def get_model(input_channels,
input_time_length,
dilations=None,
kernel_sizes=None,
padding=False):
"""
initializes a new Deep4Net and changes the kernel sizes and dilations of the network based on the input parameters
:param input_channels: 1 axis input shape
:param input_time_length: 0 axis input shape
:param dilations: dilations of the max-pool layers of the network
:param kernel_sizes: kernel sizes of the max-pool layers of the network
:param padding: if padding is to be added
:return: a Model object, the changed Deep4Net based on the kernel sizes and dilation parameters and the name
of the model based on the kernel sizes and dilatiosn
"""
if kernel_sizes is None:
kernel_sizes = [3, 3, 3, 3]
print('SBP False!!!')
model = Model(input_channels=input_channels,
n_classes=1,
input_time_length=input_time_length,
final_conv_length=2,
stride_before_pool=False)
model.make_regressor()
if cuda:
model.model = model.model.cuda()
model_name = get_model_name_from_kernel_and_dilation(
kernel_sizes, dilations)
changed_model = change_network_kernel_and_dilation(model.model,
kernel_sizes,
dilations,
remove_maxpool=False)
# print(changed_model)
return model, changed_model, model_name
