| """Eval conv deep head from a specific checkpoint step.""" |
| import os, sys, time, torch |
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) |
| from train_conv_deep import DeepConvHead, cofiber_decompose, make_locations |
|
|
| DEVICE = "cuda" |
| COCO_ROOT = os.environ["ARENA_COCO_ROOT"] |
| VAL_CACHE = os.environ["ARENA_VAL_CACHE"] |
|
|
| import argparse |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--hidden", type=int, default=256) |
| parser.add_argument("--blocks", type=int, default=10) |
| parser.add_argument("--checkpoint", required=True) |
| parser.add_argument("--with-p3", action="store_true") |
| args = parser.parse_args() |
|
|
| head = DeepConvHead(hidden=args.hidden, n_blocks=args.blocks, with_p3=args.with_p3).to(DEVICE) |
| ckpt = torch.load(args.checkpoint, map_location=DEVICE, weights_only=False) |
| head.load_state_dict(ckpt["head"]) |
| head.eval() |
| step = ckpt["step"] |
| print(f"Loaded step {step}, hidden={args.hidden}, blocks={args.blocks}, " |
| f"{sum(p.numel() for p in head.parameters()):,} params") |
|
|
| val = torch.load(VAL_CACHE, map_location="cpu", weights_only=False) |
| from pycocotools.coco import COCO |
| from pycocotools.cocoeval import COCOeval |
| coco_gt = COCO(os.path.join(COCO_ROOT, "annotations", "instances_val2017.json")) |
| cat_ids = sorted(coco_gt.getCatIds()) |
| idx_to_cat = {i: c for i, c in enumerate(cat_ids)} |
| H = 640 // 16 |
| if args.with_p3: |
| strides = [8, 16, 32, 64] |
| all_locs = torch.cat(make_locations([(H*2,H*2),(H,H),(H//2,H//2),(H//4,H//4)], strides, torch.device(DEVICE))) |
| else: |
| strides = [16, 32, 64] |
| all_locs = torch.cat(make_locations([(H,H),(H//2,H//2),(H//4,H//4)], strides, torch.device(DEVICE))) |
|
|
| all_results = [] |
| t0 = time.time() |
| with torch.no_grad(): |
| for idx in range(len(val)): |
| item = val[idx] |
| spatial = item["spatial"].unsqueeze(0).float().to(DEVICE) |
| img_id = int(item["img_id"]); scale = item["scale"] |
| cls_l, reg_l, ctr_l = head(spatial) |
| cls_s = torch.cat([c.permute(0,2,3,1).reshape(-1, 80) for c in cls_l]).sigmoid() |
| reg_s = torch.cat([r.permute(0,2,3,1).reshape(-1, 4) for r in reg_l]) |
| ctr_s = torch.cat([c.permute(0,2,3,1).reshape(-1) for c in ctr_l]).sigmoid() |
| scores = cls_s * ctr_s.unsqueeze(1) |
| max_s, max_c = scores.max(1) |
| topk = min(100, max_s.shape[0]) |
| top_s, top_i = max_s.topk(topk) |
| tc=max_c[top_i]; tr=reg_s[top_i]; tl=all_locs[top_i] |
| x1=(tl[:,0]-tr[:,0])/scale; y1=(tl[:,1]-tr[:,1])/scale |
| x2=(tl[:,0]+tr[:,2])/scale; y2=(tl[:,1]+tr[:,3])/scale |
| w=(x2-x1).clamp(min=0); h=(y2-y1).clamp(min=0) |
| for i in range(topk): |
| s = top_s[i].item() |
| if s < 0.01: continue |
| all_results.append({"image_id": img_id, "category_id": idx_to_cat[tc[i].item()], |
| "bbox": [x1[i].item(), y1[i].item(), w[i].item(), h[i].item()], |
| "score": s}) |
| if (idx+1) % 1000 == 0: |
| print(f" {idx+1}/{len(val)} ({time.time()-t0:.0f}s)", flush=True) |
|
|
| print(f"\n{len(all_results)} detections") |
|
|
| |
| import json |
| results_path = f"conv_deep_{args.hidden}h_{args.blocks}b_step{step}_coco_results.json" |
| with open(results_path, "w") as f: |
| json.dump(all_results, f) |
| print(f"Saved: {results_path}") |
|
|
| |
| try: |
| coco_dt = coco_gt.loadRes(all_results) |
| ev = COCOeval(coco_gt, coco_dt, "bbox") |
| ev.params.imgIds = sorted(coco_gt.getImgIds())[:len(val)] |
| ev.evaluate(); ev.accumulate(); ev.summarize() |
| print(f"\nConv Deep {args.hidden}h {args.blocks}b step{step}: " |
| f"mAP={ev.stats[0]:.4f} mAP50={ev.stats[1]:.4f} mAP75={ev.stats[2]:.4f}") |
| except Exception as e: |
| print(f"\npycocotools failed: {e}") |
| print(f"Use: python eval_from_results.py --results {results_path} --head conv_deep") |
|
|
