mmdet FPN_CARAFE 代码解析
我的版本是mmdet 2.23
FPN_CARAFE类的初始化参数与原始的FPN有所不同
这是PFN的构造函数
def __init__(self,
in_channels,
out_channels,
num_outs,
start_level=0,
end_level=-1,
add_extra_convs=False,
relu_before_extra_convs=False,
no_norm_on_lateral=False,
conv_cfg=None,
norm_cfg=None,
act_cfg=None,
upsample_cfg=dict(mode='nearest'),
init_cfg=dict(
type='Xavier', layer='Conv2d', distribution='uniform')):这是FPN_CARAFE的构造函数
def __init__(self,
in_channels,
out_channels,
num_outs,
start_level=0,
end_level=-1,
norm_cfg=None,
act_cfg=None,
order=('conv', 'norm', 'act'),
upsample_cfg=dict(
type='carafe',
up_kernel=5,
up_group=1,
encoder_kernel=3,
encoder_dilation=1),
init_cfg=None):区别是 FPN_CARAFE 没有了这个，add_extra_convs，这个参数可以指定额外的层来自哪，原始的FPN可选三个来源 分别是输入输出和连接层，如下代码。而在FPN_CARAFE中直接指定了额外层的的来源，可以自行查看。
FPN_CARAFE 去掉了conv_cfg，新增了norm_cfg=None, act_cfg=None, order=('conv', 'norm', 'act')，这几个参数。分别是归一化，激活，和卷积 归一化激活三者的顺序。还新增了上采样配置upsample_cfg。也就说FPN_CARAFE卷积后面都有激活和归一化，如果归一化为空的化，他会加上biase。另外初始换不再使用FPN的初始化方式，而是使用init_weights。
if self.add_extra_convs == 'on_input':
extra_source = inputs[self.backbone_end_level - 1]
elif self.add_extra_convs == 'on_lateral':
extra_source = laterals[-1]
elif self.add_extra_convs == 'on_output':
extra_source = outs[-1]在网络结构上有所不同，FPN的额外层，没有横向连接和上采样结构。如果self.add_extra_convs == 'on_lateral':那么FPN结构是这样的。
FPN_CARAFE的结构长这样，
①②③，分别是l_conv 上采样add fpn_conv
class FPN_CARAFE(BaseModule):
"""FPN_CARAFE is a more flexible implementation of FPN. It allows more
choice for upsample methods during the top-down pathway.
It can reproduce the performance of ICCV 2019 paper
CARAFE: Content-Aware ReAssembly of FEatures
Please refer to https://arxiv.org/abs/1905.02188 for more details.
Args:
in_channels (list[int]): Number of channels for each input feature map.
out_channels (int): Output channels of feature pyramids.
num_outs (int): Number of output stages.
start_level (int): Start level of feature pyramids.
(Default: 0)
end_level (int): End level of feature pyramids.
(Default: -1 indicates the last level).
norm_cfg (dict): Dictionary to construct and config norm layer.
activate (str): Type of activation function in ConvModule
(Default: None indicates w/o activation).
order (dict): Order of components in ConvModule.
upsample (str): Type of upsample layer.
upsample_cfg (dict): Dictionary to construct and config upsample layer.
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
"""
def __init__(self,
in_channels,
out_channels,
num_outs,
start_level=0,
end_level=-1,
norm_cfg=None,
act_cfg=None,
order=('conv', 'norm', 'act'),
upsample_cfg=dict(
type='carafe',
up_kernel=5,
up_group=1,
encoder_kernel=3,
encoder_dilation=1),
init_cfg=None):
assert init_cfg is None, 'To prevent abnormal initialization ' \
'behavior, init_cfg is not allowed to be set'
super(FPN_CARAFE, self).__init__(init_cfg)
assert isinstance(in_channels, list)
self.in_channels = in_channels
self.out_channels = out_channels
self.num_ins = len(in_channels)
self.num_outs = num_outs
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.with_bias = norm_cfg is None
self.upsample_cfg = upsample_cfg.copy()
self.upsample = self.upsample_cfg.get('type')
self.relu = nn.ReLU(inplace=False)
self.order = order
assert order in [('conv', 'norm', 'act'), ('act', 'conv', 'norm')]
assert self.upsample in [
'nearest', 'bilinear', 'deconv', 'pixel_shuffle', 'carafe', None
]
if self.upsample in ['deconv', 'pixel_shuffle']:
assert hasattr(
self.upsample_cfg,
'upsample_kernel') and self.upsample_cfg.upsample_kernel > 0
self.upsample_kernel = self.upsample_cfg.pop('upsample_kernel')
if end_level == -1:
self.backbone_end_level = self.num_ins
assert num_outs >= self.num_ins - start_level
else:
# if end_level < inputs, no extra level is allowed
self.backbone_end_level = end_level
assert end_level <= len(in_channels)
assert num_outs == end_level - start_level
self.start_level = start_level
self.end_level = end_level
self.lateral_convs = ModuleList()
self.fpn_convs = ModuleList()
self.upsample_modules = ModuleList()
for i in range(self.start_level, self.backbone_end_level):
l_conv = ConvModule(
in_channels[i],
out_channels,
1,
norm_cfg=norm_cfg,
bias=self.with_bias,
act_cfg=act_cfg,
inplace=False,
order=self.order)
fpn_conv = ConvModule(
out_channels,
out_channels,
3,
padding=1,
norm_cfg=self.norm_cfg,
bias=self.with_bias,
act_cfg=act_cfg,
inplace=False,
order=self.order)
if i != self.backbone_end_level - 1:
upsample_cfg_ = self.upsample_cfg.copy()
if self.upsample == 'deconv':
upsample_cfg_.update(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=self.upsample_kernel,
stride=2,
padding=(self.upsample_kernel - 1) // 2,
output_padding=(self.upsample_kernel - 1) // 2)
elif self.upsample == 'pixel_shuffle':
upsample_cfg_.update(
in_channels=out_channels,
out_channels=out_channels,
scale_factor=2,
upsample_kernel=self.upsample_kernel)
elif self.upsample == 'carafe':
upsample_cfg_.update(channels=out_channels, scale_factor=2)
else:
# suppress warnings
align_corners = (None
if self.upsample == 'nearest' else False)
upsample_cfg_.update(
scale_factor=2,
mode=self.upsample,
align_corners=align_corners)
upsample_module = build_upsample_layer(upsample_cfg_)
self.upsample_modules.append(upsample_module)
self.lateral_convs.append(l_conv)
self.fpn_convs.append(fpn_conv)
# add extra conv layers (e.g., RetinaNet)
extra_out_levels = (
num_outs - self.backbone_end_level + self.start_level)
if extra_out_levels >= 1:
for i in range(extra_out_levels):
in_channels = (
self.in_channels[self.backbone_end_level -
1] if i == 0 else out_channels)
extra_l_conv = ConvModule(
in_channels,
out_channels,
3,
stride=2,
padding=1,
norm_cfg=norm_cfg,
bias=self.with_bias,
act_cfg=act_cfg,
inplace=False,
order=self.order)
if self.upsample == 'deconv':
upsampler_cfg_ = dict(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=self.upsample_kernel,
stride=2,
padding=(self.upsample_kernel - 1) // 2,
output_padding=(self.upsample_kernel - 1) // 2)
elif self.upsample == 'pixel_shuffle':
upsampler_cfg_ = dict(
in_channels=out_channels,
out_channels=out_channels,
scale_factor=2,
upsample_kernel=self.upsample_kernel)
elif self.upsample == 'carafe':
upsampler_cfg_ = dict(
channels=out_channels,
scale_factor=2,
**self.upsample_cfg)
else:
# suppress warnings
align_corners = (None
if self.upsample == 'nearest' else False)
upsampler_cfg_ = dict(
scale_factor=2,
mode=self.upsample,
align_corners=align_corners)
upsampler_cfg_['type'] = self.upsample
upsample_module = build_upsample_layer(upsampler_cfg_)
extra_fpn_conv = ConvModule(
out_channels,
out_channels,
3,
padding=1,
norm_cfg=self.norm_cfg,
bias=self.with_bias,
act_cfg=act_cfg,
inplace=False,
order=self.order)
self.upsample_modules.append(upsample_module)
self.fpn_convs.append(extra_fpn_conv)
self.lateral_convs.append(extra_l_conv)
# default init_weights for conv(msra) and norm in ConvModule
def init_weights(self):
"""Initialize the weights of module."""
super(FPN_CARAFE, self).init_weights()
for m in self.modules():
if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
xavier_init(m, distribution='uniform')
for m in self.modules():
if isinstance(m, CARAFEPack):
m.init_weights()
def slice_as(self, src, dst):
"""Slice ``src`` as ``dst``
Note:
``src`` should have the same or larger size than ``dst``.
Args:
src (torch.Tensor): Tensors to be sliced.
dst (torch.Tensor): ``src`` will be sliced to have the same
size as ``dst``.
Returns:
torch.Tensor: Sliced tensor.
"""
assert (src.size(2) >= dst.size(2)) and (src.size(3) >= dst.size(3))
if src.size(2) == dst.size(2) and src.size(3) == dst.size(3):
return src
else:
return src[:, :, :dst.size(2), :dst.size(3)]
def tensor_add(self, a, b):
"""Add tensors ``a`` and ``b`` that might have different sizes."""
if a.size() == b.size():
c = a + b
else:
c = a + self.slice_as(b, a)
return c
def forward(self, inputs):
"""Forward function."""
assert len(inputs) == len(self.in_channels)
# build laterals
laterals = []
for i, lateral_conv in enumerate(self.lateral_convs):
if i <= self.backbone_end_level - self.start_level:
input = inputs[min(i + self.start_level, len(inputs) - 1)]
else:
input = laterals[-1]
lateral = lateral_conv(input)
laterals.append(lateral)
# build top-down path
for i in range(len(laterals) - 1, 0, -1):
if self.upsample is not None:
upsample_feat = self.upsample_modules[i - 1](laterals[i])
else:
upsample_feat = laterals[i]
laterals[i - 1] = self.tensor_add(laterals[i - 1], upsample_feat)
# build outputs
num_conv_outs = len(self.fpn_convs)
outs = []
for i in range(num_conv_outs):
out = self.fpn_convs[i](laterals[i])
outs.append(out)
return tuple(outs)