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[WIP] Add ResNet-RS models (#554)

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* Add ResNet-RS models

* Only include resnet-rs changes

* remove whitespace diff

* EOF newline

* Update time

* increase time

* Add first conv

* Try running only resnetv2_101x1_bitm on Linux runner

* Add to exclude filter

* Run test_model_forward_features for all

* Add to exclude ftrs

* back to defaults

* only run test_forward_features

* run all tests

* Run all tests

* Add bigger resnetrs to model filters to fix Github CLI

* Remove resnetv2_101x1_bitm from exclude feat features

* Remove hardcoded values

* Make sure reduction ratio in resnetrs is 0.25

* There is no bias in replaced maxpool so remove it
This commit is contained in:
Aman Arora 2021-05-05 03:59:44 +10:00 committed by GitHub
parent 9cc7dda6e5
commit 560eae38f5
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2 changed files with 93 additions and 11 deletions
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5
tests/test_models.py
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@ -22,8 +22,9 @@ NUM_NON_STD = len(NON_STD_FILTERS)
if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system():
# GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
EXCLUDE_FILTERS = [
'*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm',
'*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*'] + NON_STD_FILTERS
'*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm',
'*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*',
'*resnetrs200*', '*resnetrs270*', '*resnetrs350*', '*resnetrs420*'] + NON_STD_FILTERS
else:
EXCLUDE_FILTERS = NON_STD_FILTERS

99
timm/models/resnet.py
View File

@ -236,7 +236,23 @@ default_cfgs = {
interpolation='bicubic'),
'resnetblur50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnetblur50-84f4748f.pth',
interpolation='bicubic')
interpolation='bicubic'),
# ResNet-RS models
'resnetrs50': _cfg(
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs101': _cfg(
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs152': _cfg(
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs200': _cfg(
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs270': _cfg(
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs350': _cfg(
interpolation='bicubic', first_conv='conv1.0'),
'resnetrs420': _cfg(
interpolation='bicubic', first_conv='conv1.0'),
}
@ -318,7 +334,7 @@ class Bottleneck(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
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):
attn_layer=None, aa_layer=None, drop_block=None, drop_path=None, **kwargs):
super(Bottleneck, self).__init__()
width = int(math.floor(planes * (base_width / 64)) * cardinality)
@ -341,7 +357,7 @@ class Bottleneck(nn.Module):
self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
self.bn3 = norm_layer(outplanes)
self.se = create_attn(attn_layer, outplanes)
self.se = create_attn(attn_layer, outplanes, **kwargs)
self.act3 = act_layer(inplace=True)
self.downsample = downsample
@ -545,11 +561,12 @@ class ResNet(nn.Module):
cardinality=1, base_width=64, stem_width=64, stem_type='',
output_stride=32, block_reduce_first=1, down_kernel_size=1, avg_down=False,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, drop_rate=0.0, drop_path_rate=0.,
drop_block_rate=0., global_pool='avg', zero_init_last_bn=True, block_args=None):
drop_block_rate=0., global_pool='avg', zero_init_last_bn=True, block_args=None, replace_stem_max_pool=False):
block_args = block_args or dict()
assert output_stride in (8, 16, 32)
self.num_classes = num_classes
self.drop_rate = drop_rate
self.replace_stem_max_pool = replace_stem_max_pool
super(ResNet, self).__init__()
# Stem
@ -574,12 +591,19 @@ class ResNet(nn.Module):
self.feature_info = [dict(num_chs=inplanes, reduction=2, module='act1')]
# Stem Pooling
if aa_layer is not None:
self.maxpool = nn.Sequential(*[
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
aa_layer(channels=inplanes, stride=2)])
if not self.replace_stem_max_pool:
if aa_layer is not None:
self.maxpool = nn.Sequential(*[
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
aa_layer(channels=inplanes, stride=2)])
else:
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
else:
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.maxpool = nn.Sequential(*[
nn.Conv2d(inplanes, inplanes, 3, stride=2, padding=1, bias=False),
norm_layer(inplanes),
act_layer(inplace=True)
])
# Feature Blocks
channels = [64, 128, 256, 512]
@ -1065,6 +1089,63 @@ def ecaresnet50d(pretrained=False, **kwargs):
return _create_resnet('ecaresnet50d', pretrained, **model_args)
@register_model
def resnetrs50(pretrained=False, **kwargs):
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', replace_stem_max_pool=True,
avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs)
return _create_resnet('resnetrs50', pretrained, **model_args)
@register_model
def resnetrs101(pretrained=False, **kwargs):
model_args = dict(
block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', replace_stem_max_pool=True,
avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs)
return _create_resnet('resnetrs101', pretrained, **model_args)
@register_model
def resnetrs152(pretrained=False, **kwargs):
model_args = dict(
block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', replace_stem_max_pool=True,
avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs)
return _create_resnet('resnetrs152', pretrained, **model_args)
@register_model
def resnetrs200(pretrained=False, **kwargs):
model_args = dict(
block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', replace_stem_max_pool=True,
avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs)
return _create_resnet('resnetrs200', pretrained, **model_args)
@register_model
def resnetrs270(pretrained=False, **kwargs):
model_args = dict(
block=Bottleneck, layers=[4, 29, 53, 4], stem_width=32, stem_type='deep', replace_stem_max_pool=True,
avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs)
return _create_resnet('resnetrs270', pretrained, **model_args)
@register_model
def resnetrs350(pretrained=False, **kwargs):
model_args = dict(
block=Bottleneck, layers=[4, 36, 72, 4], stem_width=32, stem_type='deep', replace_stem_max_pool=True,
avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs)
return _create_resnet('resnetrs350', pretrained, **model_args)
@register_model
def resnetrs420(pretrained=False, **kwargs):
model_args = dict(
block=Bottleneck, layers=[4, 44, 87, 4], stem_width=32, stem_type='deep', replace_stem_max_pool=True,
avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs)
return _create_resnet('resnetrs420', pretrained, **model_args)
@register_model
def ecaresnet50d_pruned(pretrained=False, **kwargs):
"""Constructs a ResNet-50-D model pruned with eca.