"""PyTorch CspNet
A PyTorch implementation of Cross Stage Partial Networks including:
* CSPResNet50
* CSPResNeXt50
* CSPDarkNet53
* and DarkNet53 for good measure
Based on paper `CSPNet: A New Backbone that can Enhance Learning Capability of CNN` - https://arxiv.org/abs/1911.11929
Reference impl via darknet cfg files at https://github.com/WongKinYiu/CrossStagePartialNetworks
Hacked together by / Copyright 2020 Ross Wightman
"""
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
from .layers import ClassifierHead, ConvBnAct, DropPath, create_attn, get_norm_act_layer
from .registry import register_model
__all__ = ['CspNet'] # model_registry will add each entrypoint fn to this
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8),
'crop_pct': 0.887, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv1.conv', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = {
'cspresnet50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspresnet50_ra-d3e8d487.pth'),
'cspresnet50d': _cfg(url=''),
'cspresnet50w': _cfg(url=''),
'cspresnext50': _cfg(
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=''),
'darknet53': _cfg(url=''),
}
model_cfgs = dict(
cspresnet50=dict(
stem=dict(out_chs=64, kernel_size=7, stride=2, pool='max'),
stage=dict(
out_chs=(128, 256, 512, 1024),
depth=(3, 3, 5, 2),
stride=(1,) + (2,) * 3,
exp_ratio=(2.,) * 4,
bottle_ratio=(0.5,) * 4,
block_ratio=(1.,) * 4,
cross_linear=True,
)
),
cspresnet50d=dict(
stem=dict(out_chs=[32, 32, 64], kernel_size=3, stride=2, pool='max'),
stage=dict(
out_chs=(128, 256, 512, 1024),
depth=(3, 3, 5, 2),
stride=(1,) + (2,) * 3,
exp_ratio=(2.,) * 4,
bottle_ratio=(0.5,) * 4,
block_ratio=(1.,) * 4,
cross_linear=True,
)
),
cspresnet50w=dict(
stem=dict(out_chs=[32, 32, 64], kernel_size=3, stride=2, pool='max'),
stage=dict(
out_chs=(256, 512, 1024, 2048),
depth=(3, 3, 5, 2),
stride=(1,) + (2,) * 3,
exp_ratio=(1.,) * 4,
bottle_ratio=(0.25,) * 4,
block_ratio=(0.5,) * 4,
cross_linear=True,
)
),
cspresnext50=dict(
stem=dict(out_chs=64, kernel_size=7, stride=2, pool='max'),
stage=dict(
out_chs=(256, 512, 1024, 2048),
depth=(3, 3, 5, 2),
stride=(1,) + (2,) * 3,
groups=(32,) * 4,
exp_ratio=(1.,) * 4,
bottle_ratio=(1.,) * 4,
block_ratio=(0.5,) * 4,
cross_linear=True,
)
),
cspdarknet53=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,
exp_ratio=(2.,) + (1.,) * 4,
bottle_ratio=(0.5,) + (1.0,) * 4,
block_ratio=(1.,) + (0.5,) * 4,
down_growth=True,
)
),
darknet53=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,
)
)
)
def create_stem(
in_chans=3, out_chs=32, kernel_size=3, stride=2, pool='',
act_layer=None, norm_layer=None, aa_layer=None):
stem = nn.Sequential()
if not isinstance(out_chs, (tuple, list)):
out_chs = [out_chs]
assert len(out_chs)
in_c = in_chans
for i, out_c in enumerate(out_chs):
conv_name = f'conv{i + 1}'
stem.add_module(conv_name, ConvBnAct(
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
last_conv = conv_name
if pool:
if aa_layer is not None:
stem.add_module('pool', nn.MaxPool2d(kernel_size=3, stride=1, padding=1))
stem.add_module('aa', aa_layer(channels=in_c, stride=2))
else:
stem.add_module('pool', nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
return stem, dict(num_chs=in_c, reduction=stride, module='.'.join(['stem', last_conv]))
class ResBottleneck(nn.Module):
""" ResNe(X)t Bottleneck Block
"""
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):
super(ResBottleneck, self).__init__()
mid_chs = int(round(out_chs * bottle_ratio))
ckwargs = dict(act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer, drop_block=drop_block)
self.conv1 = ConvBnAct(in_chs, mid_chs, kernel_size=1, **ckwargs)
self.conv2 = ConvBnAct(mid_chs, mid_chs, kernel_size=3, dilation=dilation, groups=groups, **ckwargs)
self.attn2 = create_attn(attn_layer, channels=mid_chs) if not attn_last else None
self.conv3 = ConvBnAct(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)
def zero_init_last_bn(self):
nn.init.zeros_(self.conv3.bn.weight)
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.conv2(x)
if self.attn2 is not None:
x = self.attn2(x)
x = self.conv3(x)
if self.attn3 is not None:
x = self.attn3(x)
if self.drop_path is not None:
x = self.drop_path(x)
x = x + shortcut
# FIXME partial shortcut needed if first block handled as per original, not used for my current impl
#x[:, :shortcut.size(1)] += shortcut
x = self.act3(x)
return x
class DarkBlock(nn.Module):
""" DarkNet Block
"""
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):
super(DarkBlock, self).__init__()
mid_chs = int(round(out_chs * bottle_ratio))
ckwargs = dict(act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer, drop_block=drop_block)
self.conv1 = ConvBnAct(in_chs, mid_chs, kernel_size=1, **ckwargs)
self.conv2 = ConvBnAct(mid_chs, out_chs, kernel_size=3, dilation=dilation, groups=groups, **ckwargs)
self.attn = create_attn(attn_layer, channels=out_chs)
self.drop_path = drop_path
def zero_init_last_bn(self):
nn.init.zeros_(self.conv2.bn.weight)
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.conv2(x)
if self.attn is not None:
x = self.attn(x)
if self.drop_path is not None:
x = self.drop_path(x)
x = x + shortcut
return x
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):
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))
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:
self.conv_down = ConvBnAct(
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
# FIXME this 1x1 expansion is pushed down into the cross and block paths in the darknet cfgs. Also,
# there is also special case for the first stage for some of the model that results in uneven split
# across the two paths. I did it this way for simplicity for now.
self.conv_exp = ConvBnAct(prev_chs, exp_chs, kernel_size=1, apply_act=not cross_linear, **conv_kwargs)
prev_chs = exp_chs // 2 # output of conv_exp is always split in two
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_b = ConvBnAct(prev_chs, exp_chs // 2, kernel_size=1, **conv_kwargs)
self.conv_transition = ConvBnAct(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:]
xb = self.blocks(xb)
xb = self.conv_transition_b(xb).contiguous()
out = self.conv_transition(torch.cat([xs, xb], 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):
super(DarkStage, self).__init__()
first_dilation = first_dilation or dilation
self.conv_down = ConvBnAct(
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))
prev_chs = out_chs
block_out_chs = int(round(out_chs * block_ratio))
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
def forward(self, x):
x = self.conv_down(x)
x = self.blocks(x)
return x
def _cfg_to_stage_args(cfg, curr_stride=2, output_stride=32, drop_path_rate=0.):
# get per stage args for stage and containing blocks, calculate strides to meet target output_stride
num_stages = len(cfg['depth'])
if 'groups' not in cfg:
cfg['groups'] = (1,) * num_stages
if 'down_growth' in cfg and not isinstance(cfg['down_growth'], (list, tuple)):
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
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 = []
stage_dilations = []
stage_first_dilations = []
dilation = 1
for cfg_stride in cfg['stride']:
stage_first_dilations.append(dilation)
if curr_stride >= output_stride:
dilation *= cfg_stride
stride = 1
else:
stride = cfg_stride
curr_stride *= stride
stage_strides.append(stride)
stage_dilations.append(dilation)
cfg['stride'] = stage_strides
cfg['dilation'] = stage_dilations
cfg['first_dilation'] = stage_first_dilations
stage_args = [dict(zip(cfg.keys(), values)) for values in zip(*cfg.values())]
return stage_args
class CspNet(nn.Module):
"""Cross Stage Partial base model.
Paper: `CSPNet: A New Backbone that can Enhance Learning Capability of CNN` - https://arxiv.org/abs/1911.11929
Ref Impl: https://github.com/WongKinYiu/CrossStagePartialNetworks
NOTE: There are differences in the way I handle the 1x1 'expansion' conv in this impl vs the
darknet impl. I did it this way for simplicity and less special cases.
"""
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_bn=True, stage_fn=CrossStage, block_fn=ResBottleneck):
super().__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
assert output_stride in (8, 16, 32)
layer_args = dict(act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer)
# Construct the stem
self.stem, stem_feat_info = create_stem(in_chans, **cfg['stem'], **layer_args)
self.feature_info = [stem_feat_info]
prev_chs = stem_feat_info['num_chs']
curr_stride = stem_feat_info['reduction'] # reduction does not include pool
if cfg['stem']['pool']:
curr_stride *= 2
# Construct the stages
per_stage_args = _cfg_to_stage_args(
cfg['stage'], curr_stride=curr_stride, output_stride=output_stride, drop_path_rate=drop_path_rate)
self.stages = nn.Sequential()
for i, sa in enumerate(per_stage_args):
self.stages.add_module(
str(i), stage_fn(prev_chs, **sa, **layer_args, block_fn=block_fn))
prev_chs = sa['out_chs']
curr_stride *= sa['stride']
self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{i}')]
# Construct the head
self.num_features = prev_chs
self.head = ClassifierHead(
in_chs=prev_chs, num_classes=num_classes, pool_type=global_pool, drop_rate=drop_rate)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, mean=0.0, std=0.01)
nn.init.zeros_(m.bias)
if zero_init_last_bn:
for m in self.modules():
if hasattr(m, 'zero_init_last_bn'):
m.zero_init_last_bn()
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)
x = self.stages(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def _create_cspnet(variant, pretrained=False, **kwargs):
cfg_variant = variant.split('_')[0]
return build_model_with_cfg(
CspNet, variant, pretrained,
default_cfg=default_cfgs[variant],
feature_cfg=dict(flatten_sequential=True), model_cfg=model_cfgs[cfg_variant],
**kwargs)
@register_model
def cspresnet50(pretrained=False, **kwargs):
return _create_cspnet('cspresnet50', pretrained=pretrained, **kwargs)
@register_model
def cspresnet50d(pretrained=False, **kwargs):
return _create_cspnet('cspresnet50d', pretrained=pretrained, **kwargs)
@register_model
def cspresnet50w(pretrained=False, **kwargs):
return _create_cspnet('cspresnet50w', pretrained=pretrained, **kwargs)
@register_model
def cspresnext50(pretrained=False, **kwargs):
return _create_cspnet('cspresnext50', pretrained=pretrained, **kwargs)
@register_model
def cspresnext50_iabn(pretrained=False, **kwargs):
norm_layer = get_norm_act_layer('iabn')
return _create_cspnet('cspresnext50_iabn', pretrained=pretrained, norm_layer=norm_layer, **kwargs)
@register_model
def cspdarknet53(pretrained=False, **kwargs):
return _create_cspnet('cspdarknet53', pretrained=pretrained, block_fn=DarkBlock, **kwargs)
@register_model
def cspdarknet53_iabn(pretrained=False, **kwargs):
norm_layer = get_norm_act_layer('iabn')
return _create_cspnet('cspdarknet53_iabn', pretrained=pretrained, block_fn=DarkBlock, norm_layer=norm_layer, **kwargs)
@register_model
def darknet53(pretrained=False, **kwargs):
return _create_cspnet('darknet53', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)