You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
pytorch-image-models/timm/models/vovnet.py

407 lines
14 KiB

""" VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNet.pytorch/blob/master/models_vovnet/vovnet.py
for some reference, rewrote most of the code.
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .registry import register_model
from .helpers import build_model_with_cfg
from .layers import ConvNormAct, SeparableConvNormAct, BatchNormAct2d, ClassifierHead, DropPath,\
create_attn, create_norm_act_layer, get_norm_act_layer
# model cfgs adapted from https://github.com/youngwanLEE/vovnet-detectron2 &
# https://github.com/stigma0617/VoVNet.pytorch/blob/master/models_vovnet/vovnet.py
model_cfgs = dict(
vovnet39a=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=5,
block_per_stage=[1, 1, 2, 2],
residual=False,
depthwise=False,
attn='',
),
vovnet57a=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=5,
block_per_stage=[1, 1, 4, 3],
residual=False,
depthwise=False,
attn='',
),
ese_vovnet19b_slim_dw=dict(
stem_chs=[64, 64, 64],
stage_conv_chs=[64, 80, 96, 112],
stage_out_chs=[112, 256, 384, 512],
layer_per_block=3,
block_per_stage=[1, 1, 1, 1],
residual=True,
depthwise=True,
attn='ese',
),
ese_vovnet19b_dw=dict(
stem_chs=[64, 64, 64],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=3,
block_per_stage=[1, 1, 1, 1],
residual=True,
depthwise=True,
attn='ese',
),
ese_vovnet19b_slim=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[64, 80, 96, 112],
stage_out_chs=[112, 256, 384, 512],
layer_per_block=3,
block_per_stage=[1, 1, 1, 1],
residual=True,
depthwise=False,
attn='ese',
),
ese_vovnet19b=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=3,
block_per_stage=[1, 1, 1, 1],
residual=True,
depthwise=False,
attn='ese',
),
ese_vovnet39b=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=5,
block_per_stage=[1, 1, 2, 2],
residual=True,
depthwise=False,
attn='ese',
),
ese_vovnet57b=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=5,
block_per_stage=[1, 1, 4, 3],
residual=True,
depthwise=False,
attn='ese',
),
ese_vovnet99b=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=5,
block_per_stage=[1, 3, 9, 3],
residual=True,
depthwise=False,
attn='ese',
),
eca_vovnet39b=dict(
stem_chs=[64, 64, 128],
stage_conv_chs=[128, 160, 192, 224],
stage_out_chs=[256, 512, 768, 1024],
layer_per_block=5,
block_per_stage=[1, 1, 2, 2],
residual=True,
depthwise=False,
attn='eca',
),
)
model_cfgs['ese_vovnet39b_evos'] = model_cfgs['ese_vovnet39b']
model_cfgs['ese_vovnet99b_iabn'] = model_cfgs['ese_vovnet99b']
def _cfg(url=''):
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.fc',
}
default_cfgs = dict(
vovnet39a=_cfg(url=''),
vovnet57a=_cfg(url=''),
ese_vovnet19b_slim_dw=_cfg(url=''),
ese_vovnet19b_dw=_cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ese_vovnet19b_dw-a8741004.pth'),
ese_vovnet19b_slim=_cfg(url=''),
ese_vovnet39b=_cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ese_vovnet39b-f912fe73.pth'),
ese_vovnet57b=_cfg(url=''),
ese_vovnet99b=_cfg(url=''),
eca_vovnet39b=_cfg(url=''),
ese_vovnet39b_evos=_cfg(url=''),
ese_vovnet99b_iabn=_cfg(url=''),
)
class SequentialAppendList(nn.Sequential):
def __init__(self, *args):
super(SequentialAppendList, self).__init__(*args)
def forward(self, x: torch.Tensor, concat_list: List[torch.Tensor]) -> torch.Tensor:
for i, module in enumerate(self):
if i == 0:
concat_list.append(module(x))
else:
concat_list.append(module(concat_list[-1]))
x = torch.cat(concat_list, dim=1)
return x
class OsaBlock(nn.Module):
def __init__(self, in_chs, mid_chs, out_chs, layer_per_block, residual=False,
depthwise=False, attn='', norm_layer=BatchNormAct2d, act_layer=nn.ReLU, drop_path=None):
super(OsaBlock, self).__init__()
self.residual = residual
self.depthwise = depthwise
conv_kwargs = dict(norm_layer=norm_layer, act_layer=act_layer)
next_in_chs = in_chs
if self.depthwise and next_in_chs != mid_chs:
assert not residual
self.conv_reduction = ConvNormAct(next_in_chs, mid_chs, 1, **conv_kwargs)
else:
self.conv_reduction = None
mid_convs = []
for i in range(layer_per_block):
if self.depthwise:
conv = SeparableConvNormAct(mid_chs, mid_chs, **conv_kwargs)
else:
conv = ConvNormAct(next_in_chs, mid_chs, 3, **conv_kwargs)
next_in_chs = mid_chs
mid_convs.append(conv)
self.conv_mid = SequentialAppendList(*mid_convs)
# feature aggregation
next_in_chs = in_chs + layer_per_block * mid_chs
self.conv_concat = ConvNormAct(next_in_chs, out_chs, **conv_kwargs)
if attn:
self.attn = create_attn(attn, out_chs)
else:
self.attn = None
self.drop_path = drop_path
def forward(self, x):
output = [x]
if self.conv_reduction is not None:
x = self.conv_reduction(x)
x = self.conv_mid(x, output)
x = self.conv_concat(x)
if self.attn is not None:
x = self.attn(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.residual:
x = x + output[0]
return x
class OsaStage(nn.Module):
def __init__(self, in_chs, mid_chs, out_chs, block_per_stage, layer_per_block, downsample=True,
residual=True, depthwise=False, attn='ese', norm_layer=BatchNormAct2d, act_layer=nn.ReLU,
drop_path_rates=None):
super(OsaStage, self).__init__()
if downsample:
self.pool = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)
else:
self.pool = None
blocks = []
for i in range(block_per_stage):
last_block = i == block_per_stage - 1
if drop_path_rates is not None and drop_path_rates[i] > 0.:
drop_path = DropPath(drop_path_rates[i])
else:
drop_path = None
blocks += [OsaBlock(
in_chs, mid_chs, out_chs, layer_per_block, residual=residual and i > 0, depthwise=depthwise,
attn=attn if last_block else '', norm_layer=norm_layer, act_layer=act_layer, drop_path=drop_path)
]
in_chs = out_chs
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
if self.pool is not None:
x = self.pool(x)
x = self.blocks(x)
return x
class VovNet(nn.Module):
def __init__(self, cfg, in_chans=3, num_classes=1000, global_pool='avg', drop_rate=0., stem_stride=4,
output_stride=32, norm_layer=BatchNormAct2d, act_layer=nn.ReLU, drop_path_rate=0.):
""" VovNet (v2)
"""
super(VovNet, self).__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
assert stem_stride in (4, 2)
assert output_stride == 32 # FIXME support dilation
stem_chs = cfg["stem_chs"]
stage_conv_chs = cfg["stage_conv_chs"]
stage_out_chs = cfg["stage_out_chs"]
block_per_stage = cfg["block_per_stage"]
layer_per_block = cfg["layer_per_block"]
conv_kwargs = dict(norm_layer=norm_layer, act_layer=act_layer)
# Stem module
last_stem_stride = stem_stride // 2
conv_type = SeparableConvNormAct if cfg["depthwise"] else ConvNormAct
self.stem = nn.Sequential(*[
ConvNormAct(in_chans, stem_chs[0], 3, stride=2, **conv_kwargs),
conv_type(stem_chs[0], stem_chs[1], 3, stride=1, **conv_kwargs),
conv_type(stem_chs[1], stem_chs[2], 3, stride=last_stem_stride, **conv_kwargs),
])
self.feature_info = [dict(
num_chs=stem_chs[1], reduction=2, module=f'stem.{1 if stem_stride == 4 else 2}')]
current_stride = stem_stride
# OSA stages
stage_dpr = torch.split(torch.linspace(0, drop_path_rate, sum(block_per_stage)), block_per_stage)
in_ch_list = stem_chs[-1:] + stage_out_chs[:-1]
stage_args = dict(residual=cfg["residual"], depthwise=cfg["depthwise"], attn=cfg["attn"], **conv_kwargs)
stages = []
for i in range(4): # num_stages
downsample = stem_stride == 2 or i > 0 # first stage has no stride/downsample if stem_stride is 4
stages += [OsaStage(
in_ch_list[i], stage_conv_chs[i], stage_out_chs[i], block_per_stage[i], layer_per_block,
downsample=downsample, drop_path_rates=stage_dpr[i], **stage_args)
]
self.num_features = stage_out_chs[i]
current_stride *= 2 if downsample else 1
self.feature_info += [dict(num_chs=self.num_features, reduction=current_stride, module=f'stages.{i}')]
self.stages = nn.Sequential(*stages)
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
for n, m in self.named_modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1.)
nn.init.constant_(m.bias, 0.)
elif isinstance(m, nn.Linear):
nn.init.zeros_(m.bias)
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
def forward_features(self, x):
x = self.stem(x)
return self.stages(x)
def forward(self, x):
x = self.forward_features(x)
return self.head(x)
def _create_vovnet(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
VovNet, variant, pretrained,
default_cfg=default_cfgs[variant],
model_cfg=model_cfgs[variant],
feature_cfg=dict(flatten_sequential=True),
**kwargs)
@register_model
def vovnet39a(pretrained=False, **kwargs):
return _create_vovnet('vovnet39a', pretrained=pretrained, **kwargs)
@register_model
def vovnet57a(pretrained=False, **kwargs):
return _create_vovnet('vovnet57a', pretrained=pretrained, **kwargs)
@register_model
def ese_vovnet19b_slim_dw(pretrained=False, **kwargs):
return _create_vovnet('ese_vovnet19b_slim_dw', pretrained=pretrained, **kwargs)
@register_model
def ese_vovnet19b_dw(pretrained=False, **kwargs):
return _create_vovnet('ese_vovnet19b_dw', pretrained=pretrained, **kwargs)
@register_model
def ese_vovnet19b_slim(pretrained=False, **kwargs):
return _create_vovnet('ese_vovnet19b_slim', pretrained=pretrained, **kwargs)
@register_model
def ese_vovnet39b(pretrained=False, **kwargs):
return _create_vovnet('ese_vovnet39b', pretrained=pretrained, **kwargs)
@register_model
def ese_vovnet57b(pretrained=False, **kwargs):
return _create_vovnet('ese_vovnet57b', pretrained=pretrained, **kwargs)
@register_model
def ese_vovnet99b(pretrained=False, **kwargs):
return _create_vovnet('ese_vovnet99b', pretrained=pretrained, **kwargs)
@register_model
def eca_vovnet39b(pretrained=False, **kwargs):
return _create_vovnet('eca_vovnet39b', pretrained=pretrained, **kwargs)
# Experimental Models
@register_model
def ese_vovnet39b_evos(pretrained=False, **kwargs):
def norm_act_fn(num_features, **nkwargs):
return create_norm_act_layer('evonorms0', num_features, jit=False, **nkwargs)
return _create_vovnet('ese_vovnet39b_evos', pretrained=pretrained, norm_layer=norm_act_fn, **kwargs)
@register_model
def ese_vovnet99b_iabn(pretrained=False, **kwargs):
norm_layer = get_norm_act_layer('iabn', act_layer='leaky_relu')
return _create_vovnet(
'ese_vovnet99b_iabn', pretrained=pretrained, norm_layer=norm_layer, act_layer=nn.LeakyReLU, **kwargs)