unet node for image segmentation

.gitignore

@@ -165,3 +165,5 @@ runs/
 
 # data
 data/
+
+unet_segmetation/model/*

unet_segmentation/CMakeLists.txt

@@ -0,0 +1,22 @@
+cmake_minimum_required(VERSION 3.8)
+project(unet_segmentation)
+
+find_package(ament_cmake REQUIRED)
+find_package(ament_cmake_python REQUIRED)
+find_package(rclpy REQUIRED)
+
+ament_python_install_package(${PROJECT_NAME})
+
+install(DIRECTORY
+  launch
+  config
+  model
+  DESTINATION share/${PROJECT_NAME}
+)
+
+install(PROGRAMS
+  ${PROJECT_NAME}/unet_segmentation_node.py
+  DESTINATION lib/${PROJECT_NAME}
+)
+
+ament_package()

unet_segmentation/config/unet_segmentation.yaml

@@ -0,0 +1,17 @@
+/**:
+  ros__parameters:
+    model_file: "unet-simple-320-240-l-5-e10-b16(1).pth"
+    input_topic: "/gripper_camera/image_raw"
+    overlay_topic: "/segmentation/overlay"
+    mask_topic: "/segmentation/mask"
+    resize_width: 320
+    resize_height: 240
+    keep_original_size: true     # upsample mask/overlay back to source image size
+    mask_threshold: 0.5
+    bilinear: false
+    simple: true
+    classes: 1
+    device: "cuda"
+    pred_color: [255, 0, 0]
+    overlay_alpha: 0.4
+    qos_depth: 3

unet_segmentation/launch/unet_segmentation.launch.py

@@ -0,0 +1,53 @@
+import os
+
+from ament_index_python.packages import get_package_share_directory
+from launch import LaunchDescription
+from launch.actions import DeclareLaunchArgument, OpaqueFunction
+from launch.substitutions import LaunchConfiguration
+from launch_ros.actions import Node
+
+ALLOWED_DEVICES = ['cpu', '0']
+
+
+def validate_device(device: str):
+    if device not in ALLOWED_DEVICES:
+        raise RuntimeError(
+            f"Invalid device '{device}'. Choose one of: {', '.join(ALLOWED_DEVICES)}"
+        )
+
+
+def launch_setup(context, *args, **kwargs):
+    device = LaunchConfiguration('device').perform(context)
+    validate_device(device)
+
+    unet_params = os.path.join(
+        get_package_share_directory('unet_segmentation'),
+        'config/unet_segmentation.yaml',
+    )
+
+    unet_node = Node(
+        package='unet_segmentation',
+        executable='unet_segmentation_node.py',
+        name='unet_segmentation',
+        namespace='unet',
+        output='screen',
+        parameters=[
+            unet_params,
+            {'device': device},
+        ],
+    )
+
+    return [unet_node]
+
+
+def generate_launch_description():
+    return LaunchDescription(
+        [
+            DeclareLaunchArgument(
+                'device',
+                default_value='0',
+                description='run unet segmentation',
+            ),
+            OpaqueFunction(function=launch_setup),
+        ]
+    )

unet_segmentation/package.xml

@@ -0,0 +1,20 @@
+<?xml version="1.0"?>
+<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
+<package format="3">
+  <name>unet_segmentation</name>
+  <version>0.0.0</version>
+  <description>unet segmentation on raw image</description>
+  <maintainer email="[email protected]">gardeg</maintainer>
+  <license>MIT</license>
+
+  <buildtool_depend>ament_cmake</buildtool_depend>
+
+  <depend>rclpy</depend>
+  <depend>sensor_msgs</depend>
+  <depend>cv_bridge</depend>
+  <depend>vision_msgs</depend>
+
+  <export>
+    <build_type>ament_cmake</build_type>
+  </export>
+</package>

unet_segmentation/unet_segmentation/unet/__init__.py

@@ -0,0 +1,3 @@
+from .unet_model import UNet as UNet
+
+__all__ = ["UNet"]

unet_segmentation/unet_segmentation/unet/unet_model.py

@@ -0,0 +1,84 @@
+import torch
+from torch import nn
+
+from .unet_parts import DoubleConv, Down, OutConv, Up
+
+
+class UNet(nn.Module):
+    def __init__(self, n_channels, n_classes, simple=False, bilinear=False):
+        """The U-Net architecture.
+
+        :param n_channels: Number of input channels (e.g., 3 for RGB images)
+        :param n_classes: Number of output classes (e.g., 1 for binary segmentation)
+        :param simple: If True, creates a smaller U-Net with fewer layers.
+        :param bilinear: If True, use bilinear upsampling instead of transposed convolutions.
+        """
+        super().__init__()
+        self.n_channels = n_channels
+        self.n_classes = n_classes
+        self.bilinear = bilinear
+        self.simple = simple
+
+        factor = 2 if bilinear else 1
+
+        if not self.simple:
+            self.inc = DoubleConv(n_channels, 64)
+            self.down1 = Down(64, 128)
+            self.down2 = Down(128, 256)
+            self.down3 = Down(256, 512)
+            self.down4 = Down(512, 1024 // factor)
+            self.up1 = Up(1024, 512 // factor, bilinear)
+            self.up2 = Up(512, 256 // factor, bilinear)
+            self.up3 = Up(256, 128 // factor, bilinear)
+            self.up4 = Up(128, 64, bilinear)
+            self.outc = OutConv(64, n_classes)
+        else:
+            self.inc = DoubleConv(n_channels, 64)
+            self.down1 = Down(64, 128)
+            self.down2 = Down(128, 256 // factor)
+            self.up1 = Up(256, 128 // factor, bilinear)
+            self.up2 = Up(128, 64, bilinear)
+            self.outc = OutConv(64, n_classes)
+
+    def forward(self, x):
+        if not self.simple:
+            x1 = self.inc(x)
+            x2 = self.down1(x1)
+            x3 = self.down2(x2)
+            x4 = self.down3(x3)
+            x5 = self.down4(x4)
+            x = self.up1(x5, x4)
+            x = self.up2(x, x3)
+            x = self.up3(x, x2)
+            x = self.up4(x, x1)
+            logits = self.outc(x)
+        else:
+            x1 = self.inc(x)
+            x2 = self.down1(x1)
+            x3 = self.down2(x2)
+            x = self.up1(x3, x2)
+            x = self.up2(x, x1)
+            logits = self.outc(x)
+
+        return logits
+
+    def use_checkpointing(self):
+        """Enable gradient checkpointing to save memory, but at the cost of additional computation during backpropagation."""
+        if not self.simple:
+            self.inc = torch.utils.checkpoint.checkpoint(self.inc)
+            self.down1 = torch.utils.checkpoint.checkpoint(self.down1)
+            self.down2 = torch.utils.checkpoint.checkpoint(self.down2)
+            self.down3 = torch.utils.checkpoint.checkpoint(self.down3)
+            self.down4 = torch.utils.checkpoint.checkpoint(self.down4)
+            self.up1 = torch.utils.checkpoint.checkpoint(self.up1)
+            self.up2 = torch.utils.checkpoint.checkpoint(self.up2)
+            self.up3 = torch.utils.checkpoint.checkpoint(self.up3)
+            self.up4 = torch.utils.checkpoint.checkpoint(self.up4)
+            self.outc = torch.utils.checkpoint.checkpoint(self.outc)
+        else:
+            self.inc = torch.utils.checkpoint.checkpoint(self.inc)
+            self.down1 = torch.utils.checkpoint.checkpoint(self.down1)
+            self.down2 = torch.utils.checkpoint.checkpoint(self.down2)
+            self.up1 = torch.utils.checkpoint.checkpoint(self.up1)
+            self.up2 = torch.utils.checkpoint.checkpoint(self.up2)
+            self.outc = torch.utils.checkpoint.checkpoint(self.outc)

unet_segmentation/unet_segmentation/unet/unet_parts.py

@@ -0,0 +1,77 @@
+"""Parts of the U-Net model."""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class DoubleConv(nn.Module):
+    """(convolution => [BN] => ReLU) * 2."""
+
+    def __init__(self, in_channels, out_channels, mid_channels=None):
+        super().__init__()
+        if not mid_channels:
+            mid_channels = out_channels
+        self.double_conv = nn.Sequential(
+            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(mid_channels),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+        )
+
+    def forward(self, x):
+        return self.double_conv(x)
+
+
+class Down(nn.Module):
+    """Downscaling with maxpool then double conv."""
+
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+        self.maxpool_conv = nn.Sequential(
+            nn.MaxPool2d(2), DoubleConv(in_channels, out_channels)
+        )
+
+    def forward(self, x):
+        return self.maxpool_conv(x)
+
+
+class Up(nn.Module):
+    """Upscaling then double conv."""
+
+    def __init__(self, in_channels, out_channels, bilinear=True):
+        super().__init__()
+
+        # if bilinear, use the normal convolutions to reduce the number of channels
+        if bilinear:
+            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
+            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
+        else:
+            self.up = nn.ConvTranspose2d(
+                in_channels, in_channels // 2, kernel_size=2, stride=2
+            )
+            self.conv = DoubleConv(in_channels, out_channels)
+
+    def forward(self, x1, x2):
+        x1 = self.up(x1)
+        # input is CHW
+        diffy = x2.size()[2] - x1.size()[2]
+        diffx = x2.size()[3] - x1.size()[3]
+
+        x1 = F.pad(x1, [diffx // 2, diffx - diffx // 2, diffy // 2, diffy - diffy // 2])
+        # if you have padding issues, see
+        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
+        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
+        x = torch.cat([x2, x1], dim=1)
+        return self.conv(x)
+
+
+class OutConv(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
+
+    def forward(self, x):
+        return self.conv(x)