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import torch.nn as nn
import torch
from maskrcnn_benchmark.layers import swish
class BiFPN(nn.Module):
def __init__(self, in_channels_list, out_channels, first_time=False, epsilon=1e-4, attention=True):
super(BiFPN, self).__init__()
self.epsilon = epsilon
# Conv layers
self.conv6_up = nn.Sequential(
nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False),
nn.Conv2d(out_channels, out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
self.conv5_up = nn.Sequential(
nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False),
nn.Conv2d(out_channels, out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
self.conv4_up = nn.Sequential(
nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False),
nn.Conv2d(out_channels, out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
self.conv3_up = nn.Sequential(
nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False),
nn.Conv2d(out_channels, out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
self.conv4_down = nn.Sequential(
nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False),
nn.Conv2d(out_channels, out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
self.conv5_down = nn.Sequential(
nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False),
nn.Conv2d(out_channels, out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
self.conv6_down = nn.Sequential(
nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False),
nn.Conv2d(out_channels, out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
self.conv7_down = nn.Sequential(
nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False),
nn.Conv2d(out_channels, out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
# Feature scaling layers
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")
self.p4_downsample = nn.MaxPool2d(3, 2)
self.p5_downsample = nn.MaxPool2d(3, 2)
self.p6_downsample = nn.MaxPool2d(3, 2)
self.p7_downsample = nn.MaxPool2d(3, 2)
self.swish = swish()
self.first_time = first_time
if self.first_time:
self.p5_down_channel = nn.Sequential(
nn.Conv2d(in_channels_list[2], out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
self.p4_down_channel = nn.Sequential(
nn.Conv2d(in_channels_list[1], out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
self.p3_down_channel = nn.Sequential(
nn.Conv2d(in_channels_list[0], out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
self.p5_to_p6 = nn.Sequential(
nn.Conv2d(in_channels_list[2], out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
nn.MaxPool2d(3, 2),
)
self.p6_to_p7 = nn.Sequential(nn.MaxPool2d(3, 2))
self.p4_down_channel_2 = nn.Sequential(
nn.Conv2d(in_channels_list[1], out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
self.p5_down_channel_2 = nn.Sequential(
nn.Conv2d(in_channels_list[2], out_channels, 1),
nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
)
# Weight
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.attention = attention
def forward(self, inputs):
"""
illustration of a minimal bifpn unit
P7_0 -------------------------> P7_2 -------->
|-------------| ↑
↓ |
P6_0 ---------> P6_1 ---------> P6_2 -------->
|-------------|--------------↑ ↑
↓ |
P5_0 ---------> P5_1 ---------> P5_2 -------->
|-------------|--------------↑ ↑
↓ |
P4_0 ---------> P4_1 ---------> P4_2 -------->
|-------------|--------------↑ ↑
|--------------↓ |
P3_0 -------------------------> P3_2 -------->
"""
# downsample channels using same-padding conv2d to target phase's if not the same
# judge: same phase as target,
# if same, pass;
# elif earlier phase, downsample to target phase's by pooling
# elif later phase, upsample to target phase's by nearest interpolation
if self.attention:
p3_out, p4_out, p5_out, p6_out, p7_out = self._forward_fast_attention(inputs)
else:
p3_out, p4_out, p5_out, p6_out, p7_out = self._forward(inputs)
return p3_out, p4_out, p5_out, p6_out, p7_out
def _forward_fast_attention(self, inputs):
if self.first_time:
p3, p4, p5 = inputs[-3:]
p6_in = self.p5_to_p6(p5)
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_0, P4_0, P5_0, P6_0 and P7_0
p3_in, p4_in, p5_in, p6_in, p7_in = inputs
# P7_0 to P7_2
# 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.swish(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.swish(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.swish(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.swish(weight[0] * p3_in + weight[1] * self.p3_upsample(p4_up)))
if self.first_time:
p4_in = self.p4_down_channel_2(p4)
p5_in = self.p5_down_channel_2(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.swish(weight[0] * p4_in + weight[1] * p4_up + weight[2] * self.p4_downsample(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.swish(weight[0] * p5_in + weight[1] * p5_up + weight[2] * self.p5_downsample(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.swish(weight[0] * p6_in + weight[1] * p6_up + weight[2] * self.p6_downsample(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.swish(weight[0] * p7_in + weight[1] * self.p7_downsample(p6_out)))
return p3_out, p4_out, p5_out, p6_out, p7_out
def _forward(self, inputs):
if self.first_time:
p3, p4, p5 = inputs
p6_in = self.p5_to_p6(p5)
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_0, P4_0, P5_0, P6_0 and P7_0
p3_in, p4_in, p5_in, p6_in, p7_in = inputs
# P7_0 to P7_2
# Connections for P6_0 and P7_0 to P6_1 respectively
p6_up = self.conv6_up(self.swish(p6_in + self.p6_upsample(p7_in)))
# Connections for P5_0 and P6_1 to P5_1 respectively
p5_up = self.conv5_up(self.swish(p5_in + self.p5_upsample(p6_up)))
# Connections for P4_0 and P5_1 to P4_1 respectively
p4_up = self.conv4_up(self.swish(p4_in + self.p4_upsample(p5_up)))
# Connections for P3_0 and P4_1 to P3_2 respectively
p3_out = self.conv3_up(self.swish(p3_in + self.p3_upsample(p4_up)))
if self.first_time:
p4_in = self.p4_down_channel_2(p4)
p5_in = self.p5_down_channel_2(p5)
# Connections for P4_0, P4_1 and P3_2 to P4_2 respectively
p4_out = self.conv4_down(self.swish(p4_in + p4_up + self.p4_downsample(p3_out)))
# Connections for P5_0, P5_1 and P4_2 to P5_2 respectively
p5_out = self.conv5_down(self.swish(p5_in + p5_up + self.p5_downsample(p4_out)))
# Connections for P6_0, P6_1 and P5_2 to P6_2 respectively
p6_out = self.conv6_down(self.swish(p6_in + p6_up + self.p6_downsample(p5_out)))
# Connections for P7_0 and P6_2 to P7_2
p7_out = self.conv7_down(self.swish(p7_in + self.p7_downsample(p6_out)))
return p3_out, p4_out, p5_out, p6_out, p7_out
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