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def conv_out(in_planes, out_planes):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, bias=False)
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def recurrent_conv(in_planes, out_planes):
'3x3 convolution with padding'
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=1, padding=1, groups=1, bias=False)
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def _make_divisible(v: float, divisor: int, min_value: Optional[int]=None) -> int:
'\n This function is taken from the original tf repo.\n It ensures that all layers have a channel number that is divisible by 8\n It can be seen here:\n https://github.com/tensorflow/models/blob/master/research/slim/net... |
class ConvBNReLU(nn.Sequential):
def __init__(self, in_planes: int, out_planes: int, kernel_size: int=3, stride: int=1, groups: int=1, norm_layer: Optional[Callable[(..., nn.Module)]]=None) -> None:
padding = ((kernel_size - 1) // 2)
if (norm_layer is None):
norm_layer = nn.BatchNorm2... |
class InvertedResidual(nn.Module):
def __init__(self, inp: int, oup: int, stride: int, expand_ratio: int, rla_channel: int, norm_layer: Optional[Callable[(..., nn.Module)]]=None, ECA_ksize=None) -> None:
super(InvertedResidual, self).__init__()
self.stride = stride
assert (stride in [1, 2... |
class RLA_MobileNetV2(nn.Module):
def __init__(self, num_classes: int=1000, width_mult: float=1.0, rla_channel: int=32, inverted_residual_setting: Optional[List[List[int]]]=None, round_nearest: int=8, block: Optional[Callable[(..., nn.Module)]]=None, norm_layer: Optional[Callable[(..., nn.Module)]]=None, ECA=Fal... |
def rla_mobilenetv2(rla_channel=32):
' Constructs a RLA_MobileNetV2 model.\n default: \n rla_channel = 32, ECA=False\n '
print('Constructing rla_mobilenetv2......')
model = RLA_MobileNetV2(rla_channel=rla_channel)
return model
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def rla_mobilenetv2_eca(rla_channel=32, eca=True):
' Constructs a RLA_MobileNetV2 model.\n default: \n rla_channel = 32, ECA=False\n '
print('Constructing rla_mobilenetv2_eca......')
model = RLA_MobileNetV2(rla_channel=rla_channel, ECA=eca)
return model
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def rla_mobilenetv2_k6():
' Constructs a RLA_MobileNetV2 model.\n default: \n rla_channel = 32, ECA=False\n '
print('Constructing rla_mobilenetv2_k6......')
model = RLA_MobileNetV2(rla_channel=6)
return model
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def rla_mobilenetv2_k6_eca(eca=True):
' Constructs a RLA_MobileNetV2 model.\n default: \n rla_channel = 32, ECA=False\n '
print('Constructing rla_mobilenetv2_k6_eca......')
model = RLA_MobileNetV2(rla_channel=6, ECA=eca)
return model
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def rla_mobilenetv2_k12():
' Constructs a RLA_MobileNetV2 model.\n default: \n rla_channel = 32, ECA=False\n '
print('Constructing rla_mobilenetv2_k12......')
model = RLA_MobileNetV2(rla_channel=12)
return model
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def rla_mobilenetv2_k12_eca(eca=True):
' Constructs a RLA_MobileNetV2 model.\n default: \n rla_channel = 32, ECA=False\n '
print('Constructing rla_mobilenetv2_k12_eca......')
model = RLA_MobileNetV2(rla_channel=12, ECA=eca)
return model
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def rla_mobilenetv2_k24():
' Constructs a RLA_MobileNetV2 model.\n default: \n rla_channel = 32, ECA=False\n '
print('Constructing rla_mobilenetv2_k24......')
model = RLA_MobileNetV2(rla_channel=24)
return model
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def rla_mobilenetv2_k24_eca(eca=True):
' Constructs a RLA_MobileNetV2 model.\n default: \n rla_channel = 32, ECA=False\n '
print('Constructing rla_mobilenetv2_k24_eca......')
model = RLA_MobileNetV2(rla_channel=24, ECA=eca)
return model
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def rla_mobilenetv2_k32():
' Constructs a RLA_MobileNetV2 model.\n default: \n rla_channel = 32, ECA=False\n '
print('Constructing rla_mobilenetv2_k32......')
model = RLA_MobileNetV2(rla_channel=32)
return model
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def rla_mobilenetv2_k32_eca(eca=True):
' Constructs a RLA_MobileNetV2 model.\n default: \n rla_channel = 32, ECA=False\n '
print('Constructing rla_mobilenetv2_k32_eca......')
model = RLA_MobileNetV2(rla_channel=32, ECA=eca)
return model
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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, padding=dilation, groups=groups, bias=False, dilation=dilation)
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def conv1x1(in_planes, out_planes, stride=1):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, SE=False, ECA_size=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction=16):
super(Bottleneck, self).__init__()
if (norm_layer is None):
norm_layer = nn.Batc... |
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, SE=False, ECA=None, zero_init_last_bn=True, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None):
super(ResNet, self).__init__()
if (norm_layer is None):
norm_layer = nn.BatchNorm... |
def resnet50():
' Constructs a ResNet-50 model.\n default: \n num_classes=1000, SE=False, ECA=None\n ECA: a list of kernel sizes in ECA\n '
print('Constructing resnet50......')
model = ResNet(Bottleneck, [3, 4, 6, 3])
return model
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def resnet50_se():
' Constructs a ResNet-50_SE model.\n default: \n num_classes=1000, SE=False, ECA=None\n '
print('Constructing resnet50_se......')
model = ResNet(Bottleneck, [3, 4, 6, 3], SE=True)
return model
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def resnet50_eca(k_size=[5, 5, 5, 7]):
'Constructs a ResNet-50_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n num_classes:The classes of classification\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
print('Constructing resnet50_eca......'... |
def resnet101():
' Constructs a ResNet-101 model.\n default: \n num_classes=1000, SE=False, ECA=None\n '
print('Constructing resnet101......')
model = ResNet(Bottleneck, [3, 4, 23, 3])
return model
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def resnet101_se():
' Constructs a ResNet-101_SE model.\n default: \n num_classes=1000, SE=False, ECA=None\n '
print('Constructing resnet101_se......')
model = ResNet(Bottleneck, [3, 4, 23, 3], SE=True)
return model
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def resnet101_eca(k_size=[5, 5, 5, 7]):
'Constructs a ResNet-101_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n '
print('Constructing resnet101_eca......')
model = ResNet(Bottleneck, [3, 4, 23, 3], ECA=k_size)
return model
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def resnet152():
' Constructs a ResNet-152 model.\n default: \n num_classes=1000, SE=False, ECA=None\n '
print('Constructing resnet152......')
model = ResNet(Bottleneck, [3, 8, 36, 3])
return model
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def resnet152_se():
' Constructs a ResNet-152_SE model.\n default: \n num_classes=1000, SE=False, ECA=None\n '
print('Constructing resnet152_se......')
model = ResNet(Bottleneck, [3, 8, 36, 3], SE=True)
return model
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def resnet152_eca(k_size=[5, 5, 5, 7]):
'Constructs a ResNet-152_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n '
print('Constructing resnet152_eca......')
model = ResNet(Bottleneck, [3, 8, 36, 3], ECA=k_size)
return model
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def resnext50_32x4d():
' Constructs a ResNeXt50_32x4d model.\n default: \n num_classes=1000, SE=False, ECA=None\n '
print('Constructing resnext50_32x4d......')
model = ResNet(Bottleneck, [3, 4, 6, 3], groups=32, width_per_group=4)
return model
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def resnext50_32x4d_se():
' Constructs a ResNeXt50_32x4d_SE model.\n default: \n num_classes=1000, SE=False, ECA=None\n '
print('Constructing resnext50_32x4d_se......')
model = ResNet(Bottleneck, [3, 4, 6, 3], SE=True, groups=32, width_per_group=4)
return model
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def resnext50_32x4d_eca(k_size=[5, 5, 5, 7]):
'Constructs a ResNeXt50_32x4d_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n '
print('Constructing resnext50_32x4d_eca......')
model = ResNet(Bottleneck, [3, 4, 6, 3], ECA=k_size, groups=32, width_per_group=4)
return model
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def resnext101_32x4d():
' Constructs a ResNeXt101_32x4d model.\n default: \n num_classes=1000, SE=False, ECA=None\n '
print('Constructing resnext101_32x4d......')
model = ResNet(Bottleneck, [3, 4, 23, 3], groups=32, width_per_group=4)
return model
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def resnext101_32x4d_se():
' Constructs a ResNeXt101_32x4d_SE model.\n default: \n num_classes=1000, SE=False, ECA=None\n '
print('Constructing resnext101_32x4d_se......')
model = ResNet(Bottleneck, [3, 4, 23, 3], SE=True, groups=32, width_per_group=4)
return model
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def resnext101_32x4d_eca(k_size=[5, 5, 5, 7]):
'Constructs a ResNeXt101_32x4d_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n '
print('Constructing resnext101_32x4d_eca......')
model = ResNet(Bottleneck, [3, 4, 23, 3], ECA=k_size, groups=32, width_per_group=4)
return mode... |
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, padding=dilation, groups=groups, bias=False, dilation=dilation)
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def conv1x1(in_planes, out_planes, stride=1):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, SE=False, ECA_size=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction=16):
super(Bottleneck, self).__init__()
if (norm_layer is None):
norm_layer = nn.Batc... |
class ResNet_k(nn.Module):
def __init__(self, block, layers, num_classes=1000, SE=False, ECA=None, channel_k=32, zero_init_last_bn=True, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None):
super(ResNet_k, self).__init__()
if (norm_layer is None):
norm_la... |
def resnet50_k(channel_k=32):
' Constructs a ResNet-50 model.\n default: \n num_classes=1000, SE=False, ECA=None\n ECA: a list of kernel sizes in ECA\n '
print('Constructing resnet50_k......')
model = ResNet_k(Bottleneck, [3, 4, 6, 3], channel_k=channel_k)
return model
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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, padding=dilation, groups=groups, bias=False, dilation=dilation)
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def conv1x1(in_planes, out_planes, stride=1):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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class RLA_Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, rla_channel=32, SE=False, ECA_size=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction=16):
super(RLA_Bottleneck, self).__init__()
if (norm_layer is None):
... |
class RLA_ResNet(nn.Module):
'\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n SE: whether use SE or not \n ECA: None: not use ECA, or specify a list of kernel sizes\n '
def __init__(self, block, layers, num_classes=1000, rla_channel=32, SE=False, ECA=None, zero_init_l... |
def rla_resnet50(rla_channel=32):
' Constructs a RLA_ResNet-50 model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n ECA: a list of kernel sizes in ECA\n '
print('Constructing rla_resnet50......')
model = RLA_ResNet(RLA_Bottleneck, [3, 4, 6, 3])
return model
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def rla_resnet50_eca(rla_channel=32, k_size=[5, 5, 5, 7]):
'Constructs a RLA_ResNet-50_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n '
print('Constructing rla_resnet50_eca......')
model = RLA_R... |
def rla_resnet101(rla_channel=32):
' Constructs a RLA_ResNet-101 model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n '
print('Constructing rla_resnet101......')
model = RLA_ResNet(RLA_Bottleneck, [3, 4, 23, 3])
return model
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def rla_resnet101_eca(rla_channel=32, k_size=[5, 5, 5, 7]):
'Constructs a RLA_ResNet-101_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n '
print('Constructing rla_resnet101_eca......')
model = RL... |
def rla_resnet152(rla_channel=32):
' Constructs a RLA_ResNet-152 model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n '
print('Constructing rla_resnet152......')
model = RLA_ResNet(RLA_Bottleneck, [3, 8, 36, 3])
return model
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def rla_resnet152_eca(rla_channel=32, k_size=[5, 5, 5, 7]):
'Constructs a RLA_ResNet-101_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n '
print('Constructing rla_resnet101_eca......')
model = RL... |
def rla_resnext50_32x4d(rla_channel=32):
' Constructs a RLA_ResNeXt50_32x4d model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n '
print('Constructing rla_resnext50_32x4d......')
model = RLA_ResNet(RLA_Bottleneck, [3, 4, 6, 3], groups=32, width_per_group=4)
return m... |
def rla_resnext50_32x4d_se(rla_channel=32):
' Constructs a RLA_ResNeXt50_32x4d_SE model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n '
print('Constructing rla_resnext50_32x4d_se......')
model = RLA_ResNet(RLA_Bottleneck, [3, 4, 6, 3], SE=True, groups=32, width_per_gro... |
def rla_resnext50_32x4d_eca(rla_channel=32, k_size=[5, 5, 5, 7]):
'Constructs a RLA_ResNeXt50_32x4d_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n '
print('Constructing rla_resnext50_32x4d_eca......... |
def rla_resnext101_32x4d(rla_channel=32):
' Constructs a RLA_ResNeXt101_32x4d model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n '
print('Constructing rla_resnext101_32x4d......')
model = RLA_ResNet(RLA_Bottleneck, [3, 4, 23, 3], groups=32, width_per_group=4)
retu... |
def rla_resnext101_32x4d_se(rla_channel=32):
' Constructs a RLA_ResNeXt101_32x4d_SE model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n '
print('Constructing rla_resnext101_32x4d_se......')
model = RLA_ResNet(RLA_Bottleneck, [3, 4, 23, 3], SE=True, groups=32, width_per... |
def rla_resnext101_32x4d_eca(rla_channel=32, k_size=[5, 5, 5, 7]):
'Constructs a RLA_ResNeXt101_32x4d_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n '
print('Constructing rla_resnext101_32x4d_eca...... |
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, padding=dilation, groups=groups, bias=False, dilation=dilation)
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def conv1x1(in_planes, out_planes, stride=1):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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class RLA_Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, rla_channel=32, SE=False, ECA_size=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction=16):
super(RLA_Bottleneck, self).__init__()
if (norm_layer is None):
... |
class RLAgru_ResNet(nn.Module):
'\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n SE: whether use SE or not \n ECA: None: not use ECA, or specify a list of kernel sizes\n '
def __init__(self, block, layers, num_classes=1000, rla_channel=32, SE=False, ECA=None, zero_ini... |
def rlagru_resnet50(rla_channel=32):
' Constructs a RLAgru_ResNet-50 model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n ECA: a list of kernel sizes in ECA\n '
print('Constructing rlagru_resnet50......')
model = RLAgru_ResNet(RLA_Bottleneck, [3, 4, 6, 3])
return... |
def rlagru_resnet50_eca(rla_channel=32, k_size=[5, 5, 5, 7]):
'Constructs a RLAgru_ResNet-50_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n '
print('Constructing rlagru_resnet50_eca......')
mode... |
def rlagru_resnet101(rla_channel=32):
' Constructs a RLAgru_ResNet-101 model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n ECA: a list of kernel sizes in ECA\n '
print('Constructing rlagru_resnet101......')
model = RLAgru_ResNet(RLA_Bottleneck, [3, 4, 23, 3])
re... |
def rlagru_resnet101_eca(rla_channel=32, k_size=[5, 5, 5, 7]):
'Constructs a RLAgru_ResNet-101_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n '
print('Constructing rlagru_resnet101_eca......')
m... |
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, padding=dilation, groups=groups, bias=False, dilation=dilation)
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def conv1x1(in_planes, out_planes, stride=1):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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class RLA_Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, rla_channel=32, SE=False, ECA_size=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction=16):
super(RLA_Bottleneck, self).__init__()
if (norm_layer is None):
... |
class RLAlstm_ResNet(nn.Module):
'\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n SE: whether use SE or not \n ECA: None: not use ECA, or specify a list of kernel sizes\n '
def __init__(self, block, layers, num_classes=1000, rla_channel=32, SE=False, ECA=None, zero_in... |
def rlalstm_resnet50(rla_channel=32):
' Constructs a RLAlstm_ResNet-50 model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n ECA: a list of kernel sizes in ECA\n '
print('Constructing rlalstm_resnet50......')
model = RLAlstm_ResNet(RLA_Bottleneck, [3, 4, 6, 3])
re... |
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, padding=dilation, groups=groups, bias=False, dilation=dilation)
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def conv1x1(in_planes, out_planes, stride=1):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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class RLArh_Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, rla_channel=32, SE=False, ECA_size=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction=16):
super(RLArh_Bottleneck, self).__init__()
if (norm_layer is None):
... |
class RLArh_ResNet(nn.Module):
'\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n SE: whether use SE or not \n ECA: None: not use ECA, or specify a list of kernel sizes\n '
def __init__(self, block, layers, num_classes=1000, rla_channel=32, SE=False, ECA=None, zero_init... |
def rlarh_resnet50(rla_channel=32):
' Constructs a RLArh_ResNet-50 model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n ECA: a list of kernel sizes in ECA\n '
print('Constructing rlarh_resnet50......')
model = RLArh_ResNet(RLArh_Bottleneck, [3, 4, 6, 3])
return m... |
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, padding=dilation, groups=groups, bias=False, dilation=dilation)
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def conv1x1(in_planes, out_planes, stride=1):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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class RLAus_Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, rla_channel=32, SE=False, ECA_size=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction=16):
super(RLAus_Bottleneck, self).__init__()
if (norm_layer is None):
... |
class RLAus_ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, rla_channel=32, SE=False, ECA=None, zero_init_last_bn=True, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None):
super(RLAus_ResNet, self).__init__()
if (norm_layer is None):
... |
def rlaus_resnet50(rla_channel=32):
' Constructs a RLAus_ResNet-50 model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n ECA: a list of kernel sizes in ECA\n '
print('Constructing rlaus_resnet50......')
model = RLAus_ResNet(RLAus_Bottleneck, [3, 4, 6, 3])
return m... |
def rlaus_resnet101(rla_channel=32):
' Constructs a RLAus_ResNet-101 model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n ECA: a list of kernel sizes in ECA\n '
print('Constructing rlaus_resnet101......')
model = RLAus_ResNet(RLAus_Bottleneck, [3, 4, 23, 3])
retu... |
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, padding=dilation, groups=groups, bias=False, dilation=dilation)
|
def conv1x1(in_planes, out_planes, stride=1):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
|
class RLAv1_Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, rla_channel=32, SE=False, ECA_size=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction=16):
super(RLAv1_Bottleneck, self).__init__()
if (norm_layer is None):
... |
class RLAv1_ResNet(nn.Module):
'\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n SE: whether use SE or not \n ECA: None: not use ECA, or specify a list of kernel sizes\n '
def __init__(self, block, layers, num_classes=1000, rla_channel=32, SE=False, ECA=None, zero_init... |
def rlav1_resnet50(rla_channel=32):
' Constructs a RLAv1_ResNet-50 model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n ECA: a list of kernel sizes in ECA\n '
print('Constructing rlav1_resnet50......')
model = RLAv1_ResNet(RLAv1_Bottleneck, [3, 4, 6, 3])
return m... |
def rlav1_resnet50_eca(rla_channel=32, k_size=[5, 5, 5, 7]):
'Constructs a RLAv1_ResNet-50_ECA model.\n Args:\n k_size: Adaptive selection of kernel size\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n '
print('Constructing rlav1_resnet50_eca......')
model =... |
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, padding=dilation, groups=groups, bias=False, dilation=dilation)
|
def conv1x1(in_planes, out_planes, stride=1):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
|
class RLAv1p_Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, rla_channel=32, SE=False, ECA_size=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction=16):
super(RLAv1p_Bottleneck, self).__init__()
if (norm_layer is None):
... |
class RLAv1p_ResNet(nn.Module):
'\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n SE: whether use SE or not \n ECA: None: not use ECA, or specify a list of kernel sizes\n '
def __init__(self, block, layers, num_classes=1000, rla_channel=32, SE=False, ECA=None, zero_ini... |
def rlav1p_resnet50(rla_channel=32):
' Constructs a RLAv1p_ResNet-50 model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n ECA: a list of kernel sizes in ECA\n '
print('Constructing rlav1p_resnet50......')
model = RLAv1p_ResNet(RLAv1p_Bottleneck, [3, 4, 6, 3])
ret... |
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, padding=dilation, groups=groups, bias=False, dilation=dilation)
|
def conv1x1(in_planes, out_planes, stride=1):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
|
class RLAv2_Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, rla_channel=32, SE=False, ECA_size=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction=16):
super(RLAv2_Bottleneck, self).__init__()
if (norm_layer is None):
... |
class RLAv2_ResNet(nn.Module):
'\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n SE: whether use SE or not \n ECA: None: not use ECA, or specify a list of kernel sizes\n '
def __init__(self, block, layers, num_classes=1000, rla_channel=32, SE=False, ECA=None, zero_init... |
def rlav2_resnet50(rla_channel=32):
' Constructs a RLAv2_ResNet-50 model.\n default: \n num_classes=1000, rla_channel=32, SE=False, ECA=None\n ECA: a list of kernel sizes in ECA\n '
print('Constructing rlav2_resnet50......')
model = RLAv2_ResNet(RLAv2_Bottleneck, [3, 4, 6, 3])
return m... |
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, padding=dilation, groups=groups, bias=False, dilation=dilation)
|
def conv1x1(in_planes, out_planes, stride=1):
'1x1 convolution'
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
|
class RLAv3_Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, rla_channel=32, SE=False, ECA_size=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction=16):
super(RLAv3_Bottleneck, self).__init__()
if (norm_layer is None):
... |
class RLAv3_ResNet(nn.Module):
'\n rla_channel: the number of filters of the shared(recurrent) conv in RLA\n SE: whether use SE or not \n ECA: None: not use ECA, or specify a list of kernel sizes\n '
def __init__(self, block, layers, num_classes=1000, rla_channel=32, SE=False, ECA=None, zero_init... |
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