def initialize_network(self):
"""
changed deep supervision to False
:return:
"""
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.BatchNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.BatchNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def initialize_network(self):
"""
This is specific to the U-Net and must be adapted for other network architectures
:return:
"""
# self.print_to_log_file(self.net_num_pool_op_kernel_sizes)
# self.print_to_log_file(self.net_conv_kernel_sizes)
net_numpool = len(self.net_num_pool_op_kernel_sizes)
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, net_numpool,
self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op,
dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, False, False, lambda x: x, InitWeights_He(1e-2),
self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
self.network.inference_apply_nonlin = softmax_helper
if torch.cuda.is_available():
self.network.cuda()
def initialize_network(self):
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = Mish
net_nonlin_kwargs = {}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x,
InitWeights_He(0), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def initialize_network(self):
"""
- momentum 0.99
- SGD instead of Adam
- self.lr_scheduler = None because we do poly_lr
- deep supervision = True
- i am sure I forgot something here
Known issue: forgot to set neg_slope=0 in InitWeights_He; should not make a difference though
:return:
"""
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
for s in range(len(self.net_conv_kernel_sizes)):
for i in range(len(self.net_conv_kernel_sizes[s])):
self.net_conv_kernel_sizes[s][i] = 3
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper