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from typing import NamedTuple, List, Callable, List, Tuple, Optional
import torch
class LinData(NamedTuple):
in_dim : int # input dimension
hidden_layers : List[int] # hidden layers including the output layer
activations : List[Optional[Callable[[torch.Tensor],torch.Tensor]]] # list of activations
bns : List[bool] # list of bools
dropouts : List[Optional[float]] # list of dropouts probas
class CNNData(NamedTuple):
in_dim : int # input dimension
n_f : List[int] # num filters
kernel_size : List[Tuple] # kernel size [(5,5,5), (3,3,3),(3,3,3)]
activations : List[Optional[Callable[[torch.Tensor],torch.Tensor]]] # activation list
bns : List[bool] # batch normialization [True, True, False]
dropouts : List[Optional[float]] # # list of dropouts probas [.5,0,0]
#dropouts_ps : list # [0.5,.7, 0]
paddings : List[Optional[Tuple]] #[(0,0,0),(0,0,0), (0,0,0)]
strides : List[Optional[Tuple]] #[(1,1,1),(1,1,1),(1,1,1)]
class NetData(NamedTuple):
cnn3d : CNNData
lin : LinData
'''
class LinData(NamedTuple):
in_dim : int
#num_classes : int
hidden_layers : list
activations : list
bns : list
dropouts : list
class CNNData(NamedTuple):
in_dim : int # input dimension
n_f : list # num filters
kernel_size : list # kernel size [(5,5,5), (3,3,3),(3,3,3)]
activations : list # activation list
bns : list # batch normialization [True, True, False]
dropouts : list # [True, True, False]
#dropouts_ps : list # [0.5,.7, 0]
paddings : list #[(0,0,0),(0,0,0), (0,0,0)]
strides : list #[(1,1,1),(1,1,1),(1,1,1)]
class NetData(NamedTuple):
cnn3d : CNNData
lin : LinData
class Mdata(NamedTuple):
cm : list
ba : float
sn : float
sp : float
tn : int
fp : int
fn : int
tp : int
class MetricData(NamedTuple):
train : Mdata
test : Mdata
'''
# for outputs
class history():
def __init__(self, train, val, test):
self.train = train
self.test = test
self.val = val
class metrics():
def __init__(self, r2, loss):
self.r2 = r2
self.loss = loss
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