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eea5f0e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 | """Train the 6-channel UNet (baseline architecture) on synthetic pre/post pairs.
Label map (softmax, same as baseline): 0 background, 1 new_building, 2 tree_removal.
Scene label codes from prep.py: 1 building, 2 tree, 3 ground donor.
"""
import math
import os
import sys
import time
import cv2
import numpy as np
import segmentation_models_pytorch as smp
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
P = "/workspace/prep"
CROP = 192
MEAN = np.array([0.485, 0.456, 0.406], np.float32)
STD = np.array([0.229, 0.224, 0.225], np.float32)
MINUTES = float(sys.argv[1]) if len(sys.argv) > 1 else 25
class Synth(Dataset):
def __init__(self, n):
self.n = n
self.fx = np.load(f"{P}/flair_x.npy", mmap_mode="r"); self.fy = np.load(f"{P}/flair_y.npy", mmap_mode="r")
self.tx = np.load(f"{P}/tcd_x.npy", mmap_mode="r"); self.ty = np.load(f"{P}/tcd_y.npy", mmap_mode="r")
fy = np.load(f"{P}/flair_y.npy")
self.b_idx = np.flatnonzero((fy == 1).mean((1, 2)) > 0.01)
self.t_idx = np.flatnonzero((fy == 2).mean((1, 2)) > 0.15)
self.g_idx = np.flatnonzero((fy == 3).mean((1, 2)) > 0.6)
print("building scenes", len(self.b_idx), "tree scenes", len(self.t_idx), "ground donors", len(self.g_idx), flush=True)
def __len__(self):
return self.n
# ---------- scene sampling ----------
def scene(self, rng, kind):
if kind == "tcd":
i = rng.integers(len(self.tx)); x, y = self.tx[i], self.ty[i]
s = rng.uniform(0.85, 1.15)
size = int(round(CROP / s)); size = min(size, 320)
r, c = rng.integers(0, 321 - size, 2)
x = cv2.resize(np.ascontiguousarray(x[r:r + size, c:c + size]), (CROP, CROP), interpolation=cv2.INTER_AREA)
y = cv2.resize(np.ascontiguousarray(y[r:r + size, c:c + size]), (CROP, CROP), interpolation=cv2.INTER_NEAREST)
return x.copy(), y.copy()
pool = {"b": self.b_idx, "t": self.t_idx, "g": self.g_idx, "any": None}[kind]
i = rng.integers(len(self.fx)) if pool is None else pool[rng.integers(len(pool))]
return np.array(self.fx[i]), np.array(self.fy[i])
def donor(self, rng):
x, _ = self.scene(rng, "g")
k = rng.integers(4)
x = np.rot90(x, k).copy()
return x
@staticmethod
def paste(dst, src, region, rng):
"""Replace region of dst by src pixels with feathered edge and mean colour matching."""
if region.sum() == 0:
return dst
ring = cv2.dilate(region.astype(np.uint8), np.ones((9, 9), np.uint8)) & (~region.astype(bool))
src = src.astype(np.float32)
if ring.sum() > 10 and rng.random() < 0.7:
a = rng.uniform(0.3, 0.8)
shift = dst[ring.astype(bool)].mean(0) - src[region.astype(bool)].mean(0)
src = np.clip(src + a * shift, 0, 255)
alpha = cv2.GaussianBlur(region.astype(np.float32), (0, 0), rng.uniform(0.6, 1.2))
alpha = np.maximum(alpha, region.astype(np.float32) * 0.9)[..., None]
return (dst * (1 - alpha) + src * alpha).astype(np.uint8)
@staticmethod
def blob(rng, shape, area):
m = np.zeros(shape, np.uint8)
cy, cx = rng.integers(0, shape[0]), rng.integers(0, shape[1])
rad = math.sqrt(area / math.pi)
for _ in range(rng.integers(1, 5)):
oy, ox = rng.normal(0, rad * 0.5, 2)
ax = (int(max(3, rad * rng.uniform(0.5, 1.3))), int(max(3, rad * rng.uniform(0.4, 1.1))))
cv2.ellipse(m, (int(cx + ox), int(cy + oy)), ax, rng.uniform(0, 180), 0, 360, 1, -1)
if rng.random() < 0.5: # jagged edge
noise = cv2.GaussianBlur(rng.random(shape).astype(np.float32), (0, 0), 3)
m = ((cv2.GaussianBlur(m.astype(np.float32), (0, 0), 4) + (noise - 0.5) * 0.6) > 0.5).astype(np.uint8)
return m
# ---------- photometric: independent per epoch ----------
@staticmethod
def photo(img, rng, tree=None):
x = img.astype(np.float32)
if tree is not None and rng.random() < 0.3: # seasonal: vegetation toward brown / pale
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV).astype(np.float32)
t = cv2.GaussianBlur(tree.astype(np.float32), (0, 0), 2)
hsv[..., 0] -= t * rng.uniform(5, 25)
hsv[..., 1] *= 1 - t * rng.uniform(0, 0.5)
hsv[..., 0] %= 180
x = cv2.cvtColor(np.clip(hsv, 0, 255).astype(np.uint8), cv2.COLOR_HSV2RGB).astype(np.float32)
x = x * rng.uniform(0.7, 1.3) + rng.uniform(-30, 30) # contrast / brightness
x = x * rng.uniform(0.88, 1.12, 3) + rng.uniform(-12, 12, 3) # colour cast
g = rng.uniform(0.7, 1.4)
x = 255 * (np.clip(x, 0, 255) / 255) ** g
m = x.mean(2, keepdims=True); x = m + (x - m) * rng.uniform(0.6, 1.4) # saturation
if rng.random() < 0.3:
x = cv2.GaussianBlur(x, (0, 0), rng.uniform(0.3, 1.0))
if rng.random() < 0.3:
x = x + rng.normal(0, rng.uniform(1, 6), x.shape)
return np.clip(x, 0, 255).astype(np.uint8)
def __getitem__(self, idx):
rng = np.random.default_rng((idx * 7919 + os.getpid() * 104729 + time.time_ns()) % 2**32)
r = rng.random()
mode = "b" if r < 0.33 else ("t" if r < 0.66 else ("bt" if r < 0.74 else "neg"))
if "t" in mode:
x, y = self.scene(rng, "tcd" if rng.random() < 0.5 else "t")
elif mode == "b":
x, y = self.scene(rng, "b")
else:
x, y = self.scene(rng, ["tcd", "any", "b", "t"][rng.integers(4)])
pre, post = x.copy(), x.copy()
lab = np.zeros(y.shape, np.uint8)
bmask = (y == 1).astype(np.uint8); tmask = (y == 2).astype(np.uint8)
if "b" in mode and bmask.sum() > 20:
n, cc, stats, _ = cv2.connectedComponentsWithStats(bmask, 8)
comps = [i for i in range(1, n) if stats[i, cv2.CC_STAT_AREA] >= 12]
if comps:
k = rng.integers(1, len(comps) + 1)
sel = rng.choice(comps, size=k, replace=False)
region = np.isin(cc, sel).astype(np.uint8)
if rng.random() < 0.2: # partial extension: only part of a building is new
cut = self.blob(rng, region.shape, region.sum() * 0.5)
region = region & cut
if region.sum() >= 12:
grow = cv2.dilate(region, np.ones((3, 3), np.uint8), iterations=int(rng.integers(1, 3)))
pre = self.paste(pre, self.donor(rng), grow, rng)
lab[region.astype(bool)] = 1
if "t" in mode and tmask.sum() > 150:
for _ in range(10):
b = self.blob(rng, tmask.shape, rng.uniform(150, 6000))
region = b & cv2.dilate(tmask, np.ones((3, 3), np.uint8))
if (region & tmask).sum() >= 80:
post = self.paste(post, self.donor(rng), region, rng)
lab[(region & tmask).astype(bool) & (lab == 0)] = 2
break
if mode == "neg" and rng.random() < 0.3 and bmask.sum() > 20: # roof colour change only
post = post.astype(np.int16); post[bmask.astype(bool)] += rng.integers(-60, 60, 3).astype(np.int16)
post = np.clip(post, 0, 255).astype(np.uint8)
if lab.any() and rng.random() < 0.15: # reversed pair (demolition / regrowth) -> no target change
pre, post = post, pre
lab[:] = 0
pre = self.photo(pre, rng, tmask); post = self.photo(post, rng, tmask)
if rng.random() < 0.7: # misregistration of pre (parallax / residual shift)
M = np.float32([[1, 0, rng.uniform(-2.5, 2.5)], [0, 1, rng.uniform(-2.5, 2.5)]])
pre = cv2.warpAffine(pre, M, (CROP, CROP), borderMode=cv2.BORDER_REFLECT)
if rng.random() < 0.1: # no-data black region (straight edge), independent per image
for im in (pre, post) if rng.random() < 0.5 else ((pre,) if rng.random() < 0.5 else (post,)):
yy, xx = np.mgrid[:CROP, :CROP]
th = rng.uniform(0, 2 * np.pi); d = rng.uniform(-CROP * 0.7, -CROP * 0.2)
nd = (xx - CROP / 2) * np.cos(th) + (yy - CROP / 2) * np.sin(th) < d
im[nd] = 0
lab[nd] = 0
k = rng.integers(8) # shared dihedral transform
def d4(a):
a = np.rot90(a, k % 4)
return a[:, ::-1] if k >= 4 else a
pre, post, lab = d4(pre), d4(post), d4(lab)
inp = np.concatenate([(pre / 255.0 - MEAN) / STD, (post / 255.0 - MEAN) / STD], 2).astype(np.float32)
return torch.from_numpy(inp.transpose(2, 0, 1).copy()), torch.from_numpy(lab.copy()).long()
def dice_loss(logits, y):
p = logits.softmax(1)
loss = 0
for c in (1, 2):
t = (y == c).float(); q = p[:, c]
loss += 1 - (2 * (q * t).sum() + 1) / (q.sum() + t.sum() + 1)
return loss / 2
def main():
torch.backends.cudnn.benchmark = True
model = smp.Unet(encoder_name="resnet18", encoder_weights=None, in_channels=6, classes=3)
ck = torch.load("/workspace/unet_r18_cd.pt", map_location="cpu", weights_only=True)
model.load_state_dict(ck["state_dict"]) # start from the organizer baseline weights
model = model.cuda().to(memory_format=torch.channels_last)
ds = Synth(10**7)
dl = DataLoader(ds, batch_size=64, num_workers=30, pin_memory=True, persistent_workers=True, prefetch_factor=4)
opt = torch.optim.AdamW(model.parameters(), lr=4e-4, weight_decay=1e-4)
w = torch.tensor([1.0, 2.0, 2.0]).cuda()
t0 = time.time(); step = 0; budget = MINUTES * 60
for x, y in dl:
x = x.cuda(non_blocking=True).to(memory_format=torch.channels_last); y = y.cuda(non_blocking=True)
frac = min((time.time() - t0) / budget, 1.0)
for g in opt.param_groups:
g["lr"] = 4e-4 * (0.5 * (1 + math.cos(math.pi * frac))) * min(1, (step + 1) / 200)
with torch.autocast("cuda", dtype=torch.bfloat16):
out = model(x)
loss = F.cross_entropy(out, y, weight=w) + dice_loss(out.float(), y)
opt.zero_grad(set_to_none=True); loss.backward(); opt.step(); step += 1
if step % 100 == 0:
print(f"step {step} {time.time() - t0:.0f}s loss {loss.item():.4f} ips {step * 64 / (time.time() - t0):.0f}", flush=True)
if step % 1000 == 0 or frac >= 1:
torch.save({"state_dict": model.state_dict()}, "/workspace/unet_synth.pt")
if frac >= 1:
break
print("TRAIN_DONE", step, flush=True)
if __name__ == "__main__":
main()
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