Data core

Core functionality for gathering data
from nbdev.cli import *

The classes here provide functionality for applying a list of transforms to a set of items (TfmdLists, Datasets) or a DataLoader (TfmdDl) as well as the base class used to gather the data for model training: DataLoaders.

show_batch is a type-dispatched function that is responsible for showing decoded samples. x and y are the input and the target in the batch to be shown, and are passed along to dispatch on their types. There is a different implementation of show_batch if x is a TensorImage or a TensorText for instance (see vision.core or text.data for more details). ctxs can be passed but the function is responsible to create them if necessary. kwargs depend on the specific implementation.

show_results is a type-dispatched function that is responsible for showing decoded samples and their corresponding outs. Like in show_batch, x and y are the input and the target in the batch to be shown, and are passed along to dispatch on their types. ctxs can be passed but the function is responsible to create them if necessary. kwargs depend on the specific implementation.


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TfmdDL

 TfmdDL (dataset, bs:int=64, shuffle:bool=False, num_workers:int=None,
         verbose:bool=False, do_setup:bool=True, pin_memory=False,
         timeout=0, batch_size=None, drop_last=False, indexed=None,
         n=None, device=None, persistent_workers=False,
         pin_memory_device='', wif=None, before_iter=None,
         after_item=None, before_batch=None, after_batch=None,
         after_iter=None, create_batches=None, create_item=None,
         create_batch=None, retain=None, get_idxs=None, sample=None,
         shuffle_fn=None, do_batch=None)

Transformed DataLoader

Type Default Details
dataset Map- or iterable-style dataset from which to load the data
bs int 64 Size of batch
shuffle bool False Whether to shuffle data
num_workers int None Number of CPU cores to use in parallel (default: All available up to 16)
verbose bool False Whether to print verbose logs
do_setup bool True Whether to run setup() for batch transform(s)
pin_memory bool False
timeout int 0
batch_size NoneType None
drop_last bool False
indexed NoneType None
n NoneType None
device NoneType None
persistent_workers bool False
pin_memory_device str
wif NoneType None
before_iter NoneType None
after_item NoneType None
before_batch NoneType None
after_batch NoneType None
after_iter NoneType None
create_batches NoneType None
create_item NoneType None
create_batch NoneType None
retain NoneType None
get_idxs NoneType None
sample NoneType None
shuffle_fn NoneType None
do_batch NoneType None

A TfmdDL is a DataLoader that creates Pipeline from a list of Transforms for the callbacks after_item, before_batch and after_batch. As a result, it can decode or show a processed batch.

class _Category(int, ShowTitle): pass
#Test retain type
class NegTfm(Transform):
    def encodes(self, x): return torch.neg(x)
    def decodes(self, x): return torch.neg(x)
    
tdl = TfmdDL([(TensorImage([1]),)] * 4, after_batch=NegTfm(), bs=4, num_workers=4)
b = tdl.one_batch()
test_eq(type(b[0]), TensorImage)
b = (tensor([1.,1.,1.,1.]),)
test_eq(type(tdl.decode_batch(b)[0][0]), TensorImage)
class A(Transform): 
    def encodes(self, x): return x 
    def decodes(self, x): return TitledInt(x) 

@Transform
def f(x)->None: return fastuple((x,x))

start = torch.arange(50)
test_eq_type(f(2), fastuple((2,2)))
a = A()
tdl = TfmdDL(start, after_item=lambda x: (a(x), f(x)), bs=4)
x,y = tdl.one_batch()
test_eq(type(y), fastuple)

s = tdl.decode_batch((x,y))
test_eq(type(s[0][1]), fastuple)
tdl = TfmdDL(torch.arange(0,50), after_item=A(), after_batch=NegTfm(), bs=4)
test_eq(tdl.dataset[0], start[0])
test_eq(len(tdl), (50-1)//4+1)
test_eq(tdl.bs, 4)
test_stdout(tdl.show_batch, '0\n1\n2\n3')
test_stdout(partial(tdl.show_batch, unique=True), '0\n0\n0\n0')
class B(Transform):
    parameters = 'a'
    def __init__(self): self.a = torch.tensor(0.)
    def encodes(self, x): x
    
tdl = TfmdDL([(TensorImage([1]),)] * 4, after_batch=B(), bs=4)
test_eq(tdl.after_batch.fs[0].a.device, torch.device('cpu'))
tdl.to(default_device())
test_eq(tdl.after_batch.fs[0].a.device, default_device())

Methods


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DataLoader.one_batch

 DataLoader.one_batch ()

Return one batch from DataLoader.

tfm = NegTfm()
tdl = TfmdDL(start, after_batch=tfm, bs=4)
b = tdl.one_batch()
test_eq(tensor([0,-1,-2,-3]), b)

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TfmdDL.decode

 TfmdDL.decode (b)

Decode b using tfms

Details
b Batch to decode
test_eq(tdl.decode(b), tensor(0,1,2,3))

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TfmdDL.decode_batch

 TfmdDL.decode_batch (b, max_n:int=9, full:bool=True)

Decode b entirely

Type Default Details
b Batch to decode
max_n int 9 Maximum number of items to decode
full bool True Whether to decode all transforms. If False, decode up to the point the item knows how to show itself
test_eq(tdl.decode_batch(b), [0,1,2,3])

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TfmdDL.show_batch

 TfmdDL.show_batch (b=None, max_n:int=9, ctxs=None, show:bool=True,
                    unique:bool=False, **kwargs)

Show b (defaults to one_batch), a list of lists of pipeline outputs (i.e. output of a DataLoader)

Type Default Details
b NoneType None Batch to show
max_n int 9 Maximum number of items to show
ctxs NoneType None List of ctx objects to show data. Could be matplotlib axis, DataFrame etc
show bool True Whether to display data
unique bool False Whether to show only one
kwargs

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TfmdDL.to

 TfmdDL.to (device)

Put self and its transforms state on device

Details
device Device to put DataLoader and transforms

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DataLoaders

 DataLoaders (*loaders, path:str|pathlib.Path='.', device=None)

Basic wrapper around several DataLoaders.

dls = DataLoaders(tdl,tdl)
x = dls.train.one_batch()
x2 = first(tdl)
test_eq(x,x2)
x2 = dls.one_batch()
test_eq(x,x2)

Multiple transforms can by added to multiple dataloaders using Dataloaders.add_tfms. You can specify the dataloaders by list of names dls.add_tfms(...,'valid',...) or by index dls.add_tfms(...,1,....), by default transforms are added to all dataloaders. event is a required argument and determined when the transform will be run, for more information on events please refer to TfmdDL. tfms is a list of Transform, and is a required argument.

class _TestTfm(Transform):
    def encodes(self, o):  return torch.ones_like(o)
    def decodes(self, o):  return o
tdl1,tdl2 = TfmdDL(start, bs=4),TfmdDL(start, bs=4)
dls2 = DataLoaders(tdl1,tdl2)
dls2.add_tfms([_TestTfm()],'after_batch',['valid'])
dls2.add_tfms([_TestTfm()],'after_batch',[1])
dls2.train.after_batch,dls2.valid.after_batch,
(Pipeline: , Pipeline: _TestTfm -> _TestTfm)
class _T(Transform):  
    def encodes(self, o):  return -o
class _T2(Transform): 
    def encodes(self, o):  return o/2

#test tfms are applied on both traind and valid dl
dls_from_ds = DataLoaders.from_dsets([1,], [5,], bs=1, after_item=_T, after_batch=_T2)
b = first(dls_from_ds.train)
test_eq(b, tensor([-.5]))
b = first(dls_from_ds.valid)
test_eq(b, tensor([-2.5]))

Methods


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DataLoaders.__getitem__

 DataLoaders.__getitem__ (i)

Retrieve DataLoader at i (0 is training, 1 is validation)

x2
tensor([ 0, -1, -2, -3])
x2 = dls[0].one_batch()
test_eq(x,x2)

DataLoaders.train

 DataLoaders.train (x)

partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.


DataLoaders.valid

 DataLoaders.valid (x)

partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.


DataLoaders.train_ds

 DataLoaders.train_ds (x)

partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.


DataLoaders.valid_ds

 DataLoaders.valid_ds (x)

partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.


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FilteredBase

 FilteredBase (*args, dl_type=None, **kwargs)

Base class for lists with subsets


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FilteredBase.dataloaders

 FilteredBase.dataloaders (bs:int=64, shuffle_train:bool=None,
                           shuffle:bool=True, val_shuffle:bool=False,
                           n:int=None, path:str|Path='.',
                           dl_type:TfmdDL=None, dl_kwargs:list=None,
                           device:torch.device=None, drop_last:bool=None,
                           val_bs:int=None, num_workers:int=None,
                           verbose:bool=False, do_setup:bool=True,
                           pin_memory=False, timeout=0, batch_size=None,
                           indexed=None, persistent_workers=False,
                           pin_memory_device='', wif=None,
                           before_iter=None, after_item=None,
                           before_batch=None, after_batch=None,
                           after_iter=None, create_batches=None,
                           create_item=None, create_batch=None,
                           retain=None, get_idxs=None, sample=None,
                           shuffle_fn=None, do_batch=None)
Type Default Details
bs int 64 Batch size
shuffle_train bool None (Deprecated, use shuffle) Shuffle training DataLoader
shuffle bool True Shuffle training DataLoader
val_shuffle bool False Shuffle validation DataLoader
n int None Size of Datasets used to create DataLoader
path str | Path . Path to put in DataLoaders
dl_type TfmdDL None Type of DataLoader
dl_kwargs list None List of kwargs to pass to individual DataLoaders
device torch.device None Device to put DataLoaders
drop_last bool None Drop last incomplete batch, defaults to shuffle
val_bs int None Validation batch size, defaults to bs
num_workers int None Number of CPU cores to use in parallel (default: All available up to 16)
verbose bool False Whether to print verbose logs
do_setup bool True Whether to run setup() for batch transform(s)
pin_memory bool False
timeout int 0
batch_size NoneType None
indexed NoneType None
persistent_workers bool False
pin_memory_device str
wif NoneType None
before_iter NoneType None
after_item NoneType None
before_batch NoneType None
after_batch NoneType None
after_iter NoneType None
create_batches NoneType None
create_item NoneType None
create_batch NoneType None
retain NoneType None
get_idxs NoneType None
sample NoneType None
shuffle_fn NoneType None
do_batch NoneType None
Returns DataLoaders

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TfmdLists

 TfmdLists (items=None, *rest, use_list=False, match=None)

A Pipeline of tfms applied to a collection of items

Type Default Details
items list Items to apply Transforms to
use_list bool None Use list in L
def decode_at(o, idx):
    "Decoded item at `idx`"
    return o.decode(o[idx])

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decode_at

 decode_at (o, idx)

Decoded item at idx

def show_at(o, idx, **kwargs):
    "Show item at `idx`",
    return o.show(o[idx], **kwargs)

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show_at

 show_at (o, idx, **kwargs)

A TfmdLists combines a collection of object with a Pipeline. tfms can either be a Pipeline or a list of transforms, in which case, it will wrap them in a Pipeline. use_list is passed along to L with the items and split_idx are passed to each transform of the Pipeline. do_setup indicates if the Pipeline.setup method should be called during initialization.

class _IntFloatTfm(Transform):
    def encodes(self, o):  return TitledInt(o)
    def decodes(self, o):  return TitledFloat(o)
int2f_tfm=_IntFloatTfm()

def _neg(o): return -o
neg_tfm = Transform(_neg, _neg)
items = L([1.,2.,3.]); tfms = [neg_tfm, int2f_tfm]
tl = TfmdLists(items, tfms=tfms)
test_eq_type(tl[0], TitledInt(-1))
test_eq_type(tl[1], TitledInt(-2))
test_eq_type(tl.decode(tl[2]), TitledFloat(3.))
test_stdout(lambda: show_at(tl, 2), '-3')
test_eq(tl.types, [float, float, TitledInt])
tl
TfmdLists: [1.0, 2.0, 3.0]
tfms - [_neg:
encodes: (object,object) -> _negdecodes: (object,object) -> _neg, _IntFloatTfm:
encodes: (object,object) -> encodes
decodes: (object,object) -> decodes
]
# add splits to TfmdLists
splits = [[0,2],[1]]
tl = TfmdLists(items, tfms=tfms, splits=splits)
test_eq(tl.n_subsets, 2)
test_eq(tl.train, tl.subset(0))
test_eq(tl.valid, tl.subset(1))
test_eq(tl.train.items, items[splits[0]])
test_eq(tl.valid.items, items[splits[1]])
test_eq(tl.train.tfms.split_idx, 0)
test_eq(tl.valid.tfms.split_idx, 1)
test_eq(tl.train.new_empty().split_idx, 0)
test_eq(tl.valid.new_empty().split_idx, 1)
test_eq_type(tl.splits, L(splits))
assert not tl.overlapping_splits()
df = pd.DataFrame(dict(a=[1,2,3],b=[2,3,4]))
tl = TfmdLists(df, lambda o: o.a+1, splits=[[0],[1,2]])
test_eq(tl[1,2], [3,4])
tr = tl.subset(0)
test_eq(tr[:], [2])
val = tl.subset(1)
test_eq(val[:], [3,4])
class _B(Transform):
    def __init__(self): self.m = 0
    def encodes(self, o): return o+self.m
    def decodes(self, o): return o-self.m
    def setups(self, items): 
        print(items)
        self.m = tensor(items).float().mean().item()

# test for setup, which updates `self.m`
tl = TfmdLists(items, _B())
test_eq(tl.m, 2)
TfmdLists: [1.0, 2.0, 3.0]
tfms - []

Here’s how we can use TfmdLists.setup to implement a simple category list, getting labels from a mock file list:

class _Cat(Transform):
    order = 1
    def encodes(self, o):    return int(self.o2i[o])
    def decodes(self, o):    return TitledStr(self.vocab[o])
    def setups(self, items): self.vocab,self.o2i = uniqueify(L(items), sort=True, bidir=True)
tcat = _Cat()

def _lbl(o): return TitledStr(o.split('_')[0])

# Check that tfms are sorted by `order` & `_lbl` is called first
fns = ['dog_0.jpg','cat_0.jpg','cat_2.jpg','cat_1.jpg','dog_1.jpg']
tl = TfmdLists(fns, [tcat,_lbl])
exp_voc = ['cat','dog']
test_eq(tcat.vocab, exp_voc)
test_eq(tl.tfms.vocab, exp_voc)
test_eq(tl.vocab, exp_voc)
test_eq(tl, (1,0,0,0,1))
test_eq([tl.decode(o) for o in tl], ('dog','cat','cat','cat','dog'))
#Check only the training set is taken into account for setup
tl = TfmdLists(fns, [tcat,_lbl], splits=[[0,4], [1,2,3]])
test_eq(tcat.vocab, ['dog'])
tfm = NegTfm(split_idx=1)
tds = TfmdLists(start, A())
tdl = TfmdDL(tds, after_batch=tfm, bs=4)
x = tdl.one_batch()
test_eq(x, torch.arange(4))
tds.split_idx = 1
x = tdl.one_batch()
test_eq(x, -torch.arange(4))
tds.split_idx = 0
x = tdl.one_batch()
test_eq(x, torch.arange(4))
tds = TfmdLists(start, A())
tdl = TfmdDL(tds, after_batch=NegTfm(), bs=4)
test_eq(tdl.dataset[0], start[0])
test_eq(len(tdl), (len(tds)-1)//4+1)
test_eq(tdl.bs, 4)
test_stdout(tdl.show_batch, '0\n1\n2\n3')

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TfmdLists.subset

 TfmdLists.subset (i)

New TfmdLists with same tfms that only includes items in ith split


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TfmdLists.infer_idx

 TfmdLists.infer_idx (x)

Finds the index where self.tfms can be applied to x, depending on the type of x


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TfmdLists.infer

 TfmdLists.infer (x)

Apply self.tfms to x starting at the right tfm depending on the type of x

def mult(x): return x*2
mult.order = 2

fns = ['dog_0.jpg','cat_0.jpg','cat_2.jpg','cat_1.jpg','dog_1.jpg']
tl = TfmdLists(fns, [_lbl,_Cat(),mult])

test_eq(tl.infer_idx('dog_45.jpg'), 0)
test_eq(tl.infer('dog_45.jpg'), 2)

test_eq(tl.infer_idx(4), 2)
test_eq(tl.infer(4), 8)

test_fail(lambda: tl.infer_idx(2.0))
test_fail(lambda: tl.infer(2.0))

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Datasets

 Datasets (items:list=None, tfms:list|Pipeline=None, tls:TfmdLists=None,
           n_inp:int=None, dl_type=None, use_list:bool=None,
           do_setup:bool=True, split_idx:int=None, train_setup:bool=True,
           splits:list=None, types=None, verbose:bool=False)

A dataset that creates a tuple from each tfms

Type Default Details
items list None List of items to create Datasets
tfms list | Pipeline None List of Transform(s) or Pipeline to apply
tls TfmdLists None If None, self.tls is generated from items and tfms
n_inp int None Number of elements in Datasets tuple that should be considered part of input
dl_type NoneType None Default type of DataLoader used when function FilteredBase.dataloaders is called
use_list bool None Use list in L
do_setup bool True Call setup() for Transform
split_idx int None Apply Transform(s) to training or validation set. 0 for training set and 1 for validation set
train_setup bool True Apply Transform(s) only on training DataLoader
splits list None Indices for training and validation sets
types NoneType None Types of data in items
verbose bool False Print verbose output

A Datasets creates a tuple from items (typically input,target) by applying to them each list of Transform (or Pipeline) in tfms. Note that if tfms contains only one list of tfms, the items given by Datasets will be tuples of one element.

n_inp is the number of elements in the tuples that should be considered part of the input and will default to 1 if tfms consists of one set of transforms, len(tfms)-1 otherwise. In most cases, the number of elements in the tuples spit out by Datasets will be 2 (for input,target) but it can happen that there is 3 (Siamese networks or tabular data) in which case we need to be able to determine when the inputs end and the targets begin.

items = [1,2,3,4]
dsets = Datasets(items, [[neg_tfm,int2f_tfm], [add(1)]])
t = dsets[0]
test_eq(t, (-1,2))
test_eq(dsets[0,1,2], [(-1,2),(-2,3),(-3,4)])
test_eq(dsets.n_inp, 1)
dsets.decode(t)
(1.0, 2)
class Norm(Transform):
    def encodes(self, o): return (o-self.m)/self.s
    def decodes(self, o): return (o*self.s)+self.m
    def setups(self, items):
        its = tensor(items).float()
        self.m,self.s = its.mean(),its.std()
items = [1,2,3,4]
nrm = Norm()
dsets = Datasets(items, [[neg_tfm,int2f_tfm], [neg_tfm,nrm]])

x,y = zip(*dsets)
test_close(tensor(y).mean(), 0)
test_close(tensor(y).std(), 1)
test_eq(x, (-1,-2,-3,-4,))
test_eq(nrm.m, -2.5)
test_stdout(lambda:show_at(dsets, 1), '-2')

test_eq(dsets.m, nrm.m)
test_eq(dsets.norm.m, nrm.m)
test_eq(dsets.train.norm.m, nrm.m)
test_fns = ['dog_0.jpg','cat_0.jpg','cat_2.jpg','cat_1.jpg','kid_1.jpg']
tcat = _Cat()
dsets = Datasets(test_fns, [[tcat,_lbl]], splits=[[0,1,2], [3,4]])
test_eq(tcat.vocab, ['cat','dog'])
test_eq(dsets.train, [(1,),(0,),(0,)])
test_eq(dsets.valid[0], (0,))
test_stdout(lambda: show_at(dsets.train, 0), "dog")
inp = [0,1,2,3,4]
dsets = Datasets(inp, tfms=[None])

test_eq(*dsets[2], 2)          # Retrieve one item (subset 0 is the default)
test_eq(dsets[1,2], [(1,),(2,)])    # Retrieve two items by index
mask = [True,False,False,True,False]
test_eq(dsets[mask], [(0,),(3,)])   # Retrieve two items by mask
inp = pd.DataFrame(dict(a=[5,1,2,3,4]))
dsets = Datasets(inp, tfms=attrgetter('a')).subset(0)
test_eq(*dsets[2], 2)          # Retrieve one item (subset 0 is the default)
test_eq(dsets[1,2], [(1,),(2,)])    # Retrieve two items by index
mask = [True,False,False,True,False]
test_eq(dsets[mask], [(5,),(3,)])   # Retrieve two items by mask
#test n_inp
inp = [0,1,2,3,4]
dsets = Datasets(inp, tfms=[None])
test_eq(dsets.n_inp, 1)
dsets = Datasets(inp, tfms=[[None],[None],[None]])
test_eq(dsets.n_inp, 2)
dsets = Datasets(inp, tfms=[[None],[None],[None]], n_inp=1)
test_eq(dsets.n_inp, 1)
# splits can be indices
dsets = Datasets(range(5), tfms=[None], splits=[tensor([0,2]), [1,3,4]])

test_eq(dsets.subset(0), [(0,),(2,)])
test_eq(dsets.train, [(0,),(2,)])       # Subset 0 is aliased to `train`
test_eq(dsets.subset(1), [(1,),(3,),(4,)])
test_eq(dsets.valid, [(1,),(3,),(4,)])     # Subset 1 is aliased to `valid`
test_eq(*dsets.valid[2], 4)
#assert '[(1,),(3,),(4,)]' in str(dsets) and '[(0,),(2,)]' in str(dsets)
dsets
(#5) [(0,),(1,),(2,),(3,),(4,)]
# splits can be boolean masks (they don't have to cover all items, but must be disjoint)
splits = [[False,True,True,False,True], [True,False,False,False,False]]
dsets = Datasets(range(5), tfms=[None], splits=splits)

test_eq(dsets.train, [(1,),(2,),(4,)])
test_eq(dsets.valid, [(0,)])
# apply transforms to all items
tfm = [[lambda x: x*2,lambda x: x+1]]
splits = [[1,2],[0,3,4]]
dsets = Datasets(range(5), tfm, splits=splits)
test_eq(dsets.train,[(3,),(5,)])
test_eq(dsets.valid,[(1,),(7,),(9,)])
test_eq(dsets.train[False,True], [(5,)])
# only transform subset 1
class _Tfm(Transform):
    split_idx=1
    def encodes(self, x): return x*2
    def decodes(self, x): return TitledStr(x//2)
dsets = Datasets(range(5), [_Tfm()], splits=[[1,2],[0,3,4]])
test_eq(dsets.train,[(1,),(2,)])
test_eq(dsets.valid,[(0,),(6,),(8,)])
test_eq(dsets.train[False,True], [(2,)])
dsets
(#5) [(0,),(1,),(2,),(3,),(4,)]
#A context manager to change the split_idx and apply the validation transform on the training set
ds = dsets.train
with ds.set_split_idx(1):
    test_eq(ds,[(2,),(4,)])
test_eq(dsets.train,[(1,),(2,)])
dsets = Datasets(range(5), [_Tfm(),noop], splits=[[1,2],[0,3,4]])
test_eq(dsets.train,[(1,1),(2,2)])
test_eq(dsets.valid,[(0,0),(6,3),(8,4)])
start = torch.arange(0,50)
tds = Datasets(start, [A()])
tdl = TfmdDL(tds, after_item=NegTfm(), bs=4)
b = tdl.one_batch()
test_eq(tdl.decode_batch(b), ((0,),(1,),(2,),(3,)))
test_stdout(tdl.show_batch, "0\n1\n2\n3")
# only transform subset 1
class _Tfm(Transform):
    split_idx=1
    def encodes(self, x): return x*2

dsets = Datasets(range(8), [None], splits=[[1,2,5,7],[0,3,4,6]])
# only transform subset 1
class _Tfm(Transform):
    split_idx=1
    def encodes(self, x): return x*2

dsets = Datasets(range(8), [None], splits=[[1,2,5,7],[0,3,4,6]])
dls = dsets.dataloaders(bs=4, after_batch=_Tfm(), shuffle=False, device=torch.device('cpu'))
test_eq(dls.train, [(tensor([1,2,5, 7]),)])
test_eq(dls.valid, [(tensor([0,6,8,12]),)])
test_eq(dls.n_inp, 1)

Methods

items = [1,2,3,4]
dsets = Datasets(items, [[neg_tfm,int2f_tfm]])

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Datasets.dataloaders

 Datasets.dataloaders (bs:int=64, shuffle_train:bool=None,
                       shuffle:bool=True, val_shuffle:bool=False,
                       n:int=None, path:str|Path='.', dl_type:TfmdDL=None,
                       dl_kwargs:list=None, device:torch.device=None,
                       drop_last:bool=None, val_bs:int=None,
                       num_workers:int=None, verbose:bool=False,
                       do_setup:bool=True, pin_memory=False, timeout=0,
                       batch_size=None, indexed=None,
                       persistent_workers=False, pin_memory_device='',
                       wif=None, before_iter=None, after_item=None,
                       before_batch=None, after_batch=None,
                       after_iter=None, create_batches=None,
                       create_item=None, create_batch=None, retain=None,
                       get_idxs=None, sample=None, shuffle_fn=None,
                       do_batch=None)

Get a DataLoaders

Type Default Details
bs int 64 Batch size
shuffle_train bool None (Deprecated, use shuffle) Shuffle training DataLoader
shuffle bool True Shuffle training DataLoader
val_shuffle bool False Shuffle validation DataLoader
n int None Size of Datasets used to create DataLoader
path str | Path . Path to put in DataLoaders
dl_type TfmdDL None Type of DataLoader
dl_kwargs list None List of kwargs to pass to individual DataLoaders
device torch.device None Device to put DataLoaders
drop_last bool None Drop last incomplete batch, defaults to shuffle
val_bs int None Validation batch size, defaults to bs
num_workers int None Number of CPU cores to use in parallel (default: All available up to 16)
verbose bool False Whether to print verbose logs
do_setup bool True Whether to run setup() for batch transform(s)
pin_memory bool False
timeout int 0
batch_size NoneType None
indexed NoneType None
persistent_workers bool False
pin_memory_device str
wif NoneType None
before_iter NoneType None
after_item NoneType None
before_batch NoneType None
after_batch NoneType None
after_iter NoneType None
create_batches NoneType None
create_item NoneType None
create_batch NoneType None
retain NoneType None
get_idxs NoneType None
sample NoneType None
shuffle_fn NoneType None
do_batch NoneType None
Returns DataLoaders

Used to create dataloaders. You may prepend ‘val_’ as in val_shuffle to override functionality for the validation set. dl_kwargs gives finer per dataloader control if you need to work with more than one dataloader.


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Datasets.decode

 Datasets.decode (o, full=True)

Compose decode of all tuple_tfms then all tfms on i

test_eq(*dsets[0], -1)
test_eq(*dsets.decode((-1,)), 1)

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Datasets.show

 Datasets.show (o, ctx=None, **kwargs)

Show item o in ctx

test_stdout(lambda:dsets.show(dsets[1]), '-2')

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Datasets.new_empty

 Datasets.new_empty ()

Create a new empty version of the self, keeping only the transforms

items = [1,2,3,4]
nrm = Norm()
dsets = Datasets(items, [[neg_tfm,int2f_tfm], [neg_tfm]])
empty = dsets.new_empty()
test_eq(empty.items, [])

Add test set for inference

# only transform subset 1
class _Tfm1(Transform):
    split_idx=0
    def encodes(self, x): return x*3

dsets = Datasets(range(8), [[_Tfm(),_Tfm1()]], splits=[[1,2,5,7],[0,3,4,6]])
test_eq(dsets.train, [(3,),(6,),(15,),(21,)])
test_eq(dsets.valid, [(0,),(6,),(8,),(12,)])

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test_set

 test_set (dsets:__main__.Datasets|__main__.TfmdLists, test_items,
           rm_tfms=None, with_labels:bool=False)

Create a test set from test_items using validation transforms of dsets

Type Default Details
dsets Datasets | TfmdLists Map- or iterable-style dataset from which to load the data
test_items Items in test dataset
rm_tfms NoneType None Start index of Transform(s) from validation set in dsets to apply
with_labels bool False Whether the test items contain labels
class _Tfm1(Transform):
    split_idx=0
    def encodes(self, x): return x*3

dsets = Datasets(range(8), [[_Tfm(),_Tfm1()]], splits=[[1,2,5,7],[0,3,4,6]])
test_eq(dsets.train, [(3,),(6,),(15,),(21,)])
test_eq(dsets.valid, [(0,),(6,),(8,),(12,)])

#Tranform of the validation set are applied
tst = test_set(dsets, [1,2,3])
test_eq(tst, [(2,),(4,),(6,)])

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DataLoaders.test_dl

 DataLoaders.test_dl (test_items, rm_type_tfms=None,
                      with_labels:bool=False, bs:int=64,
                      shuffle:bool=False, num_workers:int=None,
                      verbose:bool=False, do_setup:bool=True,
                      pin_memory=False, timeout=0, batch_size=None,
                      drop_last=False, indexed=None, n=None, device=None,
                      persistent_workers=False, pin_memory_device='',
                      wif=None, before_iter=None, after_item=None,
                      before_batch=None, after_batch=None,
                      after_iter=None, create_batches=None,
                      create_item=None, create_batch=None, retain=None,
                      get_idxs=None, sample=None, shuffle_fn=None,
                      do_batch=None)

Create a test dataloader from test_items using validation transforms of dls

Type Default Details
test_items Items in test dataset
rm_type_tfms NoneType None Start index of Transform(s) from validation set in dsets to apply
with_labels bool False Whether the test items contain labels
bs int 64 Size of batch
shuffle bool False Whether to shuffle data
num_workers int None Number of CPU cores to use in parallel (default: All available up to 16)
verbose bool False Whether to print verbose logs
do_setup bool True Whether to run setup() for batch transform(s)
pin_memory bool False
timeout int 0
batch_size NoneType None
drop_last bool False
indexed NoneType None
n NoneType None
device NoneType None
persistent_workers bool False
pin_memory_device str
wif NoneType None
before_iter NoneType None
after_item NoneType None
before_batch NoneType None
after_batch NoneType None
after_iter NoneType None
create_batches NoneType None
create_item NoneType None
create_batch NoneType None
retain NoneType None
get_idxs NoneType None
sample NoneType None
shuffle_fn NoneType None
do_batch NoneType None
dsets = Datasets(range(8), [[_Tfm(),_Tfm1()]], splits=[[1,2,5,7],[0,3,4,6]])
dls = dsets.dataloaders(bs=4, device=torch.device('cpu'))
dsets = Datasets(range(8), [[_Tfm(),_Tfm1()]], splits=[[1,2,5,7],[0,3,4,6]])
dls = dsets.dataloaders(bs=4, device=torch.device('cpu'))
tst_dl = dls.test_dl([2,3,4,5])
test_eq(tst_dl._n_inp, 1)
test_eq(list(tst_dl), [(tensor([ 4,  6,  8, 10]),)])
#Test you can change transforms
tst_dl = dls.test_dl([2,3,4,5], after_item=add1)
test_eq(list(tst_dl), [(tensor([ 5,  7,  9, 11]),)])