(minor refactor) os.path to pathlib and tdqm to infer.py

.gitignore

@@ -123,3 +123,10 @@ dataset/
 
 #runs 
 runs/
+*.pt
+
+#exports
+exports/
+*.onnx
+*.trt
+*.nnx

infer.py

@@ -10,8 +10,10 @@ import cv2
 import torch
 import torch.nn.functional as F
 from torch.amp import autocast
+from tqdm import tqdm
 
 from src.wireseghr.model import WireSegHR
+from pathlib import Path
 
 
 def _pad_for_minmax(kernel: int) -> Tuple[int, int, int, int]:
@@ -179,7 +181,7 @@ def infer_image(
     save_prob: bool = False,
     prob_thresh: Optional[float] = None,
 ) -> Tuple[np.ndarray, np.ndarray]:
-    assert os.path.isfile(img_path), f"Image not found: {img_path}"
+    assert Path(img_path).is_file(), f"Image not found: {img_path}"
     bgr = cv2.imread(img_path, cv2.IMREAD_COLOR)
     assert bgr is not None, f"Failed to read {img_path}"
     rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
@@ -223,11 +225,11 @@ def infer_image(
 
     if out_dir is not None:
         os.makedirs(out_dir, exist_ok=True)
-        stem = os.path.splitext(os.path.basename(img_path))[0]
-        out_mask = os.path.join(out_dir, f"{stem}_pred.png")
-        cv2.imwrite(out_mask, pred)
+        stem = Path(img_path).stem
+        out_mask = Path(out_dir) / f"{stem}_pred.png"
+        cv2.imwrite(str(out_mask), pred)
         if save_prob:
-            out_prob = os.path.join(out_dir, f"{stem}_prob.npy")
+            out_prob = Path(out_dir) / f"{stem}_prob.npy"
             np.save(out_prob, prob_f.detach().cpu().float().numpy())
 
     # Return numpy arrays for external consumers, computed via torch
@@ -298,8 +300,8 @@ def main():
     args = parser.parse_args()
 
     cfg_path = args.config
-    if not os.path.isabs(cfg_path):
-        cfg_path = os.path.join(os.getcwd(), cfg_path)
+    if not Path(cfg_path).is_absolute():
+        cfg_path = str(Path.cwd() / cfg_path)
 
     with open(cfg_path, "r") as f:
         cfg = yaml.safe_load(f)
@@ -327,7 +329,7 @@ def main():
 
     ckpt_path = args.ckpt if args.ckpt else cfg.get("resume", "")
     if ckpt_path:
-        assert os.path.isfile(ckpt_path), f"Checkpoint not found: {ckpt_path}"
+        assert Path(ckpt_path).is_file(), f"Checkpoint not found: {ckpt_path}"
         print(f"[WireSegHR][infer] Loading checkpoint: {ckpt_path}")
         state = torch.load(ckpt_path, map_location=device)
         model.load_state_dict(state["model"])
@@ -339,7 +341,7 @@ def main():
             bench_dir = args.bench_images_dir
         else:
             bench_dir = cfg["data"]["test_images"]
-        assert os.path.isdir(bench_dir), f"Not a directory: {bench_dir}"
+        assert Path(bench_dir).is_dir(), f"Not a directory: {bench_dir}"
 
         size_filter: Optional[Tuple[int, int]] = None
         if args.bench_size_filter:
@@ -353,7 +355,7 @@ def main():
 
         img_files = sorted(
             [
-                os.path.join(bench_dir, p)
+                str(Path(bench_dir) / p)
                 for p in os.listdir(bench_dir)
                 if p.lower().endswith((".jpg", ".jpeg"))
             ]
@@ -386,7 +388,7 @@ def main():
         timings: List[Dict[str, Any]] = []
 
         # Warmup
-        for i in range(min(args.bench_warmup, len(img_files))):
+        for i in tqdm(range(min(args.bench_warmup, len(img_files))), desc="[bench] Warmup"):
             _ = infer_image(
                 model,
                 img_files[i],
@@ -399,7 +401,7 @@ def main():
             )
 
         # Timed runs
-        for p in img_files[args.bench_warmup :]:
+        for p in tqdm(img_files[args.bench_warmup :], desc="[bench] Timed"):
             # Replicate internals to time coarse vs fine separately
             bgr = cv2.imread(p, cv2.IMREAD_COLOR)
             assert bgr is not None, f"Failed to read {p}"
@@ -487,7 +489,9 @@ def main():
                 },
                 "per_image": timings,
             }
-            with open(args.bench_report_json, "w") as f:
+            report_path = args.bench_report_json
+            Path(report_path).parent.mkdir(parents=True, exist_ok=True)
+            with open(report_path, "w") as f:
                 json.dump(report, f, indent=2)
 
         return
@@ -508,14 +512,14 @@ def main():
 
     # Directory mode
     img_dir = args.images_dir
-    assert os.path.isdir(img_dir), f"Not a directory: {img_dir}"
+    assert Path(img_dir).is_dir(), f"Not a directory: {img_dir}"
     img_files = sorted(
         [p for p in os.listdir(img_dir) if p.lower().endswith((".jpg", ".jpeg"))]
     )
     assert len(img_files) > 0, f"No .jpg/.jpeg in {img_dir}"
     os.makedirs(args.out, exist_ok=True)
-    for name in img_files:
-        path = os.path.join(img_dir, name)
+    for name in tqdm(img_files, desc="[infer] Dir"):
+        path = str(Path(img_dir) / name)
         infer_image(
             model,
             path,

outputs/bench_report.json

@@ -0,0 +1,1332 @@
+{
+  "summary": {
+    "avg_ms": 546.1088118905371,
+    "p50_ms": 472.0355123281479,
+    "p95_ms": 1127.635708078742,
+    "avg_coarse_ms": 50.732872945566974,
+    "avg_fine_ms": 495.37593894497013,
+    "images": 165
+  },
+  "per_image": [
+    {
+      "path": "dataset/test/images/1035.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 45.59382610023022,
+      "t_fine_ms": 439.33012057095766,
+      "t_total_ms": 484.9239466711879
+    },
+    {
+      "path": "dataset/test/images/1044.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 45.9147822111845,
+      "t_fine_ms": 424.12184551358223,
+      "t_total_ms": 470.03662772476673
+    },
+    {
+      "path": "dataset/test/images/1084.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 49.31220877915621,
+      "t_fine_ms": 439.04411140829325,
+      "t_total_ms": 488.35632018744946
+    },
+    {
+      "path": "dataset/test/images/1094.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 43.984285555779934,
+      "t_fine_ms": 424.1811977699399,
+      "t_total_ms": 468.16548332571983
+    },
+    {
+      "path": "dataset/test/images/1099.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.372158125042915,
+      "t_fine_ms": 424.29234832525253,
+      "t_total_ms": 468.66450645029545
+    },
+    {
+      "path": "dataset/test/images/1100.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.085255824029446,
+      "t_fine_ms": 424.2294393479824,
+      "t_total_ms": 468.31469517201185
+    },
+    {
+      "path": "dataset/test/images/1104.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.348194263875484,
+      "t_fine_ms": 424.35442470014095,
+      "t_total_ms": 468.70261896401644
+    },
+    {
+      "path": "dataset/test/images/1117.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.24700140953064,
+      "t_fine_ms": 424.39749650657177,
+      "t_total_ms": 468.6444979161024
+    },
+    {
+      "path": "dataset/test/images/1131.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 46.00091464817524,
+      "t_fine_ms": 439.06145449727774,
+      "t_total_ms": 485.062369145453
+    },
+    {
+      "path": "dataset/test/images/1134.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.08291168510914,
+      "t_fine_ms": 424.22800697386265,
+      "t_total_ms": 468.3109186589718
+    },
+    {
+      "path": "dataset/test/images/1141.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.215052388608456,
+      "t_fine_ms": 436.8190476670861,
+      "t_total_ms": 481.0341000556946
+    },
+    {
+      "path": "dataset/test/images/115.jpg",
+      "H": 3024,
+      "W": 4032,
+      "t_coarse_ms": 48.61736670136452,
+      "t_fine_ms": 571.8103954568505,
+      "t_total_ms": 620.427762158215
+    },
+    {
+      "path": "dataset/test/images/1150.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.28734816610813,
+      "t_fine_ms": 424.50483050197363,
+      "t_total_ms": 468.79217866808176
+    },
+    {
+      "path": "dataset/test/images/1162.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.25444547086954,
+      "t_fine_ms": 424.3017351254821,
+      "t_total_ms": 468.5561805963516
+    },
+    {
+      "path": "dataset/test/images/1183.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 50.017811357975006,
+      "t_fine_ms": 424.20240864157677,
+      "t_total_ms": 474.2202199995518
+    },
+    {
+      "path": "dataset/test/images/1196.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.7112787514925,
+      "t_fine_ms": 424.5493831112981,
+      "t_total_ms": 469.2606618627906
+    },
+    {
+      "path": "dataset/test/images/1198.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 46.2280735373497,
+      "t_fine_ms": 424.4956923648715,
+      "t_total_ms": 470.7237659022212
+    },
+    {
+      "path": "dataset/test/images/1207.jpg",
+      "H": 2160,
+      "W": 3840,
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+      "t_fine_ms": 425.1869274303317,
+      "t_total_ms": 469.48768105357885
+    },
+    {
+      "path": "dataset/test/images/1210.jpg",
+      "H": 2160,
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+      "t_fine_ms": 424.6001597493887,
+      "t_total_ms": 468.9696133136749
+    },
+    {
+      "path": "dataset/test/images/1221.jpg",
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+      "t_total_ms": 469.07276660203934
+    },
+    {
+      "path": "dataset/test/images/1222.jpg",
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+      "t_fine_ms": 424.90849178284407,
+      "t_total_ms": 471.1316153407097
+    },
+    {
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+      "t_total_ms": 473.3728300780058
+    },
+    {
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+      "t_total_ms": 470.90417612344027
+    },
+    {
+      "path": "dataset/test/images/1248.jpg",
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+      "t_total_ms": 469.189559109509
+    },
+    {
+      "path": "dataset/test/images/1267.jpg",
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+      "t_fine_ms": 424.8329382389784,
+      "t_total_ms": 470.80390620976686
+    },
+    {
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+      "t_fine_ms": 425.51194224506617,
+      "t_total_ms": 469.7816567495465
+    },
+    {
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+      "t_total_ms": 469.18580029159784
+    },
+    {
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+      "t_total_ms": 471.58810775727034
+    },
+    {
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+      "t_total_ms": 492.99274664372206
+    },
+    {
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+      "t_fine_ms": 342.8378105163574,
+      "t_total_ms": 385.9982807189226
+    },
+    {
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+      "t_total_ms": 479.2163157835603
+    },
+    {
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+    },
+    {
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+    },
+    {
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+    },
+    {
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+      "t_total_ms": 469.3173002451658
+    },
+    {
+      "path": "dataset/test/images/1355.jpg",
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+      "t_fine_ms": 424.78822357952595,
+      "t_total_ms": 469.2654004320502
+    },
+    {
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+      "t_fine_ms": 571.7228697612882,
+      "t_total_ms": 622.5696168839931
+    },
+    {
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+      "W": 3840,
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+      "t_total_ms": 477.8418717905879
+    },
+    {
+      "path": "dataset/test/images/1386.jpg",
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+      "t_total_ms": 471.3904447853565
+    },
+    {
+      "path": "dataset/test/images/1394.jpg",
+      "H": 2160,
+      "W": 3840,
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+      "t_fine_ms": 424.9263955280185,
+      "t_total_ms": 469.420344568789
+    },
+    {
+      "path": "dataset/test/images/1397.jpg",
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+      "t_fine_ms": 434.18938014656305,
+      "t_total_ms": 478.8962919265032
+    },
+    {
+      "path": "dataset/test/images/14.jpg",
+      "H": 6000,
+      "W": 4000,
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+      "t_fine_ms": 1047.5239856168628,
+      "t_total_ms": 1141.731470823288
+    },
+    {
+      "path": "dataset/test/images/1400.jpg",
+      "H": 2160,
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+      "t_fine_ms": 437.71414924412966,
+      "t_total_ms": 490.55388011038303
+    },
+    {
+      "path": "dataset/test/images/1403.jpg",
+      "H": 2160,
+      "W": 3840,
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+      "t_fine_ms": 425.45598559081554,
+      "t_total_ms": 471.58111445605755
+    },
+    {
+      "path": "dataset/test/images/1410.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.58910785615444,
+      "t_fine_ms": 425.4433121532202,
+      "t_total_ms": 470.0324200093746
+    },
+    {
+      "path": "dataset/test/images/143.jpg",
+      "H": 3024,
+      "W": 4032,
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+      "t_fine_ms": 576.6453053802252,
+      "t_total_ms": 627.6354445144534
+    },
+    {
+      "path": "dataset/test/images/1430.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 49.46706164628267,
+      "t_fine_ms": 440.50635304301977,
+      "t_total_ms": 489.97341468930244
+    },
+    {
+      "path": "dataset/test/images/1437.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.33557949960232,
+      "t_fine_ms": 425.30061211436987,
+      "t_total_ms": 469.6361916139722
+    },
+    {
+      "path": "dataset/test/images/1438.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.27209869027138,
+      "t_fine_ms": 425.7898461073637,
+      "t_total_ms": 470.0619447976351
+    },
+    {
+      "path": "dataset/test/images/1443.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 45.066650956869125,
+      "t_fine_ms": 425.2707865089178,
+      "t_total_ms": 470.33743746578693
+    },
+    {
+      "path": "dataset/test/images/1467.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.44928374141455,
+      "t_fine_ms": 425.61439611017704,
+      "t_total_ms": 470.0636798515916
+    },
+    {
+      "path": "dataset/test/images/147.jpg",
+      "H": 5120,
+      "W": 3840,
+      "t_coarse_ms": 60.642157681286335,
+      "t_fine_ms": 854.3515391647816,
+      "t_total_ms": 914.9936968460679
+    },
+    {
+      "path": "dataset/test/images/1471.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 51.92858260124922,
+      "t_fine_ms": 430.7840187102556,
+      "t_total_ms": 482.71260131150484
+    },
+    {
+      "path": "dataset/test/images/1477.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.527141377329826,
+      "t_fine_ms": 425.5507346242666,
+      "t_total_ms": 470.07787600159645
+    },
+    {
+      "path": "dataset/test/images/1494.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.46072597056627,
+      "t_fine_ms": 425.22873543202877,
+      "t_total_ms": 469.68946140259504
+    },
+    {
+      "path": "dataset/test/images/1495.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.32813450694084,
+      "t_fine_ms": 425.8275981992483,
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+      "t_fine_ms": 425.986448302865,
+      "t_total_ms": 472.75817953050137
+    },
+    {
+      "path": "dataset/test/images/66.jpg",
+      "H": 6000,
+      "W": 4000,
+      "t_coarse_ms": 73.23215808719397,
+      "t_fine_ms": 1049.5763514190912,
+      "t_total_ms": 1122.8085095062852
+    },
+    {
+      "path": "dataset/test/images/665.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 54.276746697723866,
+      "t_fine_ms": 450.46883821487427,
+      "t_total_ms": 504.74558491259813
+    },
+    {
+      "path": "dataset/test/images/690.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.47704553604126,
+      "t_fine_ms": 442.30098370462656,
+      "t_total_ms": 486.7780292406678
+    },
+    {
+      "path": "dataset/test/images/697.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.456567615270615,
+      "t_fine_ms": 430.75701780617237,
+      "t_total_ms": 475.213585421443
+    },
+    {
+      "path": "dataset/test/images/705.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 46.252891421318054,
+      "t_fine_ms": 426.39773432165384,
+      "t_total_ms": 472.6506257429719
+    },
+    {
+      "path": "dataset/test/images/728.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.85049098730087,
+      "t_fine_ms": 426.2953819707036,
+      "t_total_ms": 471.1458729580045
+    },
+    {
+      "path": "dataset/test/images/736.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 46.267238445580006,
+      "t_fine_ms": 426.3408873230219,
+      "t_total_ms": 472.6081257686019
+    },
+    {
+      "path": "dataset/test/images/741.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.61097065359354,
+      "t_fine_ms": 426.5766916796565,
+      "t_total_ms": 471.18766233325005
+    },
+    {
+      "path": "dataset/test/images/742.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.77288480848074,
+      "t_fine_ms": 431.0416569933295,
+      "t_total_ms": 475.81454180181026
+    },
+    {
+      "path": "dataset/test/images/748.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 46.498934738337994,
+      "t_fine_ms": 426.042053848505,
+      "t_total_ms": 472.540988586843
+    },
+    {
+      "path": "dataset/test/images/759.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.73839979618788,
+      "t_fine_ms": 425.9616816416383,
+      "t_total_ms": 470.70008143782616
+    },
+    {
+      "path": "dataset/test/images/776.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.587114825844765,
+      "t_fine_ms": 426.11830681562424,
+      "t_total_ms": 470.705421641469
+    },
+    {
+      "path": "dataset/test/images/778.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.326301664114,
+      "t_fine_ms": 426.18415132164955,
+      "t_total_ms": 470.51045298576355
+    },
+    {
+      "path": "dataset/test/images/779.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.7318684309721,
+      "t_fine_ms": 426.0721895843744,
+      "t_total_ms": 470.8040580153465
+    },
+    {
+      "path": "dataset/test/images/782.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 46.11456021666527,
+      "t_fine_ms": 426.14081874489784,
+      "t_total_ms": 472.2553789615631
+    },
+    {
+      "path": "dataset/test/images/789.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.51038967818022,
+      "t_fine_ms": 426.36278830468655,
+      "t_total_ms": 470.87317798286676
+    },
+    {
+      "path": "dataset/test/images/811.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.75479107350111,
+      "t_fine_ms": 425.8342310786247,
+      "t_total_ms": 470.58902215212584
+    },
+    {
+      "path": "dataset/test/images/818.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.49161421507597,
+      "t_fine_ms": 426.4873741194606,
+      "t_total_ms": 470.97898833453655
+    },
+    {
+      "path": "dataset/test/images/822.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.6783471852541,
+      "t_fine_ms": 425.7477568462491,
+      "t_total_ms": 470.4261040315032
+    },
+    {
+      "path": "dataset/test/images/860.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 46.14970646798611,
+      "t_fine_ms": 426.2132756412029,
+      "t_total_ms": 472.36298210918903
+    },
+    {
+      "path": "dataset/test/images/865.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.77647226303816,
+      "t_fine_ms": 426.045180298388,
+      "t_total_ms": 470.82165256142616
+    },
+    {
+      "path": "dataset/test/images/866.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.464342296123505,
+      "t_fine_ms": 433.3420917391777,
+      "t_total_ms": 477.8064340353012
+    },
+    {
+      "path": "dataset/test/images/878.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.68281473964453,
+      "t_fine_ms": 442.3503763973713,
+      "t_total_ms": 487.0331911370158
+    },
+    {
+      "path": "dataset/test/images/89.jpg",
+      "H": 4496,
+      "W": 3000,
+      "t_coarse_ms": 53.19163575768471,
+      "t_fine_ms": 577.3740643635392,
+      "t_total_ms": 630.5657001212239
+    },
+    {
+      "path": "dataset/test/images/920.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 51.456608809530735,
+      "t_fine_ms": 442.13218335062265,
+      "t_total_ms": 493.5887921601534
+    },
+    {
+      "path": "dataset/test/images/929.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 46.35206889361143,
+      "t_fine_ms": 426.0200811550021,
+      "t_total_ms": 472.37215004861355
+    },
+    {
+      "path": "dataset/test/images/94.jpg",
+      "H": 4613,
+      "W": 2595,
+      "t_coarse_ms": 49.010117538273335,
+      "t_fine_ms": 539.7742157801986,
+      "t_total_ms": 588.7843333184719
+    },
+    {
+      "path": "dataset/test/images/940.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 53.503746166825294,
+      "t_fine_ms": 445.8295265212655,
+      "t_total_ms": 499.3332726880908
+    },
+    {
+      "path": "dataset/test/images/941.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 46.16930242627859,
+      "t_fine_ms": 426.11860763281584,
+      "t_total_ms": 472.28791005909443
+    },
+    {
+      "path": "dataset/test/images/947.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 46.44507262855768,
+      "t_fine_ms": 425.8964592590928,
+      "t_total_ms": 472.3415318876505
+    },
+    {
+      "path": "dataset/test/images/959.jpg",
+      "H": 2160,
+      "W": 3840,
+      "t_coarse_ms": 44.70463655889034,
+      "t_fine_ms": 426.38475075364113,
+      "t_total_ms": 471.08938731253147
+    },
+    {
+      "path": "dataset/test/images/97.jpg",
+      "H": 6000,
+      "W": 4000,
+      "t_coarse_ms": 93.69281399995089,
+      "t_fine_ms": 1047.9462426155806,
+      "t_total_ms": 1141.6390566155314
+    }
+  ]
+}

scripts/export_onnx_trt.py

@@ -9,6 +9,7 @@ import torch
 import tensorrt as trt
 
 from src.wireseghr.model import WireSegHR
+from pathlib import Path
 
 
 class CoarseModule(torch.nn.Module):
@@ -63,13 +64,13 @@ def main():
 
     ckpt_path = args.ckpt if args.ckpt else cfg.get("resume", "")
     if ckpt_path:
-        assert os.path.isfile(ckpt_path), f"Checkpoint not found: {ckpt_path}"
+        assert Path(ckpt_path).is_file(), f"Checkpoint not found: {ckpt_path}"
         print(f"[export] Loading checkpoint: {ckpt_path}")
         state = torch.load(ckpt_path, map_location=device)
         model.load_state_dict(state["model"])  # expects dict with key 'model'
     model.eval()
 
-    os.makedirs(args.out_dir, exist_ok=True)
+    Path(args.out_dir).mkdir(parents=True, exist_ok=True)
 
     # Prepare dummy inputs (static shapes for best TRT performance)
     coarse_in = torch.randn(1, 6, args.coarse_size, args.coarse_size, device=device)
@@ -77,12 +78,12 @@ def main():
 
     # Coarse export
     coarse_wrapper = CoarseModule(model).to(device).eval()
-    coarse_onnx = os.path.join(args.out_dir, f"wireseghr_coarse_{args.coarse_size}.onnx")
+    coarse_onnx = Path(args.out_dir) / f"wireseghr_coarse_{args.coarse_size}.onnx"
     print(f"[export] Exporting COARSE to {coarse_onnx}")
     torch.onnx.export(
         coarse_wrapper,
         coarse_in,
-        coarse_onnx,
+        str(coarse_onnx),
         export_params=True,
         opset_version=args.opset,
         do_constant_folding=True,
@@ -94,12 +95,12 @@ def main():
 
     # Fine export
     fine_wrapper = FineModule(model).to(device).eval()
-    fine_onnx = os.path.join(args.out_dir, f"wireseghr_fine_{args.fine_patch_size}.onnx")
+    fine_onnx = Path(args.out_dir) / f"wireseghr_fine_{args.fine_patch_size}.onnx"
     print(f"[export] Exporting FINE to {fine_onnx}")
     torch.onnx.export(
         fine_wrapper,
         fine_in,
-        fine_onnx,
+        str(fine_onnx),
         export_params=True,
         opset_version=args.opset,
         do_constant_folding=True,
@@ -111,8 +112,8 @@ def main():
     # Optional TensorRT building via trtexec; fallback to Python API if unavailable
     if args.build_trt:
         trtexec_path = args.trtexec if args.trtexec else shutil.which("trtexec")
-        coarse_engine = os.path.join(args.out_dir, f"wireseghr_coarse_{args.coarse_size}.engine")
-        fine_engine = os.path.join(args.out_dir, f"wireseghr_fine_{args.fine_patch_size}.engine")
+        coarse_engine = Path(args.out_dir) / f"wireseghr_coarse_{args.coarse_size}.engine"
+        fine_engine = Path(args.out_dir) / f"wireseghr_fine_{args.fine_patch_size}.engine"
         if trtexec_path:
             def build_engine_cli(onnx_path: str, engine_path: str):
                 print(f"[export] Building TRT engine (trtexec): {engine_path}")
@@ -125,8 +126,8 @@ def main():
                 ]
                 subprocess.run(cmd, check=True)
 
-            build_engine_cli(coarse_onnx, coarse_engine)
-            build_engine_cli(fine_onnx, fine_engine)
+            build_engine_cli(str(coarse_onnx), str(coarse_engine))
+            build_engine_cli(str(fine_onnx), str(fine_engine))
         else:
             print("[export] trtexec not found; building engines via TensorRT Python API")
 
@@ -135,7 +136,7 @@ def main():
                 builder = trt.Builder(logger)
                 network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
                 parser = trt.OnnxParser(network, logger)
-                with open(onnx_path, "rb") as f:
+                with open(str(onnx_path), "rb") as f:
                     data = f.read()
                 ok = parser.parse(data)
                 if not ok:
@@ -151,7 +152,7 @@ def main():
                 print(f"[export] Building TRT engine (Python): {engine_path}")
                 serialized = builder.build_serialized_network(network, config)
                 assert serialized is not None, "Failed to build TensorRT engine"
-                with open(engine_path, "wb") as f:
+                with open(str(engine_path), "wb") as f:
                     f.write(serialized)
 
             build_engine_py(coarse_onnx, coarse_engine)

scripts/pull_and_preprocess_wireseghr_dataset.py

@@ -46,12 +46,10 @@ def list_files_with_paths(drive, folder_id, prefix=""):
     }
     for file in drive.ListFile(params).GetList():
         if file["mimeType"] == "application/vnd.google-apps.folder":
-            sub_prefix = (
-                os.path.join(prefix, file["title"]) if prefix else file["title"]
-            )
+            sub_prefix = (f"{prefix}/{file['title']}" if prefix else file["title"]) 
             items += list_files_with_paths(drive, file["id"], sub_prefix)
         else:
-            rel_path = os.path.join(prefix, file["title"]) if prefix else file["title"]
+            rel_path = f"{prefix}/{file['title']}" if prefix else file["title"]
             size = int(file.get("fileSize", 0)) if "fileSize" in file else 0
             items.append(
                 {
@@ -67,7 +65,7 @@ def list_files_with_paths(drive, folder_id, prefix=""):
 
 def download_folder(folder_id, dest, service_account_json, workers: int):
     drive = authenticate(service_account_json)
-    os.makedirs(dest, exist_ok=True)
+    Path(dest).mkdir(parents=True, exist_ok=True)
 
     print(f"Listing files in folder {folder_id}...")
     files_with_paths = list_files_with_paths(drive, folder_id)
@@ -78,16 +76,16 @@ def download_folder(folder_id, dest, service_account_json, workers: int):
     tasks = []
     skipped = 0
     for meta in files_with_paths:
-        out_path = os.path.join(dest, meta["rel_path"])
-        os.makedirs(os.path.dirname(out_path), exist_ok=True)
+        out_path = Path(dest) / meta["rel_path"]
+        out_path.parent.mkdir(parents=True, exist_ok=True)
         if (
             meta["size"] > 0
-            and os.path.exists(out_path)
-            and os.path.getsize(out_path) == meta["size"]
+            and out_path.exists()
+            and out_path.stat().st_size == meta["size"]
         ):
             skipped += 1
             continue
-        tasks.append((meta["id"], out_path))
+        tasks.append((meta["id"], str(out_path)))
 
     print(f"Skipping {skipped} existing files; {len(tasks)} to download.")
 
@@ -213,7 +211,7 @@ def split_test_train_val(args=None):
             try:
                 if dst.exists() or dst.is_symlink():
                     dst.unlink()
-                os.symlink(src, dst)
+                os.symlink(str(src), str(dst))
             except FileExistsError:
                 pass
         else:  # copy
@@ -221,11 +219,11 @@ def split_test_train_val(args=None):
                 dst.unlink()
             # use hardlink if possible to be fast and space efficient
             try:
-                os.link(src, dst)
+                os.link(str(src), str(dst))
             except OSError:
                 import shutil
 
-                shutil.copy2(src, dst)
+                shutil.copy2(str(src), str(dst))
 
     for split_name, split_ids in (
         ("train", train_idx),

scripts/trt_infer.py

@@ -18,12 +18,13 @@ except Exception as e:  # pragma: no cover
     ) from e
 
 import yaml
+from pathlib import Path
 
 
 # ---- Utility: TRT engine wrapper ----
 class TrtEngine:
     def __init__(self, engine_path: str):
-        assert os.path.isfile(engine_path), f"Engine not found: {engine_path}"
+        assert Path(engine_path).is_file(), f"Engine not found: {engine_path}"
         logger = trt.Logger(trt.Logger.ERROR)
         with open(engine_path, 'rb') as f, trt.Runtime(logger) as runtime:
             self.engine = runtime.deserialize_cuda_engine(f.read())
@@ -270,7 +271,7 @@ def infer_image_trt(
     save_prob: bool = False,
     prob_thresh: Optional[float] = None,
 ) -> Tuple[np.ndarray, np.ndarray]:
-    assert os.path.isfile(img_path), f"Image not found: {img_path}"
+    assert Path(img_path).is_file(), f"Image not found: {img_path}"
     bgr = cv2.imread(img_path, cv2.IMREAD_COLOR)
     assert bgr is not None, f"Failed to read {img_path}"
     rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
@@ -301,13 +302,13 @@ def infer_image_trt(
     pred = (prob_f > prob_thresh).astype(np.uint8) * 255
 
     if out_dir is not None:
-        os.makedirs(out_dir, exist_ok=True)
-        stem = os.path.splitext(os.path.basename(img_path))[0]
-        out_mask = os.path.join(out_dir, f"{stem}_pred.png")
-        cv2.imwrite(out_mask, pred)
+        Path(out_dir).mkdir(parents=True, exist_ok=True)
+        stem = Path(img_path).stem
+        out_mask = Path(out_dir) / f"{stem}_pred.png"
+        cv2.imwrite(str(out_mask), pred)
         if save_prob:
-            out_prob = os.path.join(out_dir, f"{stem}_prob.npy")
-            np.save(out_prob, prob_f.astype(np.float32))
+            out_prob = Path(out_dir) / f"{stem}_prob.npy"
+            np.save(str(out_prob), prob_f.astype(np.float32))
 
     return pred, prob_f
 
@@ -341,7 +342,7 @@ def main():
 
     if args.benchmark:
         bench_dir = args.bench_images_dir or cfg["data"]["test_images"]
-        assert os.path.isdir(bench_dir), f"Not a directory: {bench_dir}"
+        assert Path(bench_dir).is_dir(), f"Not a directory: {bench_dir}"
         size_filter: Optional[Tuple[int, int]] = None
         if args.bench_size_filter:
             try:
@@ -353,7 +354,7 @@ def main():
                 )
         img_files = sorted(
             [
-                os.path.join(bench_dir, p)
+                str(Path(bench_dir) / p)
                 for p in os.listdir(bench_dir)
                 if p.lower().endswith((".jpg", ".jpeg"))
             ]
@@ -473,12 +474,12 @@ def main():
         return
 
     img_dir = args.images_dir
-    assert os.path.isdir(img_dir)
-    os.makedirs(args.out, exist_ok=True)
+    assert Path(img_dir).is_dir()
+    Path(args.out).mkdir(parents=True, exist_ok=True)
     img_files = sorted([p for p in os.listdir(img_dir) if p.lower().endswith((".jpg", ".jpeg"))])
     assert len(img_files) > 0
     for name in img_files:
-        p = os.path.join(img_dir, name)
+        p = str(Path(img_dir) / name)
         infer_image_trt(coarse, fine, p, cfg, out_dir=args.out, save_prob=args.save_prob)
     print("[TRT][infer] Done.")
 

train.py

@@ -24,6 +24,7 @@ from src.wireseghr.model.label_downsample import downsample_label_maxpool
 from src.wireseghr.data.sampler import BalancedPatchSampler
 from src.wireseghr.metrics import compute_metrics
 from infer import _coarse_forward, _tiled_fine_forward
+from pathlib import Path
 
 
 class SizeBatchSampler:
@@ -76,8 +77,8 @@ def main():
     args = parser.parse_args()
 
     cfg_path = args.config
-    if not os.path.isabs(cfg_path):
-        cfg_path = os.path.join(os.getcwd(), cfg_path)
+    if not Path(cfg_path).is_absolute():
+        cfg_path = str(Path.cwd() / cfg_path)
 
     with open(cfg_path, "r") as f:
         cfg = yaml.safe_load(f)
@@ -200,7 +201,7 @@ def main():
     start_step = 0
     best_f1 = -1.0
     resume_path = cfg.get("resume", None)
-    if resume_path and os.path.isfile(resume_path):
+    if resume_path and Path(resume_path).is_file():
         print(f"[WireSegHR][train] Resuming from {resume_path}")
         start_step, best_f1 = _load_checkpoint(
             resume_path, model, optim, scaler, device
@@ -303,7 +304,7 @@ def main():
             if val_stats["f1"] > best_f1:
                 best_f1 = val_stats["f1"]
                 _save_checkpoint(
-                    os.path.join(out_dir, "best.pt"),
+                    str(Path(out_dir) / "best.pt"),
                     step,
                     model,
                     optim,
@@ -313,7 +314,7 @@ def main():
             # Save periodic ckpt
             if ckpt_interval > 0 and (step % ckpt_interval == 0):
                 _save_checkpoint(
-                    os.path.join(out_dir, f"ckpt_{step}.pt"),
+                    str(Path(out_dir) / f"ckpt_{step}.pt"),
                     step,
                     model,
                     optim,
@@ -327,7 +328,7 @@ def main():
                     dset_test,
                     coarse_test,
                     device,
-                    os.path.join(out_dir, f"test_vis_{step}"),
+                    str(Path(out_dir) / f"test_vis_{step}"),
                     amp_enabled,
                     mm_enable,
                     mm_kernel,
@@ -341,7 +342,7 @@ def main():
 
     # Save a final checkpoint upon completion
     _save_checkpoint(
-        os.path.join(out_dir, f"ckpt_{iters}.pt"), step, model, optim, scaler, best_f1
+        str(Path(out_dir) / f"ckpt_{iters}.pt"), step, model, optim, scaler, best_f1
     )
 
     # Final test evaluation
@@ -374,10 +375,10 @@ def main():
             f"[Test Final][Coarse] IoU={test_stats['iou_coarse']:.4f} F1={test_stats['f1_coarse']:.4f} P={test_stats['precision_coarse']:.4f} R={test_stats['recall_coarse']:.4f}"
         )
         # Save final evaluation artifacts
-        final_out = os.path.join(out_dir, f"final_vis_{step}")
-        os.makedirs(final_out, exist_ok=True)
+        final_out = Path(out_dir) / f"final_vis_{step}"
+        final_out.mkdir(parents=True, exist_ok=True)
         # Dump metrics for record
-        with open(os.path.join(final_out, "metrics.yaml"), "w") as f:
+        with open(final_out / "metrics.yaml", "w") as f:
             yaml.safe_dump({**test_stats, "step": step}, f, sort_keys=False)
         # Save predictions (fine + coarse) for the whole test set
         save_final_visuals(
@@ -385,7 +386,7 @@ def main():
             dset_test,
             coarse_test,
             device,
-            final_out,
+            str(final_out),
             amp_enabled,
             amp_dtype,
             prob_thresh,
@@ -615,7 +616,7 @@ def _save_checkpoint(
     scaler: GradScaler,
     best_f1: float,
 ):
-    os.makedirs(os.path.dirname(path), exist_ok=True)
+    Path(path).parent.mkdir(parents=True, exist_ok=True)
     state = {
         "step": step,
         "model": model.state_dict(),
@@ -765,7 +766,7 @@ def save_test_visuals(
     prob_thresh: float,
     max_samples: int = 8,
 ):
-    os.makedirs(out_dir, exist_ok=True)
+    Path(out_dir).mkdir(parents=True, exist_ok=True)
     for i in range(min(max_samples, len(dset_test))):
         item = dset_test[i]
         img = item["image"].astype(np.float32) / 255.0
@@ -783,8 +784,8 @@ def save_test_visuals(
         pred = ((prob_up > prob_thresh).to(torch.uint8) * 255).cpu().numpy()
         # Save input and prediction
         img_bgr = (img[..., ::-1] * 255.0).astype(np.uint8)
-        cv2.imwrite(os.path.join(out_dir, f"{i:03d}_input.jpg"), img_bgr)
-        cv2.imwrite(os.path.join(out_dir, f"{i:03d}_pred.png"), pred)
+        cv2.imwrite(str(Path(out_dir) / f"{i:03d}_input.jpg"), img_bgr)
+        cv2.imwrite(str(Path(out_dir) / f"{i:03d}_pred.png"), pred)
 
 
 @torch.no_grad()
@@ -803,7 +804,7 @@ def save_final_visuals(
     fine_overlap: int,
     fine_batch: int,
 ):
-    os.makedirs(out_dir, exist_ok=True)
+    Path(out_dir).mkdir(parents=True, exist_ok=True)
     for i in range(len(dset_test)):
         item = dset_test[i]
         img = item["image"].astype(np.float32) / 255.0
@@ -838,9 +839,9 @@ def save_final_visuals(
         # Save input and predictions
         img_bgr = (img[..., ::-1] * 255.0).astype(np.uint8)
         base = f"{i:03d}"
-        cv2.imwrite(os.path.join(out_dir, f"{base}_input.jpg"), img_bgr)
-        cv2.imwrite(os.path.join(out_dir, f"{base}_coarse_pred.png"), pred_coarse)
-        cv2.imwrite(os.path.join(out_dir, f"{base}_fine_pred.png"), pred_fine)
+        cv2.imwrite(str(Path(out_dir) / f"{base}_input.jpg"), img_bgr)
+        cv2.imwrite(str(Path(out_dir) / f"{base}_coarse_pred.png"), pred_coarse)
+        cv2.imwrite(str(Path(out_dir) / f"{base}_fine_pred.png"), pred_fine)
 
 
 if __name__ == "__main__":