norm_cfg=norm_cfg, align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), # model training and testing settings train_cfg=dict(), test_cfg=dict(mode='whole')) 这个文件是网络架构配置,type 是用 register 注册过类,根据 type 可以找到对应的类,...
norm_cfg=dict(type='SyncBN',requires_grad=True)# 分割框架通常使用 SyncBNmodel=dict(type='EncoderDecoder',# 分割器(segmentor)的名字pretrained='open-mmlab://resnet50_v1c',# 将被加载的 ImageNet 预训练主干网络backbone=dict(type='ResNetV1c',# 主干网络的类别。 可用选项请参考 mmseg/models/backb...
norm_cfg = dict(type='BN', requires_grad=True) # 模型是预训练 好的 model = dict( type='EncoderDecoder', pretrained='open-mmlab://resnet50_v1c', backbone=dict( type='ResNetV1c', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), dilations=(1, 1, 2, 4), strides=(1, 2...
# Since we use only one GPU, BN is used instead of SyncBN cfg.norm_cfg = dict(type='BN', requires_grad=True) 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_cf...
# configs/_base_/datasets/my_dataset.py dataset_type = 'MyDataset' # 使用自定义数据集类 data_root = 'path/to/your/dataset' # 数据集根目录 img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) crop_size = (512, 512) train_pipeline = ...
norm_cfg (dict): Config dict for normalization layer.Default: dict(type='LN').""" def __init__( self, in_channels: int = 3, clip_channels: int = 768, embed_dims: int = 240, patch_size: int = 16, patch_bias: bool = True, ...
norm_cfg = dict(requires_grad=True, type='BN') optim_wrapper = dict( clip_grad=None, optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), type='OptimWrapper') optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) ...
OpenMMLab Semantic Segmentation Toolbox and Benchmark. - mmsegmentation/mmseg/models/backbones/mobilenet_v3.py at v0.17.0 · open-mmlab/mmsegmentation
dropout_ratio=0.1, # 使用的是 pascal voc 数据集,分类数目为21 num_classes=21, norm_cfg=norm_cfg, align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), # model training and testing settings train_cfg=dict(), test_cfg=dict(mode='whole')...
(type='RandomCrop',crop_size=crop_size,cat_max_ratio=0.75),dict(type='RandomFlip',prob=0.5),# dict(type='PhotoMetricDistortion'),dict(type='StrongAugCustom'),dict(type='Normalize',**img_norm_cfg),dict(type='Pad',size=crop_size,pad_val=0,seg_pad_val=255),dict(type='DefaultFormat...