from .torch_core import * from .basic_data import * from .layers import * from numbers import Integral __all__ = ['ItemList', 'CategoryList', 'MultiCategoryList', 'MultiCategoryProcessor', 'LabelList', 'ItemLists', 'get_files', 'PreProcessor', 'LabelLists', 'FloatList', 'CategoryProcessor', 'EmptyLabelList', 'MixedItem', 'MixedProcessor', 'MixedItemList'] def _decode(df): return np.array([[df.columns[i] for i,t in enumerate(x) if t==1] for x in df.values], dtype=np.object) def _maybe_squeeze(arr): return (arr if is1d(arr) else np.squeeze(arr)) def _path_to_same_str(p_fn): "path -> str, but same on nt+posix, for alpha-sort only" s_fn = str(p_fn) s_fn = s_fn.replace('\\','.') s_fn = s_fn.replace('/','.') return s_fn def _get_files(parent, p, f, extensions): p = Path(p)#.relative_to(parent) if isinstance(extensions,str): extensions = [extensions] low_extensions = [e.lower() for e in extensions] if extensions is not None else None res = [p/o for o in f if not o.startswith('.') and (extensions is None or f'.{o.split(".")[-1].lower()}' in low_extensions)] return res def get_files(path:PathOrStr, extensions:Collection[str]=None, recurse:bool=False, include:Optional[Collection[str]]=None, presort:bool=False)->FilePathList: "Return list of files in `path` that have a suffix in `extensions`; optionally `recurse`." if recurse: res = [] for i,(p,d,f) in enumerate(os.walk(path)): # skip hidden dirs if include is not None and i==0: d[:] = [o for o in d if o in include] else: d[:] = [o for o in d if not o.startswith('.')] res += _get_files(path, p, f, extensions) if presort: res = sorted(res, key=lambda p: _path_to_same_str(p), reverse=False) return res else: f = [o.name for o in os.scandir(path) if o.is_file()] res = _get_files(path, path, f, extensions) if presort: res = sorted(res, key=lambda p: _path_to_same_str(p), reverse=False) return res class PreProcessor(): "Basic class for a processor that will be applied to items at the end of the data block API." def __init__(self, ds:Collection=None): self.ref_ds = ds def process_one(self, item:Any): return item def process(self, ds:Collection): ds.items = array([self.process_one(item) for item in ds.items]) PreProcessors = Union[PreProcessor, Collection[PreProcessor]] fastai_types[PreProcessors] = 'PreProcessors' class ItemList(): "A collection of items with `__len__` and `__getitem__` with `ndarray` indexing semantics." _bunch,_processor,_label_cls,_square_show,_square_show_res = DataBunch,None,None,False,False def __init__(self, items:Iterator, path:PathOrStr='.', label_cls:Callable=None, inner_df:Any=None, processor:PreProcessors=None, x:'ItemList'=None, ignore_empty:bool=False): self.path = Path(path) self.num_parts = len(self.path.parts) self.items,self.x,self.ignore_empty = items,x,ignore_empty if not isinstance(self.items,np.ndarray): self.items = array(self.items, dtype=object) self.label_cls,self.inner_df,self.processor = ifnone(label_cls,self._label_cls),inner_df,processor self._label_list,self._split = LabelList,ItemLists self.copy_new = ['x', 'label_cls', 'path'] def __len__(self)->int: return len(self.items) or 1 def get(self, i)->Any: "Subclass if you want to customize how to create item `i` from `self.items`." return self.items[i] def __repr__(self)->str: items = [self[i] for i in range(min(5,len(self.items)))] return f'{self.__class__.__name__} ({len(self.items)} items)\n{show_some(items)}\nPath: {self.path}' def process(self, processor:PreProcessors=None): "Apply `processor` or `self.processor` to `self`." if processor is not None: self.processor = processor self.processor = listify(self.processor) for p in self.processor: p.process(self) return self def process_one(self, item:ItemBase, processor:PreProcessors=None): "Apply `processor` or `self.processor` to `item`." if processor is not None: self.processor = processor self.processor = listify(self.processor) for p in self.processor: item = p.process_one(item) return item def analyze_pred(self, pred:Tensor): "Called on `pred` before `reconstruct` for additional preprocessing." return pred def reconstruct(self, t:Tensor, x:Tensor=None): "Reconstruct one of the underlying item for its data `t`." return self[0].reconstruct(t,x) if has_arg(self[0].reconstruct, 'x') else self[0].reconstruct(t) def new(self, items:Iterator, processor:PreProcessors=None, **kwargs)->'ItemList': "Create a new `ItemList` from `items`, keeping the same attributes." processor = ifnone(processor, self.processor) copy_d = {o:getattr(self,o) for o in self.copy_new} kwargs = {**copy_d, **kwargs} return self.__class__(items=items, processor=processor, **kwargs) def add(self, items:'ItemList'): self.items = np.concatenate([self.items, items.items], 0) if self.inner_df is not None and items.inner_df is not None: self.inner_df = pd.concat([self.inner_df, items.inner_df]) else: self.inner_df = self.inner_df or items.inner_df return self def __getitem__(self,idxs:int)->Any: "returns a single item based if `idxs` is an integer or a new `ItemList` object if `idxs` is a range." idxs = try_int(idxs) if isinstance(idxs, Integral): return self.get(idxs) else: return self.new(self.items[idxs], inner_df=index_row(self.inner_df, idxs)) @classmethod def from_folder(cls, path:PathOrStr, extensions:Collection[str]=None, recurse:bool=True, include:Optional[Collection[str]]=None, processor:PreProcessors=None, presort:Optional[bool]=False, **kwargs)->'ItemList': """Create an `ItemList` in `path` from the filenames that have a suffix in `extensions`. `recurse` determines if we search subfolders.""" path = Path(path) return cls(get_files(path, extensions, recurse=recurse, include=include, presort=presort), path=path, processor=processor, **kwargs) @classmethod def from_df(cls, df:DataFrame, path:PathOrStr='.', cols:IntsOrStrs=0, processor:PreProcessors=None, **kwargs)->'ItemList': "Create an `ItemList` in `path` from the inputs in the `cols` of `df`." inputs = df.iloc[:,df_names_to_idx(cols, df)] assert not inputs.isna().any().any(), f"You have NaN values in column(s) {cols} of your dataframe, please fix it." res = cls(items=_maybe_squeeze(inputs.values), path=path, inner_df=df, processor=processor, **kwargs) return res @classmethod def from_csv(cls, path:PathOrStr, csv_name:str, cols:IntsOrStrs=0, delimiter:str=None, header:str='infer', processor:PreProcessors=None, **kwargs)->'ItemList': """Create an `ItemList` in `path` from the inputs in the `cols` of `path/csv_name`""" df = pd.read_csv(Path(path)/csv_name, delimiter=delimiter, header=header) return cls.from_df(df, path=path, cols=cols, processor=processor, **kwargs) def _relative_item_path(self, i): return self.items[i].relative_to(self.path) def _relative_item_paths(self): return [self._relative_item_path(i) for i in range_of(self.items)] def use_partial_data(self, sample_pct:float=0.01, seed:int=None)->'ItemList': "Use only a sample of `sample_pct`of the full dataset and an optional `seed`." if seed is not None: np.random.seed(seed) rand_idx = np.random.permutation(range_of(self)) cut = int(sample_pct * len(self)) return self[rand_idx[:cut]] def to_text(self, fn:str): "Save `self.items` to `fn` in `self.path`." with open(self.path/fn, 'w') as f: f.writelines([f'{o}\n' for o in self._relative_item_paths()]) def filter_by_func(self, func:Callable)->'ItemList': "Only keep elements for which `func` returns `True`." self.items = array([o for o in self.items if func(o)]) return self def filter_by_folder(self, include=None, exclude=None): "Only keep filenames in `include` folder or reject the ones in `exclude`." include,exclude = listify(include),listify(exclude) def _inner(o): if isinstance(o, Path): n = o.relative_to(self.path).parts[0] else: n = o.split(os.path.sep)[len(str(self.path).split(os.path.sep))] if include and not n in include: return False if exclude and n in exclude: return False return True return self.filter_by_func(_inner) def filter_by_rand(self, p:float, seed:int=None): "Keep random sample of `items` with probability `p` and an optional `seed`." if seed is not None: set_all_seed(seed) return self.filter_by_func(lambda o: rand_bool(p)) def no_split(self): warn("`no_split` is deprecated, please use `split_none`.") return self.split_none() def split_none(self): "Don't split the data and create an empty validation set." val = self[[]] val.ignore_empty = True return self._split(self.path, self, val) def split_by_list(self, train, valid): "Split the data between `train` and `valid`." return self._split(self.path, train, valid) def split_by_idxs(self, train_idx, valid_idx): "Split the data between `train_idx` and `valid_idx`." return self.split_by_list(self[train_idx], self[valid_idx]) def split_by_idx(self, valid_idx:Collection[int])->'ItemLists': "Split the data according to the indexes in `valid_idx`." #train_idx = [i for i in range_of(self.items) if i not in valid_idx] train_idx = np.setdiff1d(arange_of(self.items), valid_idx) return self.split_by_idxs(train_idx, valid_idx) def _get_by_folder(self, name): return [i for i in range_of(self) if (self.items[i].parts[self.num_parts] if isinstance(self.items[i], Path) else self.items[i].split(os.path.sep)[0]) == name ] def split_by_folder(self, train:str='train', valid:str='valid')->'ItemLists': "Split the data depending on the folder (`train` or `valid`) in which the filenames are." return self.split_by_idxs(self._get_by_folder(train), self._get_by_folder(valid)) def random_split_by_pct(self, valid_pct:float=0.2, seed:int=None): warn("`random_split_by_pct` is deprecated, please use `split_by_rand_pct`.") return self.split_by_rand_pct(valid_pct=valid_pct, seed=seed) def split_by_rand_pct(self, valid_pct:float=0.2, seed:int=None)->'ItemLists': "Split the items randomly by putting `valid_pct` in the validation set, optional `seed` can be passed." if valid_pct==0.: return self.split_none() if seed is not None: np.random.seed(seed) rand_idx = np.random.permutation(range_of(self)) cut = int(valid_pct * len(self)) return self.split_by_idx(rand_idx[:cut]) def split_subsets(self, train_size:float, valid_size:float, seed=None) -> 'ItemLists': "Split the items into train set with size `train_size * n` and valid set with size `valid_size * n`." assert 0 < train_size < 1 assert 0 < valid_size < 1 assert train_size + valid_size <= 1. if seed is not None: np.random.seed(seed) n = len(self.items) rand_idx = np.random.permutation(range(n)) train_cut, valid_cut = int(train_size * n), int(valid_size * n) return self.split_by_idxs(rand_idx[:train_cut], rand_idx[-valid_cut:]) def split_by_valid_func(self, func:Callable)->'ItemLists': "Split the data by result of `func` (which returns `True` for validation set)." valid_idx = [i for i,o in enumerate(self.items) if func(o)] return self.split_by_idx(valid_idx) def split_by_files(self, valid_names:'ItemList')->'ItemLists': "Split the data by using the names in `valid_names` for validation." if isinstance(self.items[0], Path): return self.split_by_valid_func(lambda o: o.name in valid_names) else: return self.split_by_valid_func(lambda o: os.path.basename(o) in valid_names) def split_by_fname_file(self, fname:PathOrStr, path:PathOrStr=None)->'ItemLists': "Split the data by using the names in `fname` for the validation set. `path` will override `self.path`." path = Path(ifnone(path, self.path)) valid_names = loadtxt_str(path/fname) return self.split_by_files(valid_names) def split_from_df(self, col:IntsOrStrs=2): "Split the data from the `col` in the dataframe in `self.inner_df`." valid_idx = np.where(self.inner_df.iloc[:,df_names_to_idx(col, self.inner_df)])[0] return self.split_by_idx(valid_idx) def get_label_cls(self, labels, label_cls:Callable=None, label_delim:str=None, **kwargs): "Return `label_cls` or guess one from the first element of `labels`." if label_cls is not None: return label_cls if self.label_cls is not None: return self.label_cls if label_delim is not None: return MultiCategoryList it = index_row(labels,0) if isinstance(it, (float, np.float32)): return FloatList if isinstance(try_int(it), (str, Integral)): return CategoryList if isinstance(it, Collection): return MultiCategoryList return ItemList #self.__class__ def _label_from_list(self, labels:Iterator, label_cls:Callable=None, from_item_lists:bool=False, **kwargs)->'LabelList': "Label `self.items` with `labels`." if not from_item_lists: raise Exception("Your data isn't split, if you don't want a validation set, please use `split_none`.") labels = array(labels, dtype=object) label_cls = self.get_label_cls(labels, label_cls=label_cls, **kwargs) y = label_cls(labels, path=self.path, **kwargs) res = self._label_list(x=self, y=y) return res def label_from_df(self, cols:IntsOrStrs=1, label_cls:Callable=None, **kwargs): "Label `self.items` from the values in `cols` in `self.inner_df`." labels = self.inner_df.iloc[:,df_names_to_idx(cols, self.inner_df)] assert labels.isna().sum().sum() == 0, f"You have NaN values in column(s) {cols} of your dataframe, please fix it." if is_listy(cols) and len(cols) > 1 and (label_cls is None or label_cls == MultiCategoryList): new_kwargs,label_cls = dict(one_hot=True, classes= cols),MultiCategoryList kwargs = {**new_kwargs, **kwargs} return self._label_from_list(_maybe_squeeze(labels), label_cls=label_cls, **kwargs) def label_const(self, const:Any=0, label_cls:Callable=None, **kwargs)->'LabelList': "Label every item with `const`." return self.label_from_func(func=lambda o: const, label_cls=label_cls, **kwargs) def label_empty(self, **kwargs): "Label every item with an `EmptyLabel`." kwargs['label_cls'] = EmptyLabelList return self.label_from_func(func=lambda o: 0., **kwargs) def label_from_func(self, func:Callable, label_cls:Callable=None, **kwargs)->'LabelList': "Apply `func` to every input to get its label." return self._label_from_list([func(o) for o in self.items], label_cls=label_cls, **kwargs) def label_from_folder(self, label_cls:Callable=None, **kwargs)->'LabelList': "Give a label to each filename depending on its folder." return self.label_from_func(func=lambda o: (o.parts if isinstance(o, Path) else o.split(os.path.sep))[-2], label_cls=label_cls, **kwargs) def label_from_re(self, pat:str, full_path:bool=False, label_cls:Callable=None, **kwargs)->'LabelList': "Apply the re in `pat` to determine the label of every filename. If `full_path`, search in the full name." pat = re.compile(pat) def _inner(o): s = str((os.path.join(self.path,o) if full_path else o).as_posix()) res = pat.search(s) assert res,f'Failed to find "{pat}" in "{s}"' return res.group(1) return self.label_from_func(_inner, label_cls=label_cls, **kwargs) def databunch(self, **kwargs): "To throw a clear error message when the data wasn't split and labeled." raise Exception("Your data is neither split nor labeled, can't turn it into a `DataBunch` yet.") class EmptyLabelList(ItemList): "Basic `ItemList` for dummy labels." def get(self, i): return EmptyLabel() def reconstruct(self, t:Tensor, x:Tensor=None): if len(t.size()) == 0: return EmptyLabel() return self.x.reconstruct(t,x) if has_arg(self.x.reconstruct, 'x') else self.x.reconstruct(t) class CategoryProcessor(PreProcessor): "`PreProcessor` that create `classes` from `ds.items` and handle the mapping." def __init__(self, ds:ItemList): self.create_classes(ds.classes) self.state_attrs,self.warns = ['classes'],[] def create_classes(self, classes): self.classes = classes if classes is not None: self.c2i = {v:k for k,v in enumerate(classes)} def generate_classes(self, items): "Generate classes from `items` by taking the sorted unique values." return uniqueify(items, sort=True) def process_one(self,item): if isinstance(item, EmptyLabel): return item res = self.c2i.get(item,None) if res is None: self.warns.append(str(item)) return res def process(self, ds): if self.classes is None: self.create_classes(self.generate_classes(ds.items)) ds.classes = self.classes ds.c2i = self.c2i super().process(ds) def __getstate__(self): return {n:getattr(self,n) for n in self.state_attrs} def __setstate__(self, state:dict): self.create_classes(state['classes']) self.state_attrs = state.keys() for n in state.keys(): if n!='classes': setattr(self, n, state[n]) class CategoryListBase(ItemList): "Basic `ItemList` for classification." def __init__(self, items:Iterator, classes:Collection=None, **kwargs): self.classes=classes self.filter_missing_y = True super().__init__(items, **kwargs) self.copy_new.append('classes') @property def c(self): return len(self.classes) class CategoryList(CategoryListBase): "Basic `ItemList` for single classification labels." _processor=CategoryProcessor def __init__(self, items:Iterator, classes:Collection=None, label_delim:str=None, **kwargs): super().__init__(items, classes=classes, **kwargs) self.loss_func = CrossEntropyFlat() def get(self, i): o = self.items[i] if o is None: return None return Category(o, self.classes[o]) def analyze_pred(self, pred, thresh:float=0.5): return pred.argmax() def reconstruct(self, t): return Category(t, self.classes[t]) class MultiCategoryProcessor(CategoryProcessor): "`PreProcessor` that create `classes` from `ds.items` and handle the mapping." def __init__(self, ds:ItemList, one_hot:bool=False): super().__init__(ds) self.one_hot = one_hot self.state_attrs.append('one_hot') def process_one(self,item): if self.one_hot or isinstance(item, EmptyLabel): return item res = [super(MultiCategoryProcessor, self).process_one(o) for o in item] return [r for r in res if r is not None] def generate_classes(self, items): "Generate classes from `items` by taking the sorted unique values." classes = set() for c in items: classes = classes.union(set(c)) classes = list(classes) classes.sort() return classes class MultiCategoryList(CategoryListBase): "Basic `ItemList` for multi-classification labels." _processor=MultiCategoryProcessor def __init__(self, items:Iterator, classes:Collection=None, label_delim:str=None, one_hot:bool=False, **kwargs): if label_delim is not None: items = array(csv.reader(items.astype(str), delimiter=label_delim)) super().__init__(items, classes=classes, **kwargs) if one_hot: assert classes is not None, "Please provide class names with `classes=...`" self.processor = [MultiCategoryProcessor(self, one_hot=True)] self.loss_func = BCEWithLogitsFlat() self.one_hot = one_hot self.copy_new += ['one_hot'] def get(self, i): o = self.items[i] if o is None: return None if self.one_hot: return self.reconstruct(o.astype(np.float32)) return MultiCategory(one_hot(o, self.c), [self.classes[p] for p in o], o) def analyze_pred(self, pred, thresh:float=0.5): return (pred >= thresh).float() def reconstruct(self, t): o = [i for i in range(self.c) if t[i] == 1.] return MultiCategory(t, [self.classes[p] for p in o], o) class FloatList(ItemList): "`ItemList` suitable for storing the floats in items for regression. Will add a `log` if this flag is `True`." def __init__(self, items:Iterator, log:bool=False, classes:Collection=None, **kwargs): super().__init__(np.array(items, dtype=np.float32), **kwargs) self.log = log self.copy_new.append('log') self.c = self.items.shape[1] if len(self.items.shape) > 1 else 1 self.loss_func = MSELossFlat() def get(self, i): o = super().get(i) return FloatItem(np.log(o) if self.log else o) def reconstruct(self,t): return FloatItem(t.numpy()) class ItemLists(): "An `ItemList` for each of `train` and `valid` (optional `test`)." def __init__(self, path:PathOrStr, train:ItemList, valid:ItemList): self.path,self.train,self.valid,self.test = Path(path),train,valid,None if not self.train.ignore_empty and len(self.train.items) == 0: warn("Your training set is empty. If this is by design, pass `ignore_empty=True` to remove this warning.") if not self.valid.ignore_empty and len(self.valid.items) == 0: warn("""Your validation set is empty. If this is by design, use `split_none()` or pass `ignore_empty=True` when labelling to remove this warning.""") if isinstance(self.train, LabelList): self.__class__ = LabelLists def __dir__(self)->List[str]: default_dir = dir(type(self)) + list(self.__dict__.keys()) add_ons = ['label_const', 'label_empty', 'label_from_df', 'label_from_folder', 'label_from_func', 'label_from_list', 'label_from_re'] return default_dir + add_ons def __repr__(self)->str: return f'{self.__class__.__name__};\n\nTrain: {self.train};\n\nValid: {self.valid};\n\nTest: {self.test}' def __getattr__(self, k): ft = getattr(self.train, k) if not isinstance(ft, Callable): return ft fv = getattr(self.valid, k) assert isinstance(fv, Callable) def _inner(*args, **kwargs): self.train = ft(*args, from_item_lists=True, **kwargs) assert isinstance(self.train, LabelList) kwargs['label_cls'] = self.train.y.__class__ self.valid = fv(*args, from_item_lists=True, **kwargs) self.__class__ = LabelLists self.process() return self return _inner def __setstate__(self,data:Any): self.__dict__.update(data) @property def lists(self): res = [self.train,self.valid] if self.test is not None: res.append(self.test) return res def label_from_lists(self, train_labels:Iterator, valid_labels:Iterator, label_cls:Callable=None, **kwargs)->'LabelList': "Use the labels in `train_labels` and `valid_labels` to label the data. `label_cls` will overwrite the default." label_cls = self.train.get_label_cls(train_labels, label_cls) self.train = self.train._label_list(x=self.train, y=label_cls(train_labels, **kwargs)) self.valid = self.valid._label_list(x=self.valid, y=self.train.y.new(valid_labels, **kwargs)) self.__class__ = LabelLists self.process() return self def transform(self, tfms:Optional[Tuple[TfmList,TfmList]]=(None,None), **kwargs): "Set `tfms` to be applied to the xs of the train and validation set." if not tfms: tfms=(None,None) assert is_listy(tfms) and len(tfms) == 2, "Please pass a list of two lists of transforms (train and valid)." self.train.transform(tfms[0], **kwargs) self.valid.transform(tfms[1], **kwargs) if self.test: self.test.transform(tfms[1], **kwargs) return self def transform_y(self, tfms:Optional[Tuple[TfmList,TfmList]]=(None,None), **kwargs): "Set `tfms` to be applied to the ys of the train and validation set." if not tfms: tfms=(None,None) self.train.transform_y(tfms[0], **kwargs) self.valid.transform_y(tfms[1], **kwargs) if self.test: self.test.transform_y(tfms[1], **kwargs) return self def databunch(self, **kwargs): "To throw a clear error message when the data wasn't labeled." raise Exception("Your data isn't labeled, can't turn it into a `DataBunch` yet!") class LabelLists(ItemLists): "A `LabelList` for each of `train` and `valid` (optional `test`)." def get_processors(self): "Read the default class processors if none have been set." procs_x,procs_y = listify(self.train.x._processor),listify(self.train.y._processor) xp = ifnone(self.train.x.processor, [p(ds=self.train.x) for p in procs_x]) yp = ifnone(self.train.y.processor, [p(ds=self.train.y) for p in procs_y]) return xp,yp def process(self): "Process the inner datasets." xp,yp = self.get_processors() for ds,n in zip(self.lists, ['train','valid','test']): ds.process(xp, yp, name=n) #progress_bar clear the outputs so in some case warnings issued during processing disappear. for ds in self.lists: if getattr(ds, 'warn', False): warn(ds.warn) return self def filter_by_func(self, func:Callable): for ds in self.lists: ds.filter_by_func(func) return self def databunch(self, path:PathOrStr=None, bs:int=64, val_bs:int=None, num_workers:int=defaults.cpus, dl_tfms:Optional[Collection[Callable]]=None, device:torch.device=None, collate_fn:Callable=data_collate, no_check:bool=False, **kwargs)->'DataBunch': "Create an `DataBunch` from self, `path` will override `self.path`, `kwargs` are passed to `DataBunch.create`." path = Path(ifnone(path, self.path)) data = self.x._bunch.create(self.train, self.valid, test_ds=self.test, path=path, bs=bs, val_bs=val_bs, num_workers=num_workers, dl_tfms=dl_tfms, device=device, collate_fn=collate_fn, no_check=no_check, **kwargs) if getattr(self, 'normalize', False):#In case a normalization was serialized norm = self.normalize data.normalize((norm['mean'], norm['std']), do_x=norm['do_x'], do_y=norm['do_y']) data.label_list = self return data def add_test(self, items:Iterator, label:Any=None, tfms=None, tfm_y=None): "Add test set containing `items` with an arbitrary `label`." # if no label passed, use label of first training item if label is None: labels = EmptyLabelList([0] * len(items)) else: labels = self.valid.y.new([label] * len(items)).process() if isinstance(items, MixedItemList): items = self.valid.x.new(items.item_lists, inner_df=items.inner_df).process() elif isinstance(items, ItemList): items = self.valid.x.new(items.items, inner_df=items.inner_df).process() else: items = self.valid.x.new(items).process() self.test = self.valid.new(items, labels, tfms=tfms, tfm_y=tfm_y) return self def add_test_folder(self, test_folder:str='test', label:Any=None, tfms=None, tfm_y=None): "Add test set containing items from `test_folder` and an arbitrary `label`." # note: labels will be ignored if available in the test dataset items = self.x.__class__.from_folder(self.path/test_folder) return self.add_test(items.items, label=label, tfms=tfms, tfm_y=tfm_y) @classmethod def load_state(cls, path:PathOrStr, state:dict): "Create a `LabelLists` with empty sets from the serialized `state`." path = Path(path) train_ds = LabelList.load_state(path, state) valid_ds = LabelList.load_state(path, state) return LabelLists(path, train=train_ds, valid=valid_ds) @classmethod def load_empty(cls, path:PathOrStr, fn:PathOrStr='export.pkl'): "Create a `LabelLists` with empty sets from the serialized file in `path/fn`." path = Path(path) state = torch.load(open(path/fn, 'rb')) return LabelLists.load_state(path, state) def _check_kwargs(ds:ItemList, tfms:TfmList, **kwargs): tfms = listify(tfms) if (tfms is None or len(tfms) == 0) and len(kwargs) == 0: return if len(ds.items) >= 1: x = ds[0] try: x.apply_tfms(tfms, **kwargs) except Exception as e: raise Exception(f"It's not possible to apply those transforms to your dataset:\n {e}") class LabelList(Dataset): "A list of inputs `x` and labels `y` with optional `tfms`." def __init__(self, x:ItemList, y:ItemList, tfms:TfmList=None, tfm_y:bool=False, **kwargs): self.x,self.y,self.tfm_y = x,y,tfm_y self.y.x = x self.item=None self.transform(tfms, **kwargs) def __len__(self)->int: return len(self.x) if self.item is None else 1 @contextmanager def set_item(self,item): "For inference, will briefly replace the dataset with one that only contains `item`." self.item = self.x.process_one(item) yield None self.item = None def __repr__(self)->str: items = [self[i] for i in range(min(5,len(self.items)))] res = f'{self.__class__.__name__} ({len(self.items)} items)\n' res += f'x: {self.x.__class__.__name__}\n{show_some([i[0] for i in items])}\n' res += f'y: {self.y.__class__.__name__}\n{show_some([i[1] for i in items])}\n' return res + f'Path: {self.path}' def predict(self, res): "Delegates predict call on `res` to `self.y`." return self.y.predict(res) @property def c(self): return self.y.c def new(self, x, y, tfms=None, tfm_y=None, **kwargs)->'LabelList': tfms,tfm_y = ifnone(tfms, self.tfms),ifnone(tfm_y, self.tfm_y) if isinstance(x, ItemList): return self.__class__(x, y, tfms=tfms, tfm_y=tfm_y, **self.tfmargs) else: return self.new(self.x.new(x, **kwargs), self.y.new(y, **kwargs), tfms=tfms, tfm_y=tfm_y).process() def __getattr__(self,k:str)->Any: x = super().__getattribute__('x') res = getattr(x, k, None) if res is not None and k not in ['classes', 'c']: return res y = super().__getattribute__('y') res = getattr(y, k, None) if res is not None: return res raise AttributeError(k) def __setstate__(self,data:Any): self.__dict__.update(data) def __getitem__(self,idxs:Union[int,np.ndarray])->'LabelList': "return a single (x, y) if `idxs` is an integer or a new `LabelList` object if `idxs` is a range." idxs = try_int(idxs) if isinstance(idxs, Integral): if self.item is None: x,y = self.x[idxs],self.y[idxs] else: x,y = self.item ,0 if self.tfms or self.tfmargs: x = x.apply_tfms(self.tfms, is_x=True, **self.tfmargs) if hasattr(self, 'tfms_y') and self.tfm_y and self.item is None: y = y.apply_tfms(self.tfms_y, is_x=False, **{**self.tfmargs_y, 'do_resolve':False}) if y is None: y=0 return x,y else: return self.new(self.x[idxs], self.y[idxs]) def to_df(self)->None: "Create `pd.DataFrame` containing `items` from `self.x` and `self.y`." return pd.DataFrame(dict(x=self.x._relative_item_paths(), y=[str(o) for o in self.y])) def to_csv(self, dest:str)->None: "Save `self.to_df()` to a CSV file in `self.path`/`dest`." self.to_df().to_csv(self.path/dest, index=False) def get_state(self, **kwargs): "Return the minimal state for export." state = {'x_cls':self.x.__class__, 'x_proc':self.x.processor, 'y_cls':self.y.__class__, 'y_proc':self.y.processor, 'tfms':self.tfms, 'tfm_y':self.tfm_y, 'tfmargs':self.tfmargs} if hasattr(self, 'tfms_y'): state['tfms_y'] = self.tfms_y if hasattr(self, 'tfmargs_y'): state['tfmargs_y'] = self.tfmargs_y return {**state, **kwargs} def export(self, fn:PathOrStr, **kwargs): "Export the minimal state and save it in `fn` to load an empty version for inference." pickle.dump(self.get_state(**kwargs), open(fn, 'wb')) @classmethod def load_empty(cls, path:PathOrStr, fn:PathOrStr): "Load the state in `fn` to create an empty `LabelList` for inference." return cls.load_state(path, pickle.load(open(Path(path)/fn, 'rb'))) @classmethod def load_state(cls, path:PathOrStr, state:dict) -> 'LabelList': "Create a `LabelList` from `state`." x = state['x_cls']([], path=path, processor=state['x_proc'], ignore_empty=True) y = state['y_cls']([], path=path, processor=state['y_proc'], ignore_empty=True) res = cls(x, y, tfms=state['tfms'], tfm_y=state['tfm_y'], **state['tfmargs']).process() if state.get('tfms_y', False): res.tfms_y = state['tfms_y'] if state.get('tfmargs_y', False): res.tfmargs_y = state['tfmargs_y'] if state.get('normalize', False): res.normalize = state['normalize'] return res def process(self, xp:PreProcessor=None, yp:PreProcessor=None, name:str=None): "Launch the processing on `self.x` and `self.y` with `xp` and `yp`." self.y.process(yp) if getattr(self.y, 'filter_missing_y', False): filt = array([o is None for o in self.y.items]) if filt.sum()>0: #Warnings are given later since progress_bar might make them disappear. self.warn = f"You are labelling your items with {self.y.__class__.__name__}.\n" self.warn += f"Your {name} set contained the following unknown labels, the corresponding items have been discarded.\n" for p in self.y.processor: if len(getattr(p, 'warns', [])) > 0: warnings = list(set(p.warns)) self.warn += ', '.join(warnings[:5]) if len(warnings) > 5: self.warn += "..." p.warns = [] self.x,self.y = self.x[~filt],self.y[~filt] self.x.process(xp) return self def filter_by_func(self, func:Callable): filt = array([func(x,y) for x,y in zip(self.x.items, self.y.items)]) self.x,self.y = self.x[~filt],self.y[~filt] return self def transform(self, tfms:TfmList, tfm_y:bool=None, **kwargs): "Set the `tfms` and `tfm_y` value to be applied to the inputs and targets." _check_kwargs(self.x, tfms, **kwargs) if tfm_y is None: tfm_y = self.tfm_y tfms_y = None if tfms is None else list(filter(lambda t: getattr(t, 'use_on_y', True), listify(tfms))) if tfm_y: _check_kwargs(self.y, tfms_y, **kwargs) self.tfms,self.tfmargs = tfms,kwargs self.tfm_y,self.tfms_y,self.tfmargs_y = tfm_y,tfms_y,kwargs return self def transform_y(self, tfms:TfmList=None, **kwargs): "Set `tfms` to be applied to the targets only." tfms_y = list(filter(lambda t: getattr(t, 'use_on_y', True), listify(self.tfms if tfms is None else tfms))) tfmargs_y = {**self.tfmargs, **kwargs} if tfms is None else kwargs _check_kwargs(self.y, tfms_y, **tfmargs_y) self.tfm_y,self.tfms_y,self.tfmargs_y=True,tfms_y,tfmargs_y return self def databunch(self, **kwargs): "To throw a clear error message when the data wasn't split." raise Exception("Your data isn't split, if you don't want a validation set, please use `split_none`") @classmethod def _databunch_load_empty(cls, path, fname:str='export.pkl'): "Load an empty `DataBunch` from the exported file in `path/fname` with optional `tfms`." sd = LabelLists.load_empty(path, fn=fname) return sd.databunch() DataBunch.load_empty = _databunch_load_empty class MixedProcessor(PreProcessor): def __init__(self, procs:Collection[Union[PreProcessor, Collection[PreProcessor]]]): self.procs = procs def process_one(self, item:Any): res = [] for procs, i in zip(self.procs, item): for p in procs: i = p.process_one(i) res.append(i) return res def process(self, ds:Collection): for procs, il in zip(self.procs, ds.item_lists): for p in procs: p.process(il) class MixedItem(ItemBase): def __init__(self, items): self.obj = items self.data = [item.data for item in items] def __repr__(self): return '\n'.join([f'{self.__class__.__name__}'] + [repr(item) for item in self.obj]) def apply_tfms(self, tfms:Collection, **kwargs): self.obj = [item.apply_tfms(t, **kwargs) for item,t in zip(self.obj, tfms)] self.data = [item.data for item in self.obj] return self class MixedItemList(ItemList): def __init__(self, item_lists, path:PathOrStr=None, label_cls:Callable=None, inner_df:Any=None, x:'ItemList'=None, ignore_empty:bool=False, processor=None): self.item_lists = item_lists if processor is None: default_procs = [[p(ds=il) for p in listify(il._processor)] for il in item_lists] processor = MixedProcessor([ifnone(il.processor, dp) for il,dp in zip(item_lists, default_procs)]) items = range_of(item_lists[0]) if len(item_lists) >= 1 else [] if path is None and len(item_lists) >= 1: path = item_lists[0].path super().__init__(items, processor=processor, path=path, label_cls=label_cls, inner_df=inner_df, x=x, ignore_empty=ignore_empty) def new(self, item_lists, processor:PreProcessor=None, **kwargs)->'ItemList': "Create a new `ItemList` from `items`, keeping the same attributes." processor = ifnone(processor, self.processor) copy_d = {o:getattr(self,o) for o in self.copy_new} kwargs = {**copy_d, **kwargs} return self.__class__(item_lists, processor=processor, **kwargs) def get(self, i): return MixedItem([il.get(i) for il in self.item_lists]) def __getitem__(self,idxs:int)->Any: idxs = try_int(idxs) if isinstance(idxs, Integral): return self.get(idxs) else: item_lists = [il.new(il.items[idxs], inner_df=index_row(il.inner_df, idxs)) for il in self.item_lists] return self.new(item_lists, inner_df=index_row(self.inner_df, idxs))