Hey guys,
Problem: How to load image files of *.RAW format into ImageBlock?
Suggested solution:
Looking at: (Data.py)
# Cell
def ImageBlock(cls=PILImage):
"A `TransformBlock` for images of `cls`"
return TransformBlock(type_tfms=cls.create, batch_tfms=IntToFloatTensor)
What if I can create another cls that is based on rawpy API?
So far it looks like that PIL API supports mostly *.JPG format.
This issue is followed by this issue:
Defining your own image class would be the right solution.
The following suggestion could work:
def load_raw_image(fn):
"Open and load a raw image with rawpy"
# your function to load a raw image from fn/Path here
# return a numpy array `arr`
return arr
class RawpyImage(PILBase):
@classmethod
def create(cls, fn:(Path,str), **kwargs)->None:
"Open a raw image from path `fn`"
arr = load_raw_image(fn)
return cls(Image.fromarray(arr))
Make sure the format of the np.array generated in load_raw_image is either uint8 (integer values between 0 and 255) or float32 (float values between 0 and 1).
Thanks man! Sounds great.
Make sure the format of the
np.arraygenerated inload_raw_imageis either uint8 (integer values between 0 and 255) or float32 (float values between 0 and 1).
JPG has about 8 bits for each pixel, so there are at most 256 different values. But RAW files have about 16 bits for each pixel, so there are at most 65536 different values. So would I need uint16, isn’t it?
In theory, yes, but IIRC, uint16 cannot be transformed to a PILImage instance. I had those problems when reading DICOM files, which are also 16 bit. Going with float values, rescaled to values between 0 and 1 might be easier.
So if I get it right, I should unpack the image file into a matrix, then divide every pixel by 65536, then convert it into np.array of the datatype of float32?
Yes. This should work.
Hi !
I want to load 16 bits png images. Does fastai do this normalization for me?
Hi BresNet !
How does this fit in a simple image classification script? Where I call this class?
Maybe here:
ds = DataBlock(blocks = (ImageBlock(cls=RawpyImage),CategoryBlock),
get_items = get_image_files,
splitter = GrandparentSplitter(),
get_y = parent_label)
I’ve tried this:
def load_16bits_image(fn):
img = Image.open(fn)
arr = np.array(img) / 65365
return arr
class Grayscale16bitsImage(PILBase):
@classmethod
def create(cls, fn : (Path, str), **kwargs)->None:
"Open a raw image from path `fn`"
arr = load_16bits_image(fn)
return cls(Image.fromarray(arr))
path = Path('data')
item_tfms = [Resize(224)]
batch_tfms = aug_transforms(
mult=2, do_flip=False, max_rotate=15
)
dblock = DataBlock(
blocks=(ImageBlock(cls=Grayscale16bitsImage), CategoryBlock),
get_items=get_image_files,
splitter=GrandparentSplitter(train_name='train', valid_name='valid'),
get_y=parent_label,
item_tfms=item_tfms,
batch_tfms=batch_tfms)
But I get the following error:
AttributeError: _tensor_cls
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-56-10885883025c> in <module>
32 print(classes)
33
---> 34 dls_patches_one.show_batch()
35
~\Anaconda3\envs\dlenv\lib\site-packages\fastai\data\core.py in show_batch(self, b, max_n, ctxs, show, unique, **kwargs)
98 old_get_idxs = self.get_idxs
99 self.get_idxs = lambda: Inf.zeros
--> 100 if b is None: b = self.one_batch()
101 if not show: return self._pre_show_batch(b, max_n=max_n)
102 show_batch(*self._pre_show_batch(b, max_n=max_n), ctxs=ctxs, max_n=max_n, **kwargs)
~\Anaconda3\envs\dlenv\lib\site-packages\fastai\data\load.py in one_batch(self)
146 def one_batch(self):
147 if self.n is not None and len(self)==0: raise ValueError(f'This DataLoader does not contain any batches')
--> 148 with self.fake_l.no_multiproc(): res = first(self)
149 if hasattr(self, 'it'): delattr(self, 'it')
150 return res
~\Anaconda3\envs\dlenv\lib\site-packages\fastcore\basics.py in first(x, f, negate, **kwargs)
545 x = iter(x)
546 if f: x = filter_ex(x, f=f, negate=negate, gen=True, **kwargs)
--> 547 return next(x, None)
548
549 # Cell
~\Anaconda3\envs\dlenv\lib\site-packages\fastai\data\load.py in __iter__(self)
107 self.before_iter()
108 self.__idxs=self.get_idxs() # called in context of main process (not workers/subprocesses)
--> 109 for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
110 if self.device is not None: b = to_device(b, self.device)
111 yield self.after_batch(b)
~\Anaconda3\envs\dlenv\lib\site-packages\torch\utils\data\dataloader.py in __next__(self)
519 if self._sampler_iter is None:
520 self._reset()
--> 521 data = self._next_data()
522 self._num_yielded += 1
523 if self._dataset_kind == _DatasetKind.Iterable and \
~\Anaconda3\envs\dlenv\lib\site-packages\torch\utils\data\dataloader.py in _next_data(self)
559 def _next_data(self):
560 index = self._next_index() # may raise StopIteration
--> 561 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
562 if self._pin_memory:
563 data = _utils.pin_memory.pin_memory(data)
~\Anaconda3\envs\dlenv\lib\site-packages\torch\utils\data\_utils\fetch.py in fetch(self, possibly_batched_index)
32 raise StopIteration
33 else:
---> 34 data = next(self.dataset_iter)
35 return self.collate_fn(data)
36
~\Anaconda3\envs\dlenv\lib\site-packages\fastai\data\load.py in create_batches(self, samps)
116 if self.dataset is not None: self.it = iter(self.dataset)
117 res = filter(lambda o:o is not None, map(self.do_item, samps))
--> 118 yield from map(self.do_batch, self.chunkify(res))
119
120 def new(self, dataset=None, cls=None, **kwargs):
~\Anaconda3\envs\dlenv\lib\site-packages\fastcore\basics.py in chunked(it, chunk_sz, drop_last, n_chunks)
214 if not isinstance(it, Iterator): it = iter(it)
215 while True:
--> 216 res = list(itertools.islice(it, chunk_sz))
217 if res and (len(res)==chunk_sz or not drop_last): yield res
218 if len(res)<chunk_sz: return
~\Anaconda3\envs\dlenv\lib\site-packages\fastai\data\load.py in do_item(self, s)
131 def prebatched(self): return self.bs is None
132 def do_item(self, s):
--> 133 try: return self.after_item(self.create_item(s))
134 except SkipItemException: return None
135 def chunkify(self, b): return b if self.prebatched else chunked(b, self.bs, self.drop_last)
~\Anaconda3\envs\dlenv\lib\site-packages\fastcore\transform.py in __call__(self, o)
198 self.fs = self.fs.sorted(key='order')
199
--> 200 def __call__(self, o): return compose_tfms(o, tfms=self.fs, split_idx=self.split_idx)
201 def __repr__(self): return f"Pipeline: {' -> '.join([f.name for f in self.fs if f.name != 'noop'])}"
202 def __getitem__(self,i): return self.fs[i]
~\Anaconda3\envs\dlenv\lib\site-packages\fastcore\transform.py in compose_tfms(x, tfms, is_enc, reverse, **kwargs)
148 for f in tfms:
149 if not is_enc: f = f.decode
--> 150 x = f(x, **kwargs)
151 return x
152
~\Anaconda3\envs\dlenv\lib\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}'
~\Anaconda3\envs\dlenv\lib\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):
~\Anaconda3\envs\dlenv\lib\site-packages\fastcore\transform.py in _do_call(self, f, x, **kwargs)
88 ret = f.returns(x) if hasattr(f,'returns') 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)
92
~\Anaconda3\envs\dlenv\lib\site-packages\fastcore\transform.py in <genexpr>(.0)
88 ret = f.returns(x) if hasattr(f,'returns') 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)
92
~\Anaconda3\envs\dlenv\lib\site-packages\fastcore\transform.py in _do_call(self, f, x, **kwargs)
87 if f is None: return x
88 ret = f.returns(x) if hasattr(f,'returns') 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)
~\Anaconda3\envs\dlenv\lib\site-packages\fastcore\dispatch.py in __call__(self, *args, **kwargs)
116 elif self.inst is not None: f = MethodType(f, self.inst)
117 elif self.owner is not None: f = MethodType(f, self.owner)
--> 118 return f(*args, **kwargs)
119
120 def __get__(self, inst, owner):
~\Anaconda3\envs\dlenv\lib\site-packages\fastai\vision\core.py in encodes(self, o)
221 # Cell
222 @ToTensor
--> 223 def encodes(self, o:PILBase): return o._tensor_cls(image2tensor(o))
224 @ToTensor
225 def encodes(self, o:PILMask): return o._tensor_cls(image2tensor(o)[0])
~\Anaconda3\envs\dlenv\lib\site-packages\PIL\Image.py in __getattr__(self, name)
544 )
545 return self._category
--> 546 raise AttributeError(name)
547
548 @property
AttributeError: _tensor_cls
Maybe load_16bits_image(fn) isn’t loading correctly the images here img = Image.open(fn). How can I see the values of fn?
My guess would be, too, that the error is in load_16bits_image_().
Try loading a file with the function only and check if you get a valid numpy array.
arr = load_16bits_image_(`path/to/file`)
arr.shape, arr.max(), arr.min()
If that works, maybe the error is in your getters. Does get_image_files return a valid file name? you could use print debugging in load_16bits_image_() function. This way, you’ll know what the values of fn are. It should be a valid FileName.
I tried to implement it now. I’ve got this far:
def loadRawImage(fn):
fn=str(fn)
raw_file=rawpy.imread(fn)
raw_converted_file=raw_file.raw_image_visible.astype(np.float32)
ndarr=np.asarray(raw_converted_file)/(65536.0)
return ndarr
class RawpyImage(PILBase):
@classmethod
def create(cls, fn:(Path, str), **kwargs)->None:
"Open a raw image from path `fn`"
arr = loadRawImage(fn)
return cls(Image.fromarray(arr, mode="RGB"))
def __repr__(self): return f'{self.__class__.__name__} mode={self.mode} size={"x".join([str(d) for d in self.size])}'
But then I got this message, which I can’t tell where it comes from: (I tried to find this error type in Rawpy package, but there isn’t that I could find)
LibRawIOError Traceback (most recent call last)
<ipython-input-54-4ad80883aa74> in <module>()
...
8 )
----> 9 dls = dblock.dataloaders(source=path,bs=btchSize,path=path)
...
/usr/local/lib/python3.7/dist-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, verbose=verbose)
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)
/usr/local/lib/python3.7/dist-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):
/usr/local/lib/python3.7/dist-packages/fastai/data/core.py in __init__(self, items, tfms, tls, n_inp, dl_type, **kwargs)
326 def __init__(self, items=None, tfms=None, tls=None, n_inp=None, dl_type=None, **kwargs):
327 super().__init__(dl_type=dl_type)
--> 328 self.tls = L(tls if tls else [TfmdLists(items, t, **kwargs) for t in L(ifnone(tfms,[None]))])
329 self.n_inp = ifnone(n_inp, max(1, len(self.tls)-1))
330
/usr/local/lib/python3.7/dist-packages/fastai/data/core.py in <listcomp>(.0)
326 def __init__(self, items=None, tfms=None, tls=None, n_inp=None, dl_type=None, **kwargs):
327 super().__init__(dl_type=dl_type)
--> 328 self.tls = L(tls if tls else [TfmdLists(items, t, **kwargs) for t in L(ifnone(tfms,[None]))])
329 self.n_inp = ifnone(n_inp, max(1, len(self.tls)-1))
330
/usr/local/lib/python3.7/dist-packages/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
95 def __call__(cls, x=None, *args, **kwargs):
96 if not args and not kwargs and x is not None and isinstance(x,cls): return x
---> 97 return super().__call__(x, *args, **kwargs)
98
99 # Cell
/usr/local/lib/python3.7/dist-packages/fastai/data/core.py in __init__(self, items, tfms, use_list, do_setup, split_idx, train_setup, splits, types, verbose, dl_type)
252 if do_setup:
253 pv(f"Setting up {self.tfms}", verbose)
--> 254 self.setup(train_setup=train_setup)
255
256 def _new(self, items, split_idx=None, **kwargs):
/usr/local/lib/python3.7/dist-packages/fastai/data/core.py in setup(self, train_setup)
274 for f in self.tfms.fs:
275 self.types.append(getattr(f, 'input_types', type(x)))
--> 276 x = f(x)
277 self.types.append(type(x))
278 types = L(t if is_listy(t) else [t] for t in self.types).concat().unique()
/usr/local/lib/python3.7/dist-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}'
/usr/local/lib/python3.7/dist-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):
/usr/local/lib/python3.7/dist-packages/fastcore/transform.py in _do_call(self, f, x, **kwargs)
87 if f is None: return x
88 ret = f.returns(x) if hasattr(f,'returns') 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)
/usr/local/lib/python3.7/dist-packages/fastcore/dispatch.py in __call__(self, *args, **kwargs)
116 elif self.inst is not None: f = MethodType(f, self.inst)
117 elif self.owner is not None: f = MethodType(f, self.owner)
--> 118 return f(*args, **kwargs)
119
120 def __get__(self, inst, owner):
<ipython-input-43-c9b93e6bca6e> in create(cls, fn, **kwargs)
15 def create(cls, fn:(Path, str), **kwargs)->None:
16 "Open a raw image from path `fn`"
---> 17 arr = loadRawImage(fn)
18 return cls(Image.fromarray(arr, mode="RGB"))
19
<ipython-input-43-c9b93e6bca6e> in loadRawImage(fn)
6 #print(fn.encode())
7 #print(str(fn))
----> 8 raw_file=rawpy.imread(fn)
9 raw_converted_file=raw_file.raw_image_visible.astype(np.float32)
10 ndarr=np.asarray(raw_converted_file)/(65536.0)
/usr/local/lib/python3.7/dist-packages/rawpy/__init__.py in imread(pathOrFile)
18 d.open_buffer(pathOrFile)
19 else:
---> 20 d.open_file(pathOrFile)
21 return d
rawpy/_rawpy.pyx in rawpy._rawpy.RawPy.open_file()
rawpy/_rawpy.pyx in rawpy._rawpy.RawPy.handle_error()
LibRawIOError: b'Input/output error'
So hell what, I don’t get it where or what does it mean:
LibRawIOError: b'Input/output error'
Any ideas?
Maybe your file paths are not correct? Or the file format is not compatible with rawpy?
I tried that function on some files somewhere else, and the function did work.
Apparently, it doesn’t work only when the DataBlock starts running it, but I can’t figure out how to debug this correctly… Because I didn’t find where it prints “b’Input/output error’”
OK, I found the solution for this error.
So what happened basically was: this error
LibRawIOError: b'Input/output error'
Told me that this command (see below) couldn’t work:
d.open_file(pathOrFile)
Which means that it couldn’t open the file. This could be because the file wasn’t in the folder, or because the path was wrong, or perhaps some other option.
I just kept adding print() at every possible step to see what happens, until I finally found out that at some point, that command was fed by a wrong auto-generated path (of the target files). I just had to change that auto-generating function, so it creates the path with the correct new format (of RAW files rather than JPG files).
So it worked.
But now I’m facing other problems, Yay! Oh wait, you guys are gonna love them. So fun them the new ones.
Great job man!
Ok, funny fact is that I encountered the same error as yours!
Here I can also track the log to see what went wrong.
Basically, the error claims that the new class that we created doesn’t include a field named “_tensor_cls”.
But checking this out, even PILBase doesn’t have that field. So I’m not even sure where I should add it myself…
Setting-up type transforms pipelines
Collecting items from /content/drive/MyDrive/Dataset12/train/Short
Found 1 items
2 datasets of sizes 1,0
Setting up Pipeline: RawpyImage.create
/content/drive/MyDrive/Dataset12/train/Short/P1200133.ORF
Setting up Pipeline: my_get_y -> RawpyImage.create
/content/drive/MyDrive/Dataset12/train/Long/P1200131.ORF
Building one sample
Pipeline: RawpyImage.create
starting from
/content/drive/MyDrive/Dataset12/train/Short/P1200133.ORF
/content/drive/MyDrive/Dataset12/train/Short/P1200133.ORF
applying RawpyImage.create gives
RawpyImage mode=RGB size=5240x3912
Pipeline: my_get_y -> RawpyImage.create
starting from
/content/drive/MyDrive/Dataset12/train/Short/P1200133.ORF
applying my_get_y gives
/content/drive/MyDrive/Dataset12/train/Long/P1200131.ORF
/content/drive/MyDrive/Dataset12/train/Long/P1200131.ORF
applying RawpyImage.create gives
RawpyImage mode=RGB size=5240x3912
/content/drive/MyDrive/Dataset12/train/Short/P1200133.ORF
/content/drive/MyDrive/Dataset12/train/Long/P1200131.ORF
Final sample: (RawpyImage mode=RGB size=5240x3912, RawpyImage mode=RGB size=5240x3912)
Collecting items from /content/drive/MyDrive/Dataset12/train/Short
Found 1 items
2 datasets of sizes 1,0
Setting up Pipeline: RawpyImage.create
/content/drive/MyDrive/Dataset12/train/Short/P1200133.ORF
Setting up Pipeline: my_get_y -> RawpyImage.create
/content/drive/MyDrive/Dataset12/train/Long/P1200131.ORF
Setting up after_item: Pipeline: ToTensor
Setting up before_batch: Pipeline:
Setting up after_batch: Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1}
/content/drive/MyDrive/Dataset12/train/Short/P1200133.ORF
/content/drive/MyDrive/Dataset12/train/Long/P1200131.ORF
Could not do one pass in your dataloader, there is something wrong in it
Building one batch
Applying item_tfms to the first sample:
/content/drive/MyDrive/Dataset12/train/Short/P1200133.ORF
/content/drive/MyDrive/Dataset12/train/Long/P1200131.ORF
Pipeline: ToTensor
starting from
(RawpyImage mode=RGB size=5240x3912, RawpyImage mode=RGB size=5240x3912)
applying ToTensor failed.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-34-585fa9d83a7f> in <module>()
----> 1 dblock.sum()
9 frames
/usr/local/lib/python3.7/dist-packages/fastai/data/block.py in summary(self, source, bs, show_batch, **kwargs)
168 if len([f for f in dls.train.after_item.fs if f.name != 'noop'])!=0:
169 print("Applying item_tfms to the first sample:")
--> 170 s = [_apply_pipeline(dls.train.after_item, dsets.train[0])]
171 print(f"\nAdding the next {bs-1} samples")
172 s += [dls.train.after_item(dsets.train[i]) for i in range(1, bs)]
/usr/local/lib/python3.7/dist-packages/fastai/data/block.py in _apply_pipeline(p, x)
137 except Exception as e:
138 print(f" applying {name} failed.")
--> 139 raise e
140 return x
141
/usr/local/lib/python3.7/dist-packages/fastai/data/block.py in _apply_pipeline(p, x)
133 name = f.name
134 try:
--> 135 x = f(x)
136 if name != "noop": print(f" applying {name} gives\n {_short_repr(x)}")
137 except Exception as e:
/usr/local/lib/python3.7/dist-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}'
/usr/local/lib/python3.7/dist-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):
/usr/local/lib/python3.7/dist-packages/fastcore/transform.py in _do_call(self, f, x, **kwargs)
88 ret = f.returns(x) if hasattr(f,'returns') 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)
92
/usr/local/lib/python3.7/dist-packages/fastcore/transform.py in <genexpr>(.0)
88 ret = f.returns(x) if hasattr(f,'returns') 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)
92
/usr/local/lib/python3.7/dist-packages/fastcore/transform.py in _do_call(self, f, x, **kwargs)
87 if f is None: return x
88 ret = f.returns(x) if hasattr(f,'returns') 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)
/usr/local/lib/python3.7/dist-packages/fastcore/dispatch.py in __call__(self, *args, **kwargs)
116 elif self.inst is not None: f = MethodType(f, self.inst)
117 elif self.owner is not None: f = MethodType(f, self.owner)
--> 118 return f(*args, **kwargs)
119
120 def __get__(self, inst, owner):
/usr/local/lib/python3.7/dist-packages/fastai/vision/core.py in encodes(self, o)
221 # Cell
222 @ToTensor
--> 223 def encodes(self, o:PILBase): return o._tensor_cls(image2tensor(o))
224 @ToTensor
225 def encodes(self, o:PILMask): return o._tensor_cls(image2tensor(o)[0])
AttributeError: 'RawpyImage' object has no attribute '_tensor_cls'
Alright guys, I managed to solve this issue by adding _tensor_cls to RawpyImage. It took me some nerve to dive into the source and search for those attributes and other issues.
But then I realized something: The whole fastai library is based on the PIL library for images files, which itself does not support any RAW files. So adding this RawpyImage class isn’t enough, as PILBase inherits from Image.Image (PIL).
Meaning, adding the RawpyImage class, that inherits from PILBase, that in turn inherits from PIL, makes just no sense - because PIL does not support RAW Files. All of the rest of Fastai classes and methods, such as “show_image” or “RandomResizedCrop”, are all based on getting PIL objects… Not Rawpy files…
I’ve come up to the thought that an approach change is needed here.
I’d love to hear your opinion about this, and maybe a little guidance for me, as maybe it’s quite easy to solve and contribute to the project.
My whole need is to have the framework of fastai support Rawpy objects. So while PIL objects are being converted into Tensor objects (of Pytorch), maybe it would be even still easy to add another layer of support, by making fastai convert Rawpy objects into Tesnor objects, so the bones of the fasiai framework still apply to those tensors for training it.
Hey folks,
I’m trying to rebuild the PILBase as RAWBase, so it will support RAW files.
What do you think could be the problem here?
My code:
class RawBase(Image.Image, metaclass=BypassNewMeta): #, metaclass=BypassNewMeta
"This is to copy class PILBase"
_bypass_type=Image.Image
_show_args = {'cmap':'viridis'}
_open_args = {'mode': 'RGB'}
@classmethod
#staticmethod
def create(cls,fn:(Path,str), *args, **kwargs)->None:
return cls(load_raw_image(fn, *args, **kwargs))
def __repr__(self): return f'{self.__class__.__name__} mode={self.mode} size={"x".join([str(d) for d in self.size])}'
Of course, RAWBase won’t inherit Image.Image eventually, because that is also part of PIL library. But there’s an error that I can’t tell where it comes from or why it does.
/usr/local/lib/python3.7/dist-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, verbose=verbose)
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)
/usr/local/lib/python3.7/dist-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):
/usr/local/lib/python3.7/dist-packages/fastai/data/core.py in __init__(self, items, tfms, tls, n_inp, dl_type, **kwargs)
327 def __init__(self, items=None, tfms=None, tls=None, n_inp=None, dl_type=None, **kwargs):
328 super().__init__(dl_type=dl_type)
--> 329 self.tls = L(tls if tls else [TfmdLists(items, t, **kwargs) for t in L(ifnone(tfms,[None]))])
330 self.n_inp = ifnone(n_inp, max(1, len(self.tls)-1))
331
/usr/local/lib/python3.7/dist-packages/fastai/data/core.py in <listcomp>(.0)
327 def __init__(self, items=None, tfms=None, tls=None, n_inp=None, dl_type=None, **kwargs):
328 super().__init__(dl_type=dl_type)
--> 329 self.tls = L(tls if tls else [TfmdLists(items, t, **kwargs) for t in L(ifnone(tfms,[None]))])
330 self.n_inp = ifnone(n_inp, max(1, len(self.tls)-1))
331
/usr/local/lib/python3.7/dist-packages/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
95 def __call__(cls, x=None, *args, **kwargs):
96 if not args and not kwargs and x is not None and isinstance(x,cls): return x
---> 97 return super().__call__(x, *args, **kwargs)
98
99 # Cell
/usr/local/lib/python3.7/dist-packages/fastai/data/core.py in __init__(self, items, tfms, use_list, do_setup, split_idx, train_setup, splits, types, verbose, dl_type)
253 if do_setup:
254 pv(f"Setting up {self.tfms}", verbose)
--> 255 self.setup(train_setup=train_setup)
256
257 def _new(self, items, split_idx=None, **kwargs):
/usr/local/lib/python3.7/dist-packages/fastai/data/core.py in setup(self, train_setup)
275 for f in self.tfms.fs:
276 self.types.append(getattr(f, 'input_types', type(x)))
--> 277 x = f(x)
278 self.types.append(type(x))
279 types = L(t if is_listy(t) else [t] for t in self.types).concat().unique()
/usr/local/lib/python3.7/dist-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}'
/usr/local/lib/python3.7/dist-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):
/usr/local/lib/python3.7/dist-packages/fastcore/transform.py in _do_call(self, f, x, **kwargs)
87 if f is None: return x
88 ret = f.returns(x) if hasattr(f,'returns') 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)
/usr/local/lib/python3.7/dist-packages/fastcore/dispatch.py in __call__(self, *args, **kwargs)
121 elif self.inst is not None: f = MethodType(f, self.inst)
122 elif self.owner is not None: f = MethodType(f, self.owner)
--> 123 return f(*args, **kwargs)
124
125 def __get__(self, inst, owner):
<ipython-input-37-5c1b39903303> in create(cls, fn, *args, **kwargs)
36 #staticmethod
37 def create(cls,fn:(Path,str), *args, **kwargs)->None:
---> 38 return cls(load_raw_image(fn, *args, **kwargs))
39
40 def show(self):
/usr/local/lib/python3.7/dist-packages/fastcore/meta.py in __call__(cls, x, *args, **kwargs)
60 if hasattr(cls, '_new_meta'): x = cls._new_meta(x, *args, **kwargs)
61 elif not isinstance(x,getattr(cls,'_bypass_type',object)) or len(args) or len(kwargs):
---> 62 x = super().__call__(*((x,)+args), **kwargs)
63 if cls!=x.__class__: x.__class__ = cls
64 return x
TypeError: __init__() takes 1 positional argument but 2 were given
Can you please show us how come float32 did work for you?
I tried your way once again, and it seems that Image.fromarray() will turn the float32 ndarray into uint8 type of image object whatsoever.
It’s been a while, since I’ve done this, so I don’t remember exactly how I did it. You can try to change the mode of the image, fromarray has an argument for this.
Is there a specific reason, you need the array to be a PIL.Image? I believe all augmentations also work with numpy arrays or tensors?
Hey man! Oh.
I’ve tried to adjust fastai to support RAW files, but it seems that a lot of the other vision methods (such as “show” and augmentations) are based on Image.Image objects. I may be wrong (hopefully I am), and that’s why I was eager to find a way to only adjust the PILBase class to support RAW.
I found out that PIL supports TIFF and BMP formats, which also share 32bits and 16bits,24bits accordingly, so I was thinking about maybe I’ll just load the RAW files as converted to BMP files.
But it turns out that PIL does support BMP, meaning that it reads it and gets its attributes, but it stores the values of the pixels in uint8 type (after scaling). That’s just a huge bummer.
So yes, I will have to rewrite a whole class just for RAW files. But I can’t tell whether the rest of fastai will support the class augumentations methods.