Remove datasets.loader and add datasets.samplers (#2482)

* remove datasets.loader and add datasets.samplers

* fix typo in the filename

.gitignore

@@ -107,6 +107,7 @@ mmdet/version.py
 data
 .vscode
 .idea
+.DS_Store
 
 # custom
 *.pkl

mmdet/datasets/__init__.py

-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'
 ]

mmdet/datasets/builder.py

 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)

mmdet/datasets/loader/__init__.py

-from .build_loader import build_dataloader
-from .sampler import DistributedGroupSampler, GroupSampler
-
-__all__ = ['GroupSampler', 'DistributedGroupSampler', 'build_dataloader']

mmdet/datasets/loader/build_loader.py

-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)

mmdet/datasets/samplers/__init__.py

+from .distributed_sampler import DistributedSampler
+from .group_sampler import DistributedGroupSampler, GroupSampler
+
+__all__ = ['DistributedSampler', 'DistributedGroupSampler', 'GroupSampler']

mmdet/datasets/samplers/distributed_sampler.py

+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)

mmdet/datasets/loader/sampler.py → 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):