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pytorch-image-models/timm/models/regnet.py

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"""RegNet
Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
Based on original PyTorch impl linked above, but re-wrote to use my own blocks (adapted from ResNet here)
and cleaned up with more descriptive variable names.
Weights from original impl have been modified
* first layer from BGR -> RGB as most PyTorch models are
* removed training specific dict entries from checkpoints and keep model state_dict only
* remap names to match the ones here
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from dataclasses import dataclass
from functools import partial
from typing import Optional, Union, Callable
import numpy as np
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg, named_apply
from .layers import ClassifierHead, AvgPool2dSame, ConvNormAct, SEModule, DropPath, GroupNormAct
from .layers import get_act_layer, get_norm_act_layer, create_conv2d
from .registry import register_model
@dataclass
class RegNetCfg:
depth: int = 21
w0: int = 80
wa: float = 42.63
wm: float = 2.66
group_size: int = 24
bottle_ratio: float = 1.
se_ratio: float = 0.
stem_width: int = 32
downsample: Optional[str] = 'conv1x1'
linear_out: bool = False
preact: bool = False
num_features: int = 0
act_layer: Union[str, Callable] = 'relu'
norm_layer: Union[str, Callable] = 'batchnorm'
# Model FLOPS = three trailing digits * 10^8
model_cfgs = dict(
# RegNet-X
regnetx_002=RegNetCfg(w0=24, wa=36.44, wm=2.49, group_size=8, depth=13),
regnetx_004=RegNetCfg(w0=24, wa=24.48, wm=2.54, group_size=16, depth=22),
regnetx_006=RegNetCfg(w0=48, wa=36.97, wm=2.24, group_size=24, depth=16),
regnetx_008=RegNetCfg(w0=56, wa=35.73, wm=2.28, group_size=16, depth=16),
regnetx_016=RegNetCfg(w0=80, wa=34.01, wm=2.25, group_size=24, depth=18),
regnetx_032=RegNetCfg(w0=88, wa=26.31, wm=2.25, group_size=48, depth=25),
regnetx_040=RegNetCfg(w0=96, wa=38.65, wm=2.43, group_size=40, depth=23),
regnetx_064=RegNetCfg(w0=184, wa=60.83, wm=2.07, group_size=56, depth=17),
regnetx_080=RegNetCfg(w0=80, wa=49.56, wm=2.88, group_size=120, depth=23),
regnetx_120=RegNetCfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19),
regnetx_160=RegNetCfg(w0=216, wa=55.59, wm=2.1, group_size=128, depth=22),
regnetx_320=RegNetCfg(w0=320, wa=69.86, wm=2.0, group_size=168, depth=23),
# RegNet-Y
regnety_002=RegNetCfg(w0=24, wa=36.44, wm=2.49, group_size=8, depth=13, se_ratio=0.25),
regnety_004=RegNetCfg(w0=48, wa=27.89, wm=2.09, group_size=8, depth=16, se_ratio=0.25),
regnety_006=RegNetCfg(w0=48, wa=32.54, wm=2.32, group_size=16, depth=15, se_ratio=0.25),
regnety_008=RegNetCfg(w0=56, wa=38.84, wm=2.4, group_size=16, depth=14, se_ratio=0.25),
regnety_016=RegNetCfg(w0=48, wa=20.71, wm=2.65, group_size=24, depth=27, se_ratio=0.25),
regnety_032=RegNetCfg(w0=80, wa=42.63, wm=2.66, group_size=24, depth=21, se_ratio=0.25),
regnety_040=RegNetCfg(w0=96, wa=31.41, wm=2.24, group_size=64, depth=22, se_ratio=0.25),
regnety_064=RegNetCfg(w0=112, wa=33.22, wm=2.27, group_size=72, depth=25, se_ratio=0.25),
regnety_080=RegNetCfg(w0=192, wa=76.82, wm=2.19, group_size=56, depth=17, se_ratio=0.25),
regnety_120=RegNetCfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19, se_ratio=0.25),
regnety_160=RegNetCfg(w0=200, wa=106.23, wm=2.48, group_size=112, depth=18, se_ratio=0.25),
regnety_320=RegNetCfg(w0=232, wa=115.89, wm=2.53, group_size=232, depth=20, se_ratio=0.25),
# Experimental
regnety_040s_gn=RegNetCfg(
w0=96, wa=31.41, wm=2.24, group_size=64, depth=22, se_ratio=0.25,
act_layer='silu', norm_layer=partial(GroupNormAct, group_size=16)),
# regnetv = 'preact regnet y'
regnetv_040=RegNetCfg(
depth=22, w0=96, wa=31.41, wm=2.24, group_size=64, se_ratio=0.25, preact=True, act_layer='silu'),
# regnetw = 'preact regnet z'
regnetw_040=RegNetCfg(
depth=28, w0=48, wa=14.5, wm=2.226, group_size=8, bottle_ratio=4.0, se_ratio=0.25,
downsample=None, preact=True, num_features=1536, act_layer='silu',
),
# RegNet-Z (unverified)
regnetz_005=RegNetCfg(
depth=21, w0=16, wa=10.7, wm=2.51, group_size=4, bottle_ratio=4.0, se_ratio=0.25,
downsample=None, linear_out=True, num_features=1024, act_layer='silu',
),
regnetz_040=RegNetCfg(
depth=28, w0=48, wa=14.5, wm=2.226, group_size=8, bottle_ratio=4.0, se_ratio=0.25,
downsample=None, linear_out=True, num_features=1536, act_layer='silu',
),
)
def _cfg(url='', **kwargs):
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.conv', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = dict(
regnetx_002=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth'),
regnetx_004=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth'),
regnetx_006=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth'),
regnetx_008=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth'),
regnetx_016=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth'),
regnetx_032=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth'),
regnetx_040=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth'),
regnetx_064=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth'),
regnetx_080=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth'),
regnetx_120=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth'),
regnetx_160=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth'),
regnetx_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth'),
regnety_002=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth'),
regnety_004=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth'),
regnety_006=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth'),
regnety_008=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth'),
regnety_016=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth'),
regnety_032=_cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth',
crop_pct=1.0, test_input_size=(3, 288, 288)),
regnety_040=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth'),
regnety_064=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth'),
regnety_080=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth'),
regnety_120=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth'),
regnety_160=_cfg(
url='https://dl.fbaipublicfiles.com/deit/regnety_160-a5fe301d.pth', # from Facebook DeiT GitHub repository
crop_pct=1.0, test_input_size=(3, 288, 288)),
regnety_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'),
regnety_040s_gn=_cfg(url=''),
regnetv_040=_cfg(url='', first_conv='stem'),
regnetw_040=_cfg(url='', first_conv='stem', input_size=(3, 256, 256), pool_size=(8, 8)),
regnetz_005=_cfg(url=''),
regnetz_040=_cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
)
def quantize_float(f, q):
"""Converts a float to closest non-zero int divisible by q."""
return int(round(f / q) * q)
def adjust_widths_groups_comp(widths, bottle_ratios, groups):
"""Adjusts the compatibility of widths and groups."""
bottleneck_widths = [int(w * b) for w, b in zip(widths, bottle_ratios)]
groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_widths)]
bottleneck_widths = [quantize_float(w_bot, g) for w_bot, g in zip(bottleneck_widths, groups)]
widths = [int(w_bot / b) for w_bot, b in zip(bottleneck_widths, bottle_ratios)]
return widths, groups
def generate_regnet(width_slope, width_initial, width_mult, depth, group_size, q=8):
"""Generates per block widths from RegNet parameters."""
assert width_slope >= 0 and width_initial > 0 and width_mult > 1 and width_initial % q == 0
# TODO dWr scaling?
# depth = int(depth * (scale ** 0.1))
# width_scale = scale ** 0.4 # dWr scale, exp 0.8 / 2, applied to both group and layer widths
widths_cont = np.arange(depth) * width_slope + width_initial
width_exps = np.round(np.log(widths_cont / width_initial) / np.log(width_mult))
widths = width_initial * np.power(width_mult, width_exps)
widths = np.round(np.divide(widths, q)) * q
num_stages, max_stage = len(np.unique(widths)), width_exps.max() + 1
groups = np.array([group_size for _ in range(num_stages)])
return widths.astype(int).tolist(), num_stages, groups.astype(int).tolist()
def downsample_conv(in_chs, out_chs, kernel_size=1, stride=1, dilation=1, norm_layer=None, preact=False):
norm_layer = norm_layer or nn.BatchNorm2d
kernel_size = 1 if stride == 1 and dilation == 1 else kernel_size
dilation = dilation if kernel_size > 1 else 1
if preact:
return create_conv2d(in_chs, out_chs, kernel_size, stride=stride, dilation=dilation)
else:
return ConvNormAct(
in_chs, out_chs, kernel_size, stride=stride, dilation=dilation, norm_layer=norm_layer, apply_act=False)
def downsample_avg(in_chs, out_chs, kernel_size=1, stride=1, dilation=1, norm_layer=None, preact=False):
""" AvgPool Downsampling as in 'D' ResNet variants. This is not in RegNet space but I might experiment."""
norm_layer = norm_layer or nn.BatchNorm2d
avg_stride = stride if dilation == 1 else 1
pool = nn.Identity()
if stride > 1 or dilation > 1:
avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
if preact:
conv = create_conv2d(in_chs, out_chs, 1, stride=1)
else:
conv = ConvNormAct(in_chs, out_chs, 1, stride=1, norm_layer=norm_layer, apply_act=False)
return nn.Sequential(*[pool, conv])
def create_shortcut(
downsample_type, in_chs, out_chs, kernel_size, stride, dilation=(1, 1), norm_layer=None, preact=False):
assert downsample_type in ('avg', 'conv1x1', '', None)
if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
dargs = dict(stride=stride, dilation=dilation[0], norm_layer=norm_layer, preact=preact)
if not downsample_type:
return None # no shortcut, no downsample
elif downsample_type == 'avg':
return downsample_avg(in_chs, out_chs, **dargs)
else:
return downsample_conv(in_chs, out_chs, kernel_size=kernel_size, **dargs)
else:
return nn.Identity() # identity shortcut (no downsample)
class Bottleneck(nn.Module):
""" RegNet Bottleneck
This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from
after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels.
"""
def __init__(
self, in_chs, out_chs, stride=1, dilation=(1, 1), bottle_ratio=1, group_size=1, se_ratio=0.25,
downsample='conv1x1', linear_out=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
drop_block=None, drop_path_rate=0.):
super(Bottleneck, self).__init__()
act_layer = get_act_layer(act_layer)
bottleneck_chs = int(round(out_chs * bottle_ratio))
groups = bottleneck_chs // group_size
cargs = dict(act_layer=act_layer, norm_layer=norm_layer)
self.conv1 = ConvNormAct(in_chs, bottleneck_chs, kernel_size=1, **cargs)
self.conv2 = ConvNormAct(
bottleneck_chs, bottleneck_chs, kernel_size=3, stride=stride, dilation=dilation[0],
groups=groups, drop_layer=drop_block, **cargs)
if se_ratio:
se_channels = int(round(in_chs * se_ratio))
self.se = SEModule(bottleneck_chs, rd_channels=se_channels, act_layer=act_layer)
else:
self.se = nn.Identity()
self.conv3 = ConvNormAct(bottleneck_chs, out_chs, kernel_size=1, apply_act=False, **cargs)
self.act3 = nn.Identity() if linear_out else act_layer()
self.downsample = create_shortcut(downsample, in_chs, out_chs, 1, stride, dilation, norm_layer=norm_layer)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
def zero_init_last(self):
nn.init.zeros_(self.conv3.bn.weight)
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.conv2(x)
x = self.se(x)
x = self.conv3(x)
if self.downsample is not None:
# NOTE stuck with downsample as the attr name due to weight compatibility
# now represents the shortcut, no shortcut if None, and non-downsample shortcut == nn.Identity()
x = self.drop_path(x) + self.downsample(shortcut)
x = self.act3(x)
return x
class PreBottleneck(nn.Module):
""" RegNet Bottleneck
This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from
after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels.
"""
def __init__(
self, in_chs, out_chs, stride=1, dilation=(1, 1), bottle_ratio=1, group_size=1, se_ratio=0.25,
downsample='conv1x1', linear_out=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
drop_block=None, drop_path_rate=0.):
super(PreBottleneck, self).__init__()
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
bottleneck_chs = int(round(out_chs * bottle_ratio))
groups = bottleneck_chs // group_size
self.norm1 = norm_act_layer(in_chs)
self.conv1 = create_conv2d(in_chs, bottleneck_chs, kernel_size=1)
self.norm2 = norm_act_layer(bottleneck_chs)
self.conv2 = create_conv2d(
bottleneck_chs, bottleneck_chs, kernel_size=3, stride=stride, dilation=dilation[0], groups=groups)
if se_ratio:
se_channels = int(round(in_chs * se_ratio))
self.se = SEModule(bottleneck_chs, rd_channels=se_channels, act_layer=act_layer)
else:
self.se = nn.Identity()
self.norm3 = norm_act_layer(bottleneck_chs)
self.conv3 = create_conv2d(bottleneck_chs, out_chs, kernel_size=1)
self.downsample = create_shortcut(downsample, in_chs, out_chs, 1, stride, dilation, preact=True)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
def zero_init_last(self):
pass
def forward(self, x):
x = self.norm1(x)
shortcut = x
x = self.conv1(x)
x = self.norm2(x)
x = self.conv2(x)
x = self.se(x)
x = self.norm3(x)
x = self.conv3(x)
if self.downsample is not None:
# NOTE stuck with downsample as the attr name due to weight compatibility
# now represents the shortcut, no shortcut if None, and non-downsample shortcut == nn.Identity()
x = self.drop_path(x) + self.downsample(shortcut)
return x
class RegStage(nn.Module):
"""Stage (sequence of blocks w/ the same output shape)."""
def __init__(
self, depth, in_chs, out_chs, stride, dilation,
drop_path_rates=None, block_fn=Bottleneck, **block_kwargs):
super(RegStage, self).__init__()
first_dilation = 1 if dilation in (1, 2) else 2
for i in range(depth):
block_stride = stride if i == 0 else 1
block_in_chs = in_chs if i == 0 else out_chs
block_dilation = (first_dilation, dilation)
dpr = drop_path_rates[i] if drop_path_rates is not None else 0.
name = "b{}".format(i + 1)
self.add_module(
name, block_fn(
block_in_chs, out_chs, stride=block_stride, dilation=block_dilation,
drop_path_rate=dpr, **block_kwargs)
)
first_dilation = dilation
def forward(self, x):
for block in self.children():
x = block(x)
return x
class RegNet(nn.Module):
"""RegNet-X, Y, and Z Models
Paper: https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
"""
def __init__(
self, cfg: RegNetCfg, in_chans=3, num_classes=1000, output_stride=32, global_pool='avg',
drop_rate=0., drop_path_rate=0., zero_init_last=True):
super().__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
assert output_stride in (8, 16, 32)
# Construct the stem
stem_width = cfg.stem_width
na_args = dict(act_layer=cfg.act_layer, norm_layer=cfg.norm_layer)
if cfg.preact:
self.stem = create_conv2d(in_chans, stem_width, 3, stride=2)
else:
self.stem = ConvNormAct(in_chans, stem_width, 3, stride=2, **na_args)
self.feature_info = [dict(num_chs=stem_width, reduction=2, module='stem')]
# Construct the stages
prev_width = stem_width
curr_stride = 2
per_stage_args, common_args = self._get_stage_args(
cfg, output_stride=output_stride, drop_path_rate=drop_path_rate)
block_fn = PreBottleneck if cfg.preact else Bottleneck
for i, stage_args in enumerate(per_stage_args):
stage_name = "s{}".format(i + 1)
self.add_module(stage_name, RegStage(in_chs=prev_width, block_fn=block_fn, **stage_args, **common_args))
prev_width = stage_args['out_chs']
curr_stride *= stage_args['stride']
self.feature_info += [dict(num_chs=prev_width, reduction=curr_stride, module=stage_name)]
# Construct the head
if cfg.num_features:
self.final_conv = ConvNormAct(prev_width, cfg.num_features, kernel_size=1, **na_args)
self.num_features = cfg.num_features
else:
final_act = cfg.linear_out or cfg.preact
self.final_conv = get_act_layer(cfg.act_layer)() if final_act else nn.Identity()
self.num_features = prev_width
self.head = ClassifierHead(
in_chs=self.num_features, num_classes=num_classes, pool_type=global_pool, drop_rate=drop_rate)
named_apply(partial(_init_weights, zero_init_last=zero_init_last), self)
def _get_stage_args(self, cfg: RegNetCfg, default_stride=2, output_stride=32, drop_path_rate=0.):
# Generate RegNet ws per block
widths, num_stages, stage_gs = generate_regnet(cfg.wa, cfg.w0, cfg.wm, cfg.depth, cfg.group_size)
# Convert to per stage format
stage_widths, stage_depths = np.unique(widths, return_counts=True)
stage_br = [cfg.bottle_ratio for _ in range(num_stages)]
stage_strides = []
stage_dilations = []
net_stride = 2
dilation = 1
for _ in range(num_stages):
if net_stride >= output_stride:
dilation *= default_stride
stride = 1
else:
stride = default_stride
net_stride *= stride
stage_strides.append(stride)
stage_dilations.append(dilation)
stage_dpr = np.split(np.linspace(0, drop_path_rate, sum(stage_depths)), np.cumsum(stage_depths[:-1]))
# Adjust the compatibility of ws and gws
stage_widths, stage_gs = adjust_widths_groups_comp(stage_widths, stage_br, stage_gs)
arg_names = ['out_chs', 'stride', 'dilation', 'depth', 'bottle_ratio', 'group_size', 'drop_path_rates']
per_stage_args = [
dict(zip(arg_names, params)) for params in
zip(stage_widths, stage_strides, stage_dilations, stage_depths, stage_br, stage_gs, stage_dpr)]
common_args = dict(
downsample=cfg.downsample, se_ratio=cfg.se_ratio, linear_out=cfg.linear_out,
act_layer=cfg.act_layer, norm_layer=cfg.norm_layer)
return per_stage_args, common_args
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):
for block in list(self.children())[:-1]:
x = block(x)
return x
def forward(self, x):
for block in self.children():
x = block(x)
return x
def _init_weights(module, name='', zero_init_last=False):
if isinstance(module, nn.Conv2d):
fan_out = module.kernel_size[0] * module.kernel_size[1] * module.out_channels
fan_out //= module.groups
module.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if module.bias is not None:
module.bias.data.zero_()
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 _filter_fn(state_dict):
""" convert patch embedding weight from manual patchify + linear proj to conv"""
if 'model' in state_dict:
# For DeiT trained regnety_160 pretraiend model
state_dict = state_dict['model']
return state_dict
def _create_regnet(variant, pretrained, **kwargs):
return build_model_with_cfg(
RegNet, variant, pretrained,
model_cfg=model_cfgs[variant],
pretrained_filter_fn=_filter_fn,
**kwargs)
@register_model
def regnetx_002(pretrained=False, **kwargs):
"""RegNetX-200MF"""
return _create_regnet('regnetx_002', pretrained, **kwargs)
@register_model
def regnetx_004(pretrained=False, **kwargs):
"""RegNetX-400MF"""
return _create_regnet('regnetx_004', pretrained, **kwargs)
@register_model
def regnetx_006(pretrained=False, **kwargs):
"""RegNetX-600MF"""
return _create_regnet('regnetx_006', pretrained, **kwargs)
@register_model
def regnetx_008(pretrained=False, **kwargs):
"""RegNetX-800MF"""
return _create_regnet('regnetx_008', pretrained, **kwargs)
@register_model
def regnetx_016(pretrained=False, **kwargs):
"""RegNetX-1.6GF"""
return _create_regnet('regnetx_016', pretrained, **kwargs)
@register_model
def regnetx_032(pretrained=False, **kwargs):
"""RegNetX-3.2GF"""
return _create_regnet('regnetx_032', pretrained, **kwargs)
@register_model
def regnetx_040(pretrained=False, **kwargs):
"""RegNetX-4.0GF"""
return _create_regnet('regnetx_040', pretrained, **kwargs)
@register_model
def regnetx_064(pretrained=False, **kwargs):
"""RegNetX-6.4GF"""
return _create_regnet('regnetx_064', pretrained, **kwargs)
@register_model
def regnetx_080(pretrained=False, **kwargs):
"""RegNetX-8.0GF"""
return _create_regnet('regnetx_080', pretrained, **kwargs)
@register_model
def regnetx_120(pretrained=False, **kwargs):
"""RegNetX-12GF"""
return _create_regnet('regnetx_120', pretrained, **kwargs)
@register_model
def regnetx_160(pretrained=False, **kwargs):
"""RegNetX-16GF"""
return _create_regnet('regnetx_160', pretrained, **kwargs)
@register_model
def regnetx_320(pretrained=False, **kwargs):
"""RegNetX-32GF"""
return _create_regnet('regnetx_320', pretrained, **kwargs)
@register_model
def regnety_002(pretrained=False, **kwargs):
"""RegNetY-200MF"""
return _create_regnet('regnety_002', pretrained, **kwargs)
@register_model
def regnety_004(pretrained=False, **kwargs):
"""RegNetY-400MF"""
return _create_regnet('regnety_004', pretrained, **kwargs)
@register_model
def regnety_006(pretrained=False, **kwargs):
"""RegNetY-600MF"""
return _create_regnet('regnety_006', pretrained, **kwargs)
@register_model
def regnety_008(pretrained=False, **kwargs):
"""RegNetY-800MF"""
return _create_regnet('regnety_008', pretrained, **kwargs)
@register_model
def regnety_016(pretrained=False, **kwargs):
"""RegNetY-1.6GF"""
return _create_regnet('regnety_016', pretrained, **kwargs)
@register_model
def regnety_032(pretrained=False, **kwargs):
"""RegNetY-3.2GF"""
return _create_regnet('regnety_032', pretrained, **kwargs)
@register_model
def regnety_040(pretrained=False, **kwargs):
"""RegNetY-4.0GF"""
return _create_regnet('regnety_040', pretrained, **kwargs)
@register_model
def regnety_064(pretrained=False, **kwargs):
"""RegNetY-6.4GF"""
return _create_regnet('regnety_064', pretrained, **kwargs)
@register_model
def regnety_080(pretrained=False, **kwargs):
"""RegNetY-8.0GF"""
return _create_regnet('regnety_080', pretrained, **kwargs)
@register_model
def regnety_120(pretrained=False, **kwargs):
"""RegNetY-12GF"""
return _create_regnet('regnety_120', pretrained, **kwargs)
@register_model
def regnety_160(pretrained=False, **kwargs):
"""RegNetY-16GF"""
return _create_regnet('regnety_160', pretrained, **kwargs)
@register_model
def regnety_320(pretrained=False, **kwargs):
"""RegNetY-32GF"""
return _create_regnet('regnety_320', pretrained, **kwargs)
@register_model
def regnety_040s_gn(pretrained=False, **kwargs):
"""RegNetY-4.0GF w/ GroupNorm """
return _create_regnet('regnety_040s_gn', pretrained, **kwargs)
@register_model
def regnetv_040(pretrained=False, **kwargs):
""""""
return _create_regnet('regnetv_040', pretrained, **kwargs)
@register_model
def regnetw_040(pretrained=False, **kwargs):
""""""
return _create_regnet('regnetw_040', pretrained, **kwargs)
@register_model
def regnetz_005(pretrained=False, **kwargs):
"""RegNetZ-500MF
NOTE: config found in https://github.com/facebookresearch/ClassyVision/blob/main/classy_vision/models/regnet.py
but it's not clear it is equivalent to paper model as not detailed in the paper.
"""
return _create_regnet('regnetz_005', pretrained, zero_init_last=False, **kwargs)
@register_model
def regnetz_040(pretrained=False, **kwargs):
"""RegNetZ-4.0GF
NOTE: config found in https://github.com/facebookresearch/ClassyVision/blob/main/classy_vision/models/regnet.py
but it's not clear it is equivalent to paper model as not detailed in the paper.
"""
return _create_regnet('regnetz_040', pretrained, zero_init_last=False, **kwargs)
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