就准备使用mmseg内置的deeplabv3plus模型来进行。 修改数据集路径 # 设置数据集的格式# dataset的类的名称dataset_type='ADE20KDataset'# 数据集的地址data_root=r'E:\note\cv\data\ADEChallengeData2016'img_norm_cfg=dict(mean=[123.675,116.28,103.53],std=[58.395,57.12,57.375],to_rgb=True)# 裁剪大小crop...
1)在mmsegmentation目录下的configs/base/datasets/下创建一个our_dataset.py文件。 修改dataset_type和data_root 2)在mmsegmentation目录下的mmseg/datasets/下创建一个our_dataset.py文件 修改CLASSES、PALETTE 3)在mmsegmentation目录下的mmseg/datasets/init.py中注册自己的数据集 4)在mmsegmentation目录下的mmseg/core/...
pipeline datasets.append(build_dataset(val_dataset)) if cfg.checkpoint_config is not None: # save mmseg version, config file content and class names in # checkpoints as meta data cfg.checkpoint_config.meta = dict( mmseg_version=f'{__version__}+{get_git_hash()[:7]}',...
'MapillaryDataset_v2', 'Albu', 'LEVIRCDDataset', 'LoadMultipleRSImageFromFile', 'LoadSingleRSImageFromFile', 'ConcatCDInput', 'BaseCDDataset', 'DSDLSegDataset', 'BDD100KDataset', 'NYUDataset', 'HSIDrive20Dataset', 'LawnSegmentationDataset' ] mmsegmentation/configs/base/datasets/lawn_segmentatio...
(model) # 构建数据集 datasets = [build_dataset(cfg.data.train)] if len(cfg.workflow) == 2: val_dataset = copy.deepcopy(cfg.data.val) val_dataset.pipeline = cfg.data.train.pipeline datasets.append(build_dataset(val_dataset)) if cfg.checkpoint_config is not None: # save mmseg version...
# train_semi.py# 修改数据集labeled_datasets=[build_dataset(cfg.data.labeled_dataset)]weak_datasets=[build_dataset(cfg.data.weak_dataset)]strong_dataset=[build_dataset(cfg.data.strong_dataset)]datasets=labeled_datasets+weak_datasets+strong_dataset ...
else: dataset = build_from_cfg(cfg, DATASETS, default_args) return dataset def build_dataloader(dataset, samples_per_gpu, workers_per_gpu, num_gpus=1, dist=True, shuffle=True, seed=None, drop_last=False, pin_memory=True, persistent_workers=True, **kwargs): """Build PyTorc...
.data.dataset.ConcatDataset`, but concat the group flag for image aspect ratio. Args: datasets (list[:obj:`Dataset`]): A list of datasets. """ def __init__(self, datasets): super(ConcatDataset, self).__init__(datasets) self.CLASSES = datasets[0].CLASSES self.PALETTE = datasets[0]...
import CityscapesDataset from .builder import DATASETS def get_rcs_class_probs(data_root, temperature): with open(osp.join(data_root, 'sample_class_stats.json'), 'r') as of: sample_class_stats = json.load(of) overall_class_stats = {} for s in sample_class_stats: s.pop('file') ...
/home/lyk/lyk/mmsegmentation-1.0.0rc6/mmseg/datasets/transforms/loading.py:78: UserWarning:reduce_zero_labelwill be deprecated, if you would like to ignore the zero label, please setreduce_zero_label=Truewhen dataset initialized warnings.warn('reduce_zero_labelwill be deprecated, ' ...