Increase pycocotools robustness (#1396)

test.py

@@ -86,8 +86,7 @@ def test(data,
         img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
         _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
         path = data['test'] if opt.task == 'test' else data['val']  # path to val/test images
-        dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt,
-                                       hyp=None, augment=False, cache=False, pad=0.5, rect=True)[0]
+        dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True)[0]
 
     seen = 0
     names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
@@ -166,7 +165,7 @@ def test(data,
                 box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
                 for p, b in zip(pred.tolist(), box.tolist()):
                     jdict.append({'image_id': int(image_id) if image_id.isnumeric() else image_id,
-                                  'category_id': coco91class[int(p[5])],
+                                  'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
                                   'bbox': [round(x, 3) for x in b],
                                   'score': round(p[4], 5)})
 

train.py

@@ -183,7 +183,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     if rank in [-1, 0]:
         ema.updates = start_epoch * nb // accumulate  # set EMA updates
         testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt,
-                                       hyp=hyp, augment=False, cache=opt.cache_images and not opt.notest, rect=True,
+                                       hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True,
                                        rank=-1, world_size=opt.world_size, workers=opt.workers)[0]  # testloader
 
         if not opt.resume: