diff --git a/.gitignore b/.gitignore
index e1a14b062a22ce209c5448dfa4fd0f0e23a7e889..d9498d9426a30e5be14f356f89248b3e7db8bc25 100644
--- a/.gitignore
+++ b/.gitignore
@@ -107,6 +107,7 @@ mmdet/version.py
data
.vscode
.idea
+.DS_Store
# custom
*.pkl
diff --git a/mmdet/datasets/__init__.py b/mmdet/datasets/__init__.py
index 7ad926d4c8afe22e983879ff2e0e21a2f5764107..0272a1c6535cb93b82ebdc903f77e635f8403bbb 100644
--- a/mmdet/datasets/__init__.py
+++ b/mmdet/datasets/__init__.py
@@ -1,10 +1,10 @@
-from .builder import build_dataset
+from .builder import build_dataloader, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .custom import CustomDataset
from .dataset_wrappers import ConcatDataset, RepeatDataset
-from .loader import DistributedGroupSampler, GroupSampler, build_dataloader
from .registry import DATASETS
+from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler
from .voc import VOCDataset
from .wider_face import WIDERFaceDataset
from .xml_style import XMLDataset
@@ -12,6 +12,6 @@ from .xml_style import XMLDataset
__all__ = [
'CustomDataset', 'XMLDataset', 'CocoDataset', 'VOCDataset',
'CityscapesDataset', 'GroupSampler', 'DistributedGroupSampler',
- 'build_dataloader', 'ConcatDataset', 'RepeatDataset', 'WIDERFaceDataset',
- 'DATASETS', 'build_dataset'
+ 'DistributedSampler', 'build_dataloader', 'ConcatDataset', 'RepeatDataset',
+ 'WIDERFaceDataset', 'DATASETS', 'build_dataset'
]
diff --git a/mmdet/datasets/builder.py b/mmdet/datasets/builder.py
index 83e0486b4c7c6ca90f19946cdd706e0309541ead..f20f48a9ed4a596434d366f59127bb3970f70a76 100644
--- a/mmdet/datasets/builder.py
+++ b/mmdet/datasets/builder.py
@@ -1,8 +1,25 @@
import copy
+import platform
+import random
+from functools import partial
+
+import numpy as np
+from mmcv.parallel import collate
+from mmcv.runner import get_dist_info
+from torch.utils.data import DataLoader
from mmdet.utils import build_from_cfg
from .dataset_wrappers import ConcatDataset, RepeatDataset
from .registry import DATASETS
+from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler
+
+if platform.system() != 'Windows':
+ # https://github.com/pytorch/pytorch/issues/973
+ import resource
+ rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
+ hard_limit = rlimit[1]
+ soft_limit = min(4096, hard_limit)
+ resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
def _concat_dataset(cfg, default_args=None):
@@ -39,3 +56,73 @@ def build_dataset(cfg, default_args=None):
dataset = build_from_cfg(cfg, DATASETS, default_args)
return dataset
+
+
+def build_dataloader(dataset,
+ imgs_per_gpu,
+ workers_per_gpu,
+ num_gpus=1,
+ dist=True,
+ shuffle=True,
+ seed=None,
+ **kwargs):
+ """Build PyTorch DataLoader.
+
+ In distributed training, each GPU/process has a dataloader.
+ In non-distributed training, there is only one dataloader for all GPUs.
+
+ Args:
+ dataset (Dataset): A PyTorch dataset.
+ imgs_per_gpu (int): Number of images on each GPU, i.e., batch size of
+ each GPU.
+ workers_per_gpu (int): How many subprocesses to use for data loading
+ for each GPU.
+ num_gpus (int): Number of GPUs. Only used in non-distributed training.
+ dist (bool): Distributed training/test or not. Default: True.
+ shuffle (bool): Whether to shuffle the data at every epoch.
+ Default: True.
+ kwargs: any keyword argument to be used to initialize DataLoader
+
+ Returns:
+ DataLoader: A PyTorch dataloader.
+ """
+ rank, world_size = get_dist_info()
+ if dist:
+ # DistributedGroupSampler will definitely shuffle the data to satisfy
+ # that images on each GPU are in the same group
+ if shuffle:
+ sampler = DistributedGroupSampler(dataset, imgs_per_gpu,
+ world_size, rank)
+ else:
+ sampler = DistributedSampler(
+ dataset, world_size, rank, shuffle=False)
+ batch_size = imgs_per_gpu
+ num_workers = workers_per_gpu
+ else:
+ sampler = GroupSampler(dataset, imgs_per_gpu) if shuffle else None
+ batch_size = num_gpus * imgs_per_gpu
+ num_workers = num_gpus * workers_per_gpu
+
+ init_fn = partial(
+ worker_init_fn, num_workers=num_workers, rank=rank,
+ seed=seed) if seed is not None else None
+
+ data_loader = DataLoader(
+ dataset,
+ batch_size=batch_size,
+ sampler=sampler,
+ num_workers=num_workers,
+ collate_fn=partial(collate, samples_per_gpu=imgs_per_gpu),
+ pin_memory=False,
+ worker_init_fn=init_fn,
+ **kwargs)
+
+ return data_loader
+
+
+def worker_init_fn(worker_id, num_workers, rank, seed):
+ # The seed of each worker equals to
+ # num_worker * rank + worker_id + user_seed
+ worker_seed = num_workers * rank + worker_id + seed
+ np.random.seed(worker_seed)
+ random.seed(worker_seed)
diff --git a/mmdet/datasets/loader/__init__.py b/mmdet/datasets/loader/__init__.py
deleted file mode 100644
index 4404615be66d7ce54e51e057652b2785c2e409ff..0000000000000000000000000000000000000000
--- a/mmdet/datasets/loader/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .build_loader import build_dataloader
-from .sampler import DistributedGroupSampler, GroupSampler
-
-__all__ = ['GroupSampler', 'DistributedGroupSampler', 'build_dataloader']
diff --git a/mmdet/datasets/loader/build_loader.py b/mmdet/datasets/loader/build_loader.py
deleted file mode 100644
index c9a0919d8913773d98b5e7bf9eb478c908e72e83..0000000000000000000000000000000000000000
--- a/mmdet/datasets/loader/build_loader.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import platform
-import random
-from functools import partial
-
-import numpy as np
-from mmcv.parallel import collate
-from mmcv.runner import get_dist_info
-from torch.utils.data import DataLoader
-
-from .sampler import DistributedGroupSampler, DistributedSampler, GroupSampler
-
-if platform.system() != 'Windows':
- # https://github.com/pytorch/pytorch/issues/973
- import resource
- rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
- hard_limit = rlimit[1]
- soft_limit = min(4096, hard_limit)
- resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
-
-
-def build_dataloader(dataset,
- imgs_per_gpu,
- workers_per_gpu,
- num_gpus=1,
- dist=True,
- shuffle=True,
- seed=None,
- **kwargs):
- """Build PyTorch DataLoader.
-
- In distributed training, each GPU/process has a dataloader.
- In non-distributed training, there is only one dataloader for all GPUs.
-
- Args:
- dataset (Dataset): A PyTorch dataset.
- imgs_per_gpu (int): Number of images on each GPU, i.e., batch size of
- each GPU.
- workers_per_gpu (int): How many subprocesses to use for data loading
- for each GPU.
- num_gpus (int): Number of GPUs. Only used in non-distributed training.
- dist (bool): Distributed training/test or not. Default: True.
- shuffle (bool): Whether to shuffle the data at every epoch.
- Default: True.
- kwargs: any keyword argument to be used to initialize DataLoader
-
- Returns:
- DataLoader: A PyTorch dataloader.
- """
- rank, world_size = get_dist_info()
- if dist:
- # DistributedGroupSampler will definitely shuffle the data to satisfy
- # that images on each GPU are in the same group
- if shuffle:
- sampler = DistributedGroupSampler(dataset, imgs_per_gpu,
- world_size, rank)
- else:
- sampler = DistributedSampler(
- dataset, world_size, rank, shuffle=False)
- batch_size = imgs_per_gpu
- num_workers = workers_per_gpu
- else:
- sampler = GroupSampler(dataset, imgs_per_gpu) if shuffle else None
- batch_size = num_gpus * imgs_per_gpu
- num_workers = num_gpus * workers_per_gpu
-
- init_fn = partial(
- worker_init_fn, num_workers=num_workers, rank=rank,
- seed=seed) if seed is not None else None
-
- data_loader = DataLoader(
- dataset,
- batch_size=batch_size,
- sampler=sampler,
- num_workers=num_workers,
- collate_fn=partial(collate, samples_per_gpu=imgs_per_gpu),
- pin_memory=False,
- worker_init_fn=init_fn,
- **kwargs)
-
- return data_loader
-
-
-def worker_init_fn(worker_id, num_workers, rank, seed):
- # The seed of each worker equals to
- # num_worker * rank + worker_id + user_seed
- worker_seed = num_workers * rank + worker_id + seed
- np.random.seed(worker_seed)
- random.seed(worker_seed)
diff --git a/mmdet/datasets/samplers/__init__.py b/mmdet/datasets/samplers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2596aeb2ccfc85b58624713c04453d34e94a4062
--- /dev/null
+++ b/mmdet/datasets/samplers/__init__.py
@@ -0,0 +1,4 @@
+from .distributed_sampler import DistributedSampler
+from .group_sampler import DistributedGroupSampler, GroupSampler
+
+__all__ = ['DistributedSampler', 'DistributedGroupSampler', 'GroupSampler']
diff --git a/mmdet/datasets/samplers/distributed_sampler.py b/mmdet/datasets/samplers/distributed_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a85619cfa0d067ed7be79d5d4fcfd770314ec0f
--- /dev/null
+++ b/mmdet/datasets/samplers/distributed_sampler.py
@@ -0,0 +1,28 @@
+import torch
+from torch.utils.data import DistributedSampler as _DistributedSampler
+
+
+class DistributedSampler(_DistributedSampler):
+
+ def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
+ super().__init__(dataset, num_replicas=num_replicas, rank=rank)
+ self.shuffle = shuffle
+
+ def __iter__(self):
+ # deterministically shuffle based on epoch
+ if self.shuffle:
+ g = torch.Generator()
+ g.manual_seed(self.epoch)
+ indices = torch.randperm(len(self.dataset), generator=g).tolist()
+ else:
+ indices = torch.arange(len(self.dataset)).tolist()
+
+ # add extra samples to make it evenly divisible
+ indices += indices[:(self.total_size - len(indices))]
+ assert len(indices) == self.total_size
+
+ # subsample
+ indices = indices[self.rank:self.total_size:self.num_replicas]
+ assert len(indices) == self.num_samples
+
+ return iter(indices)
diff --git a/mmdet/datasets/loader/sampler.py b/mmdet/datasets/samplers/group_sampler.py
similarity index 83%
rename from mmdet/datasets/loader/sampler.py
rename to mmdet/datasets/samplers/group_sampler.py
index f3dd996207fb5b64d1f865d03d29edc3bb832f10..35e21b35a87014447e06a07efa31c4286d52a7b1 100644
--- a/mmdet/datasets/loader/sampler.py
+++ b/mmdet/datasets/samplers/group_sampler.py
@@ -4,36 +4,9 @@ import math
import numpy as np
import torch
from mmcv.runner import get_dist_info
-from torch.utils.data import DistributedSampler as _DistributedSampler
from torch.utils.data import Sampler
-class DistributedSampler(_DistributedSampler):
-
- def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
- super().__init__(dataset, num_replicas=num_replicas, rank=rank)
- self.shuffle = shuffle
-
- def __iter__(self):
- # deterministically shuffle based on epoch
- if self.shuffle:
- g = torch.Generator()
- g.manual_seed(self.epoch)
- indices = torch.randperm(len(self.dataset), generator=g).tolist()
- else:
- indices = torch.arange(len(self.dataset)).tolist()
-
- # add extra samples to make it evenly divisible
- indices += indices[:(self.total_size - len(indices))]
- assert len(indices) == self.total_size
-
- # subsample
- indices = indices[self.rank:self.total_size:self.num_replicas]
- assert len(indices) == self.num_samples
-
- return iter(indices)
-
-
class GroupSampler(Sampler):
def __init__(self, dataset, samples_per_gpu=1):