def __init__(self,
in_channels,
growth_rate=48,
block_config=(6, 12, 36, 24),
num_init_features=96,
bn_size=4,
drop_rate=0,
length_aux=10,
mode=None,
nddr_drop_rate=0,
memory_efficient=True):
super(Dense_BB_ForUNet, self).__init__()
self.features = nn.Sequential(
OrderedDict([
('conv0',
nn.Conv2d(in_channels,
num_init_features,
kernel_size=7,
stride=2,
padding=3,
bias=False)),
('norm0', nn.BatchNorm2d(num_init_features)),
('relu0', nn.ReLU(inplace=True)),
('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
]))
num_features = num_init_features
for i, num_layers in enumerate(block_config):
block = _DenseBlock(num_layers=num_layers,
num_input_features=num_features,
bn_size=bn_size,
growth_rate=growth_rate,
drop_rate=drop_rate,
memory_efficient=memory_efficient)
if (i == 0):
self.block0 = block
elif (i == 1):
self.block1 = block
elif (i == 2):
self.block2 = block
elif (i == 3):
self.block3 = block
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
trans = _Transition(num_input_features=num_features,
num_output_features=num_features // 2)
if (i == 0):
self.transition0 = trans
elif (i == 1):
self.transition1 = trans
elif (i == 2):
self.transition2 = trans
num_features = num_features // 2
# Final batch norm
self.final_bn = nn.BatchNorm2d(num_features)
def __init__(self,
in_channels,
in_aux_channels,
growth_rate=48,
block_config=(6, 12, 36, 24),
num_init_features=96,
bn_size=4,
drop_rate=0,
num_classes=4,
length_aux=10,
mode='WeightMixtures',
nddr_drop_rate=0,
memory_efficient=True,
clf='PointRendBiased'):
super(BestPractice2I, self).__init__()
self.features = nn.Sequential(
OrderedDict([
('conv0',
nn.Conv2d(in_channels,
num_init_features,
kernel_size=7,
stride=2,
padding=3,
bias=False)),
('norm0', nn.BatchNorm2d(num_init_features)),
('relu0', nn.ReLU(inplace=True)),
# ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
]))
self.features_aux = nn.Sequential(
OrderedDict([
('conv0_aux',
nn.Conv2d(in_aux_channels,
num_init_features,
kernel_size=7,
stride=2,
padding=3,
bias=False)),
('norm0_aux', nn.BatchNorm2d(num_init_features)),
('relu0_aux', nn.ReLU(inplace=True)),
# ('pool0_aux', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
]))
self.nddrf = NddrLayer(net0_channels=num_init_features,
net1_channels=num_init_features,
drop_rate=nddr_drop_rate)
# Each denseblock
num_features = num_init_features
for i, num_layers in enumerate(block_config):
block = _DenseBlock(num_layers=num_layers,
num_input_features=num_features,
bn_size=bn_size,
growth_rate=growth_rate,
drop_rate=drop_rate,
memory_efficient=memory_efficient)
block_aux = _DenseBlock(num_layers=num_layers,
num_input_features=num_features,
bn_size=bn_size,
growth_rate=growth_rate,
drop_rate=drop_rate,
memory_efficient=memory_efficient)
if (i == 0):
self.block0 = block
self.block0_aux = block_aux
elif (i == 1):
self.block1 = block
self.block1_aux = block_aux
elif (i == 2):
self.block2 = block
self.block2_aux = block_aux
elif (i == 3):
self.block3 = block
self.block3_aux = block_aux
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
trans = _Transition(num_input_features=num_features,
num_output_features=num_features // 2,
asc=True)
trans_aux = _Transition(num_input_features=num_features,
num_output_features=num_features // 2,
asc=True)
if (i == 0):
self.transition0 = trans
self.transition0_aux = trans_aux
self.nddr0 = NddrLayer(net0_channels=num_features // 2,
net1_channels=num_features // 2,
drop_rate=nddr_drop_rate)
self.sluice0_conv1 = nn.Conv2d(num_features // 2, 2208, 1)
self.sluice0_aux_conv1 = nn.Conv2d(num_features // 2, 2208,
1)
elif (i == 1):
self.transition1 = trans
self.transition1_aux = trans_aux
self.nddr1 = NddrLayer(net0_channels=num_features // 2,
net1_channels=num_features // 2,
drop_rate=nddr_drop_rate)
self.sluice1_conv1 = nn.Conv2d(num_features // 2, 2208, 1)
self.sluice1_aux_conv1 = nn.Conv2d(num_features // 2, 2208,
1)
elif (i == 2):
self.transition2 = trans
self.transition2_aux = trans_aux
self.nddr2 = NddrLayer(net0_channels=num_features // 2,
net1_channels=num_features // 2,
drop_rate=nddr_drop_rate)
self.sluice2_conv1 = nn.Conv2d(num_features // 2, 2208, 1)
self.sluice2_aux_conv1 = nn.Conv2d(num_features // 2, 2208,
1)
num_features = num_features // 2
# Final batch norm
self.final_bn = nn.BatchNorm2d(num_features)
self.final_bn_aux = nn.BatchNorm2d(num_features)
self.nddr3 = NddrLayer(net0_channels=num_features,
net1_channels=num_features,
drop_rate=nddr_drop_rate)
self.cross3 = nn.Linear(num_features * 2, num_features * 2, bias=False)
self.cross4 = nn.Linear(2000, 2000, bias=False)
self.cross5 = nn.Linear(6, 6, bias=False)
self.betas_2layer = nn.Parameter(torch.tensor([0.7, 0.3]))
self.betas_8layer = nn.Parameter(
torch.tensor([0.05, 0.05, 0.1, 0.1, 0.15, 0.15, 0.2, 0.2]))
self.mode = mode
self.out_channels = 2208
self.clf = clf
if clf == 'PointRendBoth':
self.fine_grained_channels = 384
elif clf == 'PointRendBiased':
self.fine_grained_channels = 768
def __init__(self,
in_channels,
in_aux_channels,
num_classes,
growth_rate=48,
block_config=(6, 12, 36, 24),
num_init_features=96,
bn_size=4,
drop_rate=0,
length_aux=10,
mode=None,
nddr_drop_rate=0,
memory_efficient=True):
super(NDDRLSC_BB_ForUNet, self).__init__()
self.features = nn.Sequential(
OrderedDict([
('conv0',
nn.Conv2d(in_channels,
num_init_features,
kernel_size=7,
stride=2,
padding=3,
bias=False)),
('norm0', nn.BatchNorm2d(num_init_features)),
('relu0', nn.ReLU(inplace=True)),
('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
]))
self.features_aux = nn.Sequential(
OrderedDict([
('conv0_aux',
nn.Conv2d(in_aux_channels,
num_init_features,
kernel_size=7,
stride=2,
padding=3,
bias=False)),
('norm0_aux', nn.BatchNorm2d(num_init_features)),
('relu0_aux', nn.ReLU(inplace=True)),
('pool0_aux', nn.MaxPool2d(kernel_size=3, stride=2,
padding=1)),
]))
self.nddrf = NddrLayer(net0_channels=num_init_features,
net1_channels=num_init_features,
drop_rate=nddr_drop_rate)
# Each denseblock
num_features = num_init_features
for i, num_layers in enumerate(block_config):
block = _DenseBlock(num_layers=num_layers,
num_input_features=num_features,
bn_size=bn_size,
growth_rate=growth_rate,
drop_rate=drop_rate,
memory_efficient=memory_efficient)
block_aux = _DenseBlock(num_layers=num_layers,
num_input_features=num_features,
bn_size=bn_size,
growth_rate=growth_rate,
drop_rate=drop_rate,
memory_efficient=memory_efficient)
if (i == 0):
self.block0 = block
self.block0_aux = block_aux
elif (i == 1):
self.block1 = block
self.block1_aux = block_aux
elif (i == 2):
self.block2 = block
self.block2_aux = block_aux
elif (i == 3):
self.block3 = block
self.block3_aux = block_aux
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
trans = _Transition(num_input_features=num_features,
num_output_features=num_features // 2)
trans_aux = _Transition(num_input_features=num_features,
num_output_features=num_features // 2)
if (i == 0):
self.transition0 = trans
self.transition0_aux = trans_aux
self.nddr0 = NddrLayer(net0_channels=num_features // 2,
net1_channels=num_features // 2,
drop_rate=nddr_drop_rate)
self.sluice0_reshape = nn.Linear(num_features // 2, 2208)
self.sluice0_aux_reshape = nn.Linear(
num_features // 2, 2208)
# self.nddr0_conv1d = nn.Conv1d(num_features // 2 *2, num_features // 2)
# self.nddr0_conv1d_aux =
# self.nddr0_bn = nn.BatchNorm2d(num_features // 2)
# self.nddr0_bn_aux = nn.BatchNorm2d(num_features // 2)
elif (i == 1):
self.transition1 = trans
self.transition1_aux = trans_aux
self.nddr1 = NddrLayer(net0_channels=num_features // 2,
net1_channels=num_features // 2,
drop_rate=nddr_drop_rate)
self.sluice1_reshape = nn.Linear(num_features // 2, 2208)
self.sluice1_aux_reshape = nn.Linear(
num_features // 2, 2208)
elif (i == 2):
self.transition2 = trans
self.transition2_aux = trans_aux
self.nddr2 = NddrLayer(net0_channels=num_features // 2,
net1_channels=num_features // 2,
drop_rate=nddr_drop_rate)
self.sluice2_reshape = nn.Linear(num_features // 2, 2208)
self.sluice2_aux_reshape = nn.Linear(
num_features // 2, 2208)
num_features = num_features // 2
# Final batch norm
self.final_bn = nn.BatchNorm2d(num_features)
self.final_bn_aux = nn.BatchNorm2d(num_features)
self.nddr3 = NddrLayer(net0_channels=num_features,
net1_channels=num_features,
drop_rate=nddr_drop_rate)
self.sluice3_reshape = nn.Linear(num_features, 2208)
self.sluice3_aux_reshape = nn.Linear(num_features, 2208)
# Official init from torch repo.
self.betas_5layer = nn.Parameter(
torch.tensor([0.05, 0.1, 0.1, 0.25, 0.5]))
self.betas_5layer_aux = nn.Parameter(
torch.tensor([0.05, 0.1, 0.1, 0.25, 0.5]))
self.betas_6layer = nn.Parameter(
torch.tensor([0.05, 0.1, 0.1, 0.25, 0.5, 0.3]))
self.betas_6layer_aux = nn.Parameter(
torch.tensor([0.05, 0.1, 0.1, 0.25, 0.5, 0.3]))