Config命名格式：

{model}_{backbone}_[misc]_[gpu x batch_per_gpu]_{resolution}_{schedule}_{dataset}

以下为每一项的具体解释：

• {xxx}是必填字段，[yyy]是可选字段。
• {model}：型号类型等psp，deeplabv3等。
• {backbone}：骨干类型，例如r50(ResNet-50)，x101(ResNeXt-101)。
• [misc]：杂项设置/模型的插件，例如dconv，gcb，attention，mstrain。
• [gpu x batch_per_gpu]：8x2默认使用GPU和每个GPU的样本。
• {schedule}：训练时间表，20ki意味着20k次迭代。
• {dataset}：数据集cityscapes，voc12aug，ade

norm_cfg = dict(type='SyncBN', requires_grad=True) # 批归一化
backbone_norm_cfg = dict(type='LN', requires_grad=True) # LayerNorm 层归一话model = dict(type='EncoderDecoder', #语义分割模型类型pretrained='pretrain/swin_base_patch4_window7_224.pth',  # 加载ImageNet预训练的backbone,这类为swin base模型backbone=dict(type='SwinTransformer', # backone的类型pretrain_img_size=224,embed_dims=128,patch_size=4,window_size=7,mlp_ratio=4,depths=[2, 2, 18, 2],num_heads=[4, 8, 16, 32],strides=(4, 2, 2, 2),out_indices=(0, 1, 2, 3),qkv_bias=True,qk_scale=None,patch_norm=True,drop_rate=0.0,attn_drop_rate=0.0,drop_path_rate=0.3,use_abs_pos_embed=False,act_cfg=dict(type='GELU'),norm_cfg=dict(type='LN', requires_grad=True)),decode_head=dict(type='UPerHead', # 解码器类型in_channels=[128, 256, 512, 1024], # 解码器输入通道个数in_index=[0, 1, 2, 3], #选择特征图的索引pool_scales=(1, 2, 3, 6),channels=512, # 解码器头的中间通道数dropout_ratio=0.1, #分类层的之前的dropout概率num_classes=150,#分割类别的数量，norm_cfg=dict(type='SyncBN', requires_grad=True),align_corners=False,  # 用于在解码时调整大小loss_decode=dict( #  # 解码器损失函数的配置type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),auxiliary_head=dict( #  auxiliary_head(辅助头)的类型，type='FCNHead',in_channels=512, # 辅助头输入的通道个数in_index=2, # 选择特征图索引channels=256,# 解码器的中间通道num_convs=1, # FCNHead的卷积数，在auxiliary_head通常是1concat_input=False, # 是否将convs的输出与分类器之前的输入进行拼接dropout_ratio=0.1,num_classes=150,norm_cfg=dict(type='SyncBN', requires_grad=True),align_corners=False,loss_decode=dict(type='LovaszLoss', classes='present', per_image=True, reduction='mean', loss_weight=0.4)),train_cfg=dict(),test_cfg=dict(mode='whole'))
dataset_type = 'SatelliteDataset' # 数据集类型，将用于定义数据集
data_root = 'data/sensing/jpg'  # 数据的根路径
img_norm_cfg = dict( # 图像归一化配置以对输入图像进行归一化mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], to_rgb=True)
crop_size = (512, 512) #训练时的图像剪裁大小
train_pipeline = [ # # 训练流水线dict(type='LoadImageFromFile'), # 从文件中加载训练图像dict(type='LoadAnnotations', reduce_zero_label=False), # 加载标签图像，False代表标签包含0dict(type='Resize', img_scale=(512, 256), ratio_range=(0.5, 2.0)),  # 增强通道以调整图像的大小及其注释dict(type='RandomCrop', crop_size=(256, 256), cat_max_ratio=0.75), # 从当前图像中随机裁剪一patchdict(type='RandomFlip', prob=0.5),   # 翻转图像以及概率dict(type='PhotoMetricDistortion'), # 通过多种光度学方法使当前图像失真dict(type='Normalize', #  标准化输入图像的增强通道mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225],to_rgb=True),dict(type='Pad', size=(256, 256), pad_val=0, seg_pad_val=255), #  将图像填充到指定大小dict(type='DefaultFormatBundle'),  # 默认格式捆绑包，用于收集管道中的数据dict(type='Collect', keys=['img', 'gt_semantic_seg']) # 决定将数据中的哪些键传递给分割器
]
test_pipeline = [dict(type='LoadImageFromFile'),dict(type='MultiScaleFlipAug',img_scale=(512, 256),flip=False,transforms=[dict(type='Resize', keep_ratio=True),dict(type='RandomFlip'),dict(type='Normalize',mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225],to_rgb=True),dict(type='ImageToTensor', keys=['img']),dict(type='Collect', keys=['img'])])
]
data = dict(samples_per_gpu=2, #  单个GPU的批处理大小workers_per_gpu=4,# train=dict(type='SatelliteDataset',data_root='data/sensing/jpg',img_dir='images/training',ann_dir='annotations/training',pipeline=[dict(type='LoadImageFromFile'),dict(type='LoadAnnotations', reduce_zero_label=False),dict(type='Resize', img_scale=(512, 256), ratio_range=(0.5, 2.0)),dict(type='RandomCrop', crop_size=(256, 256), cat_max_ratio=0.75),dict(type='RandomFlip', prob=0.5),dict(type='PhotoMetricDistortion'),dict(type='Normalize',mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225],to_rgb=True),dict(type='Pad', size=(256, 256), pad_val=0, seg_pad_val=255),dict(type='DefaultFormatBundle'),dict(type='Collect', keys=['img', 'gt_semantic_seg'])]),val=dict(type='SatelliteDataset',data_root='data/sensing/jpg',img_dir='images/validation',ann_dir='annotations/validation',pipeline=[dict(type='LoadImageFromFile'),dict(type='MultiScaleFlipAug',img_scale=(512, 256),flip=False,transforms=[dict(type='Resize', keep_ratio=True),dict(type='RandomFlip'),dict(type='Normalize',mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225],to_rgb=True),dict(type='ImageToTensor', keys=['img']),dict(type='Collect', keys=['img'])])]),test=dict(type='SatelliteDataset',data_root='data/sensing/jpg',img_dir='images/test',ann_dir='annotations/test',pipeline=[dict(type='LoadImageFromFile'),dict(type='MultiScaleFlipAug',img_scale=(512, 256),flip=False,transforms=[dict(type='Resize', keep_ratio=True),dict(type='RandomFlip'),dict(type='Normalize',mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225],to_rgb=True),dict(type='ImageToTensor', keys=['img']),dict(type='Collect', keys=['img'])])]))
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) # 间隔打印日志
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)] #  Workflow for runner [('train'，1)]表示只有一个工作流程，名为'train'的工作流程只执行一次。
cudnn_benchmark = True #是否使用cudnn_benchmark加快速度，只适用于固定输入大小optimizer = dict(type='AdamW',lr=6e-05,betas=(0.9, 0.999),weight_decay=0.01,paramwise_cfg=dict(custom_keys=dict(absolute_pos_embed=dict(decay_mult=0.0),relative_position_bias_table=dict(decay_mult=0.0),norm=dict(decay_mult=0.0))))
optimizer_config = dict()
lr_config = dict(policy='poly',
#  # scheduler的策略还支持Step，CosineAnnealing，Cyclic等。warmup='linear',warmup_iters=1500,warmup_ratio=1e-06,power=1.0,min_lr=0.0,by_epoch=False)
runner = dict(type='IterBasedRunner', max_iters=40000)
checkpoint_config = dict(by_epoch=False, interval=10000)
evaluation = dict(interval=10000, metric='mIoU', pre_eval=True)
work_dir = './work_dirs/swin_tiny_patch4_window7_224'
gpu_ids = range(0, 1)
auto_resume = False