Image Segmentation Inference

Part 1 (2018)
1

Hello, I’ve been looking for a solution to do inference using UNet (segmentation). Sadly, I can’t find anything simple and straightforward. Is it that hard to do it? I just want to load my model and do inference in a custom dataset

However, everything I find uses an ImageBunch which requires the masks.

data = (SegmentationItemList.from_folder(path_img)
        .split_by_rand_pct()
        .label_from_func(get_y_fn, classes=codes)
        .transform(get_transforms(), tfm_y=True, size=256)
        .databunch(bs=16)
        .normalize(imagenet_stats))

(from the Inference tutorial).

Any idea how to do inference on a specific folder without passing the masks (which doesn’t make any sense)

Regards,

Luís

2

Does this work?

data = (SegmentationItemList.from_folder(path)
                            .split_none()
                            .label_empty()
                            .transform(size=256)
                            .databunch(bs=16)
                            .normalize(imagenet_stats))
1 Like
3

I did this:

data = (SegmentationItemList.from_folder('../data2/test/166144_test_001/patches')
                            .split_none()
                            .label_empty()
                            .transform(size=256)
                            .databunch(bs=16)
                            .normalize(imagenet_stats))

and then this:

learn = unet_learner(data, models.resnet34).load('20190708-rn38unet-1224')

And I got the following error:

AttributeError Traceback (most recent call last)
in
----> 1 learn = unet_learner(data, models.resnet34).load(‘20190708-rn38unet-1224’)
2 #learn.export()

/usr/local/lib/python3.6/dist-packages/fastai/vision/learner.py in unet_learner(data, arch, pretrained, blur_final, norm_type, split_on, blur, self_attention, y_range, last_cross, bottle, cut, **learn_kwargs)
114 meta = cnn_config(arch)
115 body = create_body(arch, pretrained, cut)
→ 116 model = to_device(models.unet.DynamicUnet(body, n_classes=data.c, blur=blur, blur_final=blur_final,
117 self_attention=self_attention, y_range=y_range, norm_type=norm_type, last_cross=last_cross,
118 bottle=bottle), data.device)

/usr/local/lib/python3.6/dist-packages/fastai/basic_data.py in getattr(self, k)
120 return cls(*dls, path=path, device=device, dl_tfms=dl_tfms, collate_fn=collate_fn, no_check=no_check)
121
→ 122 def getattr(self,k:int)->Any: return getattr(self.train_dl, k)
123 def setstate(self,data:Any): self.dict.update(data)
124

/usr/local/lib/python3.6/dist-packages/fastai/basic_data.py in getattr(self, k)
36
37 def len(self)->int: return len(self.dl)
—> 38 def getattr(self,k:str)->Any: return getattr(self.dl, k)
39 def setstate(self,data:Any): self.dict.update(data)
40

/usr/local/lib/python3.6/dist-packages/fastai/basic_data.py in DataLoader___getattr__(dl, k)
18 torch.utils.data.DataLoader.init = intercept_args
19
—> 20 def DataLoader___getattr__(dl, k:str)->Any: return getattr(dl.dataset, k)
21 DataLoader.getattr = DataLoader___getattr__
22

/usr/local/lib/python3.6/dist-packages/fastai/data_block.py in getattr(self, k)
637 res = getattr(y, k, None)
638 if res is not None: return res
→ 639 raise AttributeError(k)
640
641 def setstate(self,data:Any): self.dict.update(data)

AttributeError: c

Kind regards

4 
learn = load_learner('my_learner')

# Now get the preds
learn.get_preds(data.train_ds.x[0])

You get the error because when you export/save your learner you are also saving the databunch information along with it (like transforms which is uses). So you cannot create a learner with different databunch (like when you create empty_label one) and then load the previous learner object to it.

5

It makes sense. Thank you.

It still doesn’t work though. I had a custom loss metric in my model.

def dice_loss(input, target):
#     pdb.set_trace()
    smooth = 1.
    input = input[:,1,None].sigmoid()
    iflat = input.contiguous().view(-1).float()
    tflat = target.view(-1).float()
    intersection = (iflat * tflat).sum()
    return (1 - ((2. * intersection + smooth) / ((iflat + tflat).sum() +smooth)))

def combo_loss(pred, targ):
    bce_loss = CrossEntropyFlat(axis=1)
    return bce_loss(pred,targ) + dice_loss(pred,targ)

Therefore, I get this error:

AttributeError Traceback (most recent call last)
in
----> 1 learn = load_learner(‘…/data2/segmentation/images’)
2 #learn.export()

/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in load_learner(path, file, test, **db_kwargs)
608 “Load a Learner object saved with export_state in path/file with empty data, optionally add test and load on cpu. file can be file-like (file or buffer)”
609 source = Path(path)/file if is_pathlike(file) else file
→ 610 state = torch.load(source, map_location=‘cpu’) if defaults.device == torch.device(‘cpu’) else torch.load(source)
611 model = state.pop(‘model’)
612 src = LabelLists.load_state(path, state.pop(‘data’))

/usr/local/lib/python3.6/dist-packages/torch/serialization.py in load(f, map_location, pickle_module, **pickle_load_args)
384 f = f.open(‘rb’)
385 try:
→ 386 return _load(f, map_location, pickle_module, **pickle_load_args)
387 finally:
388 if new_fd:

/usr/local/lib/python3.6/dist-packages/torch/serialization.py in _load(f, map_location, pickle_module, **pickle_load_args)
571 unpickler = pickle_module.Unpickler(f, **pickle_load_args)
572 unpickler.persistent_load = persistent_load
→ 573 result = unpickler.load()
574
575 deserialized_storage_keys = pickle_module.load(f, **pickle_load_args)

AttributeError: Can’t get attribute ‘combo_loss’ on <module ‘main’>

Any idea how to sort this?

Regards

6

You must already have the code for your custom loss before loading the learner. Fastai does not serialize the code, only the objects.

7

Thank you very much! I’m really close to fix this.

However, I tried this:

learn.get_preds(data.train_ds.x[2])

But it does not seem to wor, returning:

‘DatasetType’ object has no attribute ‘data’

8

Why double bracket in get_preds

9

Ahah, was just a type. Not the reason of the problem.

I could make it work with

learn.predict(data.train_ds.x[2])

Still, I would like to run the whole batch

10

Use get_preds function of learner.

11

I did what you suggested, using:

learn.get_preds(data.train_ds.x[2])

but it doesn’t work

‘DatasetType’ object has no attribute ‘data’

12

For getpreds you only pass dataset. You can check the docs for complete guide.