model = dict( data_preprocessor=data_preprocessor, backbone=dict(backbone_cfg=dict(stdc_type='STDCNet2')), decode_head=dict(num_classes=num_classes),auxiliary_head=[ dict( type='FCNHead', in_channels=128, chann
crop_size = (256, 256) # 定义图像剪裁大小(可设置是否随机剪裁) norm_cfg = dict(type='BN', requires_grad=True) # 设置归一化为Batch Normalization,requires_grad=True设置Bath Normalization的参数训练中可以更新 data_preprocessor = dict( size=crop_size, type='SegDataPreProcessor', # mmsegmentation图...
model=dict(data_preprocessor=dict(size=crop_size),backbone=dict(norm_cfg=norm_cfg),decode_head=dict(num_classes=2,norm_cfg=norm_cfg),auxiliary_head=dict(num_classes=2,norm_cfg=norm_cfg))# 修改数据集的 type 和 rootdataset_type='StanfordBackgroundDataset'# 数据集图片和标注路径data_root='/...
type='SegDataPreProcessor') data_root = '/home/kevin/deep_learning_collection/mmsegmentation/data/lawn/' dataset_type = 'LawnSegmentationDataset' default_hooks = dict( checkpoint=dict( by_epoch=False, interval=2500, max_keep_ckpt=1, save_best='mIoU', type='CheckpointHook'), logger=dict(inte...
['projects.gid_dataset.mmseg.datasets.gid']) crop_size = (256, 256) data_preprocessor = dict(size=crop_size) model = dict( data_preprocessor=data_preprocessor, pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101), decode_head=dict(num_classes=6), auxiliary_head=dict(num_...
test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'], format_only=True) # optimizer ... # learning policy ... data_preprocessor = dict( type='SegDataPreProcessor', size = crop_size, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True, pad_val=...
importos# 创建 checkpoint 文件夹,用于存放预训练模型权重文件os.mkdir('checkpoint')# 创建 outputs 文件夹,用于存放预测结果os.mkdir('outputs')# 创建 data 文件夹,用于存放图片和视频素材os.mkdir('data') 下载预训练模型权重至checkpoint目录 Model Zoo:https://github.com/open-mmlab/mmsegmentation/blob/mast...
在上图中,红色线表示train_step,在每次训练迭代中,数据加载器(dataloader)从存储中加载图像并传输到数据预处理器(data preprocessor),数据预处理器会将图像放到特定的设备上,并将数据堆叠到批处理中,之后模型接受批处理数据作为输入,最后将模型的输出发送给优化器(optimizer)。蓝色线表示val_step和test_step。这两个...
model.data_preprocessor.size = cfg.crop_size cfg.model.backbone.norm_cfg = cfg.norm_cfg cfg.model.decode_head.norm_cfg = cfg.norm_cfg cfg.model.auxiliary_head.norm_cfg = cfg.norm_cfg # modify num classes of the model in decode/auxiliary head cfg.model.decode_head.num_classes = 2 ...
cfg.norm_cfg = dict(type='BN', requires_grad=True) # 只使用GPU时,BN取代SyncBN cfg.crop_size = (256, 256) cfg.model.data_preprocessor.size = cfg.crop_size cfg.model.backbone.norm_cfg = cfg.norm_cfg cfg.model.decode_head.norm_cfg = cfg.norm_cfg cfg.model.auxiliary_head.norm_cfg...