def __init__(self, params, se_block_type=None):
super(GenericBlock, self).__init__()
if se_block_type == se.SELayer.CSE.value:
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
elif se_block_type == se.SELayer.SSE.value:
self.SELayer = se.SpatialSELayer(params['num_filters'])
elif se_block_type == se.SELayer.CSSE.value:
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
else:
self.SELayer = None
padding_h = int((params['kernel_h'] - 1) / 2)
padding_w = int((params['kernel_w'] - 1) / 2)
self.out_channel = params['num_filters']
self.conv = nn.Conv2d(in_channels=params['num_channels'],
out_channels=params['num_filters'],
kernel_size=(params['kernel_h'],
params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.prelu = nn.PReLU()
self.batchnorm = nn.BatchNorm2d(num_features=params['num_filters'])
if params['drop_out'] > 0:
self.drop_out_needed = True
self.drop_out = nn.Dropout2d(params['drop_out'])
else:
self.drop_out_needed = False
def __init__(self, params, se_block_type=None):
# TODO: params['reduction_ratio'], kernel_d
super(FSInputBlock, self).__init__()
if se_block_type == se.SELayer.CSE.value:
self.SELayer = se.ChannelSELayer(params['num_filters'],
reduction_ratio=2)
elif se_block_type == se.SELayer.CSSE.value:
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'],
reduction_ratio=2)
elif se_block_type == se.SELayer.SSE.value:
self.SELayer = se.SpatialSELayer(params['num_filters'])
else:
self.SELayer = None
padding_h = int((params['kernel_h'] - 1) / 2)
padding_w = int((params['kernel_w'] - 1) / 2)
conv1_out_size = int(params['num_filters'])
conv2_out_size = int(params['num_filters'])
self.conv1 = nn.Conv2d(in_channels=params['num_channels'],
out_channels=params['num_filters'],
kernel_size=(params['kernel_h'],
params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv2 = nn.Conv2d(in_channels=conv1_out_size,
out_channels=params['num_filters'],
kernel_size=(params['kernel_h'],
params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv3 = nn.Conv2d(in_channels=conv2_out_size,
out_channels=params['num_filters'],
kernel_size=(1, 1),
padding=(0, 0),
stride=params['stride_conv'])
self.batchnorm0 = nn.BatchNorm2d(num_features=params['num_channels'])
self.batchnorm1 = nn.BatchNorm2d(num_features=conv1_out_size)
self.batchnorm2 = nn.BatchNorm2d(num_features=conv2_out_size)
self.batchnorm3 = nn.BatchNorm2d(num_features=conv2_out_size)
self.prelu = nn.PReLU()
if params['drop_out'] > 0:
self.drop_out_needed = True
self.drop_out = nn.Dropout(params['drop_out'])
else:
self.drop_out_needed = False
self.maxpool = nn.MaxPool2d(kernel_size=params['pool'],
stride=params['stride_pool'],
return_indices=True)
def __init__(self, params, se_block_type=None):
super(DenseBlock, self).__init__()
if se_block_type == se.SELayer.CSE.value:
self.SELayer = se.ChannelSELayer(params['num_filters'])
elif se_block_type == se.SELayer.SSE.value:
self.SELayer = se.SpatialSELayer(params['num_filters'])
elif se_block_type == se.SELayer.CSSE.value:
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
else:
self.SELayer = None
padding_h = int((params['kernel_h'] - 1) / 2)
padding_w = int((params['kernel_w'] - 1) / 2)
conv1_out_size = int(params['num_channels'] + params['num_filters'])
conv2_out_size = int(params['num_channels'] + params['num_filters'] +
params['num_filters'])
self.conv1 = nn.Conv2d(in_channels=params['num_channels'],
out_channels=params['num_filters'],
kernel_size=(params['kernel_h'],
params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv2 = nn.Conv2d(in_channels=conv1_out_size,
out_channels=params['num_filters'],
kernel_size=(params['kernel_h'],
params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv3 = nn.Conv2d(in_channels=conv2_out_size,
out_channels=params['num_filters'],
kernel_size=(1, 1),
padding=(0, 0),
stride=params['stride_conv'])
# self.norm1 = nn.BatchNorm2d(num_features=params['num_channels'], momentum=0.9)
# self.norm2 = nn.BatchNorm2d(num_features=conv1_out_size, momentum=0.9)
# self.norm3 = nn.BatchNorm2d(num_features=conv2_out_size, momentum=0.9)
# self.norm1 = GroupNorm(num_features=params['num_channels'], num_groups=2)
# self.norm2 = GroupNorm(num_features=conv1_out_size, num_groups=2)
# self.norm3 = GroupNorm(num_features=conv2_out_size, num_groups=2)
self.norm1 = nn.InstanceNorm2d(num_features=params['num_channels'])
self.norm2 = nn.InstanceNorm2d(num_features=conv1_out_size)
self.norm3 = nn.InstanceNorm2d(num_features=conv2_out_size)
self.prelu = nn.PReLU()
if params['drop_out'] > 0:
self.drop_out_needed = True
self.drop_out = nn.Dropout2d(params['drop_out'])
else:
self.drop_out_needed = False
def __init__(self, params, se_block_type=None, is_decoder=False):
super(OctaveDenseBlock, self).__init__()
print(se_block_type)
if se_block_type == se.SELayer.CSE.value:
self.SELayer = se.ChannelSELayer(params['num_filters'])
elif se_block_type == se.SELayer.SSE.value:
self.SELayer = se.SpatialSELayer(params['num_filters'])
elif se_block_type == se.SELayer.CSSE.value:
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
else:abdominal_segmentation_2] - 1) / 2)
padding_w = int((params['kernel_w'] - 1) / 2)
conv1_out_size = int(params['num_channels'] + params['num_filters'])
conv2_out_size = int(
params['num_channels'] + params['num_filters'] + params['num_filters'])
def __init__(self, params, se_block_type=None):
super(FullBayesianDenseBlock, self).__init__()
if se_block_type == se.SELayer.CSE.value:
self.SELayer = se.ChannelSELayer(params['num_filters'])
elif se_block_type == se.SELayer.SSE.value:
self.SELayer = se.SpatialSELayer(params['num_filters'])
elif se_block_type == se.SELayer.CSSE.value:
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
else:
self.SELayer = None
padding_h = int((params['kernel_h'] - 1) / 2)
padding_w = int((params['kernel_w'] - 1) / 2)
conv1_out_size = int(params['num_channels'] + params['num_filters'])
conv2_out_size = int(params['num_filters'] + params['num_filters'])
self.conv1_mu = nn.Conv2d(in_channels=params['num_channels'],
out_channels=params['num_filters'],
kernel_size=(params['kernel_h'],
params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv2_mu = nn.Conv2d(in_channels=conv1_out_size,
out_channels=params['num_filters'],
kernel_size=(params['kernel_h'],
params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv3_mu = nn.Conv2d(in_channels=conv2_out_size,
out_channels=params['num_filters'],
kernel_size=(1, 1),
padding=(0, 0),
stride=params['stride_conv'])
self.conv1_sigma = nn.Conv2d(in_channels=params['num_channels'],
out_channels=params['num_filters'],
kernel_size=(params['kernel_h'],
params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv2_sigma = nn.Conv2d(in_channels=conv1_out_size,
out_channels=params['num_filters'],
kernel_size=(params['kernel_h'],
params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv3_sigma = nn.Conv2d(in_channels=conv2_out_size,
out_channels=params['num_filters'],
kernel_size=(1, 1),
padding=(0, 0),
stride=params['stride_conv'])
self.tanh = nn.Tanh()
self.variance = 0.1
self.normal = tdist.Normal(torch.tensor([0.0]),
torch.tensor([self.variance]))
if params['drop_out'] > 0:
self.drop_out_needed = True
self.drop_out = nn.Dropout2d(params['drop_out'])
else:
self.drop_out_needed = False
def __init__(self, params):
super(FewShotSegmentorBaseLine, self).__init__()
self.conditioner = Conditioner(params)
self.segmentor = Segmentor(params)
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
self.weights = []
def __init__(self, params, se_block_type=None, is_decoder=False):
super(OctaveDenseBlock, self).__init__()
print(se_block_type)
if se_block_type == se.SELayer.CSE.value:
self.SELayer = se.ChannelSELayer(params['num_filters'])
elif se_block_type == se.SELayer.SSE.value:
self.SELayer = se.SpatialSELayer(params['num_filters'])
elif se_block_type == se.SELayer.CSSE.value:
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
else:
self.SELayer = None
padding_h = int((params['kernel_h'] - 1) / 2)
padding_w = int((params['kernel_w'] - 1) / 2)
conv1_out_size = int(params['num_channels'] + params['num_filters'])
conv2_out_size = int(params['num_channels'] + params['num_filters'] +
params['num_filters'])
self.conv1 = OctConv2d('first',
in_channels=params['num_channels'],
out_channels=params['num_filters'],
kernel_size=(params['kernel_h'],
params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv2 = OctConv2d('regular',
in_channels=conv1_out_size,
out_channels=params['num_filters'],
kernel_size=(params['kernel_h'],
params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv3 = OctConv2d('last',
in_channels=conv2_out_size,
out_channels=params['num_filters'],
kernel_size=(params['kernel_h'],
params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.avgpool1 = nn.AvgPool2d(2)
alpha_in, alpha_out = 0.5, 0.5
if is_decoder:
self.batchnorm1 = nn.BatchNorm2d(params['num_channels'])
oct_unit_decoder_channel_size = params['num_channels'] // 4
self.batchnorm2_h = nn.BatchNorm2d(
int(oct_unit_decoder_channel_size * 3))
self.batchnorm2_l = nn.BatchNorm2d(
int(oct_unit_decoder_channel_size * 3))
self.batchnorm3_h = nn.BatchNorm2d(
int(oct_unit_decoder_channel_size * 4))
self.batchnorm3_l = nn.BatchNorm2d(
int(oct_unit_decoder_channel_size * 4))
else:
self.batchnorm1 = nn.BatchNorm2d(params['num_channels'])
oct_unit_encoder_channel_size = params['num_channels'] // 2
self.batchnorm2_h = nn.BatchNorm2d(
int(oct_unit_encoder_channel_size * 2))
self.batchnorm2_l = nn.BatchNorm2d(
int(oct_unit_encoder_channel_size * 2))
self.batchnorm3_h = nn.BatchNorm2d(
int(oct_unit_encoder_channel_size * 3))
self.batchnorm3_l = nn.BatchNorm2d(
int(oct_unit_encoder_channel_size * 3))
self.prelu = nn.PReLU()
self.prelu_h = nn.PReLU()
self.prelu_l = nn.PReLU()
if params['drop_out'] > 0:
self.drop_out_needed = True
self.drop_out = nn.Dropout2d(params['drop_out'])
else:
self.drop_out_needed = False
