Increase pycocotools robustness (#1396)
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5
test.py
5
test.py
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@ -86,8 +86,7 @@ def test(data,
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img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
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_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
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path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images
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dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt,
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hyp=None, augment=False, cache=False, pad=0.5, rect=True)[0]
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dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True)[0]
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seen = 0
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names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
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@ -166,7 +165,7 @@ def test(data,
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box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
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for p, b in zip(pred.tolist(), box.tolist()):
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jdict.append({'image_id': int(image_id) if image_id.isnumeric() else image_id,
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'category_id': coco91class[int(p[5])],
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'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
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'bbox': [round(x, 3) for x in b],
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'score': round(p[4], 5)})
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2
train.py
2
train.py
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@ -183,7 +183,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
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if rank in [-1, 0]:
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ema.updates = start_epoch * nb // accumulate # set EMA updates
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testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt,
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hyp=hyp, augment=False, cache=opt.cache_images and not opt.notest, rect=True,
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hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True,
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rank=-1, world_size=opt.world_size, workers=opt.workers)[0] # testloader
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if not opt.resume:
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