File size: 8,894 Bytes
b63c30f | 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 | import argparse
import os
import json
from pathlib import Path
import numpy as np
from PIL import Image
import torch
import torch.nn.functional as F
import cv2
from train_global_unet import UNet, encode_scribble, list_test_pairs, list_train_pairs, load_palette, evaluate_predictions, tta_predict, TRAIN_H, TRAIN_W, ORIG_H, ORIG_W, TEST_PRED
def predict_threshold(prob, sc, predictor):
if predictor is None:
return 0.5
fg_scrib = (sc == 1).sum()
bg_scrib = (sc == 0).sum()
scrib_fg_ratio = fg_scrib / (fg_scrib + bg_scrib) if fg_scrib + bg_scrib > 0 else 0.5
pred_fg_frac = (prob > 0.5).mean()
prob_mean = prob.mean()
prob_std = prob.std()
p_clip = np.clip(prob, 1e-06, 1 - 1e-06)
entropy = -(p_clip * np.log(p_clip) + (1 - p_clip) * np.log(1 - p_clip)).mean()
feats = np.array([scrib_fg_ratio, pred_fg_frac, prob_mean, prob_std, entropy])
t = float(feats @ np.array(predictor['weights']) + predictor['bias'])
return float(np.clip(t, predictor['clip'][0], predictor['clip'][1]))
def load_threshold_predictor():
p = Path('threshold_predictor.json')
if not p.exists():
return None
return json.load(open(p))
def load_models(ckpt_specs, device):
models = []
for ckpt_dir, base, seed in ckpt_specs:
for fd in sorted(Path(ckpt_dir).glob('fold_*')):
ckpt = fd / 'best.pth'
if not ckpt.exists():
continue
m = UNet(in_ch=5, base=base, out_ch=1).to(device)
m.load_state_dict(torch.load(ckpt, map_location=device))
m.eval()
models.append(m)
print(f' loaded {ckpt} (base={base}, seed={seed})')
return models
def parse_specs(spec_strs):
out = []
for s in spec_strs:
parts = s.split(':')
path = parts[0]
base = int(parts[1])
seed = int(parts[2]) if len(parts) > 2 else 42
out.append((Path(path), base, seed))
return out
def filter_small_components(pred, min_area=200):
n_fg, lab_fg, stats_fg, _ = cv2.connectedComponentsWithStats(pred, connectivity=8)
out = pred.copy()
for i in range(1, n_fg):
if stats_fg[i, cv2.CC_STAT_AREA] < min_area:
out[lab_fg == i] = 0
inv = 1 - out
n_bg, lab_bg, stats_bg, _ = cv2.connectedComponentsWithStats(inv, connectivity=8)
for i in range(1, n_bg):
if stats_bg[i, cv2.CC_STAT_AREA] < min_area:
out[lab_bg == i] = 1
return out
def postprocess(pred, min_area=200, close_ks=9):
if close_ks > 1:
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (close_ks, close_ks))
pred = cv2.morphologyEx(pred, cv2.MORPH_CLOSE, kernel)
pred = filter_small_components(pred, min_area)
return pred
def predict_test1(models, device):
palette = load_palette()
predictor = load_threshold_predictor()
if predictor is not None:
print(f'Using learned per-image threshold predictor (clip={predictor['clip']})')
test_pairs = list_test_pairs()
TEST_PRED.mkdir(parents=True, exist_ok=True)
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
for stem, img_p, sc_p in test_pairs:
img = np.array(Image.open(img_p).convert('RGB'))
sc = np.array(Image.open(sc_p).convert('L'))
img_r = cv2.resize(img, (TRAIN_W, TRAIN_H), interpolation=cv2.INTER_LINEAR)
sc_r = cv2.resize(sc, (TRAIN_W, TRAIN_H), interpolation=cv2.INTER_NEAREST)
img_f = (img_r.astype(np.float32) / 255.0 - mean) / std
x = torch.cat([torch.from_numpy(img_f.transpose(2, 0, 1)), torch.from_numpy(encode_scribble(sc_r))], 0).unsqueeze(0).to(device)
prob_sum = None
for m in models:
p = tta_predict(m, x, device, scales=(0.7, 1.0, 1.3))
prob_sum = p if prob_sum is None else prob_sum + p
prob = prob_sum / len(models)
prob_full = cv2.resize(prob, (ORIG_W, ORIG_H), interpolation=cv2.INTER_LINEAR)
thresh = predict_threshold(prob_full, sc, predictor)
pred = (prob_full > thresh).astype(np.uint8)
pred = postprocess(pred, min_area=200, close_ks=9)
pred[sc == 0] = 0
pred[sc == 1] = 1
out_img = Image.fromarray(pred.astype(np.uint8), mode='P')
out_img.putpalette(palette)
out_img.save(TEST_PRED / f'{stem}.png')
print(f'Wrote {len(test_pairs)} predictions to {TEST_PRED}')
def eval_oof_ensemble(ckpt_specs, folds, seed, device, save=False):
pairs = list_train_pairs()
def fold_assignment(seed_):
rng = np.random.RandomState(seed_)
idx = np.arange(len(pairs))
rng.shuffle(idx)
fold_arr_ = np.array_split(idx, folds)
return {ii: k for k in range(folds) for ii in fold_arr_[k]}
grouped = []
for ckpt_dir, base, ckpt_seed in ckpt_specs:
fold_models = {}
for fd in sorted(Path(ckpt_dir).glob('fold_*')):
ckpt = fd / 'best.pth'
if not ckpt.exists():
continue
k = int(fd.name.split('_')[1])
m = UNet(in_ch=5, base=base, out_ch=1).to(device)
m.load_state_dict(torch.load(ckpt, map_location=device))
m.eval()
fold_models[k] = m
fold_of_seed = fold_assignment(ckpt_seed)
grouped.append((fold_models, fold_of_seed))
print(f' {ckpt_dir} has folds: {sorted(fold_models)} (seed={ckpt_seed})')
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
train_pred_dir = Path('dataset/train/predictions')
if save:
train_pred_dir.mkdir(exist_ok=True)
palette = load_palette()
predictor = load_threshold_predictor()
if predictor is not None:
print(f'Using learned per-image threshold predictor')
all_p, all_g = ([], [])
for i, (stem, img_p, sc_p, gt_p) in enumerate(pairs):
ensemble_models = []
for fold_models, fold_of_seed in grouped:
k_dir = fold_of_seed[i]
if k_dir in fold_models:
ensemble_models.append(fold_models[k_dir])
if not ensemble_models:
continue
img = np.array(Image.open(img_p).convert('RGB'))
sc = np.array(Image.open(sc_p).convert('L'))
gt = (np.array(Image.open(gt_p)) > 0).astype(np.uint8)
img_r = cv2.resize(img, (TRAIN_W, TRAIN_H), interpolation=cv2.INTER_LINEAR)
sc_r = cv2.resize(sc, (TRAIN_W, TRAIN_H), interpolation=cv2.INTER_NEAREST)
img_f = (img_r.astype(np.float32) / 255.0 - mean) / std
x = torch.cat([torch.from_numpy(img_f.transpose(2, 0, 1)), torch.from_numpy(encode_scribble(sc_r))], 0).unsqueeze(0).to(device)
prob_sum = None
for m in ensemble_models:
p = tta_predict(m, x, device, scales=(0.7, 1.0, 1.3))
prob_sum = p if prob_sum is None else prob_sum + p
prob = prob_sum / len(ensemble_models)
prob_full = cv2.resize(prob, (ORIG_W, ORIG_H), interpolation=cv2.INTER_LINEAR)
thresh = predict_threshold(prob_full, sc, predictor)
pred = (prob_full > thresh).astype(np.uint8)
pred = postprocess(pred, min_area=200, close_ks=9)
pred[sc == 0] = 0
pred[sc == 1] = 1
all_p.append(pred)
all_g.append(gt)
if save:
out_img = Image.fromarray(pred.astype(np.uint8), mode='P')
out_img.putpalette(palette)
out_img.save(train_pred_dir / f'{stem}.png')
bg, fg, miou = evaluate_predictions(all_p, all_g)
print(f'OOF ensemble: bg={bg:.4f} fg={fg:.4f} mIoU={miou:.4f} (n={len(all_p)})')
if save:
print(f'Saved {len(all_p)} OOF predictions to {train_pred_dir}')
return miou
def main():
p = argparse.ArgumentParser()
p.add_argument('--ckpt-dirs', nargs='+', required=True, help="One or more 'path:base' entries, e.g. 'runs_global_unet:48 runs_v2:64'")
p.add_argument('--gpu', type=int, default=0)
p.add_argument('--eval', action='store_true', help='Evaluate out-of-fold ensemble on training set instead of predicting test1')
p.add_argument('--save', action='store_true', help='With --eval, also save predictions to dataset/train/predictions/')
p.add_argument('--folds', type=int, default=5)
p.add_argument('--seed', type=int, default=42)
args = p.parse_args()
device = torch.device(f'cuda:{args.gpu}' if torch.cuda.is_available() else 'cpu')
ckpt_specs = parse_specs(args.ckpt_dirs)
if args.eval:
eval_oof_ensemble(ckpt_specs, args.folds, args.seed, device, save=args.save)
else:
models = load_models(ckpt_specs, device)
if not models:
print('No models found.')
return
print(f'Total models in ensemble: {len(models)}')
predict_test1(models, device)
if __name__ == '__main__':
main() |