Using transformers with fastai v2

Hi all,

I’ve added a tutorial showing how to use a transformers pretrained model in fastai, using the mid-level API. Hope you find it useful!

14 Likes

This is really cool! I was actually using their library for NLP and am happy to know I can now benefit from fastai functions.

By the way, they have the AutoModels functions which I believe are used to call the right class based on model name (gpt2, bert, distilbert…).

Would it make sense to have a transformer_learner and special datablock to accomplish all you did in the tutorial (call right transforms, add the required callback…)?

Yes, ultimately, probably in a fastai extension since it would require a new dependency. I haven’t played around with the transformers library enough to be sure this approach will work for every tuple model / problem type however.

3 Likes

Based in your tutorial I am trying to use It for creating a DataLoader for MaskRCNN:

class MaskRCNN(dict):
    
    @classmethod
    def create(cls, dictionary): 
        return cls(dict({x:dictionary[x] for x in dictionary.keys()}))
    
    def show(self, ctx=None, **kwargs): 
        dictionary = self
        
        boxes = dictionary["boxes"]
        labels = dictionary["labels"]
        masks = dictionary["masks"]
        
        result = masks
        return show_image(result, ctx=ctx, **kwargs)
def MaskRCNNBlock(): 
    return TransformBlock(type_tfms=MaskRCNN.create, batch_tfms=IntToFloatTensor)
def get_bbox(o):
    label_path = get_y_fn(o)
    mask=PILMask.create(label_path)
    pos = np.where(mask)
    xmin = np.min(pos[1])
    xmax = np.max(pos[1])
    ymin = np.min(pos[0])
    ymax = np.max(pos[0])
    
    return TensorBBox.create([xmin, ymin, xmax, ymax])
    
def get_bbox_label(o):
    
    return TensorCategory([1])
    
    
def get_mask(o):
    label_path = get_y_fn(o)
    mask=PILMask.create(label_path)
    mask=image2tensor(mask)
    return TensorMask(mask)

def get_dict(o):
    return {"boxes": get_bbox(o), "labels": get_bbox_label(o),"masks": get_mask(o)}
    

getters = [lambda o: o, get_dict]
maskrccnnDataBlock = DataBlock(
    blocks=(ImageBlock, MaskRCNNBlock),
    get_items=partial(get_image_files,folders=[manual_name]),
    getters=getters,
    splitter=RandomSplitter(valid_pct=0.1,seed=2020),
    item_tfms=Resize((size,size)),
    batch_tfms=Normalize.from_stats(*imagenet_stats)
)
maskrccnnDataBlock.summary(path_images)
dls = maskrccnnDataBlock.dataloaders(path_images,bs=bs)
dls.show_batch()

However, show_batch is not working owing to the fact that Mask is not getting resized to the same size as Image

You need to implement transforms for your MaskedRCNN since it’s not of a fastai type.

Is it possible to make Mask act as a MaskBlock? In that case, transforms will be applied no?

Sorry still confuse how this forum works, where can i get the tutorial?

Here you go!:
http://dev.fast.ai/tutorial.transformers

1 Like

I decided to go withouth them.

I tried to use this Dataloader with a Learner. I am getting a very strange error:

Traceback (most recent call last):
Traceback (most recent call last):
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/queues.py", line 236, in _feed
    obj = _ForkingPickler.dumps(obj)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/queues.py", line 236, in _feed
    obj = _ForkingPickler.dumps(obj)
Traceback (most recent call last):
Traceback (most recent call last):
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
_pickle.PicklingError: Can't pickle <class '__main__.MaskRCNN'>: it's not the same object as __main__.MaskRCNN
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/queues.py", line 236, in _feed
    obj = _ForkingPickler.dumps(obj)
Traceback (most recent call last):
_pickle.PicklingError: Can't pickle <class '__main__.MaskRCNN'>: it's not the same object as __main__.MaskRCNN
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/queues.py", line 236, in _feed
    obj = _ForkingPickler.dumps(obj)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/queues.py", line 236, in _feed
    obj = _ForkingPickler.dumps(obj)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/queues.py", line 236, in _feed
    obj = _ForkingPickler.dumps(obj)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/queues.py", line 236, in _feed
    obj = _ForkingPickler.dumps(obj)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/queues.py", line 236, in _feed
    obj = _ForkingPickler.dumps(obj)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/multiprocessing/queues.py", line 236, in _feed
    obj = _ForkingPickler.dumps(obj)

You need to restart your notebook. This errors comes from the multiprocessing so setting num_workers=0 when prototyping will get your rid of it.
If restarting is not enough, you need to put MaskRCNN in a separate module that you import.

1 Like

num_workers = 0 solve it. Thank you :smiley:

Storing in a different module is for solving multiprocessing?

In addition, I don’t know how to override the code where metrics input is set. I would like just to use the mask that returns MaskRCNN model for computing the metrics.

1 Like

Yes indeed.

For the metrics, you need to write your functions that delegates the mask part to the function you want.

My english is bad, I don’t understand what you mean by write your functions that delegates.

The metrics array that you pass into the Learner is getting executed in a callback?

It’s difficult to me to find the correct place where to make those changes. Editing one_batch with a Learner subclass was pretty easy!

Learner was working with that Loader.

learn.lr_find() got up to 10% and throw a new error related with Dataloader. Likes like he is trying to collate Images. However, I don’t want it to do it. It is being made in the first layer of the model.

It is the error:

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-28-d81c6bd29d71> in <module>
----> 1 learn.lr_find()

~/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/callback/schedule.py in lr_find(self, start_lr, end_lr, num_it, stop_div, show_plot, suggestions)
    226     n_epoch = num_it//len(self.dls.train) + 1
    227     cb=LRFinder(start_lr=start_lr, end_lr=end_lr, num_it=num_it, stop_div=stop_div)
--> 228     with self.no_logging(): self.fit(n_epoch, cbs=cb)
    229     if show_plot: self.recorder.plot_lr_find()
    230     if suggestions:

~/anaconda3/envs/seg/lib/python3.7/site-packages/fastcore/utils.py in _f(*args, **kwargs)
    428         init_args.update(log)
    429         setattr(inst, 'init_args', init_args)
--> 430         return inst if to_return else f(*args, **kwargs)
    431     return _f
    432 

~/Documents/seg/seg/models/archs/mask_rcnn.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
    114                     try:
    115                         self.epoch=epoch;          self('begin_epoch')
--> 116                         self._do_epoch_train()
    117                         self._do_epoch_validate()
    118                     except CancelEpochException:   self('after_cancel_epoch')

~/Documents/seg/seg/models/archs/mask_rcnn.py in _do_epoch_train(self)
     89         try:
     90             self.dl = self.dls.train;                        self('begin_train')
---> 91             self.all_batches()
     92         except CancelTrainException:                         self('after_cancel_train')
     93         finally:                                             self('after_train')

~/Documents/seg/seg/models/archs/mask_rcnn.py in all_batches(self)
     60     def all_batches(self):
     61         self.n_iter = len(self.dl)
---> 62         for o in enumerate(self.dl): self.one_batch(*o)
     63 
     64     def one_batch(self, i, b):

~/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py in __iter__(self)
     96         self.randomize()
     97         self.before_iter()
---> 98         for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
     99             if self.device is not None: b = to_device(b, self.device)
    100             yield self.after_batch(b)

~/anaconda3/envs/seg/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __next__(self)
    343 
    344     def __next__(self):
--> 345         data = self._next_data()
    346         self._num_yielded += 1
    347         if self._dataset_kind == _DatasetKind.Iterable and \

~/anaconda3/envs/seg/lib/python3.7/site-packages/torch/utils/data/dataloader.py in _next_data(self)
    951             if len(self._task_info[self._rcvd_idx]) == 2:
    952                 data = self._task_info.pop(self._rcvd_idx)[1]
--> 953                 return self._process_data(data)
    954 
    955             assert not self._shutdown and self._tasks_outstanding > 0

~/anaconda3/envs/seg/lib/python3.7/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)
    994         self._try_put_index()
    995         if isinstance(data, ExceptionWrapper):
--> 996             data.reraise()
    997         return data
    998 

~/anaconda3/envs/seg/lib/python3.7/site-packages/torch/_utils.py in reraise(self)
    393             # (https://bugs.python.org/issue2651), so we work around it.
    394             msg = KeyErrorMessage(msg)
--> 395         raise self.exc_type(msg)

RuntimeError: Caught RuntimeError in DataLoader worker process 10.
Original Traceback (most recent call last):
  File "/home/david/anaconda3/envs/seg/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 34, in fetch
    data = next(self.dataset_iter)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py", line 107, in create_batches
    yield from map(self.do_batch, self.chunkify(res))
  File "/home/david/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py", line 128, in do_batch
    def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py", line 127, in create_batch
    def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py", line 46, in fa_collate
    else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py", line 46, in <listcomp>
    else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/site-packages/fastai2/data/load.py", line 45, in fa_collate
    return (default_collate(t) if isinstance(b, _collate_types)
  File "/home/david/anaconda3/envs/seg/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py", line 55, in default_collate
    return torch.stack(batch, 0, out=out)
RuntimeError: stack expects each tensor to be equal size, but got [3, 966, 1296] at entry 0 and [3, 1004, 1002] at entry 1

Very cool. Where can I find the tutorial, please?

1 Like

http://dev.fast.ai/tutorial.transformers

Thank you, David.

1 Like

If I created a sub class of TfmdDL where create_batch is just a return b.

However, at training when validation is done I found something very strange:

image

image

Looks like that after passing yb to the model, it gets other values. So, my Metrics functions are just failing owing to the fact that predictions masks doesn have same shape.

[ EDIT - 07/01/2020 ] I deleted the content of this post as it was redundant with this one :
fastai v2 and Transformers | Problems not solved with DDP

1 Like