change param name copy_meta->as_copy in retain_meta`

[jph00]

No description provided.

[kmad]

FYI this completely breaks the 01_intro notebook examples (and likely other areas as well) when running on a fresh install of the docker image. Reverting these changes fixed the issue. Perhaps this should be on a different branch?

Output below:

--------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-2590e3bef449> in <module>
      6 
      7 def is_cat(x): return x[0].isupper()
----> 8 dls = ImageDataLoaders.from_name_func(
      9     path, get_image_files(path), valid_pct=0.2, seed=42,
     10     label_func=is_cat, item_tfms=Resize(224))

/opt/conda/lib/python3.8/site-packages/fastai/vision/data.py in from_name_func(cls, path, fnames, label_func, **kwargs)
    109         "Create from the name attrs of `fnames` in `path`s with `label_func`"
    110         f = using_attr(label_func, 'name')
--> 111         return cls.from_path_func(path, fnames, f, **kwargs)
    112 
    113     @classmethod

/opt/conda/lib/python3.8/site-packages/fastai/vision/data.py in from_path_func(cls, path, fnames, label_func, valid_pct, seed, item_tfms, batch_tfms, **kwargs)
    103                            item_tfms=item_tfms,
    104                            batch_tfms=batch_tfms)
--> 105         return cls.from_dblock(dblock, fnames, path=path, **kwargs)
    106 
    107     @classmethod

/opt/conda/lib/python3.8/site-packages/fastai/data/core.py in from_dblock(cls, dblock, source, path, bs, val_bs, shuffle_train, device, **kwargs)
    178     @classmethod
    179     def from_dblock(cls, dblock, source, path='.',  bs=64, val_bs=None, shuffle_train=True, device=None, **kwargs):
--> 180         return dblock.dataloaders(source, path=path, bs=bs, val_bs=val_bs, shuffle_train=shuffle_train, device=device, **kwargs)
    181 
    182     _docs=dict(__getitem__="Retrieve `DataLoader` at `i` (`0` is training, `1` is validation)",

/opt/conda/lib/python3.8/site-packages/fastai/data/block.py in dataloaders(self, source, path, verbose, **kwargs)
    111 
    112     def dataloaders(self, source, path='.', verbose=False, **kwargs):
--> 113         dsets = self.datasets(source)
    114         kwargs = {**self.dls_kwargs, **kwargs, 'verbose': verbose}
    115         return dsets.dataloaders(path=path, after_item=self.item_tfms, after_batch=self.batch_tfms, **kwargs)

/opt/conda/lib/python3.8/site-packages/fastai/data/block.py in datasets(self, source, verbose)
    108         splits = (self.splitter or RandomSplitter())(items)
    109         pv(f"{len(splits)} datasets of sizes {','.join([str(len(s)) for s in splits])}", verbose)
--> 110         return Datasets(items, tfms=self._combine_type_tfms(), splits=splits, dl_type=self.dl_type, n_inp=self.n_inp, verbose=verbose)
    111 
    112     def dataloaders(self, source, path='.', verbose=False, **kwargs):

/opt/conda/lib/python3.8/site-packages/fastai/data/core.py in __init__(self, items, tfms, tls, n_inp, dl_type, **kwargs)
    308     def __init__(self, items=None, tfms=None, tls=None, n_inp=None, dl_type=None, **kwargs):
    309         super().__init__(dl_type=dl_type)
--> 310         self.tls = L(tls if tls else [TfmdLists(items, t, **kwargs) for t in L(ifnone(tfms,[None]))])
    311         self.n_inp = ifnone(n_inp, max(1, len(self.tls)-1))
    312 

/opt/conda/lib/python3.8/site-packages/fastai/data/core.py in <listcomp>(.0)
    308     def __init__(self, items=None, tfms=None, tls=None, n_inp=None, dl_type=None, **kwargs):
    309         super().__init__(dl_type=dl_type)
--> 310         self.tls = L(tls if tls else [TfmdLists(items, t, **kwargs) for t in L(ifnone(tfms,[None]))])
    311         self.n_inp = ifnone(n_inp, max(1, len(self.tls)-1))
    312 

/opt/conda/lib/python3.8/site-packages/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
    161     def __call__(cls, x=None, *args, **kwargs):
    162         if not args and not kwargs and x is not None and isinstance(x,cls): return x
--> 163         return super().__call__(x, *args, **kwargs)
    164 
    165 # Cell

/opt/conda/lib/python3.8/site-packages/fastai/data/core.py in __init__(self, items, tfms, use_list, do_setup, split_idx, train_setup, splits, types, verbose, dl_type)
    234         if do_setup:
    235             pv(f"Setting up {self.tfms}", verbose)
--> 236             self.setup(train_setup=train_setup)
    237 
    238     def _new(self, items, split_idx=None, **kwargs):

/opt/conda/lib/python3.8/site-packages/fastai/data/core.py in setup(self, train_setup)
    256             for f in self.tfms.fs:
    257                 self.types.append(getattr(f, 'input_types', type(x)))
--> 258                 x = f(x)
    259             self.types.append(type(x))
    260         types = L(t if is_listy(t) else [t] for t in self.types).concat().unique()

/opt/conda/lib/python3.8/site-packages/fastcore/transform.py in __call__(self, x, **kwargs)
     71     @property
     72     def name(self): return getattr(self, '_name', _get_name(self))
---> 73     def __call__(self, x, **kwargs): return self._call('encodes', x, **kwargs)
     74     def decode  (self, x, **kwargs): return self._call('decodes', x, **kwargs)
     75     def __repr__(self): return f'{self.name}:\nencodes: {self.encodes}decodes: {self.decodes}'

/opt/conda/lib/python3.8/site-packages/fastcore/transform.py in _call(self, fn, x, split_idx, **kwargs)
     81     def _call(self, fn, x, split_idx=None, **kwargs):
     82         if split_idx!=self.split_idx and self.split_idx is not None: return x
---> 83         return self._do_call(getattr(self, fn), x, **kwargs)
     84 
     85     def _do_call(self, f, x, **kwargs):

/opt/conda/lib/python3.8/site-packages/fastcore/transform.py in _do_call(self, f, x, **kwargs)
     87             if f is None: return x
     88             ret = f.returns_none(x) if hasattr(f,'returns_none') else None
---> 89             return retain_type(f(x, **kwargs), x, ret)
     90         res = tuple(self._do_call(f, x_, **kwargs) for x_ in x)
     91         return retain_type(res, x)

/opt/conda/lib/python3.8/site-packages/fastcore/dispatch.py in __call__(self, *args, **kwargs)
    127         elif self.inst is not None: f = MethodType(f, self.inst)
    128         elif self.owner is not None: f = MethodType(f, self.owner)
--> 129         return f(*args, **kwargs)
    130 
    131     def __get__(self, inst, owner):

/opt/conda/lib/python3.8/site-packages/fastai/data/transforms.py in encodes(self, o)
    240     def encodes(self, o):
    241         try:
--> 242             return TensorCategory(self.vocab.o2i[o])
    243         except KeyError as e:
    244             raise KeyError(f"Label '{o}' was not included in the training dataset") from e

/opt/conda/lib/python3.8/site-packages/fastai/torch_core.py in __new__(cls, x, **kwargs)
    300 class TensorBase(Tensor):
    301     def __new__(cls, x, **kwargs):
--> 302         res = cast(tensor(x), cls)
    303         if kwargs: res._meta = kwargs
    304         return res

/opt/conda/lib/python3.8/site-packages/fastcore/dispatch.py in __call__(self, *args, **kwargs)
    127         elif self.inst is not None: f = MethodType(f, self.inst)
    128         elif self.owner is not None: f = MethodType(f, self.owner)
--> 129         return f(*args, **kwargs)
    130 
    131     def __get__(self, inst, owner):

/opt/conda/lib/python3.8/site-packages/fastcore/dispatch.py in cast(x, typ)
    188     res = typ._before_cast(x) if hasattr(typ, '_before_cast') else x
    189     if isinstance_str(res, 'ndarray'): res = res.view(typ)
--> 190     elif hasattr(res, 'as_subclass'): res = res.as_subclass(typ)
    191     else:
    192         try: res.__class__ = typ

/opt/conda/lib/python3.8/site-packages/fastai/torch_core.py in as_subclass(self, typ)
    295 def as_subclass(self:Tensor, typ):
    296     "Cast to `typ` and include `__dict__` and meta"
--> 297     return retain_meta(self, torch.as_subclass(self, typ))
    298 
    299 # Cell

/opt/conda/lib/python3.8/site-packages/fastcore/dispatch.py in retain_meta(x, res, as_copy)
    170 def retain_meta(x, res, as_copy=False):
    171     "Call `res.set_meta(x)`, if it exists"
--> 172     if hasattr(res,'set_meta'): res.set_meta(x, as_copy=as_copy)
    173     return res
    174 

/opt/conda/lib/python3.8/site-packages/fastai/torch_core.py in _f(self, *args, **kwargs)
    329         def _f(self, *args, **kwargs):
    330             cls = self.__class__
--> 331             res = getattr(super(TensorBase, self), fn)(*args, **kwargs)
    332             return retain_type(res, self, copy_meta=True)
    333         return _f

TypeError: set_meta() got an unexpected keyword argument 'as_copy'

[jph00]

Please update to the latest fastai and it should all work fine.