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Fix another bug, update all gluon resnet models to use new creation method (feature support)

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Ross Wightman 2020-06-30 16:44:15 -07:00
parent d0113f9cdb
commit 7729f40dca
3 changed files with 86 additions and 228 deletions
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302
timm/models/gluon_resnet.py
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@ -5,10 +5,9 @@ by Ross Wightman
"""
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import load_pretrained
from .layers import SEModule
from .registry import register_model
from .resnet import ResNet, Bottleneck, BasicBlock
from .resnet import _create_resnet_with_cfg, Bottleneck, BasicBlock
def _cfg(url='', **kwargs):
@ -34,9 +33,6 @@ default_cfgs = {
'gluon_resnet50_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth'),
'gluon_resnet101_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth'),
'gluon_resnet152_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth'),
'gluon_resnet50_v1e': _cfg(url=''),
'gluon_resnet101_v1e': _cfg(url=''),
'gluon_resnet152_v1e': _cfg(url=''),
'gluon_resnet50_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth'),
'gluon_resnet101_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth'),
'gluon_resnet152_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth'),
@ -50,318 +46,190 @@ default_cfgs = {
}
def _create_resnet(variant, pretrained=False, **kwargs):
default_cfg = default_cfgs[variant]
return _create_resnet_with_cfg(variant, default_cfg, pretrained=pretrained, **kwargs)
@register_model
def gluon_resnet18_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet18_v1b(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
"""
default_cfg = default_cfgs['gluon_resnet18_v1b']
model = ResNet(BasicBlock, [2, 2, 2, 2], 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
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], **kwargs)
return _create_resnet('gluon_resnet18_v1b', pretrained, **model_args)
@register_model
def gluon_resnet34_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet34_v1b(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
"""
default_cfg = default_cfgs['gluon_resnet34_v1b']
model = ResNet(BasicBlock, [3, 4, 6, 3], 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
model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], **kwargs)
return _create_resnet('gluon_resnet34_v1b', pretrained, **model_args)
@register_model
def gluon_resnet50_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet50_v1b(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
"""
default_cfg = default_cfgs['gluon_resnet50_v1b']
model = ResNet(Bottleneck, [3, 4, 6, 3], 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
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs)
return _create_resnet('gluon_resnet50_v1b', pretrained, **model_args)
@register_model
def gluon_resnet101_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet101_v1b(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
"""
default_cfg = default_cfgs['gluon_resnet101_v1b']
model = ResNet(Bottleneck, [3, 4, 23, 3], 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
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], **kwargs)
return _create_resnet('gluon_resnet101_v1b', pretrained, **model_args)
@register_model
def gluon_resnet152_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet152_v1b(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
"""
default_cfg = default_cfgs['gluon_resnet152_v1b']
model = ResNet(Bottleneck, [3, 8, 36, 3], 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
model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], **kwargs)
return _create_resnet('gluon_resnet152_v1b', pretrained, **model_args)
@register_model
def gluon_resnet50_v1c(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet50_v1c(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
"""
default_cfg = default_cfgs['gluon_resnet50_v1c']
model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=32, stem_type='deep', **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', **kwargs)
return _create_resnet('gluon_resnet50_v1c', pretrained, **model_args)
@register_model
def gluon_resnet101_v1c(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet101_v1c(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
"""
default_cfg = default_cfgs['gluon_resnet101_v1c']
model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=32, stem_type='deep', **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', **kwargs)
return _create_resnet('gluon_resnet101_v1c', pretrained, **model_args)
@register_model
def gluon_resnet152_v1c(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet152_v1c(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
"""
default_cfg = default_cfgs['gluon_resnet152_v1c']
model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=32, stem_type='deep', **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', **kwargs)
return _create_resnet('gluon_resnet152_v1c', pretrained, **model_args)
@register_model
def gluon_resnet50_v1d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet50_v1d(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
"""
default_cfg = default_cfgs['gluon_resnet50_v1d']
model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=32, stem_type='deep', avg_down=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('gluon_resnet50_v1d', pretrained, **model_args)
@register_model
def gluon_resnet101_v1d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet101_v1d(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
"""
default_cfg = default_cfgs['gluon_resnet101_v1d']
model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=32, stem_type='deep', avg_down=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('gluon_resnet101_v1d', pretrained, **model_args)
@register_model
def gluon_resnet152_v1d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet152_v1d(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
"""
default_cfg = default_cfgs['gluon_resnet152_v1d']
model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=32, stem_type='deep', avg_down=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(
block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('gluon_resnet152_v1d', pretrained, **model_args)
@register_model
def gluon_resnet50_v1e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-50-V1e model. No pretrained weights for any 'e' variants
"""
default_cfg = default_cfgs['gluon_resnet50_v1e']
model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=64, stem_type='deep', avg_down=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def gluon_resnet101_v1e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-101 model.
"""
default_cfg = default_cfgs['gluon_resnet101_v1e']
model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=64, stem_type='deep', avg_down=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def gluon_resnet152_v1e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Constructs a ResNet-152 model.
"""
default_cfg = default_cfgs['gluon_resnet152_v1e']
model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=64, stem_type='deep', avg_down=True, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
@register_model
def gluon_resnet50_v1s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet50_v1s(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
"""
default_cfg = default_cfgs['gluon_resnet50_v1s']
model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=64, stem_type='deep', **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=64, stem_type='deep', **kwargs)
return _create_resnet('gluon_resnet50_v1s', pretrained, **model_args)
@register_model
def gluon_resnet101_v1s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet101_v1s(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
"""
default_cfg = default_cfgs['gluon_resnet101_v1s']
model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=64, stem_type='deep', **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], stem_width=64, stem_type='deep', **kwargs)
return _create_resnet('gluon_resnet101_v1s', pretrained, **model_args)
@register_model
def gluon_resnet152_v1s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnet152_v1s(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
"""
default_cfg = default_cfgs['gluon_resnet152_v1s']
model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
stem_width=64, stem_type='deep', **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(
block=Bottleneck, layers=[3, 8, 36, 3], stem_width=64, stem_type='deep', **kwargs)
return _create_resnet('gluon_resnet152_v1s', pretrained, **model_args)
@register_model
def gluon_resnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnext50_32x4d(pretrained=False, **kwargs):
"""Constructs a ResNeXt50-32x4d model.
"""
default_cfg = default_cfgs['gluon_resnext50_32x4d']
model = ResNet(
Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4,
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
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs)
return _create_resnet('gluon_resnext50_32x4d', pretrained, **model_args)
@register_model
def gluon_resnext101_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnext101_32x4d(pretrained=False, **kwargs):
"""Constructs a ResNeXt-101 model.
"""
default_cfg = default_cfgs['gluon_resnext101_32x4d']
model = ResNet(
Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=4,
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
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4, **kwargs)
return _create_resnet('gluon_resnext101_32x4d', pretrained, **model_args)
@register_model
def gluon_resnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_resnext101_64x4d(pretrained=False, **kwargs):
"""Constructs a ResNeXt-101 model.
"""
default_cfg = default_cfgs['gluon_resnext101_64x4d']
model = ResNet(
Bottleneck, [3, 4, 23, 3], cardinality=64, base_width=4,
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
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4, **kwargs)
return _create_resnet('gluon_resnext101_64x4d', pretrained, **model_args)
@register_model
def gluon_seresnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_seresnext50_32x4d(pretrained=False, **kwargs):
"""Constructs a SEResNeXt50-32x4d model.
"""
default_cfg = default_cfgs['gluon_seresnext50_32x4d']
model = ResNet(
Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4,
num_classes=num_classes, in_chans=in_chans, block_args=dict(attn_layer=SEModule), **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4,
block_args=dict(attn_layer=SEModule), **kwargs)
return _create_resnet('gluon_seresnext50_32x4d', pretrained, **model_args)
@register_model
def gluon_seresnext101_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_seresnext101_32x4d(pretrained=False, **kwargs):
"""Constructs a SEResNeXt-101-32x4d model.
"""
default_cfg = default_cfgs['gluon_seresnext101_32x4d']
model = ResNet(
Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=4,
num_classes=num_classes, in_chans=in_chans, block_args=dict(attn_layer=SEModule), **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4,
block_args=dict(attn_layer=SEModule), **kwargs)
return _create_resnet('gluon_seresnext101_32x4d', pretrained, **model_args)
@register_model
def gluon_seresnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_seresnext101_64x4d(pretrained=False, **kwargs):
"""Constructs a SEResNeXt-101-64x4d model.
"""
default_cfg = default_cfgs['gluon_seresnext101_64x4d']
block_args = dict(attn_layer=SEModule)
model = ResNet(
Bottleneck, [3, 4, 23, 3], cardinality=64, base_width=4,
num_classes=num_classes, in_chans=in_chans, block_args=block_args, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4,
block_args=dict(attn_layer=SEModule), **kwargs)
return _create_resnet('gluon_seresnext101_64x4d', pretrained, **model_args)
@register_model
def gluon_senet154(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def gluon_senet154(pretrained=False, **kwargs):
"""Constructs an SENet-154 model.
"""
default_cfg = default_cfgs['gluon_senet154']
block_args = dict(attn_layer=SEModule)
model = ResNet(
Bottleneck, [3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep', down_kernel_size=3,
block_reduce_first=2, num_classes=num_classes, in_chans=in_chans, block_args=block_args, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model
model_args = dict(
block=Bottleneck, layers=[3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep',
down_kernel_size=3, block_reduce_first=2, block_args=dict(attn_layer=SEModule), **kwargs)
return _create_resnet('gluon_senet154', pretrained, **model_args)

9
timm/models/mobilenetv3.py
View File

@ -199,15 +199,6 @@ class MobileNetV3Features(nn.Module):
hooks = self.feature_info.get_by_key(keys=('module', 'hook_type'))
self.feature_hooks = FeatureHooks(hooks, self.named_modules())
def feature_channels(self, idx=None):
""" Feature Channel Shortcut
Returns feature channel count for each output index if idx == None. If idx is an integer, will
return feature channel count for that feature block index (independent of out_indices setting).
"""
if isinstance(idx, int):
return self.feature_info[idx]['num_chs']
return [self.feature_info[i]['num_chs'] for i in self.out_indices]
def forward(self, x) -> List[torch.Tensor]:
x = self.conv_stem(x)
x = self.bn1(x)

3
timm/models/resnet.py
View File

@ -525,7 +525,6 @@ def _create_resnet_with_cfg(variant, default_cfg, pretrained=False, **kwargs):
out_indices = None
if kwargs.pop('features_only', False):
features = True
kwargs.pop('num_classes', 0)
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4))
pruned = kwargs.pop('pruned', False)
@ -910,7 +909,7 @@ def seresnext26tn_32x4d(pretrained=False, **kwargs):
@register_model
def ecaresnext26tn_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
def ecaresnext26tn_32x4d(pretrained=False, **kwargs):
"""Constructs an ECA-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.