, width=1.0, pretrained=False, **kwargs):
GhostNet,
variant,
pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(flatten_sequential=True),
**model_kwargs,
)
@ -293,7 +361,7 @@ def _create_ghostnet(variant, width=1.0, pretrained=False, **kwargs):
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv_stem', 'classifier': 'classifier',
**kwargs
@ -303,8 +371,22 @@ def _cfg(url='', **kwargs):
default_cfgs = generate_default_cfgs({
'ghostnet_050.untrained': _cfg(),
'ghostnet_100.in1k': _cfg(
url='https://github.com/huawei-noah/CV-backbones/releases/download/ghostnet_pth/ghostnet_1x.pth'),
hf_hub_id='timm/',
# url='https://github.com/huawei-noah/CV-backbones/releases/download/ghostnet_pth/ghostnet_1x.pth'
),
'ghostnet_130.untrained': _cfg(),
'ghostnetv2_100.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/GhostNetV2/ck_ghostnetv2_10.pth.tar'
),
'ghostnetv2_130.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/GhostNetV2/ck_ghostnetv2_13.pth.tar'
),
'ghostnetv2_160.in1k': _cfg(
hf_hub_id='timm/',
# url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/GhostNetV2/ck_ghostnetv2_16.pth.tar'
),
})
@ -327,3 +409,24 @@ def ghostnet_130(pretrained=False, **kwargs) -> GhostNet:
""" GhostNet-1.3x """
model = _create_ghostnet('ghostnet_130', width=1.3, pretrained=pretrained, **kwargs)
return model
@register_model
def ghostnetv2_100(pretrained=False, **kwargs) -> GhostNet:
""" GhostNetV2-1.0x """
model = _create_ghostnet('ghostnetv2_100', width=1.0, pretrained=pretrained, version='v2', **kwargs)
return model
@register_model
def ghostnetv2_130(pretrained=False, **kwargs) -> GhostNet:
""" GhostNetV2-1.3x """
model = _create_ghostnet('ghostnetv2_130', width=1.3, pretrained=pretrained, version='v2', **kwargs)
return model
@register_model
def ghostnetv2_160(pretrained=False, **kwargs) -> GhostNet:
""" GhostNetV2-1.6x """
model = _create_ghostnet('ghostnetv2_160', width=1.6, pretrained=pretrained, version='v2', **kwargs)
return model