def __init__(self,
num_input_features,
growth_rate,
bn_size,
drop_rate,
memory_efficient=False):
super(_DenseLayerCOB, self).__init__()
self.add_module('norm1', BatchNorm2dCOB(num_input_features)),
self.add_module('relu1', ReLUCOB(inplace=True)),
self.add_module(
'conv1',
Conv2dCOB(num_input_features,
bn_size * growth_rate,
kernel_size=1,
stride=1,
bias=False)),
self.add_module('norm2', BatchNorm2dCOB(bn_size * growth_rate)),
self.add_module('relu2', ReLUCOB(inplace=True)),
self.add_module(
'conv2',
Conv2dCOB(bn_size * growth_rate,
growth_rate,
kernel_size=3,
stride=1,
padding=1,
bias=False)),
self.drop_rate = float(drop_rate)
self.memory_efficient = memory_efficient
self.concat = Concat()
self.dropout = DropoutCOB(self.drop_rate)
def __init__(self, in_channels=3):
super(DenseNet, self).__init__()
self.conv1 = Conv2dCOB(in_channels=in_channels,
out_channels=3,
kernel_size=3,
padding=1)
self.conv2 = Conv2dCOB(in_channels=3,
out_channels=3,
kernel_size=3,
padding=1)
self.conv3 = Conv2dCOB(in_channels=6,
out_channels=3,
kernel_size=3,
padding=1)
self.conv4 = Conv2dCOB(in_channels=3,
out_channels=3,
kernel_size=3,
padding=1)
self.relu1 = ReLUCOB()
self.relu2 = ReLUCOB()
self.relu3 = ReLUCOB()
self.relu4 = ReLUCOB()
self.concat1 = Concat()
self.flatten = FlattenCOB()
self.fc1 = LinearCOB(in_channels * 32 * 32, 10)
def __init__(self, in_ch, out_ch, bilinear=False):
super().__init__()
self.upsample = None
if bilinear:
self.upsample = UpsampleCOB(scale_factor=2, mode='bilinear', align_corners=True)
else:
self.upsample = ConvTranspose2dCOB(in_ch, in_ch // 2, kernel_size=2, stride=2)
self.conv = DoubleConvCOB(in_ch, out_ch)
self.cat = Concat()
def __init__(self,
num_layers,
num_input_features,
bn_size,
growth_rate,
drop_rate,
memory_efficient=False):
super(_DenseBlockCOB, self).__init__()
for i in range(num_layers):
layer = _DenseLayerCOB(
num_input_features + i * growth_rate,
growth_rate=growth_rate,
bn_size=bn_size,
drop_rate=drop_rate,
memory_efficient=memory_efficient,
)
self.add_module('denselayer%d' % (i + 1), layer)
self.concat = Concat()
def __init__(self):
super(DenseNet4, self).__init__()
self.conv1 = Conv2dCOB(in_channels=1,
out_channels=3,
kernel_size=3,
padding=1)
self.conv2 = Conv2dCOB(in_channels=3,
out_channels=3,
kernel_size=3,
padding=1)
self.conv3 = Conv2dCOB(in_channels=6,
out_channels=3,
kernel_size=3,
padding=1)
self.relu1 = ReLUCOB()
self.relu2 = ReLUCOB()
self.relu3 = ReLUCOB()
self.concat1 = Concat()
def __init__(self):
super(SplitConcatModel, self).__init__()
self.conv1 = Conv2dCOB(in_channels=1,
out_channels=3,
kernel_size=3,
padding=1)
self.conv21 = Conv2dCOB(in_channels=3,
out_channels=3,
kernel_size=3,
padding=1)
self.conv22 = Conv2dCOB(in_channels=3,
out_channels=3,
kernel_size=3,
padding=1)
self.conv3 = Conv2dCOB(in_channels=6,
out_channels=3,
kernel_size=3,
padding=1)
self.relu1 = ReLUCOB()
self.relu21 = ReLUCOB()
self.relu22 = ReLUCOB()
self.concat1 = Concat()