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3be9472 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 | from pathlib import Path
from shutil import rmtree
import cv2
import hydra
import numpy as np
import torch
from omegaconf import DictConfig
from torchvision.ops import box_iou
from tqdm import tqdm
from src.dl.dataset import Loader, parse_yolo_label_file
from src.dl.utils import (
abs_xyxy_to_norm_xywh,
get_latest_experiment_name,
norm_xywh_to_abs_xyxy,
vis_one_box,
)
from src.infer.torch_model import Torch_model
def norm_xywh_to_xyxy(norm_boxes: torch.Tensor) -> torch.Tensor:
# boxes: [N,4] in (cx, cy, w, h), all in [0,1]
cx, cy, w, h = norm_boxes.unbind(-1)
x1 = cx - w / 2
y1 = cy - h / 2
x2 = cx + w / 2
y2 = cy + h / 2
return torch.stack([x1, y1, x2, y2], dim=-1).clamp(0, 1)
def save_case(
case_type: str,
img: np.ndarray,
abs_box: torch.Tensor, # [4] absolute xyxy
label: int,
score: float | None,
img_path: Path,
output_dir: Path,
label_to_name: dict,
):
# Draw and save image + single-line YOLO txt with the corresponding box
out_img_dir = output_dir / case_type
out_img_dir.mkdir(parents=True, exist_ok=True)
mode = "pred" if case_type == "FPs" else "gt"
draw = img.copy()
vis_one_box(
draw,
abs_box,
int(label),
mode=mode,
label_to_name=label_to_name,
score=score if score is not None else None,
)
cv2.imwrite(str(out_img_dir / f"{Path(img_path).stem}.jpg"), draw)
def check_results(
img,
img_path,
preds,
targets,
iou_thresh: float,
conf_thresh: float,
output_dir: Path,
label_to_name: dict,
):
"""
Match predictions to targets:
- same class
- IoU >= iou_thresh (computed on normalized xyxy)
- pred score >= conf_thresh
Save unmatched predictions as FP and unmatched targets as FN.
"""
H, W = img.shape[:2]
# Prepare tensors
if len(preds) == 0:
pred_boxes_xyxy_abs = torch.zeros((0, 4), dtype=torch.float32)
pred_boxes_norm_xywh = torch.zeros((0, 4), dtype=torch.float32)
pred_labels = torch.zeros((0,), dtype=torch.long)
pred_scores = torch.zeros((0,), dtype=torch.float32)
else:
pred_boxes_xyxy_abs = torch.as_tensor(preds["boxes"], dtype=torch.float32) # abs xyxy
pred_boxes_norm_xywh = torch.as_tensor(
preds["norm_boxes"], dtype=torch.float32
) # norm xywh
pred_labels = torch.as_tensor(preds["labels"], dtype=torch.long)
pred_scores = torch.as_tensor(preds["scores"], dtype=torch.float32)
if len(targets["boxes"]) == 0:
tgt_boxes_norm_xywh = torch.zeros((0, 4), dtype=torch.float32)
tgt_labels = torch.zeros((0,), dtype=torch.long)
else:
tgt_boxes_norm_xywh = torch.as_tensor(targets["boxes"], dtype=torch.float32) # norm xywh
tgt_labels = torch.as_tensor(targets["labels"], dtype=torch.long)
# filter by conf_thresh
keep = pred_scores >= conf_thresh
pred_boxes_xyxy_abs = pred_boxes_xyxy_abs[keep]
pred_boxes_norm_xywh = pred_boxes_norm_xywh[keep]
pred_labels = pred_labels[keep]
pred_scores = pred_scores[keep]
# Early outs
if pred_boxes_norm_xywh.numel() == 0 and tgt_boxes_norm_xywh.numel() == 0:
return
if tgt_boxes_norm_xywh.numel() == 0:
# All kept preds become FP
for i in range(len(pred_labels)):
save_case(
"FPs",
img,
pred_boxes_xyxy_abs[i],
int(pred_labels[i]),
float(pred_scores[i]),
img_path,
output_dir,
label_to_name,
)
return
if pred_boxes_norm_xywh.numel() == 0:
# All targets become FN
tgt_abs_xyxy = norm_xywh_to_abs_xyxy(tgt_boxes_norm_xywh, H, W)
for j in range(len(tgt_labels)):
save_case(
"FNs",
img,
tgt_abs_xyxy[j],
int(tgt_labels[j]),
0,
img_path,
output_dir,
label_to_name,
)
return
# Compute IoU on normalized xyxy (scale doesn't matter)
pred_xyxy_norm = norm_xywh_to_xyxy(pred_boxes_norm_xywh) # [Np,4]
tgt_xyxy_norm = norm_xywh_to_xyxy(tgt_boxes_norm_xywh) # [Nt,4]
ious = box_iou(pred_xyxy_norm, tgt_xyxy_norm) # [Np,Nt]
# Greedy one-to-one matching by pred score
Np, Nt = ious.shape
tgt_matched = torch.zeros(Nt, dtype=torch.bool)
pred_matched = torch.zeros(Np, dtype=torch.bool)
order = torch.argsort(pred_scores, descending=True)
for pi in order.tolist():
# mask by class and unmatched targets
same_cls = tgt_labels == pred_labels[pi]
cand = (~tgt_matched) & same_cls & (ious[pi] >= iou_thresh)
if cand.any():
# pick best IoU target among candidates
ti = torch.argmax(ious[pi] * cand.float()).item()
tgt_matched[ti] = True
pred_matched[pi] = True
# Save FPs and FNs
for pi in torch.where(~pred_matched)[0].tolist():
save_case(
"FPs",
img,
pred_boxes_xyxy_abs[pi],
int(pred_labels[pi]),
float(pred_scores[pi]),
img_path,
output_dir,
label_to_name,
)
tgt_abs_xyxy = norm_xywh_to_abs_xyxy(tgt_boxes_norm_xywh, H, W)
for ti in torch.where(~tgt_matched)[0].tolist():
save_case(
"FNs",
img,
tgt_abs_xyxy[ti],
int(tgt_labels[ti]),
0,
img_path,
output_dir,
label_to_name,
)
def run(model, train_loader, val_loader, cfg: DictConfig) -> None:
output_dir = Path(cfg.train.infer_path).parent / "check_errors"
if output_dir.exists():
rmtree(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
for loader in [train_loader, val_loader]:
split_name = loader.dataset.mode
for batch in tqdm(loader, desc=f"Processing {split_name}"):
_, _, paths = batch
img = cv2.imread(Path(cfg.train.data_path) / "images" / paths[0])
label_path = Path(cfg.train.data_path) / "labels" / paths[0].with_suffix(".txt")
targets = [{"boxes": [], "labels": []}]
if label_path.exists() and label_path.stat().st_size > 1:
targets_raw, _ = parse_yolo_label_file(label_path)
targets = [
{
"boxes": np.array(targets_raw[:, 1:5], dtype=np.float32),
"labels": np.array(targets_raw[:, 0], dtype=np.int64),
}
]
preds = model(img)
for pred in preds:
# Model returns torch tensors, convert to cpu for processing
pred["boxes"] = pred["boxes"].cpu()
pred["labels"] = pred["labels"].cpu()
pred["scores"] = pred["scores"].cpu()
pred["norm_boxes"] = abs_xyxy_to_norm_xywh(
pred["boxes"], img.shape[0], img.shape[1]
)
check_results(
img=img,
img_path=paths[0],
preds=preds[0],
targets=targets[0],
iou_thresh=cfg.train.iou_thresh,
conf_thresh=cfg.train.conf_thresh,
output_dir=output_dir,
label_to_name=cfg.train.label_to_name,
)
@hydra.main(version_base=None, config_path="../../", config_name="config")
def main(cfg: DictConfig) -> None:
cfg.exp = get_latest_experiment_name(cfg.exp, cfg.train.path_to_save)
model = Torch_model(
model_name=cfg.model_name,
model_path=Path(cfg.train.path_to_save) / "model.pt",
n_outputs=len(cfg.train.label_to_name),
input_width=cfg.train.img_size[1],
input_height=cfg.train.img_size[0],
conf_thresh=cfg.train.conf_thresh,
rect=cfg.export.dynamic_input,
keep_ratio=cfg.train.keep_ratio,
use_nms=True, # to remove duplocated boxes on 1 GT object and not show them as FPs
)
base_loader = Loader(
root_path=Path(cfg.train.data_path),
img_size=tuple(cfg.train.img_size),
batch_size=1,
num_workers=cfg.train.num_workers,
cfg=cfg,
debug_img_processing=cfg.train.debug_img_processing,
)
train_loader, val_loader, _ = base_loader.build_dataloaders()
run(model, train_loader, val_loader, cfg)
if __name__ == "__main__":
main()
|