I am trying to train a unet model to identify objects from images that have been annotated as bounding box, however, I am having difficulty in creating the learner and successfully find lr as well as training my model. I receive this error whenI try to rung lr_find as well as fit.one_cycle:
if not loss_func: return to_detach(out), to_detach(yb[0]) loss = loss_func(out, *yb)
TypeError: __call__() takes 3 positional arguments but 4 were given
Please bear with me as I am a rookie 
As a first step I have precessed images for only one label.
Here are the steps I take to create the learner.
I have saved the annotations for the images in a coco file. So my code to extract the annotations and create the label func is:
images, lbl_bbox = get_annotations(coco_dir) img2bbox = dict(zip(images, lbl_bbox)) get_y_func = lambda o:img2bbox[os.path.basename(o)]
I created the databunch as:
src = (ObjectItemList.from_folder(dir) .split_by_rand_pct(seed=42) .label_from_func(get_y_func) .transform(get_transforms(), tfm_y=True, size=128) .databunch(bs=16, collate_fn=bb_pad_collate) .normalize(imagenet_stats) )
I extract the labes as:
codes = np.loadtxt(sample_dir/'codes.txt', dtype=str)
And create the accuracy function as:
name2id = {v:k for k,v in enumerate(codes)} void_code = name2id['void']
`def acc_bbox(input, target):
target = target.squeeze(1)
mask = target != void_code
return (input.argmax(dim=1)[mask]==target[mask]).float().mean()`
Finally I create learner as:
learn = unet_learner(src, models.resnet34, metrics= acc_bbox, wd=1e-2)
But when I run lr_find(learn), I receive the error mentioned in the beginning.
I am guessing that the problem lies in my accuracy func and possibly a need for loss func too, however, I do not know how to create them appropriately.
Can you please help me with this?