Python torch.utils.data.sampler.Sampler() Examples
The following are 30
code examples of torch.utils.data.sampler.Sampler().
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Example #1
| Source File: grouped_batch_sampler.py From detectron2 with Apache License 2.0 | 6 votes |
|
def __init__(self, sampler, group_ids, batch_size):
"""
Args:
sampler (Sampler): Base sampler.
group_ids (list[int]): If the sampler produces indices in range [0, N),
`group_ids` must be a list of `N` ints which contains the group id of each sample.
The group ids must be a set of integers in the range [0, num_groups).
batch_size (int): Size of mini-batch.
"""
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = np.asarray(group_ids)
assert self.group_ids.ndim == 1
self.batch_size = batch_size
groups = np.unique(self.group_ids).tolist()
# buffer the indices of each group until batch size is reached
self.buffer_per_group = {k: [] for k in groups}
Example #2
| Source File: sampler.py From catalyst with Apache License 2.0 | 6 votes |
|
def __init__(
self,
sampler,
num_replicas: Optional[int] = None,
rank: Optional[int] = None,
shuffle: bool = True,
):
"""
Args:
sampler: Sampler used for subsampling
num_replicas (int, optional): Number of processes participating in
distributed training
rank (int, optional): Rank of the current process
within ``num_replicas``
shuffle (bool, optional): If true (default),
sampler will shuffle the indices
"""
super(DistributedSamplerWrapper, self).__init__(
DatasetFromSampler(sampler),
num_replicas=num_replicas,
rank=rank,
shuffle=shuffle,
)
self.sampler = sampler
Example #3
| Source File: coil_sampler.py From coiltraine with MIT License | 6 votes |
|
def __init__(self, keys, executed_iterations,
batch_size, sequence_size, sequence_stride, drop_last=True):
sampler = PreSplittedSampler(keys, executed_iterations)
if not isinstance(sampler, Sampler):
raise ValueError("sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}"
.format(sampler))
if not isinstance(sequence_size, int) or isinstance(sequence_size, bool) or \
sequence_size <= 0:
raise ValueError("batch_size should be a positive integeral value, "
"but got batch_size={}".format(sequence_size))
if not isinstance(drop_last, bool):
raise ValueError("drop_last should be a boolean value, but got "
"drop_last={}".format(drop_last))
self.sampler = sampler
self.sequence_size = sequence_size
self.batch_size = batch_size
self.drop_last = drop_last
self.sequence_stride = sequence_stride
Example #4
| Source File: grouped_batch_sampler.py From detectron2 with Apache License 2.0 | 6 votes |
|
def __init__(self, sampler, group_ids, batch_size):
"""
Args:
sampler (Sampler): Base sampler.
group_ids (list[int]): If the sampler produces indices in range [0, N),
`group_ids` must be a list of `N` ints which contains the group id of each sample.
The group ids must be a set of integers in the range [0, num_groups).
batch_size (int): Size of mini-batch.
"""
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = np.asarray(group_ids)
assert self.group_ids.ndim == 1
self.batch_size = batch_size
groups = np.unique(self.group_ids).tolist()
# buffer the indices of each group until batch size is reached
self.buffer_per_group = {k: [] for k in groups}
Example #5
| Source File: multigrid_helper.py From SlowFast with Apache License 2.0 | 5 votes |
|
def __init__(self, sampler, batch_size, drop_last, cfg):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
if (
not isinstance(batch_size, _int_classes)
or isinstance(batch_size, bool)
or batch_size <= 0
):
raise ValueError(
"batch_size should be a positive integer value, "
"but got batch_size={}".format(batch_size)
)
if not isinstance(drop_last, bool):
raise ValueError(
"drop_last should be a boolean value, but got "
"drop_last={}".format(drop_last)
)
self.sampler = sampler
self.drop_last = drop_last
bs_factor = [
int(
round(
(
float(cfg.DATA.TRAIN_CROP_SIZE)
/ (s * cfg.MULTIGRID.DEFAULT_S)
)
** 2
)
)
for s in cfg.MULTIGRID.SHORT_CYCLE_FACTORS
]
self.batch_sizes = [
batch_size * bs_factor[0],
batch_size * bs_factor[1],
batch_size,
]
Example #6
| Source File: loader.py From PANet with MIT License | 5 votes |
|
def __init__(self, sampler, batch_size, drop_last):
if not isinstance(sampler, torch_sampler.Sampler):
raise ValueError("sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}"
.format(sampler))
if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
batch_size <= 0:
raise ValueError("batch_size should be a positive integeral value, "
"but got batch_size={}".format(batch_size))
if not isinstance(drop_last, bool):
raise ValueError("drop_last should be a boolean value, but got "
"drop_last={}".format(drop_last))
self.sampler = sampler
self.batch_size = batch_size
self.drop_last = drop_last
Example #7
| Source File: loader.py From PMFNet with MIT License | 5 votes |
|
def __init__(self, sampler, batch_size, drop_last):
if not isinstance(sampler, torch_sampler.Sampler):
raise ValueError("sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}"
.format(sampler))
if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
batch_size <= 0:
raise ValueError("batch_size should be a positive integeral value, "
"but got batch_size={}".format(batch_size))
if not isinstance(drop_last, bool):
raise ValueError("drop_last should be a boolean value, but got "
"drop_last={}".format(drop_last))
self.sampler = sampler
self.batch_size = batch_size
self.drop_last = drop_last
Example #8
| Source File: grouped_batch_sampler.py From DistilKoBERT with Apache License 2.0 | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = group_ids
self.batch_size = batch_size
Example #9
| Source File: grouped_batch_sampler.py From retinamask with MIT License | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #10
| Source File: loader_rel.py From Large-Scale-VRD.pytorch with MIT License | 5 votes |
|
def __init__(self, sampler, batch_size, drop_last):
if not isinstance(sampler, torch_sampler.Sampler):
raise ValueError("sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}"
.format(sampler))
if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
batch_size <= 0:
raise ValueError("batch_size should be a positive integeral value, "
"but got batch_size={}".format(batch_size))
if not isinstance(drop_last, bool):
raise ValueError("drop_last should be a boolean value, but got "
"drop_last={}".format(drop_last))
self.sampler = sampler
self.batch_size = batch_size
self.drop_last = drop_last
Example #11
| Source File: loader.py From Large-Scale-VRD.pytorch with MIT License | 5 votes |
|
def __init__(self, sampler, batch_size, drop_last):
if not isinstance(sampler, torch_sampler.Sampler):
raise ValueError("sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}"
.format(sampler))
if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
batch_size <= 0:
raise ValueError("batch_size should be a positive integeral value, "
"but got batch_size={}".format(batch_size))
if not isinstance(drop_last, bool):
raise ValueError("drop_last should be a boolean value, but got "
"drop_last={}".format(drop_last))
self.sampler = sampler
self.batch_size = batch_size
self.drop_last = drop_last
Example #12
| Source File: loader.py From detectron-self-train with MIT License | 5 votes |
|
def __init__(self, sampler, batch_size, drop_last):
if not isinstance(sampler, torch_sampler.Sampler):
raise ValueError("sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}"
.format(sampler))
if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
batch_size <= 0:
raise ValueError("batch_size should be a positive integeral value, "
"but got batch_size={}".format(batch_size))
if not isinstance(drop_last, bool):
raise ValueError("drop_last should be a boolean value, but got "
"drop_last={}".format(drop_last))
self.sampler = sampler
self.batch_size = batch_size
self.drop_last = drop_last
Example #13
| Source File: grouped_batch_sampler.py From EmbedMask with MIT License | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #14
| Source File: loader.py From DIoU-pytorch-detectron with GNU General Public License v3.0 | 5 votes |
|
def __init__(self, sampler, batch_size, drop_last):
if not isinstance(sampler, torch_sampler.Sampler):
raise ValueError("sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}"
.format(sampler))
if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
batch_size <= 0:
raise ValueError("batch_size should be a positive integeral value, "
"but got batch_size={}".format(batch_size))
if not isinstance(drop_last, bool):
raise ValueError("drop_last should be a boolean value, but got "
"drop_last={}".format(drop_last))
self.sampler = sampler
self.batch_size = batch_size
self.drop_last = drop_last
Example #15
| Source File: sampler.py From training with Apache License 2.0 | 5 votes |
|
def init_rng(self):
"""
Creates new RNG, seed depends on current epoch idx.
"""
rng = torch.Generator()
seed = self.seeds[self.epoch]
logging.info(f'Sampler for epoch {self.epoch} uses seed {seed}')
rng.manual_seed(seed)
return rng
Example #16
| Source File: grouped_batch_sampler.py From training with Apache License 2.0 | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #17
| Source File: grouped_batch_sampler.py From NAS-FCOS with BSD 2-Clause "Simplified" License | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #18
| Source File: auto.py From ignite with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def __init__(self, sampler: Sampler, num_replicas=None, rank=None):
if not isinstance(sampler, Sampler):
raise TypeError("Argument sampler should be instance of torch Sampler, but given: {}".format(type(sampler)))
if not hasattr(sampler, "__len__"):
raise TypeError("Argument sampler should have length")
super(DistributedProxySampler, self).__init__(sampler, num_replicas=num_replicas, rank=rank, shuffle=False)
self.sampler = sampler
Example #19
| Source File: grouped_batch_sampler.py From RRPN_pytorch with MIT License | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #20
| Source File: grouped_batch_sampler.py From DF-Traffic-Sign-Identification with MIT License | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #21
| Source File: grouped_batch_sampler.py From maskscoring_rcnn with MIT License | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #22
| Source File: grouped_batch_sampler.py From TinyBenchmark with MIT License | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #23
| Source File: loader.py From pcl.pytorch with MIT License | 5 votes |
|
def __init__(self, sampler, batch_size, drop_last):
if not isinstance(sampler, torch_sampler.Sampler):
raise ValueError("sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}"
.format(sampler))
if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
batch_size <= 0:
raise ValueError("batch_size should be a positive integeral value, "
"but got batch_size={}".format(batch_size))
if not isinstance(drop_last, bool):
raise ValueError("drop_last should be a boolean value, but got "
"drop_last={}".format(drop_last))
self.sampler = sampler
self.batch_size = batch_size
self.drop_last = drop_last
Example #24
| Source File: grouped_batch_sampler.py From Parsing-R-CNN with MIT License | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #25
| Source File: grouped_batch_sampler.py From R2CNN.pytorch with MIT License | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #26
| Source File: grouped_batch_sampler.py From Clothing-Detection with GNU General Public License v3.0 | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #27
| Source File: grouped_batch_sampler.py From DetNAS with MIT License | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #28
| Source File: loader.py From Detectron.pytorch with MIT License | 5 votes |
|
def __init__(self, sampler, batch_size, drop_last):
if not isinstance(sampler, torch_sampler.Sampler):
raise ValueError("sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}"
.format(sampler))
if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
batch_size <= 0:
raise ValueError("batch_size should be a positive integeral value, "
"but got batch_size={}".format(batch_size))
if not isinstance(drop_last, bool):
raise ValueError("drop_last should be a boolean value, but got "
"drop_last={}".format(drop_last))
self.sampler = sampler
self.batch_size = batch_size
self.drop_last = drop_last
Example #29
| Source File: grouped_batch_sampler.py From remote_sensing_object_detection_2019 with MIT License | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
Example #30
| Source File: data_samplers.py From training_results_v0.5 with Apache License 2.0 | 5 votes |
|
def __init__(self, sampler, group_ids, batch_size, drop_uneven=False):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = torch.as_tensor(group_ids)
assert self.group_ids.dim() == 1
self.batch_size = batch_size
self.drop_uneven = drop_uneven
self.groups = torch.unique(self.group_ids).sort(0)[0]
self._can_reuse_batches = False
