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getModel.py
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def get_model(args):
model_name = args['model_name']
if 'num_slices' in args:
num_slices = args['num_slices']
if 'pretrained' in args:
pretrained = args['pretrained']
if model_name == 'S3DSegPan3D_2D':
from model.S3DSegPan3D_2D import S3DSegPan3D_2D
model_parameters = model_parameters = {'pretrained' : pretrained,
'out_sigmoid': False,
'out_ch': 2,
'multi_output' : True,
}
model = S3DSegPan3D_2D(pretrained=model_parameters['pretrained'],
out_sigmoid=model_parameters['out_sigmoid'],
out_ch=model_parameters['out_ch'],
multi_output=model_parameters['multi_output'])
if model_name == 'S3DSegPan3D_3D':
from model.S3DSegPan3D_3D import S3DSegPan3D_3D
model_parameters = {'pretrained' : pretrained,
'out_sigmoid': False,
'out_ch': 2,
'multi_output' : True,
}
model = S3DSegPan3D_3D(pretrained=model_parameters['pretrained'],
out_sigmoid=model_parameters['out_sigmoid'],
out_ch=model_parameters['out_ch'],
multi_output=model_parameters['multi_output'],
num_slices= num_slices)
if model_name == 'S3D_EncDec_3D_3D':
from model.S3D_EncDec_3D_3D import S3D_EncDec_3D_3D
model_parameters = {'pretrained' : pretrained,
'out_sigmoid': False,
'out_ch': 2}
model = S3D_EncDec_3D_3D(pretrained=model_parameters['pretrained'],
out_sigmoid=model_parameters['out_sigmoid'],
out_ch=model_parameters['out_ch'],
num_slices= num_slices)
if model_name == 'MobileNetSegPan3D_2D':
from model.MobileNetSegPan3D_2D import MobileNetSegPan3D_2D
model_parameters = {'pretrained' : pretrained,
'out_sigmoid': False,
'out_ch': 2,
'multi_output' : True,
}
model = MobileNetSegPan3D_2D(pretrained=model_parameters['pretrained'],
out_sigmoid=model_parameters['out_sigmoid'],
out_ch=model_parameters['out_ch'],
multi_output=model_parameters['multi_output'])
if model_name == 'MobilNetSegPan3D_3D':
from model.MobilNetSegPan3D_3D import MobilNetSegPan3D_3D
model_parameters = {'pretrained' : pretrained,
'out_sigmoid': False,
'out_ch': 2,
'multi_output' : True,
}
model = MobilNetSegPan3D_3D(pretrained=model_parameters['pretrained'],
out_sigmoid=model_parameters['out_sigmoid'],
out_ch=model_parameters['out_ch'],
multi_output=model_parameters['multi_output'],
num_slices= num_slices)
if model_name == 'MobileNet_EncDec_3D_3D':
from model.MobileNet_EncDec_3D_3D import MobileNet_EncDec_3D_3D
model_parameters = {'pretrained' : pretrained,
'out_sigmoid': False,
'out_ch': 2
}
model = MobileNet_EncDec_3D_3D(pretrained=model_parameters['pretrained'],
out_sigmoid=model_parameters['out_sigmoid'],
out_ch=model_parameters['out_ch'],
num_slices= num_slices)
if model_name == 'SegPan3D_VGGBackBone':
from model.SegPan3D_VGGBackbone import SegPan3D_VGGBackBone
model_parameters = {'pretrained' : pretrained,
'out_sigmoid': False,
'out_ch': 2,
'multi_output' : True,
'arch': 'vgg16_bn'
}
model = SegPan3D_VGGBackBone(pretrained=model_parameters['pretrained'],
out_sigmoid=model_parameters['out_sigmoid'],
out_ch=model_parameters['out_ch'],
multi_output=model_parameters['multi_output'])
if model_name == 'UNet3D':
from monai.networks.nets import UNet
model_parameters = {
'dimensions': 3,
'in_channels': 1,
'out_channels': 2,
'channels': (16,32,64,128,256),
'strides' : (2,2,2,2),
'num_res_units': 2
}
model = UNet(
dimensions=model_parameters['dimensions'],
in_channels=model_parameters['in_channels'],
out_channels=model_parameters['out_channels'],
channels=model_parameters['channels'],
strides = model_parameters['strides'],
num_res_units=model_parameters['num_res_units']
)
if model_name == 'SegResNet':
from monai.networks.nets import SegResNet
model_parameters = {
'spatial_dims': 3,
'init_filters': 8,
'in_channels': 1,
'out_channels': 2
}
model = SegResNet(spatial_dims = model_parameters['spatial_dims'],
init_filters = model_parameters['init_filters'],
in_channels = model_parameters['in_channels'],
out_channels = model_parameters['out_channels']
)
if model_name == 'VNet':
from monai.networks.nets import VNet
model_parameters = {
'spatial_dims': 3,
'in_channels': 1,
'out_channels': 2,
'act': ("elu", {"inplace": True})
}
model = VNet(spatial_dims = model_parameters['spatial_dims'],
in_channels = model_parameters['in_channels'],
out_channels = model_parameters['out_channels'],
act = model_parameters['act']
)
if model_name == 'AHNet':
from monai.networks.nets import AHNet
model_parameters = {
'layers': (3,4,6,3),
'spatial_dims': 3,
'in_channels': 1,
'out_channels': 2,
'psp_block_num' : 4,
'upsample_mode' : 'transpose',
'pretrained' : True
}
model = AHNet(layers = model_parameters['layers'],
spatial_dims = model_parameters['spatial_dims'],
in_channels = model_parameters['in_channels'],
out_channels = model_parameters['out_channels'],
psp_block_num = model_parameters['psp_block_num'],
upsample_mode = model_parameters['upsample_mode'],
pretrained = model_parameters['pretrained']
)
return {'model': model, 'model_params': model_parameters}