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Add ResNeSt models

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pull/145/head
Ross Wightman 5 years ago
parent 8d8677e03b
commit f4cdc2ac31
4 changed files with 299 additions and 0 deletions
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1
README.md
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@ -130,6 +130,7 @@ Included models:
* Instagram trained / ImageNet tuned ResNeXt101-32x8d to 32x48d from from [facebookresearch](https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/) * Instagram trained / ImageNet tuned ResNeXt101-32x8d to 32x48d from from [facebookresearch](https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/)
* Res2Net (https://github.com/gasvn/Res2Net, https://arxiv.org/abs/1904.01169) * Res2Net (https://github.com/gasvn/Res2Net, https://arxiv.org/abs/1904.01169)
* Selective Kernel (SK) Nets (https://arxiv.org/abs/1903.06586) * Selective Kernel (SK) Nets (https://arxiv.org/abs/1903.06586)
* ResNeSt (code adapted from https://github.com/zhanghang1989/ResNeSt, paper https://arxiv.org/abs/2004.08955)
* DLA * DLA
* Original (https://github.com/ucbdrive/dla, https://arxiv.org/abs/1707.06484) * Original (https://github.com/ucbdrive/dla, https://arxiv.org/abs/1707.06484)
* Res2Net (https://github.com/gasvn/Res2Net, https://arxiv.org/abs/1904.01169) * Res2Net (https://github.com/gasvn/Res2Net, https://arxiv.org/abs/1904.01169)

1
timm/models/__init__.py
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@ -18,6 +18,7 @@ from .dla import *
from .hrnet import * from .hrnet import *
from .sknet import * from .sknet import *
from .tresnet import * from .tresnet import *
from .resnest import *
from .registry import * from .registry import *
from .factory import create_model from .factory import create_model

83
timm/models/layers/split_attn.py
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@ -0,0 +1,83 @@
""" Split Attention Conv2d (for ResNeSt Models)
Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl at https://github.com/zhanghang1989/ResNeSt
Modified for torchscript compat, performance, and consistency with timm by Ross Wightman
"""
import torch
import torch.nn.functional as F
from torch import nn
class RadixSoftmax(nn.Module):
def __init__(self, radix, cardinality):
super(RadixSoftmax, self).__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
if self.radix > 1:
x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
x = F.softmax(x, dim=1)
x = x.reshape(batch, -1)
else:
x = torch.sigmoid(x)
return x
class SplitAttnConv2d(nn.Module):
"""Split-Attention Conv2d
"""
def __init__(self, in_channels, channels, kernel_size, stride=1, padding=0,
dilation=1, groups=1, bias=False, radix=2, reduction_factor=4,
act_layer=nn.ReLU, norm_layer=None, drop_block=None, **kwargs):
super(SplitAttnConv2d, self).__init__()
self.radix = radix
self.cardinality = groups
self.channels = channels
mid_chs = channels * radix
attn_chs = max(in_channels * radix // reduction_factor, 32)
self.conv = nn.Conv2d(
in_channels, mid_chs, kernel_size, stride, padding, dilation,
groups=groups * radix, bias=bias, **kwargs)
self.bn0 = norm_layer(mid_chs) if norm_layer is not None else None
self.act0 = act_layer(inplace=True)
self.fc1 = nn.Conv2d(channels, attn_chs, 1, groups=self.cardinality)
self.bn1 = norm_layer(attn_chs) if norm_layer is not None else None
self.act1 = act_layer(inplace=True)
self.fc2 = nn.Conv2d(attn_chs, mid_chs, 1, groups=self.cardinality)
self.drop_block = drop_block
self.rsoftmax = RadixSoftmax(radix, groups)
def forward(self, x):
x = self.conv(x)
if self.bn0 is not None:
x = self.bn0(x)
if self.drop_block is not None:
x = self.drop_block(x)
x = self.act0(x)
B, RC, H, W = x.shape
if self.radix > 1:
x = x.reshape((B, self.radix, RC // self.radix, H, W))
x_gap = torch.sum(x, dim=1)
else:
x_gap = x
x_gap = F.adaptive_avg_pool2d(x_gap, 1)
x_gap = self.fc1(x_gap)
if self.bn1 is not None:
x_gap = self.bn1(x_gap)
x_gap = self.act1(x_gap)
x_attn = self.fc2(x_gap)
x_attn = self.rsoftmax(x_attn).view(B, -1, 1, 1)
if self.radix > 1:
out = (x * x_attn.reshape((B, self.radix, RC // self.radix, 1, 1))).sum(dim=1)
else:
out = x * x_attn
return out.contiguous()

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timm/models/resnest.py
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@ -0,0 +1,214 @@
""" ResNeSt Models
Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt
Modified for torchscript compat, and consistency with timm by Ross Wightman
"""
import math
import torch
import torch.nn.functional as F
from torch import nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropBlock2d
from .helpers import load_pretrained
from .layers import SelectiveKernelConv, ConvBnAct, create_attn
from .layers.split_attn import SplitAttnConv2d
from .registry import register_model
from .resnet import ResNet
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv1', 'classifier': 'fc',
**kwargs
}
default_cfgs = {
'resnest26d': _cfg(
url=''),
'resnest50d': _cfg(
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50-528c19ca.pth'),
'resnest101e': _cfg(
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest101-22405ba7.pth', input_size=(3, 256, 256)),
'resnest200e': _cfg(
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest200-75117900.pth', input_size=(3, 320, 320)),
'resnest269e': _cfg(
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest269-0cc87c48.pth', input_size=(3, 416, 416)),
}
class ResNestBottleneck(nn.Module):
"""ResNet Bottleneck
"""
# pylint: disable=unused-argument
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None,
radix=1, cardinality=1, base_width=64, avd=False, avd_first=False, is_first=False,
reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
super(ResNestBottleneck, self).__init__()
assert reduce_first == 1 # not supported
assert attn_layer is None # not supported
assert aa_layer is None # TODO not yet supported
assert drop_path is None # TODO not yet supported
group_width = int(planes * (base_width / 64.)) * cardinality
first_dilation = first_dilation or dilation
if avd and (stride > 1 or is_first):
avd_stride = stride
stride = 1
else:
avd_stride = 0
self.radix = radix
self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
self.bn1 = norm_layer(group_width)
self.drop_block1 = drop_block if drop_block is not None else None
self.act1 = act_layer(inplace=True)
self.avd_first = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and avd_first else None
if self.radix >= 1:
self.conv2 = SplitAttnConv2d(
group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation,
dilation=first_dilation, groups=cardinality, norm_layer=norm_layer, drop_block=drop_block)
self.bn2 = None # FIXME revisit, here to satisfy current torchscript fussyness
self.drop_block2 = None
self.act2 = None
else:
self.conv2 = nn.Conv2d(
group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation,
dilation=first_dilation, groups=cardinality, bias=False)
self.bn2 = norm_layer(group_width)
self.drop_block2 = drop_block if drop_block is not None else None
self.act2 = act_layer(inplace=True)
self.avd_last = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and not avd_first else None
self.conv3 = nn.Conv2d(group_width, planes * 4, kernel_size=1, bias=False)
self.bn3 = norm_layer(planes*4)
self.drop_block3 = drop_block if drop_block is not None else None
self.act3 = act_layer(inplace=True)
self.downsample = downsample
def zero_init_last_bn(self):
nn.init.zeros_(self.bn3.weight)
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
if self.drop_block1 is not None:
out = self.drop_block1(out)
out = self.act1(out)
if self.avd_first is not None:
out = self.avd_first(out)
out = self.conv2(out)
if self.bn2 is not None:
out = self.bn2(out)
if self.drop_block2 is not None:
out = self.drop_block2(out)
out = self.act2(out)
if self.avd_last is not None:
out = self.avd_last(out)
out = self.conv3(out)
out = self.bn3(out)
if self.drop_block3 is not None:
out = self.drop_block3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.act3(out)
return out
@register_model
def resnest26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" ResNeSt-26d model.
"""
default_cfg = default_cfgs['resnest26d']
model = ResNet(
ResNestBottleneck, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans,
stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def resnest50d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" ResNeSt-50d model. Matches paper ResNeSt-50 model, https://arxiv.org/abs/2004.08955
Since this codebase supports all possible variations, 'd' for deep stem, stem_width 32, avg in downsample.
"""
default_cfg = default_cfgs['resnest50d']
model = ResNet(
ResNestBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def resnest101e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" ResNeSt-101e model. Matches paper ResNeSt-101 model, https://arxiv.org/abs/2004.08955
Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
"""
default_cfg = default_cfgs['resnest101e']
model = ResNet(
ResNestBottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def resnest200e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" ResNeSt-200e model. Matches paper ResNeSt-200 model, https://arxiv.org/abs/2004.08955
Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
"""
default_cfg = default_cfgs['resnest200e']
model = ResNet(
ResNestBottleneck, [3, 24, 36, 3], num_classes=num_classes, in_chans=in_chans,
stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def resnest269e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" ResNeSt-269e model. Matches paper ResNeSt-269 model, https://arxiv.org/abs/2004.08955
Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
"""
default_cfg = default_cfgs['resnest269e']
model = ResNet(
ResNestBottleneck, [3, 30, 48, 8], num_classes=num_classes, in_chans=in_chans,
stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
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