""" Normalization Free Nets. NFNet, NF-RegNet, NF-ResNet (pre-activation) Models
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Paper: `High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
Official Deepmind JAX code: https://github.com/deepmind/deepmind-research/tree/master/nfnets
Status:
* These models are a work in progress, experiments ongoing.
* Two pretrained weights so far, more to come.
* Model details update to closer match official JAX code now that it's released
* NF-ResNet, NF-RegNet-B, and NFNet-F models supported
Hacked together by / copyright Ross Wightman, 2021.
"""
import math
from dataclasses import dataclass, field
from collections import OrderedDict
from typing import Tuple, Optional
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
from .registry import register_model
from .layers import ClassifierHead, DropPath, AvgPool2dSame, ScaledStdConv2d, get_act_layer, get_attn, make_divisible, get_act_fn
def _dcfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.9, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = dict(
nfnet_f0=_dcfg(
url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), first_conv='stem.conv1'),
nfnet_f1=_dcfg(
url='', pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 320, 320), first_conv='stem.conv1'),
nfnet_f2=_dcfg(
url='', pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 352, 352), first_conv='stem.conv1'),
nfnet_f3=_dcfg(
url='', pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 416, 416), first_conv='stem.conv1'),
nfnet_f4=_dcfg(
url='', pool_size=(12, 12), input_size=(3, 384, 384), test_input_size=(3, 512, 512), first_conv='stem.conv1'),
nfnet_f5=_dcfg(
url='', pool_size=(13, 13), input_size=(3, 416, 416), test_input_size=(3, 544, 544), first_conv='stem.conv1'),
nfnet_f6=_dcfg(
url='', pool_size=(14, 14), input_size=(3, 448, 448), test_input_size=(3, 576, 576), first_conv='stem.conv1'),
nfnet_f7=_dcfg(
url='', pool_size=(15, 15), input_size=(3, 480, 480), test_input_size=(3, 608, 608), first_conv='stem.conv1'),
nfnet_f0s=_dcfg(
url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), first_conv='stem.conv1'),
nfnet_f1s=_dcfg(
url='', pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 320, 320), first_conv='stem.conv1'),
nfnet_f2s=_dcfg(
url='', pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 352, 352), first_conv='stem.conv1'),
nfnet_f3s=_dcfg(
url='', pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 416, 416), first_conv='stem.conv1'),
nfnet_f4s=_dcfg(
url='', pool_size=(12, 12), input_size=(3, 384, 384), test_input_size=(3, 512, 512), first_conv='stem.conv1'),
nfnet_f5s=_dcfg(
url='', pool_size=(13, 13), input_size=(3, 416, 416), test_input_size=(3, 544, 544), first_conv='stem.conv1'),
nfnet_f6s=_dcfg(
url='', pool_size=(14, 14), input_size=(3, 448, 448), test_input_size=(3, 576, 576), first_conv='stem.conv1'),
nfnet_f7s=_dcfg(
url='', pool_size=(15, 15), input_size=(3, 480, 480), test_input_size=(3, 608, 608), first_conv='stem.conv1'),
nf_regnet_b0=_dcfg(url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256)),
nf_regnet_b1=_dcfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nf_regnet_b1_256_ra2-ad85cfef.pth',
pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 288, 288)), # NOT to paper spec
nf_regnet_b2=_dcfg(url='', pool_size=(8, 8), input_size=(3, 240, 240), test_input_size=(3, 272, 272)),
nf_regnet_b3=_dcfg(url='', pool_size=(9, 9), input_size=(3, 288, 288), test_input_size=(3, 320, 320)),
nf_regnet_b4=_dcfg(url='', pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 384, 384)),
nf_regnet_b5=_dcfg(url='', pool_size=(12, 12), input_size=(3, 384, 384), test_input_size=(3, 456, 456)),
nf_resnet26=_dcfg(url=''),
nf_resnet50=_dcfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nf_resnet50_ra2-9f236009.pth',
pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 288, 288), crop_pct=0.94),
nf_resnet101=_dcfg(url=''),
nf_seresnet26=_dcfg(url=''),
nf_seresnet50=_dcfg(url=''),
nf_seresnet101=_dcfg(url=''),
nf_ecaresnet26=_dcfg(url=''),
nf_ecaresnet50=_dcfg(url=''),
nf_ecaresnet101=_dcfg(url=''),
)
@dataclass
class NfCfg:
depths: Tuple[int, int, int, int]
channels: Tuple[int, int, int, int]
alpha: float = 0.2
gamma_in_act: bool = False
stem_type: str = '3x3'
stem_chs: Optional[int] = None
group_size: Optional[int] = None
attn_layer: Optional[str] = None
attn_kwargs: dict = None
attn_gain: float = 2.0 # NF correction gain to apply if attn layer is used
width_factor: float = 1.0
bottle_ratio: float = 0.5
num_features: int = 0 # num out_channels for final conv, no final_conv if 0
ch_div: int = 8 # round channels % 8 == 0 to keep tensor-core use optimal
reg: bool = False # enables EfficientNet-like options used in RegNet variants, expand from in_chs, se in middle
extra_conv: bool = False # extra 3x3 bottleneck convolution for NFNet models
skipinit: bool = False # disabled by default, non-trivial performance impact
zero_init_fc: bool = False
act_layer: str = 'silu'
def _nfres_cfg(
depths, channels=(256, 512, 1024, 2048), group_size=None, act_layer='relu', attn_layer=None, attn_kwargs=None):
attn_kwargs = attn_kwargs or {}
cfg = NfCfg(
depths=depths, channels=channels, stem_type='7x7_pool', stem_chs=64, bottle_ratio=0.25,
group_size=group_size, act_layer=act_layer, attn_layer=attn_layer, attn_kwargs=attn_kwargs)
return cfg
def _nfreg_cfg(depths, channels=(48, 104, 208, 440)):
num_features = 1280 * channels[-1] // 440
attn_kwargs = dict(reduction_ratio=0.5, divisor=8)
cfg = NfCfg(
depths=depths, channels=channels, stem_type='3x3', group_size=8, width_factor=0.75, bottle_ratio=2.25,
num_features=num_features, reg=True, attn_layer='se', attn_kwargs=attn_kwargs)
return cfg
def _nfnet_cfg(depths, act_layer='gelu', attn_layer='se', attn_kwargs=None):
channels = (256, 512, 1536, 1536)
num_features = channels[-1] * 2
attn_kwargs = attn_kwargs or dict(reduction_ratio=0.5, divisor=8)
cfg = NfCfg(
depths=depths, channels=channels, stem_type='nff', group_size=128, bottle_ratio=0.5, extra_conv=True,
num_features=num_features, act_layer=act_layer, attn_layer=attn_layer, attn_kwargs=attn_kwargs)
return cfg
model_cfgs = dict(
# NFNet-F models w/ GeLU
nfnet_f0=_nfnet_cfg(depths=(1, 2, 6, 3)),
nfnet_f1=_nfnet_cfg(depths=(2, 4, 12, 6)),
nfnet_f2=_nfnet_cfg(depths=(3, 6, 18, 9)),
nfnet_f3=_nfnet_cfg(depths=(4, 8, 24, 12)),
nfnet_f4=_nfnet_cfg(depths=(5, 10, 30, 15)),
nfnet_f5=_nfnet_cfg(depths=(6, 12, 36, 18)),
nfnet_f6=_nfnet_cfg(depths=(7, 14, 42, 21)),
nfnet_f7=_nfnet_cfg(depths=(8, 16, 48, 24)),
# NFNet-F models w/ SiLU (much faster in PyTorch)
nfnet_f0s=_nfnet_cfg(depths=(1, 2, 6, 3), act_layer='silu'),
nfnet_f1s=_nfnet_cfg(depths=(2, 4, 12, 6), act_layer='silu'),
nfnet_f2s=_nfnet_cfg(depths=(3, 6, 18, 9), act_layer='silu'),
nfnet_f3s=_nfnet_cfg(depths=(4, 8, 24, 12), act_layer='silu'),
nfnet_f4s=_nfnet_cfg(depths=(5, 10, 30, 15), act_layer='silu'),
nfnet_f5s=_nfnet_cfg(depths=(6, 12, 36, 18), act_layer='silu'),
nfnet_f6s=_nfnet_cfg(depths=(7, 14, 42, 21), act_layer='silu'),
nfnet_f7s=_nfnet_cfg(depths=(8, 16, 48, 24), act_layer='silu'),
# NFNet-F models w/ SiLU (much faster in PyTorch)
# FIXME add remainder if silu vs gelu proves worthwhile
# EffNet influenced RegNet defs.
# NOTE: These aren't quite the official ver, ch_div=1 must be set for exact ch counts. I round to ch_div=8.
nf_regnet_b0=_nfreg_cfg(depths=(1, 3, 6, 6)),
nf_regnet_b1=_nfreg_cfg(depths=(2, 4, 7, 7)),
nf_regnet_b2=_nfreg_cfg(depths=(2, 4, 8, 8), channels=(56, 112, 232, 488)),
nf_regnet_b3=_nfreg_cfg(depths=(2, 5, 9, 9), channels=(56, 128, 248, 528)),
nf_regnet_b4=_nfreg_cfg(depths=(2, 6, 11, 11), channels=(64, 144, 288, 616)),
nf_regnet_b5=_nfreg_cfg(depths=(3, 7, 14, 14), channels=(80, 168, 336, 704)),
# FIXME add B6-B8
# ResNet (preact, D style deep stem/avg down) defs
nf_resnet26=_nfres_cfg(depths=(2, 2, 2, 2)),
nf_resnet50=_nfres_cfg(depths=(3, 4, 6, 3)),
nf_resnet101=_nfres_cfg(depths=(3, 4, 23, 3)),
nf_seresnet26=_nfres_cfg(depths=(2, 2, 2, 2), attn_layer='se', attn_kwargs=dict(reduction_ratio=0.25)),
nf_seresnet50=_nfres_cfg(depths=(3, 4, 6, 3), attn_layer='se', attn_kwargs=dict(reduction_ratio=0.25)),
nf_seresnet101=_nfres_cfg(depths=(3, 4, 23, 3), attn_layer='se', attn_kwargs=dict(reduction_ratio=0.25)),
nf_ecaresnet26=_nfres_cfg(depths=(2, 2, 2, 2), attn_layer='eca', attn_kwargs=dict()),
nf_ecaresnet50=_nfres_cfg(depths=(3, 4, 6, 3), attn_layer='eca', attn_kwargs=dict()),
nf_ecaresnet101=_nfres_cfg(depths=(3, 4, 23, 3), attn_layer='eca', attn_kwargs=dict()),
)
class GammaAct(nn.Module):
def __init__(self, act_type='relu', gamma: float = 1.0, inplace=False):
super().__init__()
self.act_fn = get_act_fn(act_type)
self.gamma = gamma
self.inplace = inplace
def forward(self, x):
return self.gamma * self.act_fn(x, inplace=self.inplace)
def act_with_gamma(act_type, gamma: float = 1.):
def _create(inplace=False):
return GammaAct(act_type, gamma=gamma, inplace=inplace)
return _create
class DownsampleAvg(nn.Module):
def __init__(
self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None, conv_layer=ScaledStdConv2d):
""" AvgPool Downsampling as in 'D' ResNet variants. Support for dilation."""
super(DownsampleAvg, self).__init__()
avg_stride = stride if dilation == 1 else 1
if stride > 1 or dilation > 1:
avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
self.pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
else:
self.pool = nn.Identity()
self.conv = conv_layer(in_chs, out_chs, 1, stride=1)
def forward(self, x):
return self.conv(self.pool(x))
class NormFreeBlock(nn.Module):
"""Normalization-free pre-activation block.
"""
def __init__(
self, in_chs, out_chs=None, stride=1, dilation=1, first_dilation=None,
alpha=1.0, beta=1.0, bottle_ratio=0.25, group_size=None, ch_div=1, reg=True, extra_conv=False,
skipinit=False, attn_layer=None, attn_gain=2.0, act_layer=None, conv_layer=None, drop_path_rate=0.):
super().__init__()
first_dilation = first_dilation or dilation
out_chs = out_chs or in_chs
# RegNet variants scale bottleneck from in_chs, otherwise scale from out_chs like ResNet
mid_chs = make_divisible(in_chs * bottle_ratio if reg else out_chs * bottle_ratio, ch_div)
groups = 1 if not group_size else mid_chs // group_size
if group_size and group_size % ch_div == 0:
mid_chs = group_size * groups # correct mid_chs if group_size divisible by ch_div, otherwise error
self.alpha = alpha
self.beta = beta
self.attn_gain = attn_gain
if in_chs != out_chs or stride != 1 or dilation != first_dilation:
self.downsample = DownsampleAvg(
in_chs, out_chs, stride=stride, dilation=dilation, first_dilation=first_dilation, conv_layer=conv_layer)
else:
self.downsample = None
self.act1 = act_layer()
self.conv1 = conv_layer(in_chs, mid_chs, 1)
self.act2 = act_layer(inplace=True)
self.conv2 = conv_layer(mid_chs, mid_chs, 3, stride=stride, dilation=first_dilation, groups=groups)
if extra_conv:
self.act2b = act_layer(inplace=True)
self.conv2b = conv_layer(mid_chs, mid_chs, 3, stride=1, dilation=dilation, groups=groups)
else:
self.act2b = None
self.conv2b = None
if reg and attn_layer is not None:
self.attn = attn_layer(mid_chs) # RegNet blocks apply attn btw conv2 & 3
else:
self.attn = None
self.act3 = act_layer()
self.conv3 = conv_layer(mid_chs, out_chs, 1)
if not reg and attn_layer is not None:
self.attn_last = attn_layer(out_chs) # ResNet blocks apply attn after conv3
else:
self.attn_last = None
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
self.skipinit_gain = nn.Parameter(torch.tensor(0.)) if skipinit else None
def forward(self, x):
out = self.act1(x) * self.beta
# shortcut branch
shortcut = x
if self.downsample is not None:
shortcut = self.downsample(out)
# residual branch
out = self.conv1(out)
out = self.conv2(self.act2(out))
if self.conv2b is not None:
out = self.conv2b(self.act2b(out))
if self.attn is not None:
out = self.attn_gain * self.attn(out)
out = self.conv3(self.act3(out))
if self.attn_last is not None:
out = self.attn_gain * self.attn_last(out)
out = self.drop_path(out)
if self.skipinit_gain is not None:
# this really slows things down for some reason, TBD
out = out * self.skipinit_gain
out = out * self.alpha + shortcut
return out
def stem_info(stem_type):
stem_stride = 2
if 'nff' in stem_type or 'pool' in stem_type:
stem_stride = 4
stem_feat = ''
if 'nff' in stem_type:
stem_feat = 'stem.act3'
elif 'deep' in stem_type and not 'pool' in stem_type:
stem_feat = 'stem.act2'
return stem_stride, stem_feat
def create_stem(in_chs, out_chs, stem_type='', conv_layer=None, act_layer=None):
stem_stride = 2
stem_feature = ''
stem = OrderedDict()
assert stem_type in ('', 'nff', 'deep', 'deep_tiered', '3x3', '7x7', 'deep_pool', '3x3_pool', '7x7_pool')
if 'deep' in stem_type or 'nff' in stem_type:
# 3 deep 3x3 conv stack as in ResNet V1D models. NOTE: doesn't work as well here
if 'nff' in stem_type:
assert not 'pool' in stem_type
stem_chs = (16, 32, 64, out_chs)
strides = (2, 1, 1, 2)
stem_stride = 4
stem_feature = 'stem.act4'
else:
if 'tiered' in stem_type:
stem_chs = (3 * out_chs // 8, out_chs // 2, out_chs)
else:
stem_chs = (out_chs // 2, out_chs // 2, out_chs)
strides = (2, 1, 1)
stem_feature = 'stem.act3'
last_idx = len(stem_chs) - 1
for i, (c, s) in enumerate(zip(stem_chs, strides)):
stem[f'conv{i+1}'] = conv_layer(in_chs, c, kernel_size=3, stride=s)
if i != last_idx:
stem[f'act{i+2}'] = act_layer(inplace=True)
in_chs = c
elif '3x3' in stem_type:
# 3x3 stem conv as in RegNet
stem['conv'] = conv_layer(in_chs, out_chs, kernel_size=3, stride=2)
else:
# 7x7 stem conv as in ResNet
stem['conv'] = conv_layer(in_chs, out_chs, kernel_size=7, stride=2)
if 'pool' in stem_type:
stem['pool'] = nn.MaxPool2d(3, stride=2, padding=1)
stem_stride = 4
return nn.Sequential(stem), stem_stride, stem_feature
_nonlin_gamma = dict(
identity=1.0,
celu=1.270926833152771,
elu=1.2716004848480225,
gelu=1.7015043497085571,
leaky_relu=1.70590341091156,
log_sigmoid=1.9193484783172607,
log_softmax=1.0002083778381348,
relu=1.7139588594436646,
relu6=1.7131484746932983,
selu=1.0008515119552612,
sigmoid=4.803835391998291,
silu=1.7881293296813965,
softsign=2.338853120803833,
softplus=1.9203323125839233,
tanh=1.5939117670059204,
)
class NormFreeNet(nn.Module):
""" Normalization-free ResNets and RegNets
As described in `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
This model aims to cover both the NFRegNet-Bx models as detailed in the paper's code snippets and
the (preact) ResNet models described earlier in the paper.
There are a few differences:
* channels are rounded to be divisible by 8 by default (keep tensor core kernels happy),
this changes channel dim and param counts slightly from the paper models
* activation correcting gamma constants are moved into the ScaledStdConv as it has less performance
impact in PyTorch when done with the weight scaling there. This likely wasn't a concern in the JAX impl.
* a config option `gamma_in_act` can be enabled to not apply gamma in StdConv as described above, but
apply it in each activation. This is slightly slower, and yields slightly different results.
* skipinit is disabled by default, it seems to have a rather drastic impact on GPU memory use and throughput
for what it is/does. Approx 8-10% throughput loss.
"""
def __init__(self, cfg: NfCfg, num_classes=1000, in_chans=3, global_pool='avg', output_stride=32,
drop_rate=0., drop_path_rate=0.):
super().__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
assert cfg.act_layer in _nonlin_gamma, f"Please add non-linearity constants for activation ({cfg.act_layer})."
if cfg.gamma_in_act:
act_layer = act_with_gamma(cfg.act_layer, gamma=_nonlin_gamma[cfg.act_layer])
conv_layer = partial(ScaledStdConv2d, bias=True, gain=True)
else:
act_layer = get_act_layer(cfg.act_layer)
conv_layer = partial(ScaledStdConv2d, bias=True, gain=True, gamma=_nonlin_gamma[cfg.act_layer])
attn_layer = partial(get_attn(cfg.attn_layer), **cfg.attn_kwargs) if cfg.attn_layer else None
stem_chs = cfg.stem_chs or cfg.channels[0]
stem_chs = make_divisible(stem_chs * cfg.width_factor, cfg.ch_div)
self.stem, stem_stride, stem_feat = create_stem(
in_chans, stem_chs, cfg.stem_type, conv_layer=conv_layer, act_layer=act_layer)
self.feature_info = [dict(num_chs=stem_chs, reduction=2, module=stem_feat)] if stem_stride == 4 else []
drop_path_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(cfg.depths)).split(cfg.depths)]
prev_chs = stem_chs
net_stride = stem_stride
dilation = 1
expected_var = 1.0
stages = []
for stage_idx, stage_depth in enumerate(cfg.depths):
stride = 1 if stage_idx == 0 and stem_stride > 2 else 2
if stride == 2:
self.feature_info += [dict(num_chs=prev_chs, reduction=net_stride, module=f'stages.{stage_idx}.0.act1')]
if net_stride >= output_stride and stride > 1:
dilation *= stride
stride = 1
net_stride *= stride
first_dilation = 1 if dilation in (1, 2) else 2
blocks = []
for block_idx in range(cfg.depths[stage_idx]):
first_block = block_idx == 0 and stage_idx == 0
out_chs = make_divisible(cfg.channels[stage_idx] * cfg.width_factor, cfg.ch_div)
blocks += [NormFreeBlock(
in_chs=prev_chs, out_chs=out_chs,
alpha=cfg.alpha,
beta=1. / expected_var ** 0.5, # NOTE: beta used as multiplier in block
stride=stride if block_idx == 0 else 1,
dilation=dilation,
first_dilation=first_dilation,
group_size=cfg.group_size,
bottle_ratio=1. if cfg.reg and first_block else cfg.bottle_ratio,
ch_div=cfg.ch_div,
reg=cfg.reg,
extra_conv=cfg.extra_conv,
skipinit=cfg.skipinit,
attn_layer=attn_layer,
attn_gain=cfg.attn_gain,
act_layer=act_layer,
conv_layer=conv_layer,
drop_path_rate=drop_path_rates[stage_idx][block_idx],
)]
if block_idx == 0:
expected_var = 1. # expected var is reset after first block of each stage
expected_var += cfg.alpha ** 2 # Even if reset occurs, increment expected variance
first_dilation = dilation
prev_chs = out_chs
stages += [nn.Sequential(*blocks)]
self.stages = nn.Sequential(*stages)
if cfg.num_features:
# The paper NFRegNet models have an EfficientNet-like final head convolution.
self.num_features = make_divisible(cfg.width_factor * cfg.num_features, cfg.ch_div)
self.final_conv = conv_layer(prev_chs, self.num_features, 1)
# FIXME not 100% clear on gamma subtleties final conv/final act in case where it's pushed into stdconv
else:
self.num_features = prev_chs
self.final_conv = nn.Identity()
self.final_act = act_layer(inplace=cfg.num_features > 0)
self.feature_info += [dict(num_chs=self.num_features, reduction=net_stride, module='final_act')]
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
for n, m in self.named_modules():
if 'fc' in n and isinstance(m, nn.Linear):
if cfg.zero_init_fc:
nn.init.zeros_(m.weight)
else:
nn.init.normal_(m.weight, 0., .01)
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Conv2d):
# as per discussion with paper authors, original in haiku is
# hk.initializers.VarianceScaling(1.0, 'fan_in', 'normal')' w/ zero'd bias
nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='linear')
if m.bias is not None:
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)
x = self.stages(x)
x = self.final_conv(x)
x = self.final_act(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def _create_normfreenet(variant, pretrained=False, **kwargs):
model_cfg = model_cfgs[variant]
feature_cfg = dict(flatten_sequential=True)
feature_cfg['feature_cls'] = 'hook' # pre-act models need hooks to grab feat from act1 in bottleneck blocks
if 'pool' in model_cfg.stem_type and 'deep' not in model_cfg.stem_type:
feature_cfg['out_indices'] = (1, 2, 3, 4) # no stride 2 feat for stride 4, 1 layer maxpool stems
return build_model_with_cfg(
NormFreeNet, variant, pretrained,
default_cfg=default_cfgs[variant],
model_cfg=model_cfg,
feature_cfg=feature_cfg,
**kwargs)
@register_model
def nfnet_f0(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f0', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f1(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f1', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f2(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f2', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f3(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f3', pretrained=pretrained, **kwargs)
def nfnet_f4(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f4', pretrained=pretrained, **kwargs)
def nfnet_f5(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f5', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f6(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f6', pretrained=pretrained, **kwargs)
def nfnet_f7(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f7', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f0s(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f0s', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f1s(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f1s', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f2s(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f2s', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f3s(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f3s', pretrained=pretrained, **kwargs)
def nfnet_f4s(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f4s', pretrained=pretrained, **kwargs)
def nfnet_f5s(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f5s', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f6s(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f6s', pretrained=pretrained, **kwargs)
def nfnet_f7s(pretrained=False, **kwargs):
return _create_normfreenet('nfnet_f7s', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b0(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b0', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b1(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b1', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b2(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b2', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b3(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b3', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b4(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b4', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b5(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b5', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b0(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b0', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b1(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b1', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b2(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b2', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b3(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b3', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b4(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b4', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b5(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b5', pretrained=pretrained, **kwargs)
@register_model
def nf_resnet26(pretrained=False, **kwargs):
return _create_normfreenet('nf_resnet26', pretrained=pretrained, **kwargs)
@register_model
def nf_resnet50(pretrained=False, **kwargs):
return _create_normfreenet('nf_resnet50', pretrained=pretrained, **kwargs)
@register_model
def nf_resnet101(pretrained=False, **kwargs):
return _create_normfreenet('nf_resnet101', pretrained=pretrained, **kwargs)
@register_model
def nf_seresnet26(pretrained=False, **kwargs):
return _create_normfreenet('nf_seresnet26', pretrained=pretrained, **kwargs)
@register_model
def nf_seresnet50(pretrained=False, **kwargs):
return _create_normfreenet('nf_seresnet50', pretrained=pretrained, **kwargs)
@register_model
def nf_seresnet101(pretrained=False, **kwargs):
return _create_normfreenet('nf_seresnet101', pretrained=pretrained, **kwargs)
@register_model
def nf_ecaresnet26(pretrained=False, **kwargs):
return _create_normfreenet('nf_ecaresnet26', pretrained=pretrained, **kwargs)
@register_model
def nf_ecaresnet50(pretrained=False, **kwargs):
return _create_normfreenet('nf_ecaresnet50', pretrained=pretrained, **kwargs)
@register_model
def nf_ecaresnet101(pretrained=False, **kwargs):
return _create_normfreenet('nf_ecaresnet101', pretrained=pretrained, **kwargs)