Implements Feature Pyramid Network (FPN), closes #60

robosat/fpn.py

@@ -0,0 +1,141 @@
+"""Feature Pyramid Network (FPN) on top of ResNet. Comes with task-specific heads on top of it.
+
+See:
+- https://arxiv.org/abs/1612.03144 - Feature Pyramid Networks for Object Detection
+- http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf - A Unified Architecture for Instance
+                                                               and Semantic Segmentation
+
+"""
+
+import torch
+import torch.nn as nn
+
+from torchvision.models import resnet50
+
+
+class FPN(nn.Module):
+    """Feature Pyramid Network (FPN): top-down architecture with lateral connections.
+       Can be used as feature extractor for object detection or segmentation.
+    """
+
+    def __init__(self, num_filters=256, pretrained=True):
+        """Creates an `FPN` instance for feature extraction.
+
+        Args:
+          num_filters: the number of filters in each output pyramid level
+          pretrained: use ImageNet pre-trained backbone feature extractor
+        """
+
+        super().__init__()
+
+        self.resnet = resnet50(pretrained=pretrained)
+
+        # Access resnet directly in forward pass; do not store refs here due to
+        # https://github.com/pytorch/pytorch/issues/8392
+
+        self.lateral4 = Conv1x1(2048, num_filters)
+        self.lateral3 = Conv1x1(1024, num_filters)
+        self.lateral2 = Conv1x1(512, num_filters)
+        self.lateral1 = Conv1x1(256, num_filters)
+
+        self.smooth4 = Conv3x3(num_filters, num_filters)
+        self.smooth3 = Conv3x3(num_filters, num_filters)
+        self.smooth2 = Conv3x3(num_filters, num_filters)
+        self.smooth1 = Conv3x3(num_filters, num_filters)
+
+    def forward(self, x):
+        # Bottom-up pathway, from ResNet
+
+        enc0 = self.resnet.conv1(x)
+        enc0 = self.resnet.bn1(enc0)
+        enc0 = self.resnet.relu(enc0)
+        enc0 = self.resnet.maxpool(enc0)
+
+        enc1 = self.resnet.layer1(enc0)
+        enc2 = self.resnet.layer2(enc1)
+        enc3 = self.resnet.layer3(enc2)
+        enc4 = self.resnet.layer4(enc3)
+
+        # Lateral connections
+
+        lateral4 = self.lateral4(enc4)
+        lateral3 = self.lateral3(enc3)
+        lateral2 = self.lateral2(enc2)
+        lateral1 = self.lateral1(enc1)
+
+        # Top-down pathway
+
+        map4 = lateral4
+        map3 = lateral3 + nn.functional.upsample(map4, scale_factor=2, mode="nearest")
+        map2 = lateral2 + nn.functional.upsample(map3, scale_factor=2, mode="nearest")
+        map1 = lateral1 + nn.functional.upsample(map2, scale_factor=2, mode="nearest")
+
+        # Reduce aliasing effect of upsampling
+
+        map4 = self.smooth4(map4)
+        map3 = self.smooth3(map3)
+        map2 = self.smooth2(map2)
+        map1 = self.smooth1(map1)
+
+        return map1, map2, map3, map4
+
+
+class FPNSegmentation(nn.Module):
+    """Semantic segmentation model on top of a Feature Pyramid Network (FPN).
+    """
+
+    def __init__(self, num_classes, num_filters=128, num_filters_fpn=256, pretrained=True):
+        """Creates an `FPNSegmentation` instance for feature extraction.
+
+        Args:
+          num_classes: number of classes to predict
+          num_filters: the number of filters in each segmentation head pyramid level
+          num_filters_fpn: the number of filters in each FPN output pyramid level
+          pretrained: use ImageNet pre-trained backbone feature extractor
+        """
+
+        super().__init__()
+
+        # Feature Pyramid Network (FPN) with four feature maps of resolutions
+        # 1/4, 1/8, 1/16, 1/32 and `num_filters` filters for all feature maps.
+
+        self.fpn = FPN(num_filters=num_filters_fpn, pretrained=pretrained)
+
+        # The segmentation heads on top of the FPN
+
+        self.head1 = nn.Sequential(Conv3x3(num_filters_fpn, num_filters), Conv3x3(num_filters, num_filters))
+        self.head2 = nn.Sequential(Conv3x3(num_filters_fpn, num_filters), Conv3x3(num_filters, num_filters))
+        self.head3 = nn.Sequential(Conv3x3(num_filters_fpn, num_filters), Conv3x3(num_filters, num_filters))
+        self.head4 = nn.Sequential(Conv3x3(num_filters_fpn, num_filters), Conv3x3(num_filters, num_filters))
+
+        self.final = nn.Conv2d(4 * num_filters, num_classes, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        map1, map2, map3, map4 = self.fpn(x)
+
+        map4 = nn.functional.upsample(self.head4(map4), scale_factor=8, mode="nearest")
+        map3 = nn.functional.upsample(self.head3(map3), scale_factor=4, mode="nearest")
+        map2 = nn.functional.upsample(self.head2(map2), scale_factor=2, mode="nearest")
+        map1 = self.head1(map1)
+
+        final = self.final(torch.cat([map4, map3, map2, map1], dim=1))
+
+        return nn.functional.upsample(final, scale_factor=4, mode="bilinear", align_corners=False)
+
+
+class Conv1x1(nn.Module):
+    def __init__(self, num_in, num_out):
+        super().__init__()
+        self.block = nn.Conv2d(num_in, num_out, kernel_size=1, bias=False)
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class Conv3x3(nn.Module):
+    def __init__(self, num_in, num_out):
+        super().__init__()
+        self.block = nn.Conv2d(num_in, num_out, kernel_size=3, padding=1, bias=False)
+
+    def forward(self, x):
+        return self.block(x)

robosat/tools/export.py

@@ -5,7 +5,7 @@
 import torch.autograd
 
 from robosat.config import load_config
-from robosat.unet import UNet
+from robosat.fpn import FPNSegmentation
 
 
 def add_parser(subparser):
@@ -25,7 +25,7 @@ def main(args):
     dataset = load_config(args.dataset)
 
     num_classes = len(dataset["common"]["classes"])
-    net = UNet(num_classes)
+    net = FPNSegmentation(num_classes)
 
     def map_location(storage, _):
         return storage.cpu()

robosat/tools/predict.py

@@ -14,7 +14,7 @@
 from PIL import Image
 
 from robosat.datasets import BufferedSlippyMapDirectory
-from robosat.unet import UNet
+from robosat.fpn import FPNSegmentation
 from robosat.config import load_config
 from robosat.colors import continuous_palette_for_color
 from robosat.transforms import ConvertImageMode, ImageToTensor
@@ -59,8 +59,9 @@ def map_location(storage, _):
     # https://github.com/pytorch/pytorch/issues/7178
     chkpt = torch.load(args.checkpoint, map_location=map_location)
 
-    net = UNet(num_classes).to(device)
-    net = nn.DataParallel(net)
+    net = FPNSegmentation(num_classes)
+    net = DataParallel(net)
+    net = net.to(device)
 
     if cuda:
         torch.backends.cudnn.benchmark = True

robosat/tools/serve.py

@@ -16,7 +16,7 @@
 from flask import Flask, send_file, render_template, abort
 
 from robosat.tiles import fetch_image
-from robosat.unet import UNet
+from robosat.fpn import FPNSegmentation
 from robosat.config import load_config
 from robosat.colors import make_palette
 from robosat.transforms import ConvertImageMode, ImageToTensor
@@ -180,8 +180,9 @@ def map_location(storage, _):
 
         num_classes = len(self.dataset["common"]["classes"])
 
-        net = UNet(num_classes).to(self.device)
-        net = nn.DataParallel(net)
+        net = FPNSegmentation(num_classes)
+        net = DataParallel(net)
+        net = net.to(device)
 
         if self.cuda:
             torch.backends.cudnn.benchmark = True

robosat/tools/train.py

@@ -27,7 +27,7 @@
 from robosat.datasets import SlippyMapTilesConcatenation
 from robosat.metrics import Metrics
 from robosat.losses import CrossEntropyLoss2d, mIoULoss2d, FocalLoss2d, LovaszLoss2d
-from robosat.unet import UNet
+from robosat.fpn import FPNSegmentation
 from robosat.utils import plot
 from robosat.config import load_config
 from robosat.log import Log
@@ -68,7 +68,7 @@ def main(args):
     os.makedirs(model["common"]["checkpoint"], exist_ok=True)
 
     num_classes = len(dataset["common"]["classes"])
-    net = UNet(num_classes)
+    net = FPNSegmentation(num_classes)
     net = DataParallel(net)
     net = net.to(device)
 

robosat/unet.py

@@ -84,6 +84,7 @@ def __init__(self, num_classes, num_filters=32, pretrained=True):
 
         Args:
           num_classes: number of classes to predict.
+          num_filters: the number of filters for the decoder block
           pretrained: use ImageNet pre-trained backbone feature extractor
         """