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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 | """Train SiamCD (model_v2) on synthetic pairs with per-image semantics and satellite-like degradation.
usage: python train3.py MINUTES [BATCH]
"""
import math
import os
import sys
import time
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from model_v2 import SiamCD
from train2 import CROP, Synth
MINUTES = float(sys.argv[1]) if len(sys.argv) > 1 else 90
BATCH = int(sys.argv[2]) if len(sys.argv) > 2 else 32
OUT = "/workspace/siam_v2"
def degrade(img, rng):
"""Make crisp downsampled aerial imagery look like display-stretched pansharpened satellite imagery."""
x = img
if rng.random() < 0.8: # resolution loss
f = rng.uniform(1.0, 1.5)
small = cv2.resize(x, (int(CROP / f), int(CROP / f)), interpolation=cv2.INTER_AREA)
x = cv2.resize(small, (CROP, CROP), interpolation=[cv2.INTER_LINEAR, cv2.INTER_CUBIC][rng.integers(2)])
if rng.random() < 0.4: # pansharpening / sharpening halos
bl = cv2.GaussianBlur(x, (0, 0), rng.uniform(0.8, 1.6))
x = cv2.addWeighted(x, 1 + rng.uniform(0.3, 1.0), bl, -rng.uniform(0.3, 1.0), 0)
if rng.random() < 0.5: # display stretch (percentile clip per image)
lo, hi = np.percentile(x, rng.uniform(0.5, 3)), np.percentile(x, 100 - rng.uniform(0.5, 3))
x = np.clip((x.astype(np.float32) - lo) * 255.0 / max(hi - lo, 1), 0, 255).astype(np.uint8)
if rng.random() < 0.3: # haze
a = rng.uniform(0.05, 0.25)
x = (x * (1 - a) + a * rng.uniform(150, 230)).astype(np.uint8)
if rng.random() < 0.5:
ok, enc = cv2.imencode(".jpg", x, [cv2.IMWRITE_JPEG_QUALITY, int(rng.integers(40, 92))])
x = cv2.imdecode(enc, cv2.IMREAD_UNCHANGED)
return x
class Synth3(Synth):
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 < 0.30 else ("t" if r < 0.60 else ("bt" if r < 0.68 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()
sem = np.zeros(y.shape, np.uint8); sem[y == 1] = 1; sem[y == 2] = 2
sem_pre, sem_post = sem.copy(), sem.copy()
lb = np.zeros(y.shape, np.uint8); lt = 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:
sel = rng.choice(comps, size=rng.integers(1, len(comps) + 1), replace=False)
region = np.isin(cc, sel).astype(np.uint8)
if rng.random() < 0.2:
region = region & self.blob(rng, region.shape, region.sum() * 0.5)
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)
sem_pre[grow.astype(bool)] = 0
lb[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)
sem_post[region.astype(bool)] = 0
lt[(region & tmask).astype(bool)] = 1
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 mode == "neg" and rng.random() < 0.3: # field / ground appearance change (not a target)
g = (y == 3)
post = post.astype(np.int16); post[g] += rng.integers(-40, 40, 3).astype(np.int16)
post = np.clip(post, 0, 255).astype(np.uint8)
if (lb.any() or lt.any()) and rng.random() < 0.2: # demolition / regrowth -> not a target
pre, post = post, pre
sem_pre, sem_post = sem_post, sem_pre
lb[:] = 0; lt[:] = 0
season = sem == 2
pre = degrade(self.photo(pre, rng, tmask if rng.random() < 0.7 else season), rng)
post = degrade(self.photo(post, rng, tmask if rng.random() < 0.7 else season), rng)
if rng.random() < 0.8: # misregistration / parallax of pre (affine)
a = np.deg2rad(rng.uniform(-1.5, 1.5)); s = rng.uniform(0.98, 1.02)
M = cv2.getRotationMatrix2D((CROP / 2, CROP / 2), np.rad2deg(a), s)
M[:, 2] += rng.uniform(-4, 4, 2)
pre = cv2.warpAffine(pre, M, (CROP, CROP), borderMode=cv2.BORDER_REFLECT)
sem_pre = cv2.warpAffine(sem_pre, M, (CROP, CROP), flags=cv2.INTER_NEAREST, borderMode=cv2.BORDER_REFLECT)
ignore = np.zeros(y.shape, bool)
if rng.random() < 0.15:
yy, xx = np.mgrid[:CROP, :CROP]
for im, sm in ((pre, sem_pre), (post, sem_post)):
if rng.random() < 0.6:
th = rng.uniform(0, 2 * np.pi); d = rng.uniform(-CROP * 0.7, -CROP * 0.15)
nd = (xx - CROP / 2) * np.cos(th) + (yy - CROP / 2) * np.sin(th) < d
im[nd] = 0; sm[nd] = 255; ignore |= nd
lb[ignore] = 0; lt[ignore] = 0
k = rng.integers(8)
def d4(a):
a = np.rot90(a, k % 4)
return np.ascontiguousarray(a[:, ::-1] if k >= 4 else a)
pre, post, lb, lt, sem_pre, sem_post = map(d4, (pre, post, lb, lt, sem_pre, sem_post))
pres = np.array([lb.sum() >= 20, lt.sum() >= 20], np.float32)
t = lambda a: torch.from_numpy(a.transpose(2, 0, 1).copy())
return (t(pre), t(post), torch.from_numpy(np.stack([lb, lt]).astype(np.float32)), torch.from_numpy(pres),
torch.from_numpy(sem_pre.astype(np.int64)), torch.from_numpy(sem_post.astype(np.int64)))
def dice(logit, y):
p = logit.sigmoid()
inter = (p * y).sum((0, 2, 3)); den = p.sum((0, 2, 3)) + y.sum((0, 2, 3))
return (1 - (2 * inter + 1) / (den + 1)).mean()
def main():
torch.backends.cudnn.benchmark = True
model = SiamCD("/workspace/satlas/aerial_swinb_si.pth").cuda()
enc = [p for n, p in model.named_parameters() if n.startswith("enc.")]
rest = [p for n, p in model.named_parameters() if not n.startswith("enc.")]
opt = torch.optim.AdamW([{"params": enc, "lr": 1e-4, "base": 1e-4}, {"params": rest, "lr": 6e-4, "base": 6e-4}], weight_decay=1e-4)
dl = DataLoader(Synth3(10**7), batch_size=BATCH, num_workers=int(os.environ.get("WORKERS", 30)), pin_memory=True,
persistent_workers=True, prefetch_factor=4)
pw = torch.tensor([2.0, 2.0]).cuda().view(1, 2, 1, 1)
t0 = time.time(); step = 0; budget = MINUTES * 60; last_snap = t0
for pre, post, lab, pres, sp, sq in dl:
pre = pre.cuda(non_blocking=True).float() / 255; post = post.cuda(non_blocking=True).float() / 255
lab, pres, sp, sq = lab.cuda(non_blocking=True), pres.cuda(non_blocking=True), sp.cuda(non_blocking=True), sq.cuda(non_blocking=True)
frac = min((time.time() - t0) / budget, 1.0)
for g in opt.param_groups:
g["lr"] = g["base"] * 0.5 * (1 + math.cos(math.pi * frac)) * min(1, (step + 1) / 300)
with torch.autocast("cuda", dtype=torch.bfloat16):
ch, pr, sem_p, sem_q = model(pre, post)
ch, pr = ch.float(), pr.float()
loss_ch = F.binary_cross_entropy_with_logits(ch, lab, pos_weight=pw) + dice(ch, lab)
loss_sem = F.cross_entropy(sem_p.float(), sp, ignore_index=255) + F.cross_entropy(sem_q.float(), sq, ignore_index=255)
loss_pr = F.binary_cross_entropy_with_logits(pr, pres)
loss = loss_ch + 0.5 * loss_sem + 0.3 * loss_pr
opt.zero_grad(set_to_none=True); loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0); opt.step(); step += 1
if step % 100 == 0:
print(f"step {step} {time.time() - t0:.0f}s loss {loss.item():.4f} ch {loss_ch.item():.3f} sem {loss_sem.item():.3f} "
f"pr {loss_pr.item():.3f} ips {step * BATCH / (time.time() - t0):.0f}", flush=True)
if step % 1000 == 0 or frac >= 1:
torch.save({"state_dict": model.state_dict()}, OUT + ".pt")
if time.time() - last_snap > 1800:
last_snap = time.time()
torch.save({"state_dict": model.state_dict()}, OUT + f"_m{int((last_snap - t0) // 60)}.pt")
if frac >= 1:
break
print("TRAIN_DONE", step, flush=True)
if __name__ == "__main__":
main()
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