Python DATASETS.get方法代码示例

本文整理汇总了Python中mmdet.datasets.DATASETS.get方法的典型用法代码示例。如果您正苦于以下问题：Python DATASETS.get方法的具体用法？Python DATASETS.get怎么用？Python DATASETS.get使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mmdet.datasets.DATASETS的用法示例。

在下文中一共展示了DATASETS.get方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: _non_dist_train

# 需要导入模块: from mmdet.datasets import DATASETS [as 别名]
# 或者: from mmdet.datasets.DATASETS import get [as 别名]
def _non_dist_train(model, dataset, cfg, validate=False):
    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    data_loaders = [
        build_dataloader(
            ds,
            cfg.data.imgs_per_gpu,
            cfg.data.workers_per_gpu,
            cfg.gpus,
            dist=False) for ds in dataset
    ]
    # put model on gpus
    model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()

    # build runner
    optimizer = build_optimizer(model, cfg.optimizer)
    runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
                    cfg.log_level)
    # fp16 setting
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        optimizer_config = Fp16OptimizerHook(
            **cfg.optimizer_config, **fp16_cfg, distributed=False)
    else:
        optimizer_config = cfg.optimizer_config
    runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config)

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)

开发者ID:xieenze，项目名称:PolarMask，代码行数:35，代码来源:train.py

示例2: _non_dist_train

# 需要导入模块: from mmdet.datasets import DATASETS [as 别名]
# 或者: from mmdet.datasets.DATASETS import get [as 别名]
def _non_dist_train(model, dataset, cfg, validate=False):
    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    data_loaders = [
        build_dataloader(
            ds,
            cfg.data.imgs_per_gpu,
            cfg.data.workers_per_gpu,
            cfg.gpus,
            dist=False) for ds in dataset
    ]
    # put model on gpus
    model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()

    # build runner
    optimizer = build_optimizer(model, cfg.optimizer)
    runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
                    cfg.log_level)
    # fp16 setting
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        optimizer_config = Fp16OptimizerHook(
            **cfg.optimizer_config, **fp16_cfg, distributed=False)
    else:
        optimizer_config = cfg.optimizer_config
    runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config)
    #####
    runner.register_hook(CheckpointHook(interval=500))

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)

开发者ID:wangsr126，项目名称:RDSNet，代码行数:37，代码来源:train.py

示例3: _non_dist_train

# 需要导入模块: from mmdet.datasets import DATASETS [as 别名]
# 或者: from mmdet.datasets.DATASETS import get [as 别名]
def _non_dist_train(model, dataset, cfg, validate=False):
    if validate:
        raise NotImplementedError('Built-in validation is not implemented '
                                  'yet in not-distributed training. Use '
                                  'distributed training or test.py and '
                                  '*eval.py scripts instead.')
    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    data_loaders = [
        build_dataloader(
            ds,
            cfg.data.imgs_per_gpu,
            cfg.data.workers_per_gpu,
            cfg.gpus,
            dist=False) for ds in dataset
    ]
    # put model on gpus
    model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()

    # build runner
    optimizer = build_optimizer(model, cfg.optimizer)
    runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
                    cfg.log_level)
    # fp16 setting
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        optimizer_config = Fp16OptimizerHook(
            **cfg.optimizer_config, **fp16_cfg, distributed=False)
    else:
        optimizer_config = cfg.optimizer_config
    runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config)

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)

开发者ID:zl1994，项目名称:IoU-Uniform-R-CNN，代码行数:40，代码来源:train.py

示例4: _dist_train

# 需要导入模块: from mmdet.datasets import DATASETS [as 别名]
# 或者: from mmdet.datasets.DATASETS import get [as 别名]
def _dist_train(model, dataset, cfg, validate=False):
    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    data_loaders = [
        build_dataloader(
            ds, cfg.data.imgs_per_gpu, cfg.data.workers_per_gpu, dist=True)
        for ds in dataset
    ]
    # put model on gpus
    model = MMDistributedDataParallel(model.cuda())

    # build runner
    optimizer = build_optimizer(model, cfg.optimizer)
    runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
                    cfg.log_level)

    # fp16 setting
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
                                             **fp16_cfg)
    else:
        optimizer_config = DistOptimizerHook(**cfg.optimizer_config)

    # register hooks
    runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config)
    runner.register_hook(DistSamplerSeedHook())
    # register eval hooks
    if validate:
        val_dataset_cfg = cfg.data.val
        eval_cfg = cfg.get('evaluation', {})
        if isinstance(model.module, RPN):
            # TODO: implement recall hooks for other datasets
            runner.register_hook(
                CocoDistEvalRecallHook(val_dataset_cfg, **eval_cfg))
        else:
            dataset_type = DATASETS.get(val_dataset_cfg.type)
            if issubclass(dataset_type, datasets.CocoDataset):
                runner.register_hook(
                    CocoDistEvalmAPHook(val_dataset_cfg, **eval_cfg))
            else:
                runner.register_hook(
                    DistEvalmAPHook(val_dataset_cfg, **eval_cfg))

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)

开发者ID:xieenze，项目名称:PolarMask，代码行数:52，代码来源:train.py

示例5: _dist_train

# 需要导入模块: from mmdet.datasets import DATASETS [as 别名]
# 或者: from mmdet.datasets.DATASETS import get [as 别名]
def _dist_train(model, dataset, cfg, validate=False):
    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    data_loaders = [
        build_dataloader(
            ds, cfg.data.imgs_per_gpu, cfg.data.workers_per_gpu, dist=True)
        for ds in dataset
    ]
    # put model on gpus
    model = MMDistributedDataParallel(model.cuda())

    # build runner
    optimizer = build_optimizer(model, cfg.optimizer)
    runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
                    cfg.log_level)

    # fp16 setting
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
                                             **fp16_cfg)
    else:
        optimizer_config = DistOptimizerHook(**cfg.optimizer_config)

    # register hooks
    runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config)
    runner.register_hook(DistSamplerSeedHook())
    # register eval hooks
    if validate:
        val_dataset_cfg = cfg.data.val
        eval_cfg = cfg.get('evaluation', {})
        if isinstance(model.module, RPN):
            # TODO: implement recall hooks for other datasets
            runner.register_hook(
                CocoDistEvalRecallHook(val_dataset_cfg, **eval_cfg))
        else:
            dataset_type = DATASETS.get(val_dataset_cfg.type)
            if issubclass(dataset_type, datasets.CocoDataset):
                runner.register_hook(
                    CocoDistEvalmAPHook(val_dataset_cfg, **eval_cfg))
            else:
                runner.register_hook(
                    DistEvalF1Hook(val_dataset_cfg, **eval_cfg))

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)

开发者ID:tascj，项目名称:kaggle-kuzushiji-recognition，代码行数:52，代码来源:train.py

示例6: _dist_train

# 需要导入模块: from mmdet.datasets import DATASETS [as 别名]
# 或者: from mmdet.datasets.DATASETS import get [as 别名]
def _dist_train(model, dataset, cfg, validate=False):
    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    data_loaders = [
        build_dataloader(
            ds, cfg.data.imgs_per_gpu, cfg.data.workers_per_gpu, dist=True)
        for ds in dataset
    ]
    # put model on gpus
    model = MMDistributedDataParallel(model.cuda())

    # build runner
    optimizer = build_optimizer(model, cfg.optimizer)
    runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
                    cfg.log_level)

    # fp16 setting
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
                                             **fp16_cfg)
    else:
        optimizer_config = DistOptimizerHook(**cfg.optimizer_config)

    # register hooks
    runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config)
    runner.register_hook(DistSamplerSeedHook())
    # register eval hooks
    if validate:
        val_dataset_cfg = cfg.data.val
        eval_cfg = cfg.get('evaluation', {})
        if isinstance(model.module, (RPN, CascadeRPN)):
            # TODO: implement recall hooks for other datasets
            runner.register_hook(
                CocoDistEvalRecallHook(val_dataset_cfg, **eval_cfg))
        else:
            dataset_type = DATASETS.get(val_dataset_cfg.type)
            if issubclass(dataset_type, datasets.CocoDataset):
                runner.register_hook(
                    CocoDistEvalmAPHook(val_dataset_cfg, **eval_cfg))
            else:
                runner.register_hook(
                    DistEvalmAPHook(val_dataset_cfg, **eval_cfg))

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)

开发者ID:thangvubk，项目名称:Cascade-RPN，代码行数:52，代码来源:train.py

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