def __init__(self):
super(SingleBNN, self).__init__()
self.convolutions = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64), BinaryHardtanh(),
BinarizeConv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64), BinaryHardtanh(),
BinarizeConv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64), BinaryHardtanh(),
BinarizeConv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64), BinaryHardtanh(),
nn.MaxPool2d(kernel_size=2, stride=2),
BinarizeConv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64), BinaryHardtanh(),
BinarizeConv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64), BinaryHardtanh(),
nn.MaxPool2d(kernel_size=2, stride=2),
BinarizeConv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64), BinaryHardtanh(),
BinarizeConv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64), BinaryHardtanh(),
nn.MaxPool2d(kernel_size=2, stride=2),
BinarizeConv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64), BinaryHardtanh(),
BinarizeConv2d(64, 10, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(10), nn.AvgPool2d(kernel_size=8))
self.softmax = nn.Sequential(nn.LogSoftmax(dim=1))
def __init__(self, *argv, **argn):
super(sConv2d, self).__init__()
if 'factor' in argn:
self.init_factor = argn['factor']
del argn['factor']
else:
self.init_factor = 1
if 'max_scales' in argn:
self.max_scales = argn['max_scales']
del argn['max_scales']
else:
self.max_scales = 4
if 'usf' in argn:
self.usf = argn['usf']
del argn['usf']
else:
self.usf = False
if 'bnorm' in argn:
self.bnorm = argn['bnorm']
del argn['bnorm']
else:
self.bnorm = True
if 'lf' in argn:
self.lf = argn['lf']
del argn['lf']
else:
self.lf = False
if 'binarized' in argn:
self.binarized = argn['binarized']
del argn['binarized']
else:
self.binarized = False
if 'last' in argn:
self.last = argn['last']
del argn['last']
else:
self.last = False
self.alpha = nn.Parameter(torch.ones(self.max_scales)) # *0.001)
if self.lf:
self.factor = nn.Parameter(torch.ones(1) * self.init_factor)
else:
self.factor = self.init_factor
self.interp = F.interpolate
if self.binarized:
self.conv = nn.ModuleList([BinarizeConv2d(*argv, **argn, bias=True) for _ in range(1)])
if self.bnorm:
self.bn = nn.ModuleList([nn.BatchNorm2d(argv[1]) for _ in range(self.max_scales)])
self.relu = nn.ModuleList([nn.ReLU() for _ in range(self.max_scales)])
else:
self.conv = nn.ModuleList([nn.Conv2d(*argv, **argn, bias=True) for _ in range(self.max_scales)])
if self.bnorm:
self.bn = nn.ModuleList([nn.BatchNorm2d(argv[1]) for _ in range(self.max_scales)])
self.relu = nn.ModuleList([nn.ReLU() for _ in range(self.max_scales)])
self.lastbn = nn.BatchNorm2d(argv[1])
if not self.last:
self.hardtanh = BinaryHardtanh()