Adds feature pyramid attention (FPA) module, resolves #167

robosat/fpa.py

@@ -0,0 +1,59 @@
+"""Feature Pyramid Attention blocks
+
+See:
+- https://arxiv.org/abs/1805.10180 - Pyramid Attention Network for Semantic Segmentation
+
+"""
+
+import torch.nn as nn
+
+
+class FeaturePyramidAttention(nn.Module):
+    """Feature Pyramid Attetion (FPA) block
+       See https://arxiv.org/abs/1805.10180 Figure 3 b
+    """
+
+    def __init__(self, num_in, num_out):
+        super().__init__()
+
+        # no batch norm for tensors of shape NxCx1x1
+        self.top1x1 = nn.Sequential(nn.Conv2d(num_in, num_out, 1, bias=False), nn.ReLU(inplace=True))
+
+        self.mid1x1 = ConvBnRelu(num_in, num_out, 1)
+
+        self.bot5x5 = ConvBnRelu(num_in, num_in, 5, stride=2, padding=2)
+        self.bot3x3 = ConvBnRelu(num_in, num_in, 3, stride=2, padding=1)
+
+        self.lat5x5 = ConvBnRelu(num_in, num_out, 5, stride=1, padding=2)
+        self.lat3x3 = ConvBnRelu(num_in, num_out, 3, stride=1, padding=1)
+
+    def forward(self, x):
+        assert x.size()[-1] % 8 == 0 and x.size()[-2] % 8 == 0, "size has to be divisible by 8 for fpa"
+
+        # global pooling top pathway
+        top = self.top1x1(nn.functional.adaptive_avg_pool2d(x, 1))
+        top = nn.functional.interpolate(top, size=x.size()[-2:], mode="bilinear")
+
+        # conv middle pathway
+        mid = self.mid1x1(x)
+
+        # multi-scale bottom and lateral pathways
+        bot0 = self.bot5x5(x)
+        bot1 = self.bot3x3(bot0)
+
+        lat0 = self.lat5x5(bot0)
+        lat1 = self.lat3x3(bot1)
+
+        # upward accumulation pathways
+        up = lat0 + nn.functional.interpolate(lat1, scale_factor=2, mode="bilinear")
+        up = nn.functional.interpolate(up, scale_factor=2, mode="bilinear")
+
+        return up * mid + top
+
+
+def ConvBnRelu(num_in, num_out, kernel_size, stride=1, padding=0, bias=False):
+    return nn.Sequential(
+        nn.Conv2d(num_in, num_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias),
+        nn.BatchNorm2d(num_out, num_out),
+        nn.ReLU(inplace=True),
+    )

robosat/unet.py

@@ -14,6 +14,8 @@
 
 from torchvision.models import resnet50
 
+from robosat.fpa import FeaturePyramidAttention
+
 
 class ConvRelu(nn.Module):
     """3x3 convolution followed by ReLU activation building block.
@@ -96,6 +98,8 @@ def __init__(self, num_classes, num_filters=32, pretrained=True):
         # Access resnet directly in forward pass; do not store refs here due to
         # https://github.com/pytorch/pytorch/issues/8392
 
+        self.fpa = FeaturePyramidAttention(2048, 2048)
+
         self.center = DecoderBlock(2048, num_filters * 8)
 
         self.dec0 = DecoderBlock(2048 + num_filters * 8, num_filters * 8)
@@ -129,7 +133,9 @@ def forward(self, x):
         enc3 = self.resnet.layer3(enc2)
         enc4 = self.resnet.layer4(enc3)
 
-        center = self.center(nn.functional.max_pool2d(enc4, kernel_size=2, stride=2))
+        fpa = self.fpa(enc4)
+
+        center = self.center(nn.functional.max_pool2d(fpa, kernel_size=2, stride=2))
 
         dec0 = self.dec0(torch.cat([enc4, center], dim=1))
         dec1 = self.dec1(torch.cat([enc3, dec0], dim=1))