Core functionality for gathering data
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.
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.
add_docs(TfmdDL,
decode="Decode `b` using `tfms`",
decode_batch="Decode `b` entirely",
new="Create a new version of self with a few changed attributes",
show_batch="Show `b` (defaults to `one_batch`), a list of lists of pipeline outputs (i.e. output of a `DataLoader`)",
show_results="Show each item of `b` and `out`",
before_iter="override",
to="Put self and its transforms state on `device`")
class _Category(int, ShowTitle): pass
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())
tfm = NegTfm()
tdl = TfmdDL(start, after_batch=tfm, bs=4)
b = tdl.one_batch()
test_eq(tensor([0,-1,-2,-3]), b)
test_eq(tdl.decode(b), tensor(0,1,2,3))
test_eq(tdl.decode_batch(b), [0,1,2,3])
dls = DataLoaders(tdl,tdl)
x = dls.train.one_batch()
x2 = first(tdl)
test_eq(x,x2)
x2 = dls.one_batch()
test_eq(x,x2)
x2 = dls[0].one_batch()
test_eq(x,x2)
add_docs(TfmdLists,
setup="Transform setup with self",
decode="From `Pipeline",
show="From `Pipeline",
overlapping_splits="All splits that are in more than one split",
subset="New `TfmdLists` with same tfms that only includes items in `i`th split",
infer_idx="Finds the index where `self.tfms` can be applied to `x`, depending on the type of `x`",
infer="Apply `self.tfms` to `x` starting at the right tfm depending on the type of `x`",
new_empty="A new version of `self` but with no items")
def decode_at(o, idx):
"Decoded item at `idx`"
return o.decode(o[idx])
def show_at(o, idx, **kwargs):
"Show item at `idx`",
return o.show(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
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)
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'))
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')
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))
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)
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
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)
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
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,)])
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,)])
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
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")
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]])
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_train=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)
items = [1,2,3,4]
dsets = Datasets(items, [[neg_tfm,int2f_tfm]])
test_eq(*dsets[0], -1)
test_eq(*dsets.decode((-1,)), 1)
test_stdout(lambda:dsets.show(dsets[1]), '-2')
items = [1,2,3,4]
nrm = Norm()
dsets = Datasets(items, [[neg_tfm,int2f_tfm], [neg_tfm]])
empty = dsets.new_empty()
test_eq(empty.items, [])
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,)])
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,)])
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]),)])