def __init__(self, args, fourier): super(res_SF, self).__init__() self.conv = transfer_img(fourier) self.resnet = resnet50_F() self.cnn_c = cnn_C() self.sf = SF(args, (24, 8, 8), 2)
def __init__(self, args, fourier): super(dcnn, self).__init__() self.batch_size = args[0] self.cuda = args[1] cuda = self.cuda self.conv = transfer_img(fourier) self.cnn = cnn() self.fc = FC(24, 8, 8, 2, cuda)
def __init__(self, args, fourier): super(resnet152, self).__init__() self.batch_size = args[0] self.cuda = args[1] cuda = self.cuda self.conv = transfer_img(fourier) self.resnet = resnet152_F() self.cnn_c = cnn_C() self.fc = FC(24, 8, 8, 2, cuda)
def __init__(self, args, fourier): super(res_TN, self).__init__() self.batch_size = args[0] self.cuda = args[1] b = self.batch_size cuda = self.cuda self.conv = transfer_img(fourier) self.resnet = resnet50_F() self.cnn_c = cnn_C() self.tn = TN(b, 24, 8, 8, 2, cuda=cuda, method=1)
def __init__(self, args, fourier): super(res_test, self).__init__() self.conv = transfer_img(fourier) self.resnet = resnet50_F() self.tr = TR(args, (24, 8, 8), 64) self.sf = SF(args, (24, 8, 8), 64) self.cnn_c = cnn_C() self.final = nn.Sequential(nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, 2))
def __init__(self, args, fourier): super(beauty_net, self).__init__() cuda = args[1] self.conv = transfer_img(fourier) self.resnet_a = resnet50_A() self.cnn_a = cnn_A() self.resnet_b = resnet50_B() self.cnn_b = cnn_B() self.resnet_c = resnet50_C() self.cnn_c = cnn_C() self.fc_1 = FC(24, 8, 8, 64, cuda) self.fc_2 = FC(24, 8, 8, 64, cuda) self.fc_3 = FC(24, 8, 8, 64, cuda) self.final = nn.Sequential(nn.Linear(192, 128), nn.BatchNorm1d(128), nn.ReLU(), nn.Linear(128, 64), nn.BatchNorm1d(64), nn.ReLU(), nn.Linear(64, 2))
def __init__(self, args, fourier): super(beauty_TN, self).__init__() b = args[0] cuda = args[1] self.conv = transfer_img(fourier) self.resnet_a = resnet50_A() self.cnn_a = cnn_A() self.resnet_b = resnet50_B() self.cnn_b = cnn_B() self.resnet_c = resnet50_C() self.cnn_c = cnn_C() self.tn_1 = TN(b, 24, 8, 8, 64, cuda=cuda, method=1) self.tn_2 = TN(b, 24, 8, 8, 64, cuda=cuda, method=1) self.tn_3 = TN(b, 24, 8, 8, 64, cuda=cuda, method=1) self.final = nn.Sequential(nn.Linear(192, 128), nn.BatchNorm1d(128), nn.ReLU(), nn.Linear(128, 64), nn.BatchNorm1d(64), nn.ReLU(), nn.Linear(64, 2))