def __init__(self):
super(Binarizer, self).__init__()
self.conv = nn.Conv2d(512, 32, kernel_size=1, bias=False)
for param in self.conv.parameters():
param.requires_grad = False
self.sign = Sign()
def __init__(self):
super(Binarizer, self).__init__()
self.conv = nn.Conv2d(512, 32, kernel_size=1, bias=False)
for param in self.conv.parameters():
param.requires_grad = False
self.customConv = BatchConv2dContext(512,
32,
kernel_size=1,
stride=1,
padding=0,
bias=False)
self.sign = Sign()
def __init__(self, bits):
super(Binarizer, self).__init__()
self.conv = nn.Conv2d(512, bits, kernel_size=1, bias=False)
self.sign = Sign()
def __init__(self):
super(Binarizer, self).__init__()
self.conv = nn.Conv2d(512, 32, kernel_size=1, bias=False) # changed output channels from 32 to 16
self.sign = Sign()
def __init__(self, bottleneck):
super(Binarizer, self).__init__()
self.conv = nn.Conv2d(512, bottleneck, kernel_size=1, bias=False)
#elf.conv = nn.Conv2d(32, 16, kernel_size=1, bias=False)
self.tanh = nn.Tanh()
self.sign = Sign()
def __init__(self):
super(Binarizer, self).__init__()
#self.conv1 = nn.Conv2d(NUM_FEAT1, NUM_FEAT1, kernel_size=1, bias=False)
#self.conv4 = nn.Conv2d(NUM_FEAT2, NUM_FEAT2, kernel_size=1, bias=False)
self.sign = Sign()
