def __init__(self, in_chan, out_chan, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(in_chan, out_chan, stride)
self.bn1 = BatchNorm2d(out_chan)
self.conv2 = conv3x3(out_chan, out_chan)
self.bn2 = BatchNorm2d(out_chan, activation='none')
self.relu = nn.ReLU(inplace=True)
self.downsample = None
if in_chan != out_chan or stride != 1:
self.downsample = nn.Sequential(
nn.Conv2d(in_chan, out_chan,
kernel_size=1, stride=stride, bias=False),
BatchNorm2d(out_chan, activation='none'),
)
def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
super(ConvBNReLU, self).__init__()
self.conv = nn.Conv2d(in_chan,
out_chan,
kernel_size = ks,
stride = stride,
padding = padding,
bias = False)
self.bn = BatchNorm2d(out_chan)
self.init_weight()
def __init__(self, in_chan, out_chan, *args, **kwargs):
super(AttentionRefinementModule, self).__init__()
self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
self.conv_atten = nn.Conv2d(out_chan,
out_chan,
kernel_size=1,
bias=False)
self.bn_atten = BatchNorm2d(out_chan, activation='none')
self.sigmoid_atten = nn.Sigmoid()
self.init_weight()
def __init__(self):
super(Resnet18, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = BatchNorm2d(64)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
self.init_weight()
def __init__(self,
in_planes,
out_planes,
ksize,
stride,
pad,
dilation=1,
groups=1,
has_bias=False):
super(ConvBnRelu, self).__init__()
self.conv = nn.Conv2d(in_planes,
out_planes,
kernel_size=ksize,
stride=stride,
padding=pad,
dilation=dilation,
groups=groups,
bias=has_bias)
self.bn = BatchNorm2d(out_planes)