Yes, I upgraded yesterday morning so I’m on the latest version.
I found the bug. Will submit a PR now, please replace the last line in
comb_similarity by these two:
t = torch.mm(t1, t2.t()) / (w1 * w2.t()).clamp(min=1e-8)
return torch.tril(t, diagonal=-1)
Works great, thanks!
Thank you for catching this!
How to identify images with no information?
Hello @lesscomfortable. I just tested your ImageCleaner widget (I used
ImageDataBunch.from_folder() to create my batches). Great widget!
1) How to get validation images in the widget?
I started with
DatasetFormatter().from_toplosses(learn) (code below). It created the file
cleaned.csv with the list of my training images.
ds, idxs = DatasetFormatter().from_toplosses(learn) ImageCleaner(ds, idxs, path)
Then, I added the argument
ds_type=DatasetType.Valid as coded below, but it gave me again images from my training dataset.
ds, idxs = DatasetFormatter().from_toplosses(learn, ds_type=DatasetType.Valid) ImageCleaner(ds, idxs, path)
2) How to end?
It looks like the ImageCleaner widget never ends. It displays again and again the same images. How to deal with that ?
3) Call from_similars() delete cleaned.csv and create a new one (gloups )
After, I ran the following code that replaced my cleaned.csv. How to avoid that?
ds, idxs = DatasetFormatter().from_similars(learn) ImageCleaner(ds, idxs, path, duplicates=True)
4) How to display the labels?
It would be great to see the labels in order to decide which image to delete (screenshot below).
Pierre, thanks for the feedback! Let me answer your questions one by one:
You need to create your databunch using
db = (ImageItemList.from_folder(path)
If you do it like this, all your dataset will be considered when running the widget.
You don’t need to end it. It recreates the csv every time you click on
next batch. Just stop when you are done.
That’s fine, but you should run from_similars loading from the previous csv. This way the csv created when running
from_similarswill include the changes made using the first widget and the second widget.
There is no way yet to display the labels (I didn’t think it was useful since you have from_toplosses to change labels). However now that you mention it, I understand that it might be useful. I am a bit tight on time but if I find some time I’ll include it. You are welcome to submit a PR if you want to do it yourself too.
Great. The idea is indeed to have all data in training. Thanks.
But I want to end it Once I made a choice for an image, I do not want to see it again.
That’s what I want but it is not working like that. When I run
from_similars, it creates a new cleaned.csv (ie, it destroys the existing one).
Yes, it is definitively important as I can not make any decision without knowing the label of the image (for example, how to choose the one to delete when there are 2 identical images?).
Great if you can solve these 3 points (when you have time and if you agree on them of course):
- do not show 2 times the same image
- do not delete cleaned.csv when it exists
- display labels
I also wanted to clean the (training + validation) datasets.
By following @lesscomfortable’s recommendation, what i did is:
db = (ImageItemList.from_folder(path) .no_split() .label_from_folder() .databunch()) learn = create_cnn(db, models.resnet34, metrics=error_rate) learn.load('stage-2') ds, idxs = DatasetFormatter().from_toplosses(learn)
But it ended up with error as below:
RuntimeError Traceback (most recent call last) <ipython-input-20-59787071ac27> in <module> 6 learn = create_cnn(db, models.resnet34, metrics=error_rate) 7 learn.load('stage-2') ----> 8 ds, idxs = DatasetFormatter().from_toplosses(learn) /opt/anaconda3/lib/python3.7/site-packages/fastai/widgets/image_cleaner.py in from_toplosses(cls, learn, n_imgs, **kwargs) 17 def from_toplosses(cls, learn, n_imgs=None, **kwargs): 18 "Gets indices with top losses." ---> 19 train_ds, train_idxs = cls.get_toplosses_idxs(learn, n_imgs, **kwargs) 20 return train_ds, train_idxs 21 /opt/anaconda3/lib/python3.7/site-packages/fastai/widgets/image_cleaner.py in get_toplosses_idxs(cls, learn, n_imgs, **kwargs) 25 dl = learn.data.fix_dl 26 if not n_imgs: n_imgs = len(dl.dataset) ---> 27 _,_,top_losses = learn.get_preds(ds_type=DatasetType.Fix, with_loss=True) 28 idxs = torch.topk(top_losses, n_imgs) 29 return cls.padded_ds(dl.dataset, **kwargs), idxs /opt/anaconda3/lib/python3.7/site-packages/fastai/basic_train.py in get_preds(self, ds_type, with_loss, n_batch, pbar) 253 lf = self.loss_func if with_loss else None 254 return get_preds(self.model, self.dl(ds_type), cb_handler=CallbackHandler(self.callbacks), --> 255 activ=_loss_func2activ(self.loss_func), loss_func=lf, n_batch=n_batch, pbar=pbar) 256 257 def pred_batch(self, ds_type:DatasetType=DatasetType.Valid, batch:Tuple=None, reconstruct:bool=False) -> List[Tensor]: /opt/anaconda3/lib/python3.7/site-packages/fastai/basic_train.py in get_preds(model, dl, pbar, cb_handler, activ, loss_func, n_batch) 38 "Tuple of predictions and targets, and optional losses (if `loss_func`) using `dl`, max batches `n_batch`." 39 res = [torch.cat(o).cpu() for o in ---> 40 zip(*validate(model, dl, cb_handler=cb_handler, pbar=pbar, average=False, n_batch=n_batch))] 41 if loss_func is not None: res.append(calc_loss(res, res, loss_func)) 42 if activ is not None: res = activ(res) /opt/anaconda3/lib/python3.7/site-packages/fastai/basic_train.py in validate(model, dl, loss_func, cb_handler, pbar, average, n_batch) 50 val_losses,nums = , 51 if cb_handler: cb_handler.set_dl(dl) ---> 52 for xb,yb in progress_bar(dl, parent=pbar, leave=(pbar is not None)): 53 if cb_handler: xb, yb = cb_handler.on_batch_begin(xb, yb, train=False) 54 val_losses.append(loss_batch(model, xb, yb, loss_func, cb_handler=cb_handler)) /opt/anaconda3/lib/python3.7/site-packages/fastprogress/fastprogress.py in __iter__(self) 63 self.update(0) 64 try: ---> 65 for i,o in enumerate(self._gen): 66 yield o 67 if self.auto_update: self.update(i+1) /opt/anaconda3/lib/python3.7/site-packages/fastai/basic_data.py in __iter__(self) 69 def __iter__(self): 70 "Process and returns items from `DataLoader`." ---> 71 for b in self.dl: yield self.proc_batch(b) 72 73 @classmethod /opt/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __next__(self) 635 self.reorder_dict[idx] = batch 636 continue --> 637 return self._process_next_batch(batch) 638 639 next = __next__ # Python 2 compatibility /opt/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py in _process_next_batch(self, batch) 656 self._put_indices() 657 if isinstance(batch, ExceptionWrapper): --> 658 raise batch.exc_type(batch.exc_msg) 659 return batch 660 RuntimeError: Traceback (most recent call last): File "/opt/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 138, in _worker_loop samples = collate_fn([dataset[i] for i in batch_indices]) File "/opt/anaconda3/lib/python3.7/site-packages/fastai/torch_core.py", line 110, in data_collate return torch.utils.data.dataloader.default_collate(to_data(batch)) File "/opt/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 232, in default_collate return [default_collate(samples) for samples in transposed] File "/opt/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 232, in <listcomp> return [default_collate(samples) for samples in transposed] File "/opt/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 209, in default_collate return torch.stack(batch, 0, out=out) RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 333 and 282 in dimension 2 at /opt/conda/conda-bld/pytorch_1544202130060/work/aten/src/TH/generic/THTensorMoreMath.cpp:1333
look like it is looking for
the losses when calling
DatasetFormatter().from_toplosses(learn), but actually I don’t have it because I only load the model’s weights, i didn’t run
Can you please help here.
You won’t! The widget does not show deleted images.
Yes, this is expected. It includes both the old changes and the new changes in a brand new csv. Is this ok for you?
My assumption was that once you run the relabeler widget, it doesn’t make a difference which to delete because they should be well labeled. However, if you run the duplicate detector without running the relabeler first, what you say makes sense.
I think this has to do with the images being of different dimensions. Try transforming them to have the same dimensions before loading them into the model. See here for help on how to do this.
.no_split() will create an DataBunch with no validation set, then there won’t be any valid_loss or error_rate shown if we fit on that data (as expected). So how do we add the validation set back in?
@lesscomfortable is there any way that DatasetFormatter.from_top_losses could honor the dataset type like it used to? It seems like ds_type was hardcoded to be
Test at some point before being changed to
Fix. This was a huge source of confusion for me while running through lesson 2. Ideally I would think that from_top_losses should look at the entire dataset(not just Test/Validation) separately.
It seems kind of hacky to require users to create a new databunch without a validation set in order to be able to achieve the standard use-case functionality. I’d be happy to work on a PR for this if you think it would be helpful.
Incidentally, what is the
I am having exactly the same problem. I upgraded to latest version.
Fix DatasetType is the
train dataset without shuffling. Since we built the databunch using
train dataset contains all the images in the dataset. By specifying
.Fix we get the whole dataset without shuffling, which is what we want.
If you can write a PR that removes the need to create a new databunch for the widget, that would be great!
Inconsistent result between ClassificationInterpretation.top_losses() and DatasetFormatter().from_toplosses()
This bug is solved, are you sure you are using the last version?
I upgraded yes but still the same problem. I am working in a Kaggle Kernel so maybe I cannot upgrade the fastai package myself within my kernel.
Yeah, it would be worth to try in a Jupyter Notebook running locally or in a server so you can understand where the problem is coming from.
Hey my question is in Cleaned.csv everything is included rather than images of each category in each separate file. In this case, how can we recreate ImageDataBunch for this cleaned.csv file?
Kaggle now seems to be using latest version and so from_similars works fine. Thanks!
You can create the ImageDataBunch from the csv, like so (assuming your csv is in folder named ‘data’):
data = ImageDataBunch.from_csv('data',csv_labels='cleaned.csv',label_col=1,valid_pct=0.2,ds_tfms=get_transforms(), size=224, num_workers=0).normalize(imagenet_stats)