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from typing import List
import torch
import torch.nn as nn
from mmcv.cnn.bricks import Swish
from mmengine.model import BaseModule
from mmdet.registry import MODELS
from mmdet.utils import MultiConfig, OptConfigType
from .utils import DepthWiseConvBlock, DownChannelBlock, MaxPool2dSamePadding
class BiFPNStage(nn.Module):
"""
in_channels: List[int], input dim for P3, P4, P5
out_channels: int, output dim for P2 - P7
first_time: int, whether is the first bifpnstage
conv_bn_act_pattern: bool, whether use conv_bn_act_pattern
norm_cfg: (:obj:`ConfigDict` or dict, optional): Config dict for
normalization layer.
epsilon: float, hyperparameter in fusion features
"""
def __init__(self,
in_channels: List[int],
out_channels: int,
first_time: bool = False,
apply_bn_for_resampling: bool = True,
conv_bn_act_pattern: bool = False,
norm_cfg: OptConfigType = dict(
type='BN', momentum=1e-2, eps=1e-3),
epsilon: float = 1e-4) -> None:
super().__init__()
assert isinstance(in_channels, list)
self.in_channels = in_channels
self.out_channels = out_channels
self.first_time = first_time
self.apply_bn_for_resampling = apply_bn_for_resampling
self.conv_bn_act_pattern = conv_bn_act_pattern
self.norm_cfg = norm_cfg
self.epsilon = epsilon
if self.first_time:
self.p5_down_channel = DownChannelBlock(
self.in_channels[-1],
self.out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
self.p4_down_channel = DownChannelBlock(
self.in_channels[-2],
self.out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
self.p3_down_channel = DownChannelBlock(
self.in_channels[-3],
self.out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
self.p5_to_p6 = nn.Sequential(
DownChannelBlock(
self.in_channels[-1],
self.out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg), MaxPool2dSamePadding(3, 2))
self.p6_to_p7 = MaxPool2dSamePadding(3, 2)
self.p4_level_connection = DownChannelBlock(
self.in_channels[-2],
self.out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
self.p5_level_connection = DownChannelBlock(
self.in_channels[-1],
self.out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
self.p6_upsample = nn.Upsample(scale_factor=2, mode='nearest')
self.p5_upsample = nn.Upsample(scale_factor=2, mode='nearest')
self.p4_upsample = nn.Upsample(scale_factor=2, mode='nearest')
self.p3_upsample = nn.Upsample(scale_factor=2, mode='nearest')
# bottom to up: feature map down_sample module
self.p4_down_sample = MaxPool2dSamePadding(3, 2)
self.p5_down_sample = MaxPool2dSamePadding(3, 2)
self.p6_down_sample = MaxPool2dSamePadding(3, 2)
self.p7_down_sample = MaxPool2dSamePadding(3, 2)
# Fuse Conv Layers
self.conv6_up = DepthWiseConvBlock(
out_channels,
out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
self.conv5_up = DepthWiseConvBlock(
out_channels,
out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
self.conv4_up = DepthWiseConvBlock(
out_channels,
out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
self.conv3_up = DepthWiseConvBlock(
out_channels,
out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
self.conv4_down = DepthWiseConvBlock(
out_channels,
out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
self.conv5_down = DepthWiseConvBlock(
out_channels,
out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
self.conv6_down = DepthWiseConvBlock(
out_channels,
out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
self.conv7_down = DepthWiseConvBlock(
out_channels,
out_channels,
apply_norm=self.apply_bn_for_resampling,
conv_bn_act_pattern=self.conv_bn_act_pattern,
norm_cfg=norm_cfg)
# weights
self.p6_w1 = nn.Parameter(
torch.ones(2, dtype=torch.float32), requires_grad=True)
self.p6_w1_relu = nn.ReLU()
self.p5_w1 = nn.Parameter(
torch.ones(2, dtype=torch.float32), requires_grad=True)
self.p5_w1_relu = nn.ReLU()
self.p4_w1 = nn.Parameter(
torch.ones(2, dtype=torch.float32), requires_grad=True)
self.p4_w1_relu = nn.ReLU()
self.p3_w1 = nn.Parameter(
torch.ones(2, dtype=torch.float32), requires_grad=True)
self.p3_w1_relu = nn.ReLU()
self.p4_w2 = nn.Parameter(
torch.ones(3, dtype=torch.float32), requires_grad=True)
self.p4_w2_relu = nn.ReLU()
self.p5_w2 = nn.Parameter(
torch.ones(3, dtype=torch.float32), requires_grad=True)
self.p5_w2_relu = nn.ReLU()
self.p6_w2 = nn.Parameter(
torch.ones(3, dtype=torch.float32), requires_grad=True)
self.p6_w2_relu = nn.ReLU()
self.p7_w2 = nn.Parameter(
torch.ones(2, dtype=torch.float32), requires_grad=True)
self.p7_w2_relu = nn.ReLU()
self.swish = Swish()
def combine(self, x):
if not self.conv_bn_act_pattern:
x = self.swish(x)
return x
def forward(self, x):
if self.first_time:
p3, p4, p5 = x
# build feature map P6
p6_in = self.p5_to_p6(p5)
# build feature map P7
p7_in = self.p6_to_p7(p6_in)
p3_in = self.p3_down_channel(p3)
p4_in = self.p4_down_channel(p4)
p5_in = self.p5_down_channel(p5)
else:
p3_in, p4_in, p5_in, p6_in, p7_in = x
# Weights for P6_0 and P7_0 to P6_1
p6_w1 = self.p6_w1_relu(self.p6_w1)
weight = p6_w1 / (torch.sum(p6_w1, dim=0) + self.epsilon)
# Connections for P6_0 and P7_0 to P6_1 respectively
p6_up = self.conv6_up(
self.combine(weight[0] * p6_in +
weight[1] * self.p6_upsample(p7_in)))
# Weights for P5_0 and P6_1 to P5_1
p5_w1 = self.p5_w1_relu(self.p5_w1)
weight = p5_w1 / (torch.sum(p5_w1, dim=0) + self.epsilon)
# Connections for P5_0 and P6_1 to P5_1 respectively
p5_up = self.conv5_up(
self.combine(weight[0] * p5_in +
weight[1] * self.p5_upsample(p6_up)))
# Weights for P4_0 and P5_1 to P4_1
p4_w1 = self.p4_w1_relu(self.p4_w1)
weight = p4_w1 / (torch.sum(p4_w1, dim=0) + self.epsilon)
# Connections for P4_0 and P5_1 to P4_1 respectively
p4_up = self.conv4_up(
self.combine(weight[0] * p4_in +
weight[1] * self.p4_upsample(p5_up)))
# Weights for P3_0 and P4_1 to P3_2
p3_w1 = self.p3_w1_relu(self.p3_w1)
weight = p3_w1 / (torch.sum(p3_w1, dim=0) + self.epsilon)
# Connections for P3_0 and P4_1 to P3_2 respectively
p3_out = self.conv3_up(
self.combine(weight[0] * p3_in +
weight[1] * self.p3_upsample(p4_up)))
if self.first_time:
p4_in = self.p4_level_connection(p4)
p5_in = self.p5_level_connection(p5)
# Weights for P4_0, P4_1 and P3_2 to P4_2
p4_w2 = self.p4_w2_relu(self.p4_w2)
weight = p4_w2 / (torch.sum(p4_w2, dim=0) + self.epsilon)
# Connections for P4_0, P4_1 and P3_2 to P4_2 respectively
p4_out = self.conv4_down(
self.combine(weight[0] * p4_in + weight[1] * p4_up +
weight[2] * self.p4_down_sample(p3_out)))
# Weights for P5_0, P5_1 and P4_2 to P5_2
p5_w2 = self.p5_w2_relu(self.p5_w2)
weight = p5_w2 / (torch.sum(p5_w2, dim=0) + self.epsilon)
# Connections for P5_0, P5_1 and P4_2 to P5_2 respectively
p5_out = self.conv5_down(
self.combine(weight[0] * p5_in + weight[1] * p5_up +
weight[2] * self.p5_down_sample(p4_out)))
# Weights for P6_0, P6_1 and P5_2 to P6_2
p6_w2 = self.p6_w2_relu(self.p6_w2)
weight = p6_w2 / (torch.sum(p6_w2, dim=0) + self.epsilon)
# Connections for P6_0, P6_1 and P5_2 to P6_2 respectively
p6_out = self.conv6_down(
self.combine(weight[0] * p6_in + weight[1] * p6_up +
weight[2] * self.p6_down_sample(p5_out)))
# Weights for P7_0 and P6_2 to P7_2
p7_w2 = self.p7_w2_relu(self.p7_w2)
weight = p7_w2 / (torch.sum(p7_w2, dim=0) + self.epsilon)
# Connections for P7_0 and P6_2 to P7_2
p7_out = self.conv7_down(
self.combine(weight[0] * p7_in +
weight[1] * self.p7_down_sample(p6_out)))
return p3_out, p4_out, p5_out, p6_out, p7_out
@MODELS.register_module()
class BiFPN(BaseModule):
"""
num_stages: int, bifpn number of repeats
in_channels: List[int], input dim for P3, P4, P5
out_channels: int, output dim for P2 - P7
start_level: int, Index of input features in backbone
epsilon: float, hyperparameter in fusion features
apply_bn_for_resampling: bool, whether use bn after resampling
conv_bn_act_pattern: bool, whether use conv_bn_act_pattern
norm_cfg: (:obj:`ConfigDict` or dict, optional): Config dict for
normalization layer.
init_cfg: MultiConfig: init method
"""
def __init__(self,
num_stages: int,
in_channels: List[int],
out_channels: int,
start_level: int = 0,
epsilon: float = 1e-4,
apply_bn_for_resampling: bool = True,
conv_bn_act_pattern: bool = False,
norm_cfg: OptConfigType = dict(
type='BN', momentum=1e-2, eps=1e-3),
init_cfg: MultiConfig = None) -> None:
super().__init__(init_cfg=init_cfg)
self.start_level = start_level
self.bifpn = nn.Sequential(*[
BiFPNStage(
in_channels=in_channels,
out_channels=out_channels,
first_time=True if _ == 0 else False,
apply_bn_for_resampling=apply_bn_for_resampling,
conv_bn_act_pattern=conv_bn_act_pattern,
norm_cfg=norm_cfg,
epsilon=epsilon) for _ in range(num_stages)
])
def forward(self, x):
x = x[self.start_level:]
x = self.bifpn(x)
return x
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