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Add tiered narrow ResNet (tn) and weights for seresnext26tn_32x4d

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Ross Wightman 2020-01-11 10:50:06 -08:00
parent cfa951bceb
commit a28117ea46
1 changed files with 24 additions and 3 deletions
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27
timm/models/resnet.py
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@ -97,6 +97,9 @@ default_cfgs = {
'seresnext26t_32x4d': _cfg( 'seresnext26t_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26t_32x4d-361bc1c4.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26t_32x4d-361bc1c4.pth',
interpolation='bicubic'), interpolation='bicubic'),
'seresnext26tn_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26tn_32x4d-569cb627.pth',
interpolation='bicubic'),
} }
@ -318,7 +321,7 @@ class ResNet(nn.Module):
stem_chs_1 = stem_chs_2 = stem_width stem_chs_1 = stem_chs_2 = stem_width
if 'tiered' in stem_type: if 'tiered' in stem_type:
stem_chs_1 = 3 * (stem_width // 4) stem_chs_1 = 3 * (stem_width // 4)
stem_chs_2 = 6 * (stem_width // 4) stem_chs_2 = stem_width if 'narrow' in stem_type else 6 * (stem_width // 4)
self.conv1 = nn.Sequential(*[ self.conv1 = nn.Sequential(*[
nn.Conv2d(in_chans, stem_chs_1, 3, stride=2, padding=1, bias=False), nn.Conv2d(in_chans, stem_chs_1, 3, stride=2, padding=1, bias=False),
norm_layer(stem_chs_1), norm_layer(stem_chs_1),
@ -893,7 +896,8 @@ def swsl_resnext101_32x16d(pretrained=True, **kwargs):
@register_model @register_model
def seresnext26d_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): def seresnext26d_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a SE-ResNeXt-26-D model. """Constructs a SE-ResNeXt-26-D model.
This is technically a 28 layer ResNet, sticking with 'D' modifier from Gluon / bag-of-tricks. This is technically a 28 layer ResNet, using the 'D' modifier from Gluon / bag-of-tricks for
combination of deep stem and avg_pool in downsample.
""" """
default_cfg = default_cfgs['seresnext26d_32x4d'] default_cfg = default_cfgs['seresnext26d_32x4d']
model = ResNet( model = ResNet(
@ -910,7 +914,7 @@ def seresnext26d_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs)
def seresnext26t_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs): def seresnext26t_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a SE-ResNet-26-T model. """Constructs a SE-ResNet-26-T model.
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 48, 64 channels This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 48, 64 channels
in the deep stem. Stem channel counts suggested by Jeremy Howard. in the deep stem.
""" """
default_cfg = default_cfgs['seresnext26t_32x4d'] default_cfg = default_cfgs['seresnext26t_32x4d']
model = ResNet( model = ResNet(
@ -921,3 +925,20 @@ def seresnext26t_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs)
if pretrained: if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans) load_pretrained(model, default_cfg, num_classes, in_chans)
return model return model
@register_model
def seresnext26tn_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a SE-ResNeXt-26-TN model.
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
in the deep stem. The channel number of the middle stem conv is narrower than the 'T' variant.
"""
default_cfg = default_cfgs['seresnext26tn_32x4d']
model = ResNet(
Bottleneck, [2, 2, 2, 2], cardinality=32, base_width=4,
stem_width=32, stem_type='deep_tiered_narrow', avg_down=True, use_se=True,
num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model