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rlizzorlizzo
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initial work on adding sampler
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-4
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3 files changed

+234
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‎src/hangar/arrayset.py

Lines changed: 13 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1,3 +1,4 @@
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from collections import defaultdict
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import hashlib
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import os
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import warnings
@@ -307,6 +308,18 @@ def backend_opts(self):
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"""
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return self._dflt_backend_opts
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@property
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def sample_classes(self):
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grouped_spec_names = defaultdict(list)
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for name, bespec in self._sspecs.items():
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grouped_spec_names[bespec].append(name)
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grouped_data_names = {}
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for spec, names in grouped_spec_names.items():
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data = self._fs[spec.backend].read_data(spec)
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grouped_data_names[tuple(data.tolist())] = names
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return grouped_data_names
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def keys(self, local: bool = False) -> Iterator[Union[str, int]]:
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"""generator which yields the names of every sample in the arrayset
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‎src/hangar/dataloaders/common.py

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -33,20 +33,20 @@ def __init__(self,
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if len(arraysets) == 0:
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raise ValueError('len(arraysets) cannot == 0')
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aset_lens = set()
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# aset_lens = set()
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all_keys = []
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all_remote_keys = []
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for aset in arraysets:
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if aset.iswriteable is True:
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raise TypeError(f'Cannot load arraysets opened in `write-enabled` checkout.')
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self.arrayset_array.append(aset)
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self.arrayset_names.append(aset.name)
44-
aset_lens.add(len(aset))
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# aset_lens.add(len(aset))
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all_keys.append(set(aset.keys()))
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all_remote_keys.append(set(aset.remote_reference_keys))
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48-
if len(aset_lens) > 1:
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warnings.warn('Arraysets do not contain equal number of samples', UserWarning)
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# if len(aset_lens) > 1:
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# warnings.warn('Arraysets do not contain equal number of samples', UserWarning)
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common_keys = set.intersection(*all_keys)
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remote_keys = set.union(*all_remote_keys)

‎src/hangar/dataloaders/sampler.py

Lines changed: 217 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,217 @@
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import numpy as np
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3+
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def pnorm(p):
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if not isinstance(p, (list, tuple)):
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raise ValueError(f'probability map {p} must be of type (list, tuple), not {type(p)}')
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ptot = np.sum(p)
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if not np.allclose(ptot, 1):
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p = [i / ptot for i in p]
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return p
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def multinomial(num_samples, p):
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valid_p = pnorm(p)
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res = np.random.multinomial(num_samples, valid_p)
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return res
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class Sampler(object):
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r"""Base class for all Samplers.
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Every Sampler subclass has to provide an :meth:`__iter__` method, providing a
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way to iterate over indices of dataset elements, and a :meth:`__len__` method
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that returns the length of the returned iterators.
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.. note:: The :meth:`__len__` method isn't strictly required by
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:class:`~torch.utils.data.DataLoader`, but is expected in any
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calculation involving the length of a :class:`~torch.utils.data.DataLoader`.
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"""
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def __init__(self, data_source):
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pass
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def __iter__(self):
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raise NotImplementedError
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# NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
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#
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# Many times we have an abstract class representing a collection/iterable of
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# data, e.g., `torch.utils.data.Sampler`, with its subclasses optionally
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# implementing a `__len__` method. In such cases, we must make sure to not
40+
# provide a default implementation, because both straightforward default
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# implementations have their issues:
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#
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# + `return NotImplemented`:
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# Calling `len(subclass_instance)` raises:
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# TypeError: 'NotImplementedType' object cannot be interpreted as an integer
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#
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# + `raise NotImplementedError()`:
48+
# This prevents triggering some fallback behavior. E.g., the built-in
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# `list(X)` tries to call `len(X)` first, and executes a different code
50+
# path if the method is not found or `NotImplemented` is returned, while
51+
# raising an `NotImplementedError` will propagate and and make the call
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# fail where it could have use `__iter__` to complete the call.
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#
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# Thus, the only two sensible things to do are
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#
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# + **not** provide a default `__len__`.
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#
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# + raise a `TypeError` instead, which is what Python uses when users call
59+
# a method that is not defined on an object.
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# (@ssnl verifies that this works on at least Python 3.7.)
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62+
63+
class SequentialSampler(Sampler):
64+
r"""Samples elements sequentially, always in the same order.
65+
Arguments:
66+
data_source (Dataset): dataset to sample from
67+
"""
68+
69+
def __init__(self, data_source):
70+
self.data_source = data_source
71+
72+
def __iter__(self):
73+
return iter(self.data_source.keys())
74+
75+
def __len__(self):
76+
return len(self.data_source)
77+
78+
79+
class RandomSampler(Sampler):
80+
r"""Samples elements randomly. If without replacement, then sample from a shuffled dataset.
81+
If with replacement, then user can specify :attr:`num_samples` to draw.
82+
Arguments:
83+
data_source (Dataset): dataset to sample from
84+
replacement (bool): samples are drawn with replacement if ``True``, default=``False``
85+
num_samples (int): number of samples to draw, default=`len(dataset)`. This argument
86+
is supposed to be specified only when `replacement` is ``True``.
87+
"""
88+
89+
def __init__(self, data_source, replacement=False, num_samples=None):
90+
self.data_source = data_source
91+
self.replacement = replacement
92+
self._num_samples = num_samples
93+
94+
if not isinstance(self.replacement, bool):
95+
raise ValueError("replacement should be a boolean value, but got "
96+
"replacement={}".format(self.replacement))
97+
98+
if self._num_samples is not None and not replacement:
99+
raise ValueError("With replacement=False, num_samples should not be specified, "
100+
"since a random permute will be performed.")
101+
102+
if not isinstance(self.num_samples, int) or self.num_samples <= 0:
103+
raise ValueError("num_samples should be a positive integer "
104+
"value, but got num_samples={}".format(self.num_samples))
105+
106+
@property
107+
def num_samples(self):
108+
# dataset size might change at runtime
109+
if self._num_samples is None:
110+
return len(self.data_source)
111+
return self._num_samples
112+
113+
def __iter__(self):
114+
n = len(self.data_source)
115+
keys = list(self.data_source.keys())
116+
if self.replacement:
117+
choose = np.random.randint(low=0, high=n, size=(self.num_samples,), dtype=np.int64).tolist()
118+
return (keys[x] for x in choose)
119+
choose = np.random.permutation(self.num_samples)
120+
return (keys[x] for x in choose)
121+
122+
def __len__(self):
123+
return self.num_samples
124+
125+
126+
class SubsetRandomSampler(Sampler):
127+
r"""Samples elements randomly from a given list of indices, without replacement.
128+
Arguments:
129+
indices (sequence): a sequence of indices
130+
"""
131+
132+
def __init__(self, indices):
133+
self.indices = indices
134+
135+
def __iter__(self):
136+
choose = np.random.permutation(len(self.indices))
137+
return (self.indices[x] for x in choose)
138+
139+
def __len__(self):
140+
return len(self.indices)
141+
142+
143+
class WeightedRandomSampler(Sampler):
144+
r"""Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights).
145+
Args:
146+
weights (sequence) : a sequence of weights, not necessary summing up to one
147+
num_samples (int): number of samples to draw
148+
replacement (bool): if ``True``, samples are drawn with replacement.
149+
If not, they are drawn without replacement, which means that when a
150+
sample index is drawn for a row, it cannot be drawn again for that row.
151+
Example:
152+
>>> list(WeightedRandomSampler([0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True))
153+
[0, 0, 0, 1, 0]
154+
>>> list(WeightedRandomSampler([0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False))
155+
[0, 1, 4, 3, 2]
156+
"""
157+
158+
def __init__(self, weights, num_samples):
159+
if not isinstance(num_samples, int) or isinstance(num_samples, bool) or \
160+
num_samples <= 0:
161+
raise ValueError("num_samples should be a positive integer "
162+
"value, but got num_samples={}".format(num_samples))
163+
self.weights = tuple(weights)
164+
self.num_samples = num_samples
165+
166+
def __iter__(self):
167+
return iter(multinomial(self.num_samples, self.weights))
168+
169+
def __len__(self):
170+
return self.num_samples
171+
172+
173+
class BatchSampler(Sampler):
174+
r"""Wraps another sampler to yield a mini-batch of indices.
175+
Args:
176+
sampler (Sampler): Base sampler.
177+
batch_size (int): Size of mini-batch.
178+
drop_last (bool): If ``True``, the sampler will drop the last batch if
179+
its size would be less than ``batch_size``
180+
Example:
181+
>>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=False))
182+
[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
183+
>>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True))
184+
[[0, 1, 2], [3, 4, 5], [6, 7, 8]]
185+
"""
186+
187+
def __init__(self, sampler, batch_size, drop_last):
188+
if not isinstance(sampler, Sampler):
189+
raise ValueError("sampler should be an instance of "
190+
"torch.utils.data.Sampler, but got sampler={}"
191+
.format(sampler))
192+
if not isinstance(batch_size, int) or isinstance(batch_size, bool) or \
193+
batch_size <= 0:
194+
raise ValueError("batch_size should be a positive integer value, "
195+
"but got batch_size={}".format(batch_size))
196+
if not isinstance(drop_last, bool):
197+
raise ValueError("drop_last should be a boolean value, but got "
198+
"drop_last={}".format(drop_last))
199+
self.sampler = sampler
200+
self.batch_size = batch_size
201+
self.drop_last = drop_last
202+
203+
def __iter__(self):
204+
batch = []
205+
for idx in self.sampler:
206+
batch.append(idx)
207+
if len(batch) == self.batch_size:
208+
yield batch
209+
batch = []
210+
if len(batch) > 0 and not self.drop_last:
211+
yield batch
212+
213+
def __len__(self):
214+
if self.drop_last:
215+
return len(self.sampler) // self.batch_size
216+
else:
217+
return (len(self.sampler) + self.batch_size - 1) // self.batch_size

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