def __init__(self):
super(Resnet50_AVG, self).__init__()
self.base = nn.Sequential(
OrderedDict(
list(models.resnet50(pretrained=True).named_children())[:-2]))
self.pool = torch.nn.AdaptiveAvgPool2d((1, 1))
self.norm = L2N()
def __init__(self):
super(Resnet50_RMAC, self).__init__()
self.base = nn.Sequential(
OrderedDict(
list(models.resnet50(pretrained=True).named_children())[:-2]))
self.pool = RMAC()
self.norm = L2N()
def __init__(self):
super(DenseNet_AVG, self).__init__()
self.base = nn.Sequential(
*list(models.densenet121(pretrained=True).features.children()),
nn.ReLU(inplace=True))
self.pool = torch.nn.AdaptiveAvgPool2d((1, 1))
self.norm = L2N()
def __init__(self):
super(DenseNet_RMAC, self).__init__()
self.base = nn.Sequential(
*list(models.densenet121(pretrained=True).features.children()),
nn.ReLU(inplace=True))
self.pool = RMAC()
self.norm = L2N()
def __init__(self):
super(MobileNet_GeM, self).__init__()
self.base = nn.Sequential(
OrderedDict([
*list(
models.mobilenet_v2(
pretrained=True).features.named_children())
]))
self.pool = GeM()
self.norm = L2N()
def __init__(self):
super(MobileNet_AVG, self).__init__()
self.base = nn.Sequential(
OrderedDict([
*list(
models.mobilenet_v2(
pretrained=True).features.named_children())
]))
self.pool = torch.nn.AdaptiveAvgPool2d((1, 1))
self.norm = L2N()
def __init__(self):
super(DenseNet_GeM, self).__init__()
self.base = nn.Sequential(
OrderedDict([
*list(
models.densenet121(
pretrained=True).features.named_children())
] + [('relu', nn.ReLU(inplace=True))]))
self.pool = GeM()
self.norm = L2N()
def __init__(self):
super(Segment_Maxpooling, self).__init__()
self.pool = torch.nn.AdaptiveAvgPool1d(1)
self.norm = L2N()