def __init__(self, stride=32, *args, **kwargs): super(Resnet101, self).__init__() assert stride in (8, 16, 32) dils = [1, 1 ] if stride == 32 else [el * (16 // stride) for el in (1, 2)] strds = [2 if el == 1 else 1 for el in dils] 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, dilation=1, ceil_mode=False) self.layer1 = create_stage(64, 256, 3, stride=1, dilation=1) self.layer2 = create_stage(256, 512, 4, stride=2, dilation=1) self.layer3 = create_stage(512, 1024, 23, stride=strds[0], dilation=dils[0]) self.layer4 = create_stage(1024, 2048, 3, stride=strds[1], dilation=dils[1]) self.init_weight()
def __init__(self, in_chan, out_chan, stride=1, stride_at_1x1=False, dilation=1, *args, **kwargs): super(Bottleneck, self).__init__(*args, **kwargs) stride1x1, stride3x3 = (stride, 1) if stride_at_1x1 else (1, stride) assert out_chan % 4 == 0 mid_chan = int(out_chan / 4) self.conv1 = nn.Conv2d(in_chan, mid_chan, kernel_size=1, stride=stride1x1, bias=False) self.bn1 = BatchNorm2d(mid_chan) self.conv2 = nn.Conv2d(mid_chan, mid_chan, kernel_size=3, stride=stride3x3, padding=dilation, dilation=dilation, bias=False) self.bn2 = BatchNorm2d(mid_chan) self.conv3 = nn.Conv2d(mid_chan, out_chan, kernel_size=1, bias=False) self.bn3 = 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')) self.init_weight()
def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, dilation=1, *args, **kwargs): super(ConvBNReLU, self).__init__() self.conv = nn.Conv2d(in_chan, out_chan, kernel_size = ks, stride = stride, padding = padding, dilation = dilation, bias = True) self.bn = BatchNorm2d(out_chan) self.init_weight()
def __init__(self, in_chan, out_chan, ks=3, stride=1, pad=0, dilation=1, slope=0.1, *args, **kwargs): super(ConvBNLeaky, self).__init__() self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, padding=pad, dilation=dilation, stride=stride, bias=False) self.bn = BatchNorm2d(out_chan, slope=slope) self._init_weight()
def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, dilation=1, bias=False, *args, **kwargs): super(SepConvBNReLU, self).__init__() self.conv = nn.Conv2d(in_chan, in_chan, kernel_size=ks, stride=stride, padding=dilation, dilation=dilation, groups=in_chan, bias=bias) self.bn = BatchNorm2d(in_chan) self.pairwise = nn.Conv2d(in_chan, out_chan, kernel_size=1, bias=bias) self.init_weight()