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Add resnest14, resnest26, and two of the abalation grouped resnest50 models

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Ross Wightman 2020-05-12 12:21:52 -07:00
parent f4cdc2ac31
commit 2f884a0ce5
1 changed files with 41 additions and 5 deletions
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46
timm/models/resnest.py
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@ -1,8 +1,8 @@
""" ResNeSt Models
Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt
Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang
Modified for torchscript compat, and consistency with timm by Ross Wightman
"""
@ -31,8 +31,10 @@ def _cfg(url='', **kwargs):
}
default_cfgs = {
'resnest14d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest14-9c8fe254.pth'),
'resnest26d': _cfg(
url=''),
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest26-50eb607c.pth'),
'resnest50d': _cfg(
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50-528c19ca.pth'),
'resnest101e': _cfg(
@ -41,6 +43,12 @@ default_cfgs = {
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)),
'resnest50d_4s2x40d': _cfg(
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50_fast_4s2x40d-41d14ed0.pth',
interpolation='bicubic'),
'resnest50d_1s4x24d': _cfg(
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50_fast_1s4x24d-d4a4f76f.pth',
interpolation='bicubic')
}
@ -78,7 +86,7 @@ class ResNestBottleneck(nn.Module):
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)
dilation=first_dilation, groups=cardinality, radix=radix, 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
@ -135,9 +143,24 @@ class ResNestBottleneck(nn.Module):
return out
@register_model
def resnest14d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" ResNeSt-14d model. Weights ported from GluonCV.
"""
default_cfg = default_cfgs['resnest14d']
model = ResNet(
ResNestBottleneck, [1, 1, 1, 1], 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 resnest26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" ResNeSt-26d model.
""" ResNeSt-26d model. Weights ported from GluonCV.
"""
default_cfg = default_cfgs['resnest26d']
model = ResNet(
@ -212,3 +235,16 @@ def resnest269e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def resnest50d_1s4x24d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
default_cfg = default_cfgs['resnest50d_1s4x24d']
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=24, cardinality=4,
block_args=dict(radix=1, avd=True, avd_first=True), **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model