Migrations
/
pytorch-image-models
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
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.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'e2cc481310'
${ noResults }
pytorch-image-models/timm/models/regnet.py

506 lines
20 KiB
Raw Blame History

"""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
"""
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .features import FeatureNet
from .helpers import load_pretrained
from .layers import SelectAdaptivePool2d, AvgPool2dSame, ConvBnAct, SEModule
from .registry import register_model
def _mcfg(**kwargs):
cfg = dict(se_ratio=0., bottle_ratio=1., stem_width=32)
cfg.update(**kwargs)
return cfg
# Model FLOPS = three trailing digits * 10^8
model_cfgs = dict(
regnetx_002=_mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13),
regnetx_004=_mcfg(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22),
regnetx_006=_mcfg(w0=48, wa=36.97, wm=2.24, group_w=24, depth=16),
regnetx_008=_mcfg(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16),
regnetx_016=_mcfg(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18),
regnetx_032=_mcfg(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25),
regnetx_040=_mcfg(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23),
regnetx_064=_mcfg(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17),
regnetx_080=_mcfg(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23),
regnetx_120=_mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19),
regnetx_160=_mcfg(w0=216, wa=55.59, wm=2.1, group_w=128, depth=22),
regnetx_320=_mcfg(w0=320, wa=69.86, wm=2.0, group_w=168, depth=23),
regnety_002=_mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13, se_ratio=0.25),
regnety_004=_mcfg(w0=48, wa=27.89, wm=2.09, group_w=8, depth=16, se_ratio=0.25),
regnety_006=_mcfg(w0=48, wa=32.54, wm=2.32, group_w=16, depth=15, se_ratio=0.25),
regnety_008=_mcfg(w0=56, wa=38.84, wm=2.4, group_w=16, depth=14, se_ratio=0.25),
regnety_016=_mcfg(w0=48, wa=20.71, wm=2.65, group_w=24, depth=27, se_ratio=0.25),
regnety_032=_mcfg(w0=80, wa=42.63, wm=2.66, group_w=24, depth=21, se_ratio=0.25),
regnety_040=_mcfg(w0=96, wa=31.41, wm=2.24, group_w=64, depth=22, se_ratio=0.25),
regnety_064=_mcfg(w0=112, wa=33.22, wm=2.27, group_w=72, depth=25, se_ratio=0.25),
regnety_080=_mcfg(w0=192, wa=76.82, wm=2.19, group_w=56, depth=17, se_ratio=0.25),
regnety_120=_mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, se_ratio=0.25),
regnety_160=_mcfg(w0=200, wa=106.23, wm=2.48, group_w=112, depth=18, se_ratio=0.25),
regnety_320=_mcfg(w0=232, wa=115.89, wm=2.53, group_w=232, depth=20, se_ratio=0.25),
)
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.conv', 'classifier': 'head.fc',
}
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-regnet/regnety_032-62b47782.pth'),
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://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth'),
regnety_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'),
)
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, 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
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
widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist()
return widths, num_stages, max_stage, widths_cont
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, bottleneck_ratio=1, group_width=1, se_ratio=0.25,
downsample=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None,
drop_block=None, drop_path=None):
super(Bottleneck, self).__init__()
bottleneck_chs = int(round(out_chs * bottleneck_ratio))
groups = bottleneck_chs // group_width
cargs = dict(act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer, drop_block=drop_block)
self.conv1 = ConvBnAct(in_chs, bottleneck_chs, kernel_size=1, **cargs)
self.conv2 = ConvBnAct(
bottleneck_chs, bottleneck_chs, kernel_size=3, stride=stride, dilation=dilation,
groups=groups, **cargs)
if se_ratio:
se_channels = int(round(in_chs * se_ratio))
self.se = SEModule(bottleneck_chs, reduction_channels=se_channels)
else:
self.se = None
cargs['act_layer'] = None
self.conv3 = ConvBnAct(bottleneck_chs, out_chs, kernel_size=1, **cargs)
self.act3 = act_layer(inplace=True)
self.downsample = downsample
self.drop_path = drop_path
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.se is not None:
x = self.se(x)
x = self.conv3(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.downsample is not None:
shortcut = self.downsample(shortcut)
x += shortcut
x = self.act3(x)
return x
def downsample_conv(
in_chs, out_chs, kernel_size, stride=1, dilation=1, norm_layer=None):
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
return ConvBnAct(
in_chs, out_chs, kernel_size, stride=stride, dilation=dilation, norm_layer=norm_layer, act_layer=None)
def downsample_avg(
in_chs, out_chs, kernel_size, stride=1, dilation=1, norm_layer=None):
""" 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)
return nn.Sequential(*[
pool, ConvBnAct(in_chs, out_chs, 1, stride=1, norm_layer=norm_layer, act_layer=None)])
class RegStage(nn.Module):
"""Stage (sequence of blocks w/ the same output shape)."""
def __init__(self, in_chs, out_chs, stride, dilation, depth, bottle_ratio, group_width,
block_fn=Bottleneck, se_ratio=0.):
super(RegStage, self).__init__()
block_kwargs = {} # FIXME setup to pass various aa, norm, act layer common args
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 if i == 0 else dilation
if (block_in_chs != out_chs) or (block_stride != 1):
proj_block = downsample_conv(block_in_chs, out_chs, 1, block_stride, block_dilation)
else:
proj_block = None
name = "b{}".format(i + 1)
self.add_module(
name, block_fn(
block_in_chs, out_chs, block_stride, block_dilation, bottle_ratio, group_width, se_ratio,
downsample=proj_block, **block_kwargs)
)
def forward(self, x):
for block in self.children():
x = block(x)
return x
class ClassifierHead(nn.Module):
"""Head."""
def __init__(self, in_chs, num_classes, pool_type='avg', drop_rate=0.):
super(ClassifierHead, self).__init__()
self.drop_rate = drop_rate
self.global_pool = SelectAdaptivePool2d(pool_type=pool_type)
if num_classes > 0:
self.fc = nn.Linear(in_chs, num_classes, bias=True)
else:
self.fc = nn.Identity()
def forward(self, x):
x = self.global_pool(x).flatten(1)
if self.drop_rate:
x = F.dropout(x, p=float(self.drop_rate), training=self.training)
x = self.fc(x)
return x
class RegNet(nn.Module):
"""RegNet model.
Paper: https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
"""
def __init__(self, cfg, in_chans=3, num_classes=1000, output_stride=32, global_pool='avg', drop_rate=0.,
zero_init_last_bn=True):
super().__init__()
# TODO add drop block, drop path, anti-aliasing, custom bn/act args
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']
self.stem = ConvBnAct(in_chans, stem_width, 3, stride=2)
self.feature_info = [dict(num_chs=stem_width, reduction=2, module='stem')]
# Construct the stages
prev_width = stem_width
curr_stride = 2
stage_params = self._get_stage_params(cfg, output_stride=output_stride)
se_ratio = cfg['se_ratio']
for i, stage_args in enumerate(stage_params):
stage_name = "s{}".format(i + 1)
self.add_module(stage_name, RegStage(prev_width, **stage_args, se_ratio=se_ratio))
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
self.num_features = prev_width
self.head = ClassifierHead(
in_chs=prev_width, 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_stage_params(self, cfg, default_stride=2, output_stride=32):
# Generate RegNet ws per block
w_a, w_0, w_m, d = cfg['wa'], cfg['w0'], cfg['wm'], cfg['depth']
widths, num_stages, _, _ = generate_regnet(w_a, w_0, w_m, d)
# Convert to per stage format
stage_widths, stage_depths = np.unique(widths, return_counts=True)
# Use the same group width, bottleneck mult and stride for each stage
stage_groups = [cfg['group_w'] for _ in range(num_stages)]
stage_bottle_ratios = [cfg['bottle_ratio'] for _ in range(num_stages)]
stage_strides = []
stage_dilations = []
total_stride = 2
dilation = 1
for _ in range(num_stages):
if total_stride >= output_stride:
dilation *= default_stride
stride = 1
else:
stride = default_stride
total_stride *= stride
stage_strides.append(stride)
stage_dilations.append(dilation)
# Adjust the compatibility of ws and gws
stage_widths, stage_groups = adjust_widths_groups_comp(stage_widths, stage_bottle_ratios, stage_groups)
param_names = ['out_chs', 'stride', 'dilation', 'depth', 'bottle_ratio', 'group_width']
stage_params = [
dict(zip(param_names, params)) for params in
zip(stage_widths, stage_strides, stage_dilations, stage_depths, stage_bottle_ratios, stage_groups)]
return stage_params
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 _regnet(variant, pretrained, **kwargs):
features = False
out_indices = None
if kwargs.pop('features_only', False):
features = True
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4))
model_cfg = model_cfgs[variant]
model = RegNet(model_cfg, **kwargs)
model.default_cfg = default_cfgs[variant]
if pretrained:
load_pretrained(
model,
num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3), strict=not features)
if features:
model = FeatureNet(model, out_indices=out_indices)
return model
@register_model
def regnetx_002(pretrained=False, **kwargs):
"""RegNetX-200MF"""
return _regnet('regnetx_002', pretrained, **kwargs)
@register_model
def regnetx_004(pretrained=False, **kwargs):
"""RegNetX-400MF"""
return _regnet('regnetx_004', pretrained, **kwargs)
@register_model
def regnetx_006(pretrained=False, **kwargs):
"""RegNetX-600MF"""
return _regnet('regnetx_006', pretrained, **kwargs)
@register_model
def regnetx_008(pretrained=False, **kwargs):
"""RegNetX-800MF"""
return _regnet('regnetx_008', pretrained, **kwargs)
@register_model
def regnetx_016(pretrained=False, **kwargs):
"""RegNetX-1.6GF"""
return _regnet('regnetx_016', pretrained, **kwargs)
@register_model
def regnetx_032(pretrained=False, **kwargs):
"""RegNetX-3.2GF"""
return _regnet('regnetx_032', pretrained, **kwargs)
@register_model
def regnetx_040(pretrained=False, **kwargs):
"""RegNetX-4.0GF"""
return _regnet('regnetx_040', pretrained, **kwargs)
@register_model
def regnetx_064(pretrained=False, **kwargs):
"""RegNetX-6.4GF"""
return _regnet('regnetx_064', pretrained, **kwargs)
@register_model
def regnetx_080(pretrained=False, **kwargs):
"""RegNetX-8.0GF"""
return _regnet('regnetx_080', pretrained, **kwargs)
@register_model
def regnetx_120(pretrained=False, **kwargs):
"""RegNetX-12GF"""
return _regnet('regnetx_120', pretrained, **kwargs)
@register_model
def regnetx_160(pretrained=False, **kwargs):
"""RegNetX-16GF"""
return _regnet('regnetx_160', pretrained, **kwargs)
@register_model
def regnetx_320(pretrained=False, **kwargs):
"""RegNetX-32GF"""
return _regnet('regnetx_320', pretrained, **kwargs)
@register_model
def regnety_002(pretrained=False, **kwargs):
"""RegNetY-200MF"""
return _regnet('regnety_002', pretrained, **kwargs)
@register_model
def regnety_004(pretrained=False, **kwargs):
"""RegNetY-400MF"""
return _regnet('regnety_004', pretrained, **kwargs)
@register_model
def regnety_006(pretrained=False, **kwargs):
"""RegNetY-600MF"""
return _regnet('regnety_006', pretrained, **kwargs)
@register_model
def regnety_008(pretrained=False, **kwargs):
"""RegNetY-800MF"""
return _regnet('regnety_008', pretrained, **kwargs)
@register_model
def regnety_016(pretrained=False, **kwargs):
"""RegNetY-1.6GF"""
return _regnet('regnety_016', pretrained, **kwargs)
@register_model
def regnety_032(pretrained=False, **kwargs):
"""RegNetY-3.2GF"""
return _regnet('regnety_032', pretrained, **kwargs)
@register_model
def regnety_040(pretrained=False, **kwargs):
"""RegNetY-4.0GF"""
return _regnet('regnety_040', pretrained, **kwargs)
@register_model
def regnety_064(pretrained=False, **kwargs):
"""RegNetY-6.4GF"""
return _regnet('regnety_064', pretrained, **kwargs)
@register_model
def regnety_080(pretrained=False, **kwargs):
"""RegNetY-8.0GF"""
return _regnet('regnety_080', pretrained, **kwargs)
@register_model
def regnety_120(pretrained=False, **kwargs):
"""RegNetY-12GF"""
return _regnet('regnety_120', pretrained, **kwargs)
@register_model
def regnety_160(pretrained=False, **kwargs):
"""RegNetY-16GF"""
return _regnet('regnety_160', pretrained, **kwargs)
@register_model
def regnety_320(pretrained=False, **kwargs):
"""RegNetY-32GF"""
return _regnet('regnety_320', pretrained, **kwargs)
Reference in new issue View Git Blame Copy Permalink