def __init__(self, J, num_channels, num_features, track, normalizer_name, activation_name, inter_factor):
super(Bottleneck, self).__init__()
inter_channels = inter_factor*num_features
self.bn1 = get_normalizer(normalizer_name, J*num_channels, track)
self.conv1 = nn.Conv2d(J*num_channels, inter_channels, 1)
self.act1 = get_activation_function(activation_name)
self.bn2 = get_normalizer(normalizer_name, inter_channels, track)
self.conv2 = nn.Conv2d(inter_channels, num_features, 1)
self.act2 = get_activation_function(activation_name)
def __init__(self, J, num_channels, num_out_channels, track,
normalizer_name, activation_name):
super(Transition, self).__init__()
self.bn = get_normalizer(normalizer_name, num_channels * J,
track).to(device)
self.conv = nn.Conv2d(J * num_channels, num_out_channels, 1)
self.act = get_activation_function(activation_name)
def __init__(self, J, num_channels, num_features, track, normalizer_name,
activation_name):
# number of in channels is num_channels; number of out channels is num_channels+num_features
# i.e. output dim = input dim + num_features
super(SingleLayer, self).__init__()
num_output = int(num_features / 2)
self.conv1 = nn.Conv2d(num_channels * J, num_output, 1).to(device)
self.conv2 = nn.Conv2d(num_channels * J, num_output, 1).to(device)
self.bn = get_normalizer(normalizer_name, num_features,
track).to(device)
self.act = get_activation_function(activation_name)
def __init__(self, J, num_channels, num_features, track, normalizer_name, activation_name):
super(SingleLayer, self).__init__()
self.bn = get_normalizer(normalizer_name, num_channels*J, track)
self.conv = nn.Conv2d(num_channels*J, num_features, 1)
self.act = get_activation_function(activation_name)
