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A few more additions to Gluon Xception models to match interface of others.

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pull/24/head
Ross Wightman 5 years ago
parent 4d505e0785
commit 3b4868f6dc
2 changed files with 63 additions and 18 deletions
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1
README.md
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@ -120,6 +120,7 @@ I've leveraged the training scripts in this repository to train a few of the mod
| gluon_resnet152_v1c | 79.916 (20.084) | 94.842 (5.158) | 60.21 | bicubic | 224 | |
| gluon_seresnext50_32x4d | 79.912 (20.088) | 94.818 (5.182) | 27.56 | bicubic | 224 | |
| gluon_resnet152_v1b | 79.692 (20.308) | 94.738 (5.262) | 60.19 | bicubic | 224 | |
| gluon_xception65 | 79.604 (20.396) | 94.748 (5.252) | 39.92 | bicubic | 299 | |
| gluon_resnet101_v1c | 79.544 (20.456) | 94.586 (5.414) | 44.57 | bicubic | 224 | |
| gluon_resnext50_32x4d | 79.356 (20.644) | 94.424 (5.576) | 25.03 | bicubic | 224 | |
| gluon_resnet101_v1b | 79.304 (20.696) | 94.524 (5.476) | 44.55 | bicubic | 224 | |

80
timm/models/gluon_xception.py
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@ -23,6 +23,7 @@ default_cfgs = {
'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_xception-7015a15c.pth',
'input_size': (3, 299, 299),
'crop_pct': 0.875,
'pool_size': (10, 10),
'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN,
'std': IMAGENET_DEFAULT_STD,
@ -35,6 +36,7 @@ default_cfgs = {
'url': '',
'input_size': (3, 299, 299),
'crop_pct': 0.875,
'pool_size': (10, 10),
'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN,
'std': IMAGENET_DEFAULT_STD,
@ -181,7 +183,9 @@ class Xception65(nn.Module):
def __init__(self, num_classes=1000, in_chans=3, output_stride=32, norm_layer=nn.BatchNorm2d,
norm_kwargs=None, drop_rate=0., global_pool='avg'):
super(Xception65, self).__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
self.global_pool = global_pool
norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
if output_stride == 32:
entry_block3_stride = 2
@ -240,14 +244,26 @@ class Xception65(nn.Module):
norm_layer=norm_layer, norm_kwargs=norm_kwargs)
self.bn4 = norm_layer(num_features=1536, **norm_kwargs)
self.num_features = 2048
self.conv5 = SeparableConv2d(
1536, 2048, 3, stride=1, dilation=exit_block_dilations[1],
1536, self.num_features, 3, stride=1, dilation=exit_block_dilations[1],
norm_layer=norm_layer, norm_kwargs=norm_kwargs)
self.bn5 = norm_layer(num_features=2048, **norm_kwargs)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(in_features=2048, out_features=num_classes)
self.bn5 = norm_layer(num_features=self.num_features, **norm_kwargs)
self.fc = nn.Linear(in_features=self.num_features, out_features=num_classes)
def get_classifier(self):
return self.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool = global_pool
del self.fc
if num_classes:
self.fc = nn.Linear(self.num_features, num_classes)
else:
self.fc = None
def forward(self, x):
def forward_features(self, x, pool=True):
# Entry flow
x = self.conv1(x)
x = self.bn1(x)
@ -284,10 +300,15 @@ class Xception65(nn.Module):
x = self.bn5(x)
x = self.relu(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
if pool:
x = select_adaptive_pool2d(x, pool_type=self.global_pool)
x = x.view(x.size(0), -1)
return x
def forward(self, x):
x = self.forward_features(x)
if self.drop_rate:
F.dropout(x, self.drop_rate, training=self.training)
x = self.fc(x)
return x
@ -299,7 +320,9 @@ class Xception71(nn.Module):
def __init__(self, num_classes=1000, in_chans=3, output_stride=32, norm_layer=nn.BatchNorm2d,
norm_kwargs=None, drop_rate=0., global_pool='avg'):
super(Xception71, self).__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
self.global_pool = global_pool
norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
if output_stride == 32:
entry_block3_stride = 2
@ -365,14 +388,26 @@ class Xception71(nn.Module):
norm_layer=norm_layer, norm_kwargs=norm_kwargs)
self.bn4 = norm_layer(num_features=1536, **norm_kwargs)
self.num_features = 2048
self.conv5 = SeparableConv2d(
1536, 2048, 3, stride=1, dilation=exit_block_dilations[1],
1536, self.num_features, 3, stride=1, dilation=exit_block_dilations[1],
norm_layer=norm_layer, norm_kwargs=norm_kwargs)
self.bn5 = norm_layer(num_features=2048, **norm_kwargs)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(in_features=2048, out_features=num_classes)
self.bn5 = norm_layer(num_features=self.num_features, **norm_kwargs)
self.fc = nn.Linear(in_features=self.num_features, out_features=num_classes)
def get_classifier(self):
return self.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool = global_pool
del self.fc
if num_classes:
self.fc = nn.Linear(self.num_features, num_classes)
else:
self.fc = None
def forward(self, x):
def forward_features(self, x, pool=True):
# Entry flow
x = self.conv1(x)
x = self.bn1(x)
@ -409,16 +444,23 @@ class Xception71(nn.Module):
x = self.bn5(x)
x = self.relu(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
if pool:
x = select_adaptive_pool2d(x, pool_type=self.global_pool)
x = x.view(x.size(0), -1)
return x
def forward(self, x):
x = self.forward_features(x)
if self.drop_rate:
F.dropout(x, self.drop_rate, training=self.training)
x = self.fc(x)
return x
@register_model
def gluon_xception65(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" Modified Aligned Xception-65
"""
default_cfg = default_cfgs['gluon_xception65']
model = Xception65(num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg
@ -429,6 +471,8 @@ def gluon_xception65(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
@register_model
def gluon_xception71(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
""" Modified Aligned Xception-71
"""
default_cfg = default_cfgs['gluon_xception71']
model = Xception71(num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg

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