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Richard Zhang authored and Richard Zhang committed Oct 22, 2020
1 parent 5e2f926 commit bf2f1af
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Showing 5 changed files with 137 additions and 64 deletions.
8 changes: 6 additions & 2 deletions antialiased_cnns/alexnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,7 @@
'alexnet_lpf3': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/alexnet_lpf3-f9bbc410.pth',
'alexnet_lpf4': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/alexnet_lpf4-0114fe25.pth',
'alexnet_lpf5': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/alexnet_lpf5-4fa3706a.pth',
'alexnet_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/alexnet_lpf4_finetune-20598a7a.pth',
}


Expand Down Expand Up @@ -106,7 +107,7 @@ def forward(self, x):
return x


def alexnet(pretrained=False, filter_size=4, **kwargs):
def alexnet(pretrained=False, filter_size=4, _force_nonfinetuned=False, **kwargs):
"""AlexNet model architecture from the
`"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
Expand All @@ -116,7 +117,10 @@ def alexnet(pretrained=False, filter_size=4, **kwargs):
"""
model = AlexNet(filter_size=filter_size, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['alexnet_lpf%i'%filter_size], map_location='cpu', check_hash=True)['state_dict'])
if(filter_size==4 and not _force_nonfinetuned):
model.load_state_dict(model_zoo.load_url(model_urls['alexnet_lpf4_finetune'], map_location='cpu', check_hash=True)['state_dict'])
else:
model.load_state_dict(model_zoo.load_url(model_urls['alexnet_lpf%i'%filter_size], map_location='cpu', check_hash=True)['state_dict'])
return model


Expand Down
35 changes: 24 additions & 11 deletions antialiased_cnns/densenet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,6 +53,10 @@
'densenet121_lpf3': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/densenet121_lpf3-0f267ad8.pth',
'densenet121_lpf4': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/densenet121_lpf4-edeaab00.pth',
'densenet121_lpf5': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/densenet121_lpf5-ebc7880c.pth',
'densenet121_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/densenet121_lpf4_finetune-eceaa619.pth',
'densenet161_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/densenet161_lpf4_finetune-a5e0f328.pth',
'densenet169_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/densenet169_lpf4_finetune-992131e6.pth',
'densenet201_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/densenet201_lpf4_finetune-736979b2.pth',
}


Expand Down Expand Up @@ -191,7 +195,7 @@ def _load_state_dict(model, model_url):
model.load_state_dict(state_dict)


def densenet121(pretrained=False, filter_size=4, pool_only=True, **kwargs):
def densenet121(pretrained=False, filter_size=4, pool_only=True, _force_nonfinetuned=False, **kwargs):
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
Expand All @@ -202,11 +206,14 @@ def densenet121(pretrained=False, filter_size=4, pool_only=True, **kwargs):
model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16),
filter_size=filter_size, pool_only=pool_only, **kwargs)
if pretrained:
_load_state_dict(model, model_urls['densenet121_lpf%i'%filter_size])
if(filter_size==4 and not _force_nonfinetuned):
_load_state_dict(model, model_urls['densenet121_lpf4_finetune'])
else:
_load_state_dict(model, model_urls['densenet121_lpf%i'%filter_size])
return model


def densenet169(pretrained=False, filter_size=4, pool_only=True, **kwargs):
def densenet169(pretrained=False, filter_size=4, pool_only=True, _force_nonfinetuned=False, **kwargs):
r"""Densenet-169 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
Expand All @@ -216,12 +223,14 @@ def densenet169(pretrained=False, filter_size=4, pool_only=True, **kwargs):
model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32),
filter_size=filter_size, pool_only=pool_only, **kwargs)
if pretrained:
raise ValueError('No pretrained model available')
# _load_state_dict(model, model_urls['densenet169'])
if(filter_size==4):
_load_state_dict(model, model_urls['densenet169_lpf4_finetune'])
else:
raise ValueError('No pretrained model available')
return model


def densenet201(pretrained=False, filter_size=4, pool_only=True, **kwargs):
def densenet201(pretrained=False, filter_size=4, pool_only=True, _force_nonfinetuned=False, **kwargs):
r"""Densenet-201 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
Expand All @@ -231,12 +240,14 @@ def densenet201(pretrained=False, filter_size=4, pool_only=True, **kwargs):
model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32),
filter_size=filter_size, pool_only=pool_only, **kwargs)
if pretrained:
raise ValueError('No pretrained model available')
# _load_state_dict(model, model_urls['densenet201'])
if(filter_size==4):
_load_state_dict(model, model_urls['densenet201_lpf4_finetune'])
else:
raise ValueError('No pretrained model available')
return model


def densenet161(pretrained=False, filter_size=4, pool_only=True, **kwargs):
def densenet161(pretrained=False, filter_size=4, pool_only=True, _force_nonfinetuned=False, **kwargs):
r"""Densenet-161 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
Expand All @@ -246,6 +257,8 @@ def densenet161(pretrained=False, filter_size=4, pool_only=True, **kwargs):
model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24),
filter_size=filter_size, pool_only=pool_only, **kwargs)
if pretrained:
raise ValueError('No pretrained model available')
# _load_state_dict(model, model_urls['densenet161'])
if(filter_size==4):
_load_state_dict(model, model_urls['densenet161_lpf4_finetune'])
else:
raise ValueError('No pretrained model available')
return model
11 changes: 6 additions & 5 deletions antialiased_cnns/mobilenet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,7 @@
'mobilenet_v2_lpf3': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/mobilenet_v2_lpf3-23b2e2ee.pth',
'mobilenet_v2_lpf4': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/mobilenet_v2_lpf4-2e0b9cb9.pth',
'mobilenet_v2_lpf5': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/mobilenet_v2_lpf5-ab8fe968.pth',
'mobilenet_v2_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/mobilenet_v2_lpf4_finetune-7eed94b1.pth',
}


Expand Down Expand Up @@ -160,7 +161,7 @@ def forward(self, x):
return x


def mobilenet_v2(pretrained=False, filter_size=4, **kwargs):
def mobilenet_v2(pretrained=False, filter_size=4, _force_nonfinetuned=False, **kwargs):
"""
Constructs a MobileNetV2 architecture from
`"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.
Expand All @@ -170,9 +171,9 @@ def mobilenet_v2(pretrained=False, filter_size=4, **kwargs):
"""
model = MobileNetV2(filter_size=filter_size, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['mobilenet_v2_lpf%i'%filter_size], map_location='cpu', check_hash=True)['state_dict'])
# state_dict = load_state_dict_from_url(model_urls['mobilenet_v2'],
# progress=progress)
# model.load_state_dict(state_dict)
if(filter_size==4 and not _force_nonfinetuned):
model.load_state_dict(model_zoo.load_url(model_urls['mobilenet_v2_lpf4_finetune'], map_location='cpu', check_hash=True)['state_dict'])
else:
model.load_state_dict(model_zoo.load_url(model_urls['mobilenet_v2_lpf%i'%filter_size], map_location='cpu', check_hash=True)['state_dict'])
return model

74 changes: 52 additions & 22 deletions antialiased_cnns/resnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,9 +64,16 @@
'resnet101_lpf4': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/resnet101_lpf4-f8a116ff.pth',
'resnet101_lpf5': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/resnet101_lpf5-1f3745af.pth',
'resnet18_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/resnet18_lpf4_finetune-8cc58f59.pth',
'resnet34_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/resnet34_lpf4_finetune-db622952.pth',
'resnet50_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/resnet50_lpf4_finetune-cad66808.pth',
'resnet101_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/resnet101_lpf4_finetune-9280acb0.pth',
'resnet152_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/resnet152_lpf4_finetune-7f67d9ae.pth',
'resnext50_32x4d_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/resnext50_32x4d_lpf4_finetune-9106e549.pth',
'resnext101_32x8d_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/resnext101_32x8d_lpf4_finetune-8f13a25d.pth',
'wide_resnet50_2_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/wide_resnet50_2_lpf4_finetune-02a183f7.pth',
'wide_resnet101_2_lpf4_finetune': 'https://antialiased-cnns.s3.us-east-2.amazonaws.com/weights_v0.1/wide_resnet101_2_lpf4_finetune-da4eae04.pth',
}


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
Expand Down Expand Up @@ -293,75 +300,94 @@ def resnet18(pretrained=False, filter_size=4, pool_only=True, _force_nonfinetune
return model


def resnet34(pretrained=False, filter_size=4, pool_only=True, **kwargs):
def resnet34(pretrained=False, filter_size=4, pool_only=True, _force_nonfinetuned=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
filter_size (int): Antialiasing filter size
pool_only (bool): [True] don't antialias the first downsampling operation (which is costly to antialias)
_force_nonfinetuned (bool): [False] If True, load the trained-from scratch pretrained model (if available)
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], filter_size=filter_size, pool_only=pool_only, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34_lpf%i'%filter_size], map_location='cpu', check_hash=True)['state_dict'])
if(filter_size==4 and not _force_nonfinetuned):
model.load_state_dict(model_zoo.load_url(model_urls['resnet34_lpf4_finetune'], map_location='cpu', check_hash=True)['state_dict'])
else:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34_lpf%i'%filter_size], map_location='cpu', check_hash=True)['state_dict'])
return model


def resnet50(pretrained=False, filter_size=4, pool_only=True, **kwargs):
def resnet50(pretrained=False, filter_size=4, pool_only=True, _force_nonfinetuned=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
filter_size (int): Antialiasing filter size
pool_only (bool): [True] don't antialias the first downsampling operation (which is costly to antialias)
_force_nonfinetuned (bool): [False] If True, load the trained-from scratch pretrained model (if available)
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], filter_size=filter_size, pool_only=pool_only, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50_lpf%i'%filter_size], map_location='cpu', check_hash=True)['state_dict'])
if(filter_size==4 and not _force_nonfinetuned):
model.load_state_dict(model_zoo.load_url(model_urls['resnet50_lpf4_finetune'], map_location='cpu', check_hash=True)['state_dict'])
else:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50_lpf%i'%filter_size], map_location='cpu', check_hash=True)['state_dict'])
return model


def resnet101(pretrained=False, filter_size=4, pool_only=True, **kwargs):
def resnet101(pretrained=False, filter_size=4, pool_only=True, _force_nonfinetuned=False, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
filter_size (int): Antialiasing filter size
pool_only (bool): [True] don't antialias the first downsampling operation (which is costly to antialias)
_force_nonfinetuned (bool): [False] If True, load the trained-from scratch pretrained model (if available)
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], filter_size=filter_size, pool_only=pool_only, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101_lpf%i'%filter_size], map_location='cpu', check_hash=True)['state_dict'])
if(filter_size==4 and not _force_nonfinetuned):
model.load_state_dict(model_zoo.load_url(model_urls['resnet101_lpf4_finetune'], map_location='cpu', check_hash=True)['state_dict'])
else:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101_lpf%i'%filter_size], map_location='cpu', check_hash=True)['state_dict'])
return model


def resnet152(pretrained=False, filter_size=4, pool_only=True, **kwargs):
def resnet152(pretrained=False, filter_size=4, pool_only=True, _force_nonfinetuned=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
filter_size (int): Antialiasing filter size
pool_only (bool): [True] don't antialias the first downsampling operation (which is costly to antialias)
_force_nonfinetuned (bool): [False] If True, load the trained-from scratch pretrained model (if available)
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], filter_size=filter_size, pool_only=pool_only, **kwargs)
if pretrained:
raise ValueError('No pretrained model available')
# model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
if(filter_size==4):
model.load_state_dict(model_zoo.load_url(model_urls['resnet152_lpf4_finetune'], map_location='cpu', check_hash=True)['state_dict'])
else:
raise ValueError('No pretrained model available')
return model


def resnext50_32x4d(pretrained=False, filter_size=4, pool_only=True, **kwargs):
def resnext50_32x4d(pretrained=False, filter_size=4, pool_only=True, _force_nonfinetuned=False, **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3], groups=32, width_per_group=4, filter_size=filter_size, pool_only=pool_only, **kwargs)
if pretrained:
raise ValueError('No pretrained model available')
# model.load_state_dict(model_zoo.load_url(model_urls['resnext50_32x4d']))
if(filter_size==4):
model.load_state_dict(model_zoo.load_url(model_urls['resnext50_32x4d_lpf4_finetune'], map_location='cpu', check_hash=True)['state_dict'])
else:
raise ValueError('No pretrained model available')
return model


def resnext101_32x8d(pretrained=False, filter_size=4, pool_only=True, **kwargs):
def resnext101_32x8d(pretrained=False, filter_size=4, pool_only=True, _force_nonfinetuned=False, **kwargs):
model = ResNet(Bottleneck, [3, 4, 23, 3], groups=32, width_per_group=8, filter_size=filter_size, pool_only=pool_only, **kwargs)
if pretrained:
raise ValueError('No pretrained model available')
# model.load_state_dict(model_zoo.load_url(model_urls['resnext101_32x8d']))
if(filter_size==4):
model.load_state_dict(model_zoo.load_url(model_urls['resnext101_32x8d_lpf4_finetune'], map_location='cpu', check_hash=True)['state_dict'])
else:
raise ValueError('No pretrained model available')
return model


def wide_resnet50_2(pretrained=False, filter_size=4, **kwargs):
def wide_resnet50_2(pretrained=False, filter_size=4, _force_nonfinetuned=False, **kwargs):
"""Wide ResNet-50-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
Expand All @@ -376,11 +402,13 @@ def wide_resnet50_2(pretrained=False, filter_size=4, **kwargs):
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], width_per_group=64*2, filter_size=filter_size, **kwargs)
if pretrained:
raise ValueError('No pretrained model available')
model.load_state_dict(state_dict)
if(filter_size==4):
model.load_state_dict(model_zoo.load_url(model_urls['wide_resnet50_2_lpf4_finetune'], map_location='cpu', check_hash=True)['state_dict'])
else:
raise ValueError('No pretrained model available')
return model

def wide_resnet101_2(pretrained=False, filter_size=4, **kwargs):
def wide_resnet101_2(pretrained=False, filter_size=4, _force_nonfinetuned=False, **kwargs):
"""Wide ResNet-101-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
Expand All @@ -395,6 +423,8 @@ def wide_resnet101_2(pretrained=False, filter_size=4, **kwargs):
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], width_per_group=64*2, filter_size=filter_size, **kwargs)
if pretrained:
raise ValueError('No pretrained model available')
model.load_state_dict(state_dict)
if(filter_size==4):
model.load_state_dict(model_zoo.load_url(model_urls['wide_resnet101_2_lpf4_finetune'], map_location='cpu', check_hash=True)['state_dict'])
else:
raise ValueError('No pretrained model available')
return model
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