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冒险岛私服定制:GM助手简单源码解读

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12.11MB | 更新于2025-02-02 | 168 浏览量 | 3 评论 | 0 下载量 举报 收藏
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根据提供的文件信息,我们可以推断出相关的IT知识点。以下是详细的知识点: ### 标题知识点: **GM助手源码简单版.7z** - **GM助手**:GM是Game Master(游戏管理员)的简称。GM助手通常是指为游戏管理员提供辅助功能的软件工具。它可能包括一系列用于管理在线游戏服务器的功能,例如封禁违规玩家、发放游戏内物品、调整游戏参数等。 - **源码**:源代码是程序员编写的用以创建计算机软件程序的文本代码。源码是软件开发过程中的中间产品,通常包含指令序列,用于指导计算机硬件如何执行特定的任务。源码简单版意味着这份源代码是基础或者入门级别的,可能只包含最基本的功能。 - **.7z**:这是7-Zip软件所使用的一种压缩格式。7-Zip是一款开源的文件压缩工具,使用7z压缩格式能够提供高压缩率。此格式通常用于打包文件,以便于文件的传输或备份。 ### 描述知识点: **GM助手源码简单版** - 描述表明文件内容是GM助手的源代码,且为简化版本。这可能意味着源码不包含复杂的游戏管理功能,或者是专门为初学者准备的教学示例。 ### 标签知识点: **冒险岛私服定制** - **冒险岛私服**:私服是相对于官方服务器(官服)而言的,是指未经官方许可私自开设的游戏服务器。私服由私人搭建,通常会修改官方游戏内容以吸引玩家。冒险岛是一款非常受欢迎的在线多人角色扮演游戏(MMORPG),私服版本往往意味着该游戏已经被非官方实体修改和控制。 - **定制**:定制指的是根据特定需求对产品进行修改和设计的过程。在GM助手源码简单版的上下文中,定制可能涉及到为特定的冒险岛私服版本编写GM工具,以适应私服的特殊规则和需求。定制也可能意味着有相关的文档和教程,帮助GM管理员更有效地使用这款工具。 ### 压缩包子文件的文件名称列表: **源码** - 文件名称列表中只有一个项目“源码”,这表明解压缩文件后,用户将直接接触到GM助手的源代码文件。这可能包括多种编程语言的文件,如C++、C#、Java等,具体取决于GM助手是用何种编程语言编写的。文件结构可能包含多个项目文件、资源文件、配置文件等。 ### 综合知识点: - **软件定制开发**:GM助手源码简单版涉及到软件定制开发的概念,即根据特定需求开发软件。在这个案例中,软件是为冒险岛私服定制的GM管理工具。 - **开源软件与压缩格式**:源码的打包方式暗示了开源社区的协作文化。开源软件社区经常使用各种压缩格式来共享他们的工作成果,7z格式则是众多压缩格式中的一种,它提供了高效率的压缩率,有助于源码的快速分享和下载。 - **私服运营风险**:运营私服本身可能涉及到法律风险,因为它通常违反了游戏公司的服务条款。因此,有关GM助手的使用和开发可能会涉及道德和法律上的考量。 - **IT安全与监控**:GM助手在正常操作下可能需要实现对游戏服务器的安全性监控和异常行为的检测,以维护游戏环境的健康和公平。 总结起来,GM助手源码简单版.7z文件涉及到了软件开发、IT安全、开源文化、压缩技术以及法律风险等多个领域的知识。这些知识点不仅对从事相关工作的IT专业人员有重要的参考价值,同时也为对这一领域感兴趣的初学者提供了学习的途径。

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解决一下(nnUNet) root@autodl-container-54694c8faa-39a99183:/autodl-fs/data/nnU-Net/nnUNet# nnUNetv2_train 4 2d 0 ############################ INFO: You are using the old nnU-Net default plans. We have updated our recommendations. Please consider using those instead! Read more here: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/resenc_presets.md ############################ Using device: cuda:0 ####################################################################### Please cite the following paper when using nnU-Net: Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. ####################################################################### 2025-05-12 19:33:23.235735: Using torch.compile... 2025-05-12 19:33:23.251931: do_dummy_2d_data_aug: False 2025-05-12 19:33:23.261743: Using splits from existing split file: /root/autodl-fs/nnU-Net/nnUNet/nnUNet_preprocessed/Dataset004_Hippocampus/splits_final.json 2025-05-12 19:33:23.262319: The split file contains 5 splits. 2025-05-12 19:33:23.262368: Desired fold for training: 0 2025-05-12 19:33:23.262403: This split has 208 training and 52 validation cases. using pin_memory on device 0 using pin_memory on device 0 This is the configuration used by this training: Configuration name: 2d {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 366, 'patch_size': [56, 40], 'median_image_size_in_voxels': [50.0, 35.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 4, 'features_per_stage': [32, 64, 128, 256], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2]], 'n_conv_per_stage': [2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True} These are the global plan.json settings: {'dataset_name': 'Dataset004_Hippocampus', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [36, 50, 35], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 486420.21875, 'mean': 22360.326171875, 'median': 362.88250732421875, 'min': 0.0, 'percentile_00_5': 28.0, 'percentile_99_5': 277682.03125, 'std': 60656.1328125}}} 2025-05-12 19:33:25.892359: Unable to plot network architecture: nnUNet_compile is enabled! 2025-05-12 19:33:25.901765: 2025-05-12 19:33:25.902487: Epoch 0 2025-05-12 19:33:25.902790: Current learning rate: 0.01 Traceback (most recent call last): File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/output_graph.py", line 670, in call_user_compiler compiled_fn = compiler_fn(gm, self.fake_example_inputs()) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/debug_utils.py", line 1055, in debug_wrapper compiled_gm = compiler_fn(gm, example_inputs) File "/root/miniconda3/lib/python3.10/site-packages/torch/__init__.py", line 1388, in __call__ from torch._inductor.compile_fx import compile_fx File "/root/miniconda3/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 21, in <module> from . import config, metrics, overrides, pattern_matcher File "/root/miniconda3/lib/python3.10/site-packages/torch/_inductor/pattern_matcher.py", line 19, in <module> from .lowering import lowerings as L File "/root/miniconda3/lib/python3.10/site-packages/torch/_inductor/lowering.py", line 3868, in <module> import_submodule(kernel) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 1304, in import_submodule importlib.import_module(f"{mod.__name__}.{filename[:-3]}") File "/root/miniconda3/lib/python3.10/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "/root/miniconda3/lib/python3.10/site-packages/torch/_inductor/kernel/conv.py", line 22, in <module> from ..utils import ( ImportError: cannot import name 'is_ones' from 'torch._inductor.utils' (/root/miniconda3/lib/python3.10/site-packages/torch/_inductor/utils.py) The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/root/miniconda3/bin/nnUNetv2_train", line 8, in <module> sys.exit(run_training_entry()) File "/autodl-fs/data/nnU-Net/nnUNet/nnunetv2/run/run_training.py", line 267, in run_training_entry run_training(args.dataset_name_or_id, args.configuration, args.fold, args.tr, args.p, args.pretrained_weights, File "/autodl-fs/data/nnU-Net/nnUNet/nnunetv2/run/run_training.py", line 207, in run_training nnunet_trainer.run_training() File "/autodl-fs/data/nnU-Net/nnUNet/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py", line 1371, in run_training train_outputs.append(self.train_step(next(self.dataloader_train))) File "/autodl-fs/data/nnU-Net/nnUNet/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py", line 989, in train_step output = self.network(data) File "/root/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 82, in forward return self.dynamo_ctx(self._orig_mod.forward)(*args, **kwargs) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 209, in _fn return fn(*args, **kwargs) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 337, in catch_errors return callback(frame, cache_size, hooks) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 404, in _convert_frame result = inner_convert(frame, cache_size, hooks) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 104, in _fn return fn(*args, **kwargs) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 262, in _convert_frame_assert return _compile( File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 163, in time_wrapper r = func(*args, **kwargs) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 324, in _compile out_code = transform_code_object(code, transform) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 445, in transform_code_object transformations(instructions, code_options) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 311, in transform tracer.run() File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1726, in run super().run() File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 576, in run and self.step() File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 540, in step getattr(self, inst.opname)(inst) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1792, in RETURN_VALUE self.output.compile_subgraph( File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/output_graph.py", line 541, in compile_subgraph self.compile_and_call_fx_graph(tx, pass2.graph_output_vars(), root) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/output_graph.py", line 588, in compile_and_call_fx_graph compiled_fn = self.call_user_compiler(gm) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 163, in time_wrapper r = func(*args, **kwargs) File "/root/miniconda3/lib/python3.10/site-packages/torch/_dynamo/output_graph.py", line 675, in call_user_compiler raise BackendCompilerFailed(self.compiler_fn, e) from e torch._dynamo.exc.BackendCompilerFailed: debug_wrapper raised ImportError: cannot import name 'is_ones' from 'torch._inductor.utils' (/root/miniconda3/lib/python3.10/site-packages/torch/_inductor/utils.py) Set torch._dynamo.config.verbose=True for more information You can suppress this exception and fall back to eager by setting: torch._dynamo.config.suppress_errors = True Exception in thread Thread-2 (results_loop): Traceback (most recent call last): File "/root/miniconda3/lib/python3.10/threading.py", line 1016, in _bootstrap_inner Exception in thread Thread-1 (results_loop): Traceback (most recent call last): File "/root/miniconda3/lib/python3.10/threading.py", line 1016, in _bootstrap_inner self.run() File "/root/miniconda3/lib/python3.10/threading.py", line 953, in run self.run() self._target(*self._args, **self._kwargs) File "/root/miniconda3/lib/python3.10/threading.py", line 953, in run File "/root/miniconda3/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 125, in results_loop self._target(*self._args, **self._kwargs) File "/root/miniconda3/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 125, in results_loop raise e File "/root/miniconda3/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 103, in results_loop raise e File "/root/miniconda3/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 103, in results_loop raise RuntimeError("One or more background workers are no longer alive. Exiting. Please check the " RuntimeError: One or more background workers are no longer alive. Exiting. Please check the print statements above for the actual error message raise RuntimeError("One or more background workers are no longer alive. Exiting. Please check the " RuntimeError: One or more background workers are no longer alive. Exiting. Please check the print statements above for the actual error message

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白小俗
2025.07.10
冒险岛私服定制新手入门神器,简单易懂。🐷
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袁大岛
2025.07.04
想要自定义冒险岛私服功能,这个资源非常适合。
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仙夜子
2025.05.12
对于GM初学者来说,这款源码是个不错的起点。🏆
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