Well, that’s embarrassing 
We’re all still learning new things 
Thanks for the explanation!
2 Likes
I knew you could pass callbacks both at init and at fit, but I wasn’t aware of the difference either! I expected the difference in behaviour should be built into each callback.
1 Like
This is a bit of a general question. I understand that when we use the high level DataBlock, ToTensor is being applied automatically, but if I look at what it is it’s simply:
class ToTensor(Transform):
"Convert item to appropriate tensor class"
order = 5
I then went in and tried to look at Transform to see if I could find anywhere where it’s explicitly done but I couldn’t figure out where the magic is happening. Any hints to what I’m missing?
Aha! That’s what I was missing. It simply has the Transform base class and then the encodes functionalities are assigned in the submodules. That makes perfect sense. Thanks @scart97 
(Along with the tensor classes being assigned to each Pillow type)
I want to clarify something before I make a PR, does passing get_x and/or get_y to DataBlock only works when n_inp=1?
I’m asking this because of the following lines of code:
if self.get_x: self.getters[0] = self.get_x
if self.get_y: self.getters[1] = self.get_y
Now, let’s say I have 2 inputs and 1 output. If I pass get_y it will be wrongly assigned to the second input.
I think we should do something like:
if self.get_x: getters[:n_inp] = self.get_x
if self.get_y: getters[n_inp:] = self.get_y
We also need to check that get_x is a list with length 2 in this case.
5 Likes
Yes, for now, get_x/get_y only works for one input and one target. In other cases, you are supposed to provide getters.
But I like your approach! Feel free to suggest a PR with it!
1 Like
Me too.
1 Like
Hi,
Is there already a loss function that can be used with a multi-target classification? In my Dataloaders I end up with a collated tensor of one hot encoded vectors but after that the Learner fails on the loss calculation (I tried LabelSmoothingCrossEntropy and CrossEntropyLossFlat). I went through the notebooks but cannot find any example that would cover that (only the planet dataset but it focuses on how to prepare the data). Thanks for your help!
That’s because if you choose the “MultiCategoryBlock” the proper loss function is already assigned (if using cnn_learner), which is BCELossLogitsFlat
2 Likes
Ah ok, thank you. I need to dig deeper into that bit.
I’m not sure what does PointScaler do with TensorBBox?
@PointScaler
def encodes(self, x:TensorBBox):
pnts = self.encodes(cast(x.view(-1,2), TensorPoint))
return cast(pnts.view(-1, 4), TensorBBox)
@PointScaler
def decodes(self, x:TensorBBox):
pnts = self.decodes(cast(x.view(-1,2), TensorPoint))
return cast(pnts.view(-1, 4), TensorBBox)
I see the method calling encodes on self, but there’s no encodes implementation in TensorBBox and TensorPoint
EDIT: I found scale_pnts being called for TensorPoint but not getting what it’s doing. Any reference to learn about this scaling?
I’m trying to understand what it does to TensorBBox, the code works with Pipeline but not with Datasets
# PointScaler expects `img_size` in _meta
class AddImsize(Transform):
def __init__(self,sz=128): self.sz=sz
def encodes(self, x:TensorBBox):
x._meta = {'img_size': self.sz}
return x
# This works
p = Pipeline([img2bbox.__getitem__, TensorBBox.create, AddImsize, PointScaler]); p(imgs[0])
# But this does not
itfms = [lambda o: path/'train'/o, PILImage.create]
bbtfms = [img2bbox.__getitem__, TensorBBox.create, AddImsize, PointScaler]
ds = Datasets(imgs,[itfms,bbtfms])
Causes AttributeError: do_item
Here’s the complete stack trace
<ipython-input-80-178eb5fcaa07> in <module>()
7 itfms = [lambda o: path/'train'/o, PILImage.create]
8 bbtfms = [img2bbox.__getitem__, TensorBBox.create, AddImsize(), PointScaler]
----> 9 tds = Datasets(imgs,[itfms,bbtfms])
10 # p = Pipeline([img2bbox.__getitem__, TensorBBox.create, AddImsize, PointScaler]); p(imgs[0])
11 # s = p(imgs[0])
12 frames
/usr/local/lib/python3.6/dist-packages/fastai2/data/core.py in __init__(self, items, tfms, tls, n_inp, dl_type, **kwargs)
272 def __init__(self, items=None, tfms=None, tls=None, n_inp=None, dl_type=None, **kwargs):
273 super().__init__(dl_type=dl_type)
--> 274 self.tls = L(tls if tls else [TfmdLists(items, t, **kwargs) for t in L(ifnone(tfms,[None]))])
275 self.n_inp = (1 if len(self.tls)==1 else len(self.tls)-1) if n_inp is None else n_inp
276
/usr/local/lib/python3.6/dist-packages/fastai2/data/core.py in <listcomp>(.0)
272 def __init__(self, items=None, tfms=None, tls=None, n_inp=None, dl_type=None, **kwargs):
273 super().__init__(dl_type=dl_type)
--> 274 self.tls = L(tls if tls else [TfmdLists(items, t, **kwargs) for t in L(ifnone(tfms,[None]))])
275 self.n_inp = (1 if len(self.tls)==1 else len(self.tls)-1) if n_inp is None else n_inp
276
/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
39 return x
40
---> 41 res = super().__call__(*((x,) + args), **kwargs)
42 res._newchk = 0
43 return res
/usr/local/lib/python3.6/dist-packages/fastai2/data/core.py in __init__(self, items, tfms, use_list, do_setup, split_idx, train_setup, splits, types, verbose)
212 if do_setup:
213 pv(f"Setting up {self.tfms}", verbose)
--> 214 self.setup(train_setup=train_setup)
215
216 def _new(self, items, **kwargs): return super()._new(items, tfms=self.tfms, do_setup=False, types=self.types, **kwargs)
/usr/local/lib/python3.6/dist-packages/fastai2/data/core.py in setup(self, train_setup)
226
227 def setup(self, train_setup=True):
--> 228 self.tfms.setup(self, train_setup)
229 if len(self) != 0:
230 x = super().__getitem__(0) if self.splits is None else super().__getitem__(self.splits[0])[0]
/usr/local/lib/python3.6/dist-packages/fastcore/transform.py in setup(self, items, train_setup)
177 tfms = self.fs[:]
178 self.fs.clear()
--> 179 for t in tfms: self.add(t,items, train_setup)
180
181 def add(self,t, items=None, train_setup=False):
/usr/local/lib/python3.6/dist-packages/fastcore/transform.py in add(self, t, items, train_setup)
180
181 def add(self,t, items=None, train_setup=False):
--> 182 t.setup(items, train_setup)
183 self.fs.append(t)
184
/usr/local/lib/python3.6/dist-packages/fastcore/transform.py in setup(self, items, train_setup)
76 def setup(self, items=None, train_setup=False):
77 train_setup = train_setup if self.train_setup is None else self.train_setup
---> 78 return self.setups(getattr(items, 'train', items) if train_setup else items)
79
80 def _call(self, fn, x, split_idx=None, **kwargs):
/usr/local/lib/python3.6/dist-packages/fastcore/dispatch.py in __call__(self, *args, **kwargs)
96 if not f: return args[0]
97 if self.inst is not None: f = MethodType(f, self.inst)
---> 98 return f(*args, **kwargs)
99
100 def __get__(self, inst, owner):
/usr/local/lib/python3.6/dist-packages/fastai2/vision/core.py in setups(self, dl)
235
236 def setups(self, dl):
--> 237 its = dl.do_item(0)
238 for t in its:
239 if isinstance(t, TensorPoint): self.c = t.numel()
/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in __getattr__(self, k)
228 if self._component_attr_filter(k):
229 attr = getattr(self,self._default,None)
--> 230 if attr is not None: return getattr(attr,k)
231 raise AttributeError(k)
232 def __dir__(self): return custom_dir(self,self._dir())
/usr/local/lib/python3.6/dist-packages/fastcore/transform.py in __getattr__(self, k)
187 def __getitem__(self,i): return self.fs[i]
188 def __setstate__(self,data): self.__dict__.update(data)
--> 189 def __getattr__(self,k): return gather_attrs(self, k, 'fs')
190 def __dir__(self): return super().__dir__() + gather_attr_names(self, 'fs')
191
/usr/local/lib/python3.6/dist-packages/fastcore/transform.py in gather_attrs(o, k, nm)
151 att = getattr(o,nm)
152 res = [t for t in att.attrgot(k) if t is not None]
--> 153 if not res: raise AttributeError(k)
154 return res[0] if len(res)==1 else L(res)
155
AttributeError: do_item
Interesting note: when I pass in Pipelines of transform, surprisingly it works
p1 = Pipeline(itfms)
p2 = Pipeline(bbtfms)
ds = Datasets(imgs,[p1,p2])
This transform is not meant to be used on its own in a Datasets, but at the batch level as after_item. It needs the tuple (image, point) to work.
Hi,
I’m trying to create a TfmdDL subclass, but I need to be able to pass a new collate_fn. Is there any way to do that in fastai2 similar to how it was done in fastai?
If your collate function is just an operation on samples (like padding) you should pass it to before_batch (expects an array of samples and should return the modified array).
If it’s a very custom collation in itself, it’s the function create_batch you want to modify (defaults to fa_collate of fa_convert depending of if your data loader has a batch size or not)
1 Like
I’m trying to run TTA on test set, but it is using validation set instead of the test set.
get_preds works fine though.
I’m using current fastai2 github version.
Has anyone experienced this?
tst_dl = dls.test_dl([path.ls()[0]/x for x in testdf.Image.values])
preds=learn.tta(dl=tst_dl)
preds[0].shape #==> torch.Size([600, 4])
preds=learn.get_preds(dl=tst_dl)
preds[0].shape #==> torch.Size([3219, 4])
Ah yes, it was using the ds_idx internally and not the dl. Should be fixed now.
2 Likes
I’ve noticed that get_image_files() from data.transforms reads not all the image files.
Investigation showed that image_extensions is not initialized with all possible mimetypes.
Fix: add mimetypes.init()
mimetypes.init()
image_extensions = set(k for k,v in mimetypes.types_map.items() if v.startswith('image/'))
See what difference this makes.
6 Likes