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Add feature support to legacy senets, add 32x32 resnext models to exclude list for feature testing.

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pull/175/head
Ross Wightman 4 years ago
parent 701dba303e
commit 2ac663f340
3 changed files with 65 additions and 104 deletions
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4
tests/test_models.py
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@ -110,8 +110,10 @@ def test_model_forward_torchscript(model_name, batch_size):
EXCLUDE_FEAT_FILTERS = [
'hrnet*', '*pruned*', # hopefully fix at some point
'legacy*', # not going to bother
]
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_FEAT_FILTERS += ['*resnext101_32x32d']
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS))

160
timm/models/senet.py
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@ -18,7 +18,7 @@ import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import load_pretrained
from .helpers import build_model_with_cfg
from .layers import SelectAdaptivePool2d
from .registry import register_model
@ -229,8 +229,8 @@ class SEResNetBlock(nn.Module):
class SENet(nn.Module):
def __init__(self, block, layers, groups, reduction, drop_rate=0.2,
in_chans=3, inplanes=128, input_3x3=True, downsample_kernel_size=3,
downsample_padding=1, num_classes=1000, global_pool='avg'):
in_chans=3, inplanes=64, input_3x3=False, downsample_kernel_size=1,
downsample_padding=0, num_classes=1000, global_pool='avg'):
"""
Parameters
----------
@ -297,10 +297,10 @@ class SENet(nn.Module):
('bn1', nn.BatchNorm2d(inplanes)),
('relu1', nn.ReLU(inplace=True)),
]
# To preserve compatibility with Caffe weights `ceil_mode=True`
# is used instead of `padding=1`.
layer0_modules.append(('pool', nn.MaxPool2d(3, stride=2, ceil_mode=True)))
self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
# To preserve compatibility with Caffe weights `ceil_mode=True` is used instead of `padding=1`.
self.pool0 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
self.feature_info = [dict(num_chs=inplanes, reduction=2, module='layer0')]
self.layer1 = self._make_layer(
block,
planes=64,
@ -310,6 +310,7 @@ class SENet(nn.Module):
downsample_kernel_size=1,
downsample_padding=0
)
self.feature_info += [dict(num_chs=64 * block.expansion, reduction=4, module='layer1')]
self.layer2 = self._make_layer(
block,
planes=128,
@ -320,6 +321,7 @@ class SENet(nn.Module):
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.feature_info += [dict(num_chs=128 * block.expansion, reduction=8, module='layer2')]
self.layer3 = self._make_layer(
block,
planes=256,
@ -330,6 +332,7 @@ class SENet(nn.Module):
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.feature_info += [dict(num_chs=256 * block.expansion, reduction=16, module='layer3')]
self.layer4 = self._make_layer(
block,
planes=512,
@ -340,8 +343,9 @@ class SENet(nn.Module):
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.avg_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.feature_info += [dict(num_chs=512 * block.expansion, reduction=32, module='layer4')]
self.num_features = 512 * block.expansion
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.last_linear = nn.Linear(self.num_features, num_classes)
for m in self.modules():
@ -352,14 +356,13 @@ class SENet(nn.Module):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=downsample_kernel_size, stride=stride,
padding=downsample_padding, bias=False),
nn.Conv2d(
self.inplanes, planes * block.expansion, kernel_size=downsample_kernel_size,
stride=stride, padding=downsample_padding, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = [block(
self.inplanes, planes, groups, reduction, stride, downsample)]
layers = [block(self.inplanes, planes, groups, reduction, stride, downsample)]
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, groups, reduction))
@ -371,15 +374,16 @@ class SENet(nn.Module):
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.avg_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
if num_classes:
num_features = self.num_features * self.avg_pool.feat_mult()
num_features = self.num_features * self.global_pool.feat_mult()
self.last_linear = nn.Linear(num_features, num_classes)
else:
self.last_linear = nn.Identity()
def forward_features(self, x):
x = self.layer0(x)
x = self.pool0(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
@ -387,7 +391,7 @@ class SENet(nn.Module):
return x
def logits(self, x):
x = self.avg_pool(x).flatten(1)
x = self.global_pool(x).flatten(1)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
x = self.last_linear(x)
@ -399,116 +403,70 @@ class SENet(nn.Module):
return x
def _create_senet(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
SENet, variant, default_cfg=default_cfgs[variant], pretrained=pretrained, **kwargs)
@register_model
def legacy_seresnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
default_cfg = default_cfgs['seresnet18']
model = SENet(SEResNetBlock, [2, 2, 2, 2], groups=1, reduction=16,
inplanes=64, input_3x3=False,
downsample_kernel_size=1, downsample_padding=0,
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
def legacy_seresnet18(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBlock, layers=[2, 2, 2, 2], groups=1, reduction=16, **kwargs)
return _create_senet('seresnet18', pretrained, **model_args)
@register_model
def legacy_seresnet34(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
default_cfg = default_cfgs['seresnet34']
model = SENet(SEResNetBlock, [3, 4, 6, 3], groups=1, reduction=16,
inplanes=64, input_3x3=False,
downsample_kernel_size=1, downsample_padding=0,
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
def legacy_seresnet34(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBlock, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs)
return _create_senet('seresnet34', pretrained, **model_args)
@register_model
def legacy_seresnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
default_cfg = default_cfgs['seresnet50']
model = SENet(SEResNetBottleneck, [3, 4, 6, 3], groups=1, reduction=16,
inplanes=64, input_3x3=False,
downsample_kernel_size=1, downsample_padding=0,
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=SEResNetBottleneck, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs)
return _create_senet('seresnet50', pretrained, **model_args)
@register_model
def legacy_seresnet101(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
default_cfg = default_cfgs['seresnet101']
model = SENet(SEResNetBottleneck, [3, 4, 23, 3], groups=1, reduction=16,
inplanes=64, input_3x3=False,
downsample_kernel_size=1, downsample_padding=0,
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=SEResNetBottleneck, layers=[3, 4, 23, 3], groups=1, reduction=16, **kwargs)
return _create_senet('seresnet101', pretrained, **model_args)
@register_model
def legacy_seresnet152(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
default_cfg = default_cfgs['seresnet152']
model = SENet(SEResNetBottleneck, [3, 8, 36, 3], groups=1, reduction=16,
inplanes=64, input_3x3=False,
downsample_kernel_size=1, downsample_padding=0,
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
def legacy_seresnet152(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBottleneck, layers=[3, 8, 36, 3], groups=1, reduction=16, **kwargs)
return _create_senet('seresnet152', pretrained, **model_args)
@register_model
def legacy_senet154(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
default_cfg = default_cfgs['senet154']
model = SENet(SEBottleneck, [3, 8, 36, 3], groups=64, reduction=16,
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
def legacy_senet154(pretrained=False, **kwargs):
model_args = dict(
block=SEBottleneck, layers=[3, 8, 36, 3], groups=64, reduction=16,
downsample_kernel_size=3, downsample_padding=1, inplanes=128, input_3x3=True, **kwargs)
return _create_senet('senet154', pretrained, **model_args)
@register_model
def legacy_seresnext26_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
default_cfg = default_cfgs['seresnext26_32x4d']
model = SENet(SEResNeXtBottleneck, [2, 2, 2, 2], groups=32, reduction=16,
inplanes=64, input_3x3=False,
downsample_kernel_size=1, downsample_padding=0,
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
def legacy_seresnext26_32x4d(pretrained=False, **kwargs):
model_args = dict(
block=SEResNeXtBottleneck, layers=[2, 2, 2, 2], groups=32, reduction=16, **kwargs)
return _create_senet('seresnext26_32x4d', pretrained, **model_args)
@register_model
def legacy_seresnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
default_cfg = default_cfgs['seresnext50_32x4d']
model = SENet(SEResNeXtBottleneck, [3, 4, 6, 3], groups=32, reduction=16,
inplanes=64, input_3x3=False,
downsample_kernel_size=1, downsample_padding=0,
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
def legacy_seresnext50_32x4d(pretrained=False, **kwargs):
model_args = dict(
block=SEResNeXtBottleneck, layers=[3, 4, 6, 3], groups=32, reduction=16, **kwargs)
return _create_senet('seresnext50_32x4d', pretrained, **model_args)
@register_model
def legacy_seresnext101_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
default_cfg = default_cfgs['seresnext101_32x4d']
model = SENet(SEResNeXtBottleneck, [3, 4, 23, 3], groups=32, reduction=16,
inplanes=64, input_3x3=False,
downsample_kernel_size=1, downsample_padding=0,
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
def legacy_seresnext101_32x4d(pretrained=False, **kwargs):
model_args = dict(
block=SEResNeXtBottleneck, layers=[3, 4, 23, 3], groups=32, reduction=16, **kwargs)
return _create_senet('seresnext101_32x4d', pretrained, **model_args)

5
timm/models/xception_aligned.py
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@ -1,6 +1,7 @@
"""Pytorch impl of Aligned Xception
"""Pytorch impl of Aligned Xception 41, 65, 71
This is a correct impl of Aligned Xception (Deeplab) models compatible with TF definition.
This is a correct, from scratch impl of Aligned Xception (Deeplab) models compatible with TF weights at
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
Hacked together by Ross Wightman
"""

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