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aa4d14b ba4abf7 aa4d14b ba4abf7 aa4d14b ba4abf7 aa4d14b 99e1f27 aa4d14b 99e1f27 aa4d14b 99e1f27 aa4d14b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 | """
Siamese U-Net for satellite change detection.
A lightweight Siamese encoder shares weights between before/after images,
fuses features via concatenation + difference, and decodes into a binary
change probability map.
Designed for CPU inference (< 2s per 256x256 tile).
"""
import logging
import os
from pathlib import Path
import cv2
import numpy as np
logger = logging.getLogger(__name__)
_MODEL = None
_DEVICE = None
_AVAILABLE = None
_WEIGHTS_DIR = Path(__file__).parent / "weights"
_WEIGHTS_FILE = _WEIGHTS_DIR / "siamese_unet_cd.pt"
def _try_torch():
try:
import torch
import torch.nn as nn
return torch, nn
except ImportError:
return None, None
# ---------------------------------------------------------------------------
# Model architecture
# ---------------------------------------------------------------------------
def _build_model():
torch, nn = _try_torch()
if torch is None:
return None
class ConvBlock(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.block = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1, bias=False),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1, bias=False),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.block(x)
class Encoder(nn.Module):
def __init__(self, in_ch=3, base=32):
super().__init__()
self.enc1 = ConvBlock(in_ch, base)
self.enc2 = ConvBlock(base, base * 2)
self.enc3 = ConvBlock(base * 2, base * 4)
self.enc4 = ConvBlock(base * 4, base * 8)
self.pool = nn.MaxPool2d(2)
def forward(self, x):
e1 = self.enc1(x)
e2 = self.enc2(self.pool(e1))
e3 = self.enc3(self.pool(e2))
e4 = self.enc4(self.pool(e3))
return [e1, e2, e3, e4]
class SiameseUNet(nn.Module):
"""
Siamese U-Net: shared encoder processes before/after images independently.
Decoder fuses features via concatenation of both streams + their absolute
difference, providing the decoder with explicit change information.
"""
def __init__(self, in_ch=3, base=32, out_ch=2):
super().__init__()
self.encoder = Encoder(in_ch, base)
b = base
# Decoder: at each level receives [enc_a, enc_b, |enc_a-enc_b|] = 3x channels
self.up4 = nn.ConvTranspose2d(b * 8, b * 4, 2, stride=2)
self.dec4 = ConvBlock(b * 4 + b * 4 * 3, b * 4)
self.up3 = nn.ConvTranspose2d(b * 4, b * 2, 2, stride=2)
self.dec3 = ConvBlock(b * 2 + b * 2 * 3, b * 2)
self.up2 = nn.ConvTranspose2d(b * 2, b, 2, stride=2)
self.dec2 = ConvBlock(b + b * 3, b)
self.head = nn.Conv2d(b, out_ch, 1)
def forward(self, img_a, img_b):
feats_a = self.encoder(img_a)
feats_b = self.encoder(img_b)
# Bottleneck: fuse deepest features
bot = torch.cat([feats_a[3], feats_b[3], torch.abs(feats_a[3] - feats_b[3])], dim=1)
import torch.nn.functional as F
# Level 3
d4 = self.up4(feats_a[3])
skip3 = torch.cat([feats_a[2], feats_b[2], torch.abs(feats_a[2] - feats_b[2])], dim=1)
d4 = self.dec4(torch.cat([d4, skip3], dim=1))
# Level 2
d3 = self.up3(d4)
skip2 = torch.cat([feats_a[1], feats_b[1], torch.abs(feats_a[1] - feats_b[1])], dim=1)
d3 = self.dec3(torch.cat([d3, skip2], dim=1))
# Level 1
d2 = self.up2(d3)
skip1 = torch.cat([feats_a[0], feats_b[0], torch.abs(feats_a[0] - feats_b[0])], dim=1)
d2 = self.dec2(torch.cat([d2, skip1], dim=1))
return self.head(d2)
return SiameseUNet
# ---------------------------------------------------------------------------
# Model loading (singleton)
# ---------------------------------------------------------------------------
def has_siamese_weights():
"""True only when a trained weights file is present."""
return _WEIGHTS_FILE.is_file()
def is_siamese_available():
"""PyTorch installed and pretrained weights available."""
global _AVAILABLE
if _AVAILABLE is not None:
return _AVAILABLE
torch, _ = _try_torch()
_AVAILABLE = torch is not None and has_siamese_weights()
return _AVAILABLE
def _load_siamese():
global _MODEL, _DEVICE
if _MODEL is not None:
return _MODEL
torch, _ = _try_torch()
if torch is None:
raise RuntimeError("PyTorch not installed")
_DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
ModelClass = _build_model()
model = ModelClass(in_ch=3, base=32, out_ch=2)
if _WEIGHTS_FILE.exists():
logger.info("Loading Siamese U-Net weights from %s", _WEIGHTS_FILE)
state = torch.load(str(_WEIGHTS_FILE), map_location=_DEVICE, weights_only=True)
model.load_state_dict(state)
else:
logger.info("No pretrained weights found at %s — using random init "
"(model will still produce change maps but accuracy depends on "
"classical fusion weighting)", _WEIGHTS_FILE)
model.to(_DEVICE)
model.eval()
_MODEL = model
return _MODEL
# ---------------------------------------------------------------------------
# Inference
# ---------------------------------------------------------------------------
_TILE = 256
def predict_siamese(img1, img2, threshold=0.5):
"""
Run Siamese U-Net inference on two RGB uint8 arrays.
Tile-based with overlap stitching (same pattern as AdaptFormer).
Returns (uint8 mask [0|255], float32 probability map [0-1]).
"""
torch, _ = _try_torch()
model = _load_siamese()
if img1.shape != img2.shape:
img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0]))
from .model_utils import tiled_score_map
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
def _score_tile(t1, t2):
n1 = (t1.astype(np.float32) / 255.0 - mean) / std
n2 = (t2.astype(np.float32) / 255.0 - mean) / std
ta = torch.from_numpy(n1.transpose(2, 0, 1)).unsqueeze(0).to(_DEVICE)
tb = torch.from_numpy(n2.transpose(2, 0, 1)).unsqueeze(0).to(_DEVICE)
logits = model(ta, tb)
probs = torch.softmax(logits, dim=1)
return probs[0, 1].cpu().numpy()
with torch.no_grad():
avg = tiled_score_map(_score_tile, img1, img2,
tile_size=_TILE, overlap=_TILE // 4)
mask = (avg >= threshold).astype(np.uint8) * 255
return mask, avg
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