def __init__(self,in_channels, out_channels, acti_func, acti_func_param):
super(UNetBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.acti_func = acti_func
self.conv1 = ConvolutionLayer(in_channels, out_channels, 3,
dim = 2, padding = 1, acti_func=get_acti_func(acti_func, acti_func_param))
self.conv2 = ConvolutionLayer(out_channels, out_channels, 3,
dim = 2, padding = 1, acti_func=get_acti_func(acti_func, acti_func_param))
def __init__(self, channels, acti_func, acti_func_param):
super(PEBlock, self).__init__()
self.channels = channels
self.acti_func = acti_func
self.conv1 = ConvolutionLayer(channels, int(channels / 2), 1,
dim = 2, padding = 0, conv_group = 1, norm_type = None, norm_group = 1,
acti_func=get_acti_func(acti_func, acti_func_param))
self.conv2 = ConvolutionLayer(int(channels / 2), channels, 1,
dim = 2, padding = 0, conv_group = 1, norm_type = None, norm_group = 1,
acti_func=nn.Sigmoid())
def __init__(self,in_channels, out_channels, norm_type, groups, acti_func, acti_func_param):
super(UNetBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.acti_func = acti_func
group1 = 1 if (in_channels < 8) else groups
self.conv1 = ConvolutionLayer(in_channels, out_channels, 1,
dim = 2, padding = 0, conv_group = group1, norm_type = norm_type, norm_group = group1,
acti_func=get_acti_func(acti_func, acti_func_param))
self.conv2 = ConvolutionLayer(out_channels, out_channels, 3,
dim = 2, padding = 1, conv_group = groups, norm_type = norm_type, norm_group = groups,
acti_func=get_acti_func(acti_func, acti_func_param))
def __init__(self, in_channels, out_channels, kernel_size, paddding,
acti_func, acti_func_param):
super(ResidualBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.acti_func = acti_func
self.relu = nn.ReLU(inplace=True)
self.conv1 = ConvolutionLayer(in_channels,
out_channels,
kernel_size=kernel_size,
padding=paddding,
acti_func=get_acti_func(
acti_func, acti_func_param))
self.conv2 = ConvolutionLayer(out_channels,
out_channels,
kernel_size=kernel_size,
padding=paddding,
acti_func=get_acti_func(
acti_func, acti_func_param))
def __init__(self, in_channels, out_chnannels,
dim, resample, acti_func, acti_func_param):
super(UNetBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_chnannels
self.dim = dim
self.resample = resample # resample should be 'down', 'up', or None
self.acti_func = acti_func
self.conv1 = ConvolutionLayer(in_channels, out_chnannels, kernel_size = 3, padding=1,
dim = self.dim, acti_func=get_acti_func(acti_func, acti_func_param))
self.conv2 = ConvolutionLayer(out_chnannels, out_chnannels, kernel_size = 3, padding=1,
dim = self.dim, acti_func=get_acti_func(acti_func, acti_func_param))
if(self.resample == 'down'):
if(self.dim == 2):
self.resample_layer = nn.MaxPool2d(kernel_size = 2, stride = 2)
else:
self.resample_layer = nn.MaxPool3d(kernel_size = 2, stride = 2)
elif(self.resample == 'up'):
self.resample_layer = DeconvolutionLayer(out_chnannels, out_chnannels, kernel_size = 2,
dim = self.dim, stride = 2, acti_func = get_acti_func(acti_func, acti_func_param))
else:
assert(self.resample == None)
def __init__(self,in_channels, out_channels, norm_type, groups, acti_func, acti_func_param):
super(ResBlock_DWGC_CF, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.acti_func = acti_func
self.groups = groups
groups2 = int(out_channels / groups)
self.conv1 = ConvolutionLayer(in_channels, out_channels, 1,
dim = 2, padding = 0, conv_group = 1, norm_type = norm_type, norm_group = 1,
acti_func=get_acti_func(acti_func, acti_func_param))
self.conv2 = DepthSeperableConvolutionLayer(out_channels, out_channels, 3,
dim = 2, padding = 1, conv_group = groups, norm_type = norm_type, norm_group = groups,
acti_func=get_acti_func(acti_func, acti_func_param))
self.conv3 = DepthSeperableConvolutionLayer(out_channels, out_channels, 3,
dim = 2, padding = 1, conv_group = groups2, norm_type = norm_type, norm_group = groups2,
acti_func=get_acti_func(acti_func, acti_func_param))
def __init__(self,in_channels, out_channels, norm_type, groups, acti_func, acti_func_param):
super(ResBlock_DWGC_BE_CPF, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.acti_func = acti_func
chns_half = int(out_channels / 2)
group1 = 1 if (in_channels < 8) else groups
self.conv1 = ConvolutionLayer(in_channels, out_channels, 1,
dim = 2, padding = 0,conv_group = group1, norm_type = norm_type, norm_group = group1,
acti_func=get_acti_func(acti_func, acti_func_param))
self.conv2 = DepthSeperableConvolutionLayer(chns_half, self.out_chns, 3,
dim = 2, padding = 1, conv_group = groups, norm_type = norm_type, norm_group = groups,
acti_func=get_acti_func(acti_func, acti_func_param))
self.conv3 = DepthSeperableConvolutionLayer(self.out_chns, chns_half, 3,
dim = 2, padding = 1, conv_group = groups, norm_type = norm_type, norm_group = groups,
acti_func=get_acti_func(acti_func, acti_func_param))
