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from transformers import PretrainedConfig
class GPUNetConfig(PretrainedConfig):
model_type = "GPUNet"
def __init__(
self,
in_channels=1,
n_classes=3,
depth=3,
wf=6,
padding=True,
batch_norm=False,
up_mode="sinc",
dropout=True,
Relu="Relu",
out_act="None",
**kwargs):
self.in_channels = in_channels
self.n_classes = n_classes
self.depth = depth
self.wf = wf
self.padding = padding
self.batch_norm = batch_norm
self.up_mode = up_mode
self.dropout = dropout
self.Relu = Relu
self.out_act = out_act
super().__init__(**kwargs)
class GPReconResNetConfig(PretrainedConfig):
model_type = "GPReconResNet"
def __init__(
self,
in_channels=1,
n_classes=3,
res_blocks=14,
starting_nfeatures=64,
updown_blocks=2,
is_relu_leaky=True,
do_batchnorm=False,
res_drop_prob=0.5,
out_act="None",
forwardV=0,
upinterp_algo='sinc',
post_interp_convtrans=False,
is3D=False,
**kwargs):
self.in_channels = in_channels
self.n_classes = n_classes
self.res_blocks = res_blocks
self.starting_nfeatures = starting_nfeatures
self.updown_blocks = updown_blocks
self.is_relu_leaky = is_relu_leaky
self.do_batchnorm = do_batchnorm
self.res_drop_prob = res_drop_prob
self.out_act = out_act
self.forwardV = forwardV
self.upinterp_algo = upinterp_algo
self.post_interp_convtrans = post_interp_convtrans
self.is3D = is3D
super().__init__(**kwargs)
class GPShuffleUNetConfig(PretrainedConfig):
model_type = "GPShuffleUNet"
def __init__(
self,
d=2,
in_ch=1,
num_features=64,
n_levels=3,
out_ch=3,
kernel_size=3,
stride=1,
dropout=True,
out_act="None",
**kwargs):
self.d = d
self.in_ch = in_ch
self.num_features = num_features
self.n_levels = n_levels
self.out_ch = out_ch
self.kernel_size = kernel_size
self.stride = stride
self.dropout = dropout
self.out_act = out_act
super().__init__(**kwargs) |