"`fastai.data` loads and manages datasets with `DataBunch`"
from .torch_core import *
from torch.utils.data.dataloader import default_collate
DatasetType = Enum('DatasetType', 'Train Valid Test Single Fix')
__all__ = ['DataBunch', 'DeviceDataLoader', 'DatasetType', 'load_data']
old_dl_init = torch.utils.data.DataLoader.__init__
def intercept_args(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,
num_workers=0, collate_fn=default_collate, pin_memory=True, drop_last=False,
timeout=0, worker_init_fn=None):
self.init_kwargs = {'batch_size':batch_size, 'shuffle':shuffle, 'sampler':sampler, 'batch_sampler':batch_sampler,
'num_workers':num_workers, 'collate_fn':collate_fn, 'pin_memory':pin_memory,
'drop_last': drop_last, 'timeout':timeout, 'worker_init_fn':worker_init_fn}
old_dl_init(self, dataset, **self.init_kwargs)
torch.utils.data.DataLoader.__init__ = intercept_args
def DataLoader___getattr__(dl, k:str)->Any: return getattr(dl.dataset, k)
DataLoader.__getattr__ = DataLoader___getattr__
def DataLoader___setstate__(dl, data:Any): dl.__dict__.update(data)
DataLoader.__setstate__ = DataLoader___setstate__
@dataclass
class DeviceDataLoader():
"Bind a `DataLoader` to a `torch.device`."
dl: DataLoader
device: torch.device
tfms: List[Callable]=None
collate_fn: Callable=data_collate
def __post_init__(self):
self.dl.collate_fn=self.collate_fn
self.tfms = listify(self.tfms)
def __len__(self)->int: return len(self.dl)
def __getattr__(self,k:str)->Any: return getattr(self.dl, k)
def __setstate__(self,data:Any): self.__dict__.update(data)
@property
def batch_size(self): return self.dl.batch_size
@batch_size.setter
def batch_size(self,v):
new_kwargs = {**self.dl.init_kwargs, 'batch_size':v, 'collate_fn':self.collate_fn}
self.dl = self.dl.__class__(self.dl.dataset, **new_kwargs)
if hasattr(self.dl.dataset, 'bs'): self.dl.dataset.bs = v
@property
def num_workers(self): return self.dl.num_workers
@num_workers.setter
def num_workers(self,v): self.dl.num_workers = v
def add_tfm(self,tfm:Callable)->None:
"Add `tfm` to `self.tfms`."
self.tfms.append(tfm)
def remove_tfm(self,tfm:Callable)->None:
"Remove `tfm` from `self.tfms`."
if tfm in self.tfms: self.tfms.remove(tfm)
def new(self, **kwargs):
"Create a new copy of `self` with `kwargs` replacing current values."
new_kwargs = {**self.dl.init_kwargs, **kwargs}
return DeviceDataLoader(self.dl.__class__(self.dl.dataset, **new_kwargs), self.device, self.tfms,
self.collate_fn)
def proc_batch(self,b:Tensor)->Tensor:
"Process batch `b` of `TensorImage`."
b = to_device(b, self.device)
for f in listify(self.tfms): b = f(b)
return b
def __iter__(self):
"Process and returns items from `DataLoader`."
for b in self.dl: yield self.proc_batch(b)
@classmethod
def create(cls, dataset:Dataset, bs:int=64, shuffle:bool=False, device:torch.device=defaults.device,
tfms:Collection[Callable]=tfms, num_workers:int=defaults.cpus, collate_fn:Callable=data_collate, **kwargs:Any):
"Create DeviceDataLoader from `dataset` with `bs` and `shuffle`: process using `num_workers`."
return cls(DataLoader(dataset, batch_size=bs, shuffle=shuffle, num_workers=num_workers, **kwargs),
device=device, tfms=tfms, collate_fn=collate_fn)
class DataBunch():
"Bind `train_dl`,`valid_dl` and `test_dl` in a data object."
def __init__(self, train_dl:DataLoader, valid_dl:DataLoader, fix_dl:DataLoader=None, test_dl:Optional[DataLoader]=None,
device:torch.device=None, dl_tfms:Optional[Collection[Callable]]=None, path:PathOrStr='.',
collate_fn:Callable=data_collate, no_check:bool=False):
self.dl_tfms = listify(dl_tfms)
self.device = defaults.device if device is None else device
assert not isinstance(train_dl,DeviceDataLoader)
def _create_dl(dl, **kwargs):
if dl is None: return None
return DeviceDataLoader(dl, self.device, self.dl_tfms, collate_fn, **kwargs)
self.train_dl,self.valid_dl,self.fix_dl,self.test_dl = map(_create_dl, [train_dl,valid_dl,fix_dl,test_dl])
if fix_dl is None: self.fix_dl = self.train_dl.new(shuffle=False, drop_last=False)
self.single_dl = _create_dl(DataLoader(valid_dl.dataset, batch_size=1, num_workers=0))
self.path = Path(path)
if not no_check: self.sanity_check()
def __repr__(self)->str:
return f'{self.__class__.__name__};\n\nTrain: {self.train_ds};\n\nValid: {self.valid_ds};\n\nTest: {self.test_ds}'
@staticmethod
def _init_ds(train_ds:Dataset, valid_ds:Dataset, test_ds:Optional[Dataset]=None):
# train_ds, but without training tfms
fix_ds = valid_ds.new(train_ds.x, train_ds.y) if hasattr(valid_ds,'new') else train_ds
return [o for o in (train_ds,valid_ds,fix_ds,test_ds) if o is not None]
@classmethod
def create(cls, train_ds:Dataset, valid_ds:Dataset, test_ds:Optional[Dataset]=None, path:PathOrStr='.', 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, **dl_kwargs)->'DataBunch':
"Create a `DataBunch` from `train_ds`, `valid_ds` and maybe `test_ds` with a batch size of `bs`. Passes `**dl_kwargs` to `DataLoader()`"
datasets = cls._init_ds(train_ds, valid_ds, test_ds)
val_bs = ifnone(val_bs, bs)
dls = [DataLoader(d, b, shuffle=s, drop_last=s, num_workers=num_workers, **dl_kwargs) for d,b,s in
zip(datasets, (bs,val_bs,val_bs,val_bs), (True,False,False,False)) if d is not None]
return cls(*dls, path=path, device=device, dl_tfms=dl_tfms, collate_fn=collate_fn, no_check=no_check)
def __getattr__(self,k:int)->Any: return getattr(self.train_dl, k)
def __setstate__(self,data:Any): self.__dict__.update(data)
def dl(self, ds_type:DatasetType=DatasetType.Valid)->DeviceDataLoader:
"Returns appropriate `Dataset` for validation, training, or test (`ds_type`)."
#TODO: refactor
return (self.train_dl if ds_type == DatasetType.Train else
self.test_dl if ds_type == DatasetType.Test else
self.valid_dl if ds_type == DatasetType.Valid else
self.single_dl if ds_type == DatasetType.Single else
self.fix_dl)
@property
def dls(self)->List[DeviceDataLoader]:
"Returns a list of all DeviceDataLoaders. If you need a specific DeviceDataLoader, access via the relevant property (`train_dl`, `valid_dl`, etc) as the index of DLs in this list is not guaranteed to remain constant."
res = [self.train_dl, self.fix_dl, self.single_dl]
# Preserve the original ordering of Train, Valid, Fix, Single, Test Data Loaders
# (Unknown/not verified as of 1.0.47 whether there are other methods explicitly using DLs their list index)
if self.valid_dl: res.insert(1, self.valid_dl)
return res if not self.test_dl else res + [self.test_dl]
def add_tfm(self,tfm:Callable)->None:
for dl in self.dls: dl.add_tfm(tfm)
def remove_tfm(self,tfm:Callable)->None:
for dl in self.dls: dl.remove_tfm(tfm)
def save(self, file:PathLikeOrBinaryStream= 'data_save.pkl')->None:
"Save the `DataBunch` in `self.path/file`. `file` can be file-like (file or buffer)"
if not getattr(self, 'label_list', False):
warn("Serializing the `DataBunch` only works when you created it using the data block API.")
return
try_save(self.label_list, self.path, file)
def add_test(self, items:Iterator, label:Any=None)->None:
"Add the `items` as a test set. Pass along `label` otherwise label them with `EmptyLabel`."
self.label_list.add_test(items, label=label)
vdl = self.valid_dl
dl = DataLoader(self.label_list.test, vdl.batch_size, shuffle=False, drop_last=False, num_workers=vdl.num_workers)
self.test_dl = DeviceDataLoader(dl, vdl.device, vdl.tfms, vdl.collate_fn)
def one_batch(self, ds_type:DatasetType=DatasetType.Train, detach:bool=True, denorm:bool=True, cpu:bool=True)->Collection[Tensor]:
"Get one batch from the data loader of `ds_type`. Optionally `detach` and `denorm`."
dl = self.dl(ds_type)
w = self.num_workers
self.num_workers = 0
try: x,y = next(iter(dl))
finally: self.num_workers = w
if detach: x,y = to_detach(x,cpu=cpu),to_detach(y,cpu=cpu)
norm = getattr(self,'norm',False)
if denorm and norm:
x = self.denorm(x)
if norm.keywords.get('do_y',False): y = self.denorm(y, do_x=True)
return x,y
def one_item(self, item, detach:bool=False, denorm:bool=False, cpu:bool=False):
"Get `item` into a batch. Optionally `detach` and `denorm`."
ds = self.single_ds
with ds.set_item(item):
return self.one_batch(ds_type=DatasetType.Single, detach=detach, denorm=denorm, cpu=cpu)
def show_batch(self, rows:int=5, ds_type:DatasetType=DatasetType.Train, reverse:bool=False, **kwargs)->None:
"Show a batch of data in `ds_type` on a few `rows`."
x,y = self.one_batch(ds_type, True, True)
if reverse: x,y = x.flip(0),y.flip(0)
n_items = rows **2 if self.train_ds.x._square_show else rows
if self.dl(ds_type).batch_size < n_items: n_items = self.dl(ds_type).batch_size
xs = [self.train_ds.x.reconstruct(grab_idx(x, i)) for i in range(n_items)]
#TODO: get rid of has_arg if possible
if has_arg(self.train_ds.y.reconstruct, 'x'):
ys = [self.train_ds.y.reconstruct(grab_idx(y, i), x=x) for i,x in enumerate(xs)]
else : ys = [self.train_ds.y.reconstruct(grab_idx(y, i)) for i in range(n_items)]
self.train_ds.x.show_xys(xs, ys, **kwargs)
def export(self, file:PathLikeOrBinaryStream='export.pkl'):
"Export the minimal state of `self` for inference in `self.path/file`. `file` can be file-like (file or buffer)"
xtra = dict(normalize=self.norm.keywords) if getattr(self, 'norm', False) else {}
try_save(self.valid_ds.get_state(**xtra), self.path, file)
def _grab_dataset(self, dl:DataLoader):
ds = dl.dl.dataset
while hasattr(ds, 'dataset'): ds = ds.dataset
return ds
@property
def train_ds(self)->Dataset: return self._grab_dataset(self.train_dl)
@property
def valid_ds(self)->Dataset: return self._grab_dataset(self.valid_dl)
@property
def single_ds(self)->Dataset: return self._grab_dataset(self.single_dl)
@property
def loss_func(self)->OptLossFunc:
return getattr(self.train_ds.y, 'loss_func', F.nll_loss) if hasattr(self.train_ds, 'y') else F.nll_loss
@property
def test_ds(self)->Dataset:
return self._grab_dataset(self.test_dl) if self.test_dl is not None else None
@property
def empty_val(self)->bool:
if not hasattr(self, 'valid_dl') or self.valid_dl is None: return True
if hasattr(self.valid_ds, 'items') and len(self.valid_ds.items) == 0: return True
return (len(self.valid_ds) == 0)
@property
def is_empty(self)->bool:
return not ((self.train_dl and len(self.train_ds.items) != 0) or
(self.valid_dl and len(self.valid_ds.items) != 0) or
(self.test_dl and len(self.test_ds.items) != 0))
@property
def batch_size(self): return self.train_dl.batch_size
@batch_size.setter
def batch_size(self,v):
self.train_dl.batch_size,self.valid_dl.batch_size = v,v
if self.test_dl is not None: self.test_dl.batch_size = v
def sanity_check(self):
"Check the underlying data in the training set can be properly loaded."
final_message = "You can deactivate this warning by passing `no_check=True`."
if not hasattr(self.train_ds, 'items') or len(self.train_ds.items) == 0 or not hasattr(self.train_dl, 'batch_sampler'): return
if len(self.train_dl) == 0:
warn(f"""Your training dataloader is empty, you have only {len(self.train_dl.dataset)} items in your training set.
Your batch size is {self.train_dl.batch_size}, you should lower it.""")
print(final_message)
return
idx = next(iter(self.train_dl.batch_sampler))
samples,fails = [],[]
for i in idx:
try: samples.append(self.train_dl.dataset[i])
except: fails.append(i)
if len(fails) > 0:
warn_msg = "There seems to be something wrong with your dataset, for example, in the first batch can't access"
if len(fails) == len(idx):
warn_msg += f" any element of self.train_ds.\nTried: {show_some(idx)}"
else:
warn_msg += f" these elements in self.train_ds: {show_some(fails)}"
warn(warn_msg)
print(final_message)
return
try: batch = self.collate_fn(samples)
except:
message = "It's not possible to collate samples of your dataset together in a batch."
try:
shapes = [[o[i].data.shape for o in samples] for i in range(2)]
message += f'\nShapes of the inputs/targets:\n{shapes}'
except: pass
warn(message)
print(final_message)
def load_data(path:PathOrStr, file:PathLikeOrBinaryStream='data_save.pkl', 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:
"Load a saved `DataBunch` from `path/file`. `file` can be file-like (file or buffer)"
source = Path(path)/file if is_pathlike(file) else file
ll = torch.load(source, map_location='cpu') if defaults.device == torch.device('cpu') else torch.load(source)
return ll.databunch(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)