mirrors/mmsegmentation
1
0
Fork 0
mirror of https://github.com/open-mmlab/mmsegmentation.git synced 2025-06-03 22:03:48 +08:00
Code Issues Projects Releases Wiki Activity

[Enhance] Add extra dataloader settings in configs (#1435)

Browse Source 
* [Enhance] Add extra dataloader settings in configs

* val default samples

* val default samples

* del unuse

* del unused
This commit is contained in:
Miao Zheng 2022-04-13 13:30:10 +08:00 committed by GitHub
parent 91b1bcb9d8
commit 3f797072d8
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 47 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
mmseg/apis/train.py
View File

@ -79,17 +79,25 @@ def train_segmentor(model,
# prepare data loaders # prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset] dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [ # The default loader config
build_dataloader( loader_cfg = dict(
ds, # cfg.gpus will be ignored if distributed
cfg.data.samples_per_gpu, num_gpus=len(cfg.gpu_ids),
cfg.data.workers_per_gpu, dist=distributed,
# cfg.gpus will be ignored if distributed seed=cfg.seed,
len(cfg.gpu_ids), drop_last=True)
dist=distributed, # The overall dataloader settings
seed=cfg.seed, loader_cfg.update({
drop_last=True) for ds in dataset k: v
] for k, v in cfg.data.items() if k not in [
'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
'test_dataloader'
]
})
# The specific dataloader settings
train_loader_cfg = {**loader_cfg, **cfg.data.get('train_dataloader', {})}
data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]
# put model on gpus # put model on gpus
if distributed: if distributed:
@ -142,12 +150,14 @@ def train_segmentor(model,
# register eval hooks # register eval hooks
if validate: if validate:
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True)) val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
val_dataloader = build_dataloader( # The specific dataloader settings
val_dataset, val_loader_cfg = {
samples_per_gpu=1, **loader_cfg,
workers_per_gpu=cfg.data.workers_per_gpu, 'samples_per_gpu': 1,
dist=distributed, 'shuffle': False, # Not shuffle by default
shuffle=False) **cfg.data.get('val_dataloader', {}),
}
val_dataloader = build_dataloader(val_dataset, **val_loader_cfg)
eval_cfg = cfg.get('evaluation', {}) eval_cfg = cfg.get('evaluation', {})
eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner' eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
eval_hook = DistEvalHook if distributed else EvalHook eval_hook = DistEvalHook if distributed else EvalHook

24
tools/test.py
View File

@ -191,12 +191,28 @@ def main():
# build the dataloader # build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed) # TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test) dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader( # The default loader config
dataset, loader_cfg = dict(
samples_per_gpu=1, # cfg.gpus will be ignored if distributed
workers_per_gpu=cfg.data.workers_per_gpu, num_gpus=len(cfg.gpu_ids),
dist=distributed, dist=distributed,
shuffle=False) shuffle=False)
# The overall dataloader settings
loader_cfg.update({
k: v
for k, v in cfg.data.items() if k not in [
'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
'test_dataloader'
]
})
test_loader_cfg = {
**loader_cfg,
'samples_per_gpu': 1,
'shuffle': False, # Not shuffle by default
**cfg.data.get('test_dataloader', {})
}
# build the dataloader
data_loader = build_dataloader(dataset, **test_loader_cfg)
# build the model and load checkpoint # build the model and load checkpoint
cfg.model.train_cfg = None cfg.model.train_cfg = None