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Seg_All_In_One_MMSeg/※使用手册/mmseg数据集生成相关

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+_base_ = [
+    '../_base_/models/vars_file.alg_base_dir',
+    '../_base_/datasets/vars_file.dataset_file_name', #换成自己定义的数据集
+    '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_vars_file.schedule_k_timesk.py'
+]
+crop_size = (vars_file.crop_size_w, vars_file.crop_size_h)
+data_preprocessor = dict(size=crop_size)
+model = dict(
+    pretrained='open-mmlab://vars_file.pretrained_model',
+    backbone=dict(depth=vars_file.pretrained_depth),
+    data_preprocessor=data_preprocessor,
+    decode_head=dict(
+        num_classes=vars_file.class_num, # TODO 设置不同分类种类
+        loss_decode=dict(type='DiceLoss', use_sigmoid=False, loss_weight=1.0), # TODO 设置不同分类种类，它根据预测结果和真实标签的重叠区域来度量相似性
+        # align_corners=True,
+        # align_corners=False, # 在不用slide时
+    ),
+    auxiliary_head=dict(
+        num_classes=vars_file.class_num, # TODO 设置不同分类种类
+        loss_decode=dict(type='DiceLoss', use_sigmoid=False, loss_weight=1.0), # TODO 设置不同分类种类，它根据预测结果和真实标签的重叠区域来度量相似性
+        # align_corners=True,
+        # align_corners=False, # 在不用slide时
+    ),
+    # test_cfg=dict(mode='slide', crop_size=(vars_file.crop_size_w, vars_file.crop_size_h), stride=(vars_file.crop_size_w, vars_file.crop_size_w))
+)
+
+# optimizer（优化器设计）TODO
+optim_wrapper = dict(
+    type='OptimWrapper',
+    _delete_=True,
+    optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0005),
+    clip_grad=dict(max_norm=1, norm_type=2))
+
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
+    dict(
+        type='PolyLR',
+        power=1.0,
+        begin=1500,
+        end=160000,
+        eta_min=0.0,
+        by_epoch=False,
+    )
+]
+
+# optim_wrapper = dict(
+#     _delete_=True,
+#     type='OptimWrapper',
+#     optimizer=dict(type='AdamW', lr=0.0005, weight_decay=0.05),
+#     clip_grad=dict(max_norm=1, norm_type=2))
+# # learning policy
+# param_scheduler = [
+#     dict(
+#         type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
+#         end=1000),
+#     dict(
+#         type='MultiStepLR',
+#         begin=1000,
+#         end=80000,
+#         by_epoch=False,
+#         milestones=[60000, 72000],
+#     )
+# ]