pafpn.py
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# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import auto_fp16
from ..builder import NECKS
from .fpn import FPN
@NECKS.register_module()
class PAFPN(FPN):
"""Path Aggregation Network for Instance Segmentation.
This is an implementation of the `PAFPN in Path Aggregation Network
<https://arxiv.org/abs/1803.01534>`_.
Args:
in_channels (List[int]): Number of input channels per scale.
out_channels (int): Number of output channels (used at each scale)
num_outs (int): Number of output scales.
start_level (int): Index of the start input backbone level used to
build the feature pyramid. Default: 0.
end_level (int): Index of the end input backbone level (exclusive) to
build the feature pyramid. Default: -1, which means the last level.
add_extra_convs (bool | str): If bool, it decides whether to add conv
layers on top of the original feature maps. Default to False.
If True, it is equivalent to `add_extra_convs='on_input'`.
If str, it specifies the source feature map of the extra convs.
Only the following options are allowed
- 'on_input': Last feat map of neck inputs (i.e. backbone feature).
- 'on_lateral': Last feature map after lateral convs.
- 'on_output': The last output feature map after fpn convs.
relu_before_extra_convs (bool): Whether to apply relu before the extra
conv. Default: False.
no_norm_on_lateral (bool): Whether to apply norm on lateral.
Default: False.
conv_cfg (dict): Config dict for convolution layer. Default: None.
norm_cfg (dict): Config dict for normalization layer. Default: None.
act_cfg (str): Config dict for activation layer in ConvModule.
Default: None.
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
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,
init_cfg=dict(
type='Xavier', layer='Conv2d', distribution='uniform')):
super(PAFPN, self).__init__(
in_channels,
out_channels,
num_outs,
start_level,
end_level,
add_extra_convs,
relu_before_extra_convs,
no_norm_on_lateral,
conv_cfg,
norm_cfg,
act_cfg,
init_cfg=init_cfg)
# add extra bottom up pathway
self.downsample_convs = nn.ModuleList()
self.pafpn_convs = nn.ModuleList()
for i in range(self.start_level + 1, self.backbone_end_level):
d_conv = ConvModule(
out_channels,
out_channels,
3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
inplace=False)
pafpn_conv = ConvModule(
out_channels,
out_channels,
3,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
inplace=False)
self.downsample_convs.append(d_conv)
self.pafpn_convs.append(pafpn_conv)
@auto_fp16()
def forward(self, inputs):
"""Forward function."""
assert len(inputs) == len(self.in_channels)
# build laterals
laterals = [
lateral_conv(inputs[i + self.start_level])
for i, lateral_conv in enumerate(self.lateral_convs)
]
# build top-down path
used_backbone_levels = len(laterals)
for i in range(used_backbone_levels - 1, 0, -1):
prev_shape = laterals[i - 1].shape[2:]
laterals[i - 1] += F.interpolate(
laterals[i], size=prev_shape, mode='nearest')
# build outputs
# part 1: from original levels
inter_outs = [
self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)
]
# part 2: add bottom-up path
for i in range(0, used_backbone_levels - 1):
inter_outs[i + 1] += self.downsample_convs[i](inter_outs[i])
outs = []
outs.append(inter_outs[0])
outs.extend([
self.pafpn_convs[i - 1](inter_outs[i])
for i in range(1, used_backbone_levels)
])
# part 3: add extra levels
if self.num_outs > len(outs):
# use max pool to get more levels on top of outputs
# (e.g., Faster R-CNN, Mask R-CNN)
if not self.add_extra_convs:
for i in range(self.num_outs - used_backbone_levels):
outs.append(F.max_pool2d(outs[-1], 1, stride=2))
# add conv layers on top of original feature maps (RetinaNet)
else:
if self.add_extra_convs == 'on_input':
orig = inputs[self.backbone_end_level - 1]
outs.append(self.fpn_convs[used_backbone_levels](orig))
elif self.add_extra_convs == 'on_lateral':
outs.append(self.fpn_convs[used_backbone_levels](
laterals[-1]))
elif self.add_extra_convs == 'on_output':
outs.append(self.fpn_convs[used_backbone_levels](outs[-1]))
else:
raise NotImplementedError
for i in range(used_backbone_levels + 1, self.num_outs):
if self.relu_before_extra_convs:
outs.append(self.fpn_convs[i](F.relu(outs[-1])))
else:
outs.append(self.fpn_convs[i](outs[-1]))
return tuple(outs)