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configs.yml
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configs.yml
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unet-small:
input_size: !!python/tuple [128,128]
in_channels: 3
out_channels: 1
encoder_layers: 4
dim: 64
weights_filename: "unet-c1-l4-d64"
unet:
input_size: !!python/tuple [128,128]
in_channels: 3
out_channels: 1
encoder_layers: 5
dim: 64
weights_filename: "unet-big-c1-l5-d64"
fno2d:
in_dim: 3
n_hidden: 48
freq_dim: 48
out_dim: 1
modes: 14
num_spectral_layers: 6
return_freq: True
activation: silu
last_activation: False
add_grad_channel: True
dropout: 0.0
weights_filename: "fno2d-base"
fno2d-big:
in_dim: 3
n_hidden: 64
freq_dim: 64
out_dim: 1
modes: 16
num_spectral_layers: 8
return_freq: True
activation: silu
last_activation: False
add_grad_channel: True
dropout: 0.05
weights_filename: "fno2d-big"
uit:
input_size: !!python/tuple [128,128]
in_channels: 3
out_channels: 1
dim: 64
head: 4
add_grad_channel: True
sinosoidal_pe: False
pe_trainable: False
hadamard: False
softmax: False
batch_norm: True
weights_filename: "uit-base-c1-d64"
uit-c3:
input_size: !!python/tuple [128,128]
in_channels: 9
out_channels: 1
dim: 64
head: 4
add_grad_channel: True
sinosoidal_pe: False
pe_trainable: False
hadamard: False
softmax: False
batch_norm: True
weights_filename: "uit-c3-d64"
ut:
input_size: !!python/tuple [128,128]
in_channels: 3
out_channels: 1
dim: 64
head: 4
add_grad_channel: True
sinosoidal_pe: False
pe_trainable: False
hadamard: False
softmax: True
batch_norm: True
attn_norm_type: None
weights_filename: "ut-st-base"
xut:
input_size: !!python/tuple [128,128]
in_channels: 3
out_channels: 1
dim: 64
head: 4
add_grad_channel: True
sinosoidal_pe: True
pe_trainable: False
hadamard: True
softmax: True
batch_norm: True
weights_filename: "xut-base"
hut:
input_size: !!python/tuple [128,128]
in_channels: 3
out_channels: 1
dim: 32
num_blocks: !!python/list [1,1,1]
num_heads: !!python/list [4,4,4]
block_list: '234'
reduce_size: 8
projection: 'interp'
attn_drop: 0.1
proj_drop: 0.1
rel_pos: True
aux_loss: True
maxpool: True
bias: True
add_grad_channel: True
weights_filename: "hybrid-ut"