def __init__(self,
num_input_features,
growth_rate,
bn_size,
drop_rate,
norm_type='Unknown'):
super(_DenseLayer, self).__init__()
self.add_module('norm1', get_norm(norm_type, num_input_features)),
self.add_module('relu1', nn.ReLU(inplace=True)),
self.add_module(
'conv1',
nn.Conv2d(num_input_features,
bn_size * growth_rate,
kernel_size=1,
stride=1,
bias=False)),
self.add_module('norm2', get_norm(norm_type, bn_size * growth_rate)),
self.add_module('relu2', nn.ReLU(inplace=True)),
self.add_module(
'conv2',
nn.Conv2d(bn_size * growth_rate,
growth_rate,
kernel_size=3,
stride=1,
padding=1,
bias=False)),
self.drop_rate = drop_rate
def __init__(self,
in_channels,
out_channels,
norm_type='Unknown',
**kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
self.norm = get_norm(norm_type, out_channels, eps=0.001)
def __init__(self, in_ch, out_ch, kernel_size=3, stride=1, padding=0,
bias=True, norm_type='Unknown'):
super(Conv2dNormRelu, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size, stride, padding, bias=bias),
get_norm(norm_type, out_ch),
nn.ReLU(inplace=True))
def make_layers(cfg, batch_norm=False,
norm_type='Unknown'):
layers = []
in_channels = 3
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, get_norm(norm_type, v),
nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return nn.Sequential(*layers)
def __init__(self,
num_input_features,
num_output_features,
norm_type='Unknown'):
super(_Transition, self).__init__()
self.add_module('norm', get_norm(norm_type, num_input_features))
self.add_module('relu', nn.ReLU(inplace=True))
self.add_module(
'conv',
nn.Conv2d(
num_input_features,
num_output_features, # noqa
kernel_size=1,
stride=1,
bias=False))
self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
def __init__(self,
growth_rate=32,
block_config=(6, 12, 24, 16),
norm_type='Unknown',
num_init_features=64,
bn_size=4,
drop_rate=0,
num_classes=1000): # noqa
super(DenseNet, self).__init__()
# First convolution
self.features = nn.Sequential(
OrderedDict([
('conv0',
nn.Conv2d(3,
num_init_features,
kernel_size=7,
stride=2,
padding=3,
bias=False)), # noqa
('norm0', get_norm(norm_type, num_init_features)),
('relu0', nn.ReLU(inplace=True)),
('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
]))
# 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,
norm_type=norm_type, # noqa
bn_size=bn_size,
growth_rate=growth_rate,
drop_rate=drop_rate) # noqa
self.features.add_module('denseblock%d' % (i + 1), 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,
norm_type=norm_type) # noqa
self.features.add_module('transition%d' % (i + 1), trans)
num_features = num_features // 2
# Final batch norm
self.features.add_module('norm5', get_norm(norm_type, num_features))
# Linear layer
self.classifier = nn.Linear(num_features, num_classes)
self.num_features = num_features
# Official init from torch repo.
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.GroupNorm):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.InstanceNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)