Image classification¶

Setting-up type transforms pipelines
Collecting items from /home/jhoward/.fastai/data/oxford-iiit-pet/images
Found 7390 items
2 datasets of sizes 5912,1478
Setting up Pipeline: PILBase.create
Setting up Pipeline: partial -> Categorize

Building one sample
  Pipeline: PILBase.create
    starting from
      /home/jhoward/.fastai/data/oxford-iiit-pet/images/american_pit_bull_terrier_31.jpg
    applying PILBase.create gives
      PILImage mode=RGB size=500x414
  Pipeline: partial -> Categorize
    starting from
      /home/jhoward/.fastai/data/oxford-iiit-pet/images/american_pit_bull_terrier_31.jpg
    applying partial gives
      american_pit_bull_terrier
    applying Categorize gives
      TensorCategory(13)

Final sample: (PILImage mode=RGB size=500x414, TensorCategory(13))


Setting up after_item: Pipeline: ToTensor
Setting up before_batch: Pipeline: 
Setting up after_batch: Pipeline: IntToFloatTensor

Building one batch
Applying item_tfms to the first sample:
  Pipeline: ToTensor
    starting from
      (PILImage mode=RGB size=500x414, TensorCategory(13))
    applying ToTensor gives
      (TensorImage of size 3x414x500, TensorCategory(13))

Adding the next 3 samples

No before_batch transform to apply

Collating items in a batch
Error! It's not possible to collate your items in a batch
Could not collate the 0-th members of your tuples because got the following shapes
torch.Size([3, 414, 500]),torch.Size([3, 375, 500]),torch.Size([3, 500, 281]),torch.Size([3, 203, 300])

----------------------------------------------------------------
RuntimeError                   Traceback (most recent call last)
<ipython-input-18-8c0a3d421ca2> in <module>
      4                  splitter=RandomSplitter(seed=42),
      5                  get_y=using_attr(RegexLabeller(r'(.+)_\d+.jpg$'), 'name'))
----> 6 pets1.summary(path/"images")

~/git/fastai2/fastai2/data/block.py in summary(self, source, bs, **kwargs)
    172         why = _find_fail_collate(s)
    173         print("Make sure all parts of your samples are tensors of the same size" if why is None else why)
--> 174         raise e
    175 
    176     if len([f for f in dls.train.after_batch.fs if f.name != 'noop'])!=0:

~/git/fastai2/fastai2/data/block.py in summary(self, source, bs, **kwargs)
    166     print("\nCollating items in a batch")
    167     try:
--> 168         b = dls.train.create_batch(s)
    169         b = retain_types(b, s[0] if is_listy(s) else s)
    170     except Exception as e:

~/git/fastai2/fastai2/data/load.py in create_batch(self, b)
    124     def retain(self, res, b):  return retain_types(res, b[0] if is_listy(b) else b)
    125     def create_item(self, s):  return next(self.it) if s is None else self.dataset[s]
--> 126     def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b)
    127     def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
    128     def one_batch(self):

~/git/fastai2/fastai2/data/load.py in fa_collate(t)
     44     b = t[0]
     45     return (default_collate(t) if isinstance(b, _collate_types)
---> 46             else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
     47             else default_collate(t))
     48 

~/git/fastai2/fastai2/data/load.py in <listcomp>(.0)
     44     b = t[0]
     45     return (default_collate(t) if isinstance(b, _collate_types)
---> 46             else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
     47             else default_collate(t))
     48 

~/git/fastai2/fastai2/data/load.py in fa_collate(t)
     43 def fa_collate(t):
     44     b = t[0]
---> 45     return (default_collate(t) if isinstance(b, _collate_types)
     46             else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
     47             else default_collate(t))

~/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py in default_collate(batch)
     53             storage = elem.storage()._new_shared(numel)
     54             out = elem.new(storage)
---> 55         return torch.stack(batch, 0, out=out)
     56     elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
     57             and elem_type.__name__ != 'string_':

RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 414 and 375 in dimension 2 at /opt/conda/conda-bld/pytorch_1579022060824/work/aten/src/TH/generic/THTensor.cpp:612

Minimum/10: 8.32e-03, steepest point: 6.31e-03