Migrations
/
pytorch-image-models
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

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

Browse Source
pull/175/head
Ross Wightman 5 years ago
parent d0113f9cdb
commit 7729f40dca
3 changed files with 86 additions and 228 deletions
Show Stats Download Patch File Download Diff File

302
timm/models/gluon_resnet.py
Unescape View File

@ -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
@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
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_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
Unescape 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
Unescape 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.

Write Preview
Loading…
Cancel
Save