效果表现:虽然理论上 SyncBN 可以提供更稳定的训练,但在单卡设置下,普通的 BN 通常能够足够好地处理批归一化任务,因此效果可能并不会显著提升。 建议:在单卡训练中,建议将norm_cfg设置为普通的 Batch Normalization(例如dict(type='BN', requires_grad=True)),以避免由于 SyncBN 带来的额外开销和复杂性。这样可...
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(requires_grad=True, type='BN'), num_classes=6, pool_scales=( 1, 2, 3, 6, ), type='PSPHead'), pretrained='open-mmlab://resnet50_v1c', test_cfg=dict(mode='whole'), train_cfg=dict(), type='EncoderDecoder') norm_cfg = dict(requires_grad=True, type='BN') opt...
# 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 = dict(type='LN'), ): super().__init__() self.patch_embed = PatchEmbed( in_channels=in_channels, embed_dims=embed_dims, conv_type='Conv2d', kernel_size=patch_size, stride=patch_size, padding=0, ...
norm_cfg (dict): dictionary to construct and config norm layer. stride (int): stride of the first block. Default: 1 dilation (int): dilation rate for convs layers. Default: 1. init_cfg (dict, optional): Initialization config dict. Default: None. """ def __init__(self, in_cha...
OpenMMLab Semantic Segmentation Toolbox and Benchmark. - mmsegmentation/mmseg/models/backbones/mobilenet_v3.py at v0.17.0 · open-mmlab/mmsegmentation
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, ...
(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...