Migrations
/
pytorch-image-models
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add more experimental darknet and 'cs2' darknet variants (different cross stage setup, closer to newer YOLO backbones) for train trials.

Browse Source
pull/1327/head
Ross Wightman 3 years ago
parent a050fde5cd
commit 82c311d082
2 changed files with 352 additions and 51 deletions
Show Stats Download Patch File Download Diff File

368
timm/models/cspnet.py
Unescape View File

@ -16,6 +16,7 @@ from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg, named_apply, MATCH_PREV_GROUP
@ -46,11 +47,21 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspresnext50_ra_224-648b4713.pth',
input_size=(3, 224, 224), pool_size=(7, 7), crop_pct=0.875 # FIXME I trained this at 224x224, not 256 like ref impl
),
'cspresnext50_iabn': _cfg(url=''),
'cspdarknet53': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspdarknet53_ra_256-d05c7c21.pth'),
'cspdarknet53_iabn': _cfg(url=''),
'darknet17': _cfg(url=''),
'darknet21': _cfg(url=''),
'darknet53': _cfg(url=''),
'cs2darknet_m': _cfg(
url=''),
'cs2darknet_l': _cfg(
url=''),
'cs2darknet_f_m': _cfg(
url=''),
'cs2darknet_f_l': _cfg(
url=''),
}
@ -116,6 +127,37 @@ model_cfgs = dict(
down_growth=True,
)
),
darknet17=dict(
stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
stage=dict(
out_chs=(64, 128, 256, 512, 1024),
depth=(1,) * 5,
stride=(2,) * 5,
bottle_ratio=(0.5,) * 5,
block_ratio=(1.,) * 5,
)
),
darknet21=dict(
stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
stage=dict(
out_chs=(64, 128, 256, 512, 1024),
depth=(1, 1, 1, 2, 2),
stride=(2,) * 5,
bottle_ratio=(0.5,) * 5,
block_ratio=(1.,) * 5,
)
),
sedarknet21=dict(
stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
stage=dict(
out_chs=(64, 128, 256, 512, 1024),
depth=(1, 1, 1, 2, 2),
stride=(2,) * 5,
bottle_ratio=(0.5,) * 5,
block_ratio=(1.,) * 5,
attn_layer=('se',) * 5,
)
),
darknet53=dict(
stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
stage=dict(
@ -125,13 +167,81 @@ model_cfgs = dict(
bottle_ratio=(0.5,) * 5,
block_ratio=(1.,) * 5,
)
),
darknetaa53=dict(
stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
stage=dict(
out_chs=(64, 128, 256, 512, 1024),
depth=(1, 2, 8, 8, 4),
stride=(2,) * 5,
bottle_ratio=(0.5,) * 5,
block_ratio=(1.,) * 5,
avg_down=True,
),
),
cs2darknet_m=dict(
stem=dict(out_chs=(24, 48), kernel_size=3, stride=2, pool=''),
stage=dict(
out_chs=(96, 192, 384, 768),
depth=(2, 4, 6, 2),
stride=(2,) * 4,
bottle_ratio=(1.,) * 4,
block_ratio=(0.5,) * 4,
avg_down=False,
),
),
cs2darknet_f_m=dict(
stem=dict(out_chs=48, kernel_size=6, stride=2, padding=2, pool=''),
stage=dict(
out_chs=(96, 192, 384, 768),
depth=(2, 4, 6, 2),
stride=(2,) * 4,
bottle_ratio=(1.,) * 4,
block_ratio=(0.5,) * 4,
avg_down=False,
),
),
cs2darknet_l=dict(
stem=dict(out_chs=(32, 64), kernel_size=3, stride=2, pool=''),
stage=dict(
out_chs=(128, 256, 512, 1024),
depth=(3, 6, 9, 3),
stride=(2,) * 4,
bottle_ratio=(1.,) * 4,
block_ratio=(0.5,) * 4,
avg_down=False,
),
),
cs2darknet_f_l=dict(
stem=dict(out_chs=64, kernel_size=6, stride=2, padding=2, pool=''),
stage=dict(
out_chs=(128, 256, 512, 1024),
depth=(3, 6, 9, 3),
stride=(2,) * 4,
bottle_ratio=(1.,) * 4,
block_ratio=(0.5,) * 4,
avg_down=False,
),
)
)
def create_stem(
in_chans=3, out_chs=32, kernel_size=3, stride=2, pool='',
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None):
in_chans=3,
out_chs=32,
kernel_size=3,
stride=2,
pool='',
padding='',
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
aa_layer=None
):
stem = nn.Sequential()
if not isinstance(out_chs, (tuple, list)):
out_chs = [out_chs]
@ -140,8 +250,12 @@ def create_stem(
for i, out_c in enumerate(out_chs):
conv_name = f'conv{i + 1}'
stem.add_module(conv_name, ConvNormAct(
in_c, out_c, kernel_size, stride=stride if i == 0 else 1,
act_layer=act_layer, norm_layer=norm_layer))
in_c, out_c, kernel_size,
stride=stride if i == 0 else 1,
padding=padding if i == 0 else '',
act_layer=act_layer,
norm_layer=norm_layer
))
in_c = out_c
last_conv = conv_name
if pool:
@ -158,9 +272,20 @@ class ResBottleneck(nn.Module):
"""
def __init__(
self, in_chs, out_chs, dilation=1, bottle_ratio=0.25, groups=1,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_last=False,
attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
self,
in_chs,
out_chs,
dilation=1,
bottle_ratio=0.25,
groups=1,
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
attn_last=False,
attn_layer=None,
aa_layer=None,
drop_block=None,
drop_path=None
):
super(ResBottleneck, self).__init__()
mid_chs = int(round(out_chs * bottle_ratio))
ckwargs = dict(act_layer=act_layer, norm_layer=norm_layer)
@ -173,7 +298,7 @@ class ResBottleneck(nn.Module):
self.conv3 = ConvNormAct(mid_chs, out_chs, kernel_size=1, apply_act=False, **ckwargs)
self.attn3 = create_attn(attn_layer, channels=out_chs) if attn_last else None
self.drop_path = drop_path
self.act3 = act_layer(inplace=True)
self.act3 = act_layer()
def zero_init_last(self):
nn.init.zeros_(self.conv3.bn.weight)
@ -201,9 +326,19 @@ class DarkBlock(nn.Module):
"""
def __init__(
self, in_chs, out_chs, dilation=1, bottle_ratio=0.5, groups=1,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_layer=None, aa_layer=None,
drop_block=None, drop_path=None):
self,
in_chs,
out_chs,
dilation=1,
bottle_ratio=0.5,
groups=1,
act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d,
attn_layer=None,
aa_layer=None,
drop_block=None,
drop_path=None
):
super(DarkBlock, self).__init__()
mid_chs = int(round(out_chs * bottle_ratio))
ckwargs = dict(act_layer=act_layer, norm_layer=norm_layer)
@ -211,7 +346,7 @@ class DarkBlock(nn.Module):
self.conv2 = ConvNormActAa(
mid_chs, out_chs, kernel_size=3, dilation=dilation, groups=groups,
aa_layer=aa_layer, drop_layer=drop_block, **ckwargs)
self.attn = create_attn(attn_layer, channels=out_chs)
self.attn = create_attn(attn_layer, channels=out_chs, act_layer=act_layer)
self.drop_path = drop_path
def zero_init_last(self):
@ -232,23 +367,44 @@ class DarkBlock(nn.Module):
class CrossStage(nn.Module):
"""Cross Stage."""
def __init__(
self, in_chs, out_chs, stride, dilation, depth, block_ratio=1., bottle_ratio=1., exp_ratio=1.,
groups=1, first_dilation=None, down_growth=False, cross_linear=False, block_dpr=None,
block_fn=ResBottleneck, **block_kwargs):
self,
in_chs,
out_chs,
stride,
dilation,
depth,
block_ratio=1.,
bottle_ratio=1.,
exp_ratio=1.,
groups=1,
first_dilation=None,
avg_down=False,
down_growth=False,
cross_linear=False,
block_dpr=None,
block_fn=ResBottleneck,
**block_kwargs
):
super(CrossStage, self).__init__()
first_dilation = first_dilation or dilation
down_chs = out_chs if down_growth else in_chs # grow downsample channels to output channels
exp_chs = int(round(out_chs * exp_ratio))
self.exp_chs = exp_chs = int(round(out_chs * exp_ratio))
block_out_chs = int(round(out_chs * block_ratio))
conv_kwargs = dict(act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'))
if stride != 1 or first_dilation != dilation:
if avg_down:
self.conv_down = nn.Sequential(
nn.AvgPool2d(3, 2, 1) if stride == 2 else nn.Identity(), # FIXME dilation handling
ConvNormActAa(in_chs, out_chs, kernel_size=1, stride=1, groups=groups, **conv_kwargs)
)
else:
self.conv_down = ConvNormActAa(
in_chs, down_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
aa_layer=block_kwargs.get('aa_layer', None), **conv_kwargs)
prev_chs = down_chs
else:
self.conv_down = None
self.conv_down = nn.Identity()
prev_chs = in_chs
# FIXME this 1x1 expansion is pushed down into the cross and block paths in the darknet cfgs. Also,
@ -269,30 +425,115 @@ class CrossStage(nn.Module):
self.conv_transition = ConvNormAct(exp_chs, out_chs, kernel_size=1, **conv_kwargs)
def forward(self, x):
if self.conv_down is not None:
x = self.conv_down(x)
x = self.conv_exp(x)
split = x.shape[1] // 2
xs, xb = x[:, :split], x[:, split:]
xs, xb = x.split(self.exp_chs // 2, dim=1)
xb = self.blocks(xb)
xb = self.conv_transition_b(xb).contiguous()
out = self.conv_transition(torch.cat([xs, xb], dim=1))
return out
class CrossStage2(nn.Module):
"""Cross Stage v2.
Similar to CrossStage, but with one transition conv for the concat output.
"""
def __init__(
self,
in_chs,
out_chs,
stride,
dilation,
depth,
block_ratio=1.,
bottle_ratio=1.,
exp_ratio=1.,
groups=1,
first_dilation=None,
avg_down=False,
down_growth=False,
cross_linear=False,
block_dpr=None,
block_fn=ResBottleneck,
**block_kwargs
):
super(CrossStage2, self).__init__()
first_dilation = first_dilation or dilation
down_chs = out_chs if down_growth else in_chs # grow downsample channels to output channels
self.exp_chs = exp_chs = int(round(out_chs * exp_ratio))
block_out_chs = int(round(out_chs * block_ratio))
conv_kwargs = dict(act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'))
if stride != 1 or first_dilation != dilation:
if avg_down:
self.conv_down = nn.Sequential(
nn.AvgPool2d(3, 2, 1) if stride == 2 else nn.Identity(), # FIXME dilation handling
ConvNormActAa(in_chs, out_chs, kernel_size=1, stride=1, groups=groups, **conv_kwargs)
)
else:
self.conv_down = ConvNormActAa(
in_chs, down_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
aa_layer=block_kwargs.get('aa_layer', None), **conv_kwargs)
prev_chs = down_chs
else:
self.conv_down = None
prev_chs = in_chs
# expansion conv
self.conv_exp = ConvNormAct(prev_chs, exp_chs, kernel_size=1, apply_act=not cross_linear, **conv_kwargs)
prev_chs = exp_chs // 2 # expanded output is split in 2 for blocks and cross stage
self.blocks = nn.Sequential()
for i in range(depth):
drop_path = DropPath(block_dpr[i]) if block_dpr and block_dpr[i] else None
self.blocks.add_module(str(i), block_fn(
prev_chs, block_out_chs, dilation, bottle_ratio, groups, drop_path=drop_path, **block_kwargs))
prev_chs = block_out_chs
# transition convs
self.conv_transition = ConvNormAct(exp_chs, out_chs, kernel_size=1, **conv_kwargs)
def forward(self, x):
x = self.conv_down(x)
x = self.conv_exp(x)
x1, x2 = x.split(self.exp_chs // 2, dim=1)
x1 = self.blocks(x1)
out = self.conv_transition(torch.cat([x1, x2], dim=1))
return out
class DarkStage(nn.Module):
"""DarkNet stage."""
def __init__(
self, in_chs, out_chs, stride, dilation, depth, block_ratio=1., bottle_ratio=1., groups=1,
first_dilation=None, block_fn=ResBottleneck, block_dpr=None, **block_kwargs):
self,
in_chs,
out_chs,
stride,
dilation,
depth,
block_ratio=1.,
bottle_ratio=1.,
groups=1,
first_dilation=None,
avg_down=False,
block_fn=ResBottleneck,
block_dpr=None,
**block_kwargs
):
super(DarkStage, self).__init__()
first_dilation = first_dilation or dilation
conv_kwargs = dict(act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'))
if avg_down:
self.conv_down = nn.Sequential(
nn.AvgPool2d(3, 2, 1) if stride == 2 else nn.Identity(), # FIXME dilation handling
ConvNormActAa(in_chs, out_chs, kernel_size=1, stride=1, groups=groups, **conv_kwargs)
)
else:
self.conv_down = ConvNormActAa(
in_chs, out_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'),
aa_layer=block_kwargs.get('aa_layer', None))
aa_layer=block_kwargs.get('aa_layer', None), **conv_kwargs)
prev_chs = out_chs
block_out_chs = int(round(out_chs * block_ratio))
@ -318,6 +559,8 @@ def _cfg_to_stage_args(cfg, curr_stride=2, output_stride=32, drop_path_rate=0.):
cfg['down_growth'] = (cfg['down_growth'],) * num_stages
if 'cross_linear' in cfg and not isinstance(cfg['cross_linear'], (list, tuple)):
cfg['cross_linear'] = (cfg['cross_linear'],) * num_stages
if 'avg_down' in cfg and not isinstance(cfg['avg_down'], (list, tuple)):
cfg['avg_down'] = (cfg['avg_down'],) * num_stages
cfg['block_dpr'] = [None] * num_stages if not drop_path_rate else \
[x.tolist() for x in torch.linspace(0, drop_path_rate, sum(cfg['depth'])).split(cfg['depth'])]
stage_strides = []
@ -352,9 +595,20 @@ class CspNet(nn.Module):
"""
def __init__(
self, cfg, in_chans=3, num_classes=1000, output_stride=32, global_pool='avg', drop_rate=0.,
act_layer=nn.LeakyReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, drop_path_rate=0.,
zero_init_last=True, stage_fn=CrossStage, block_fn=ResBottleneck):
self,
cfg,
in_chans=3,
num_classes=1000,
output_stride=32,
global_pool='avg',
act_layer=nn.LeakyReLU,
norm_layer=nn.BatchNorm2d,
aa_layer=None,
drop_rate=0.,
drop_path_rate=0.,
zero_init_last=True,
stage_fn=CrossStage,
block_fn=ResBottleneck):
super().__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
@ -427,23 +681,22 @@ class CspNet(nn.Module):
def _init_weights(module, name, zero_init_last=False):
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(module, nn.BatchNorm2d):
nn.init.ones_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.01)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif zero_init_last and hasattr(module, 'zero_init_last'):
module.zero_init_last()
def _create_cspnet(variant, pretrained=False, **kwargs):
cfg_variant = variant.split('_')[0]
# NOTE: DarkNet is one of few models with stride==1 features w/ 6 out_indices [0..5]
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4, 5) if 'darknet' in variant else (0, 1, 2, 3, 4))
return build_model_with_cfg(
CspNet, variant, pretrained,
model_cfg=model_cfgs[cfg_variant],
model_cfg=model_cfgs[variant],
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs)
@ -469,22 +722,55 @@ def cspresnext50(pretrained=False, **kwargs):
@register_model
def cspresnext50_iabn(pretrained=False, **kwargs):
norm_layer = get_norm_act_layer('iabn', act_layer='leaky_relu')
return _create_cspnet('cspresnext50_iabn', pretrained=pretrained, norm_layer=norm_layer, **kwargs)
def cspdarknet53(pretrained=False, **kwargs):
return _create_cspnet('cspdarknet53', pretrained=pretrained, block_fn=DarkBlock, **kwargs)
@register_model
def cspdarknet53(pretrained=False, **kwargs):
return _create_cspnet('cspdarknet53', pretrained=pretrained, block_fn=DarkBlock, **kwargs)
def darknet17(pretrained=False, **kwargs):
return _create_cspnet('darknet17', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
@register_model
def darknet21(pretrained=False, **kwargs):
return _create_cspnet('darknet21', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
@register_model
def cspdarknet53_iabn(pretrained=False, **kwargs):
norm_layer = get_norm_act_layer('iabn', act_layer='leaky_relu')
return _create_cspnet('cspdarknet53_iabn', pretrained=pretrained, block_fn=DarkBlock, norm_layer=norm_layer, **kwargs)
def sedarknet21(pretrained=False, **kwargs):
return _create_cspnet('sedarknet21', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
@register_model
def darknet53(pretrained=False, **kwargs):
return _create_cspnet('darknet53', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
@register_model
def darknetaa53(pretrained=False, **kwargs):
return _create_cspnet(
'darknetaa53', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
@register_model
def cs2darknet_m(pretrained=False, **kwargs):
return _create_cspnet(
'cs2darknet_m', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs)
@register_model
def cs2darknet_l(pretrained=False, **kwargs):
return _create_cspnet(
'cs2darknet_l', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs)
@register_model
def cs2darknet_f_m(pretrained=False, **kwargs):
return _create_cspnet(
'cs2darknet_f_m', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs)
@register_model
def cs2darknet_f_l(pretrained=False, **kwargs):
return _create_cspnet(
'cs2darknet_f_l', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs)

19
timm/models/layers/conv_bn_act.py
Unescape View File

@ -2,6 +2,7 @@
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
@ -40,12 +41,26 @@ class ConvNormAct(nn.Module):
ConvBnAct = ConvNormAct
def create_aa(aa_layer, channels, stride=2, enable=True):
if not aa_layer or not enable:
return nn.Identity()
if isinstance(aa_layer, functools.partial):
if issubclass(aa_layer.func, nn.AvgPool2d):
return aa_layer()
else:
return aa_layer(channels)
elif issubclass(aa_layer, nn.AvgPool2d):
return aa_layer(stride)
else:
return aa_layer(channels=channels, stride=stride)
class ConvNormActAa(nn.Module):
def __init__(
self, in_channels, out_channels, kernel_size=1, stride=1, padding='', dilation=1, groups=1,
bias=False, apply_act=True, norm_layer=nn.BatchNorm2d, act_layer=nn.ReLU, aa_layer=None, drop_layer=None):
super(ConvNormActAa, self).__init__()
use_aa = aa_layer is not None
use_aa = aa_layer is not None and stride == 2
self.conv = create_conv2d(
in_channels, out_channels, kernel_size, stride=1 if use_aa else stride,
@ -56,7 +71,7 @@ class ConvNormActAa(nn.Module):
# NOTE for backwards (weight) compatibility, norm layer name remains `.bn`
norm_kwargs = dict(drop_layer=drop_layer) if drop_layer is not None else {}
self.bn = norm_act_layer(out_channels, apply_act=apply_act, **norm_kwargs)
self.aa = aa_layer(channels=out_channels) if stride == 2 and use_aa else nn.Identity()
self.aa = create_aa(aa_layer, out_channels, stride=stride, enable=use_aa)
@property
def in_channels(self):

Write Preview
Loading…
Cancel
Save