def __init__(self,
in_channels,
output_channels,
feature_maps,
hidden_neurons=128):
super(CNNDS2DDeconvWind_DK, self).__init__()
# scale up input and then use a bigger kernel size to increase receptive field
self.upscale = nn.ConvTranspose2d(in_channels,
in_channels,
kernel_size=2,
stride=2)
# 5x8x8
self.doubleDSConv = DoubleDSConv(in_channels, feature_maps)
self.dsConv1 = DepthwiseSeparableConv(feature_maps,
output_channels=feature_maps //
2,
kernel_size=3,
padding=0)
# ((W−F+2P)/S)+1 --> (8x8)-3+1 = (6x6)
self.dsConv2 = DepthwiseSeparableConv(feature_maps // 2,
output_channels=feature_maps //
4,
kernel_size=3,
padding=0)
# (6x6)-3+1 = (4x4)
self.bn = nn.BatchNorm2d(feature_maps // 4)
self.doubleDense = DoubleDense(feature_maps // 4 * 4 * 4,
hidden_neurons=hidden_neurons,
output_channels=output_channels)
def __init__(self,
in_channels,
output_channels,
feature_maps,
hidden_neurons=128):
super(CNN2DWind_DK, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels,
out_channels=feature_maps,
kernel_size=2)
# Correctly calculate input features (depends on conv2d)
# ((W−F+2P)/S)+1 --> ((4-2+0)/1)+1 = 3
self.dd = DoubleDense(feature_maps * 3 * 3,
hidden_neurons=hidden_neurons,
output_channels=output_channels)
def __init__(self,
in_channels,
output_channels,
feature_maps,
hidden_neurons=128):
super(CNN3DWind_NL, self).__init__()
self.conv1 = nn.Conv3d(in_channels=in_channels,
out_channels=feature_maps,
kernel_size=(2, 2, 2))
# Correctly calculate input features (depends on conv3d)
# ((W−F+2P)/S)+1 --> (7x6x6)-2+1 = 6x5x5
self.dd = DoubleDense(feature_maps * 6 * 5 * 5,
hidden_neurons=hidden_neurons,
output_channels=output_channels)
def __init__(self,
in_channels,
output_channels,
feature_maps,
hidden_neurons=128):
super(CNN2DAttWind_DK, self).__init__()
self.conv1 = AugmentedConv(in_channels=in_channels,
out_channels=feature_maps,
kernel_size=2,
dk=feature_maps // 2,
dv=feature_maps // 2,
Nh=feature_maps // 2,
shape=0,
relative=False,
stride=1)
# Correctly calculate input features (depends on conv2d)
# ((W−F+2P)/S)+1 --> ((4-2+0)/1)+1 = 3
self.dd = DoubleDense(feature_maps * 3 * 3,
hidden_neurons=hidden_neurons,
output_channels=output_channels)