nnUNetV2修改笔记-D4

Wang Zihao2024-06-172024-09-26

* 动机

由于跑完的验证集没有GT，所以只能在https://ipp.cbica.upenn.edu/ 等他们给我验证，但是得先请求数据集使用，然后再验证验证集。等吧。现在就想先把他们搞好的TotalSegmentator的预训练数据集进行转换，在基于nnUNetv2的STUnet上使用。

查看模型内容

首先先写一个脚本看看到底他们有什么不同：
经过与AI的斗智斗勇，写出了这个能用的版本

import torch

# 加载 PyTorch 保存的数据集文件
dataset = torch.load(r"D:\nnUNetWeb\STUNet\Pre-trained Models\checkpoint_best_modified_2.pth")

# 创建一个txt文件用于保存输出信息
with open("checkpoint_best_modified_2.txt", "w") as file:
    # 遍历数据集字典的每个键值对
    for key, value in dataset.items():
        file.write("键: {}\n".format(key))
        file.write("值的类型: {}\n".format(type(value)))
        
        # 如果值是字典,递归显示其结构
        if isinstance(value, dict):
            file.write("值的结构:\n")
            for sub_key, sub_value in value.items():
                file.write("  键: {}\n".format(sub_key))
                file.write("  值的类型: {}\n".format(type(sub_value)))
                if isinstance(sub_value, torch.Tensor):
                    file.write("  值的形状: {}\n".format(sub_value.shape))
        

        # 如果值是张量,显示其形状
        elif isinstance(value, torch.Tensor):
            file.write("值的形状: {}\n".format(value.shape))
        
        # 如果值是其他类型,直接显示其值
        else:
            file.write("值: {}\n".format(value))
        
        file.write("\n")

原始预训练模型的结构

然后打开作者提供的TotalSegmentator的base模型的预训练模型：

base_ep4k.model
键: epoch
值的类型: <class 'int'>
值: 4000

键: state_dict
值的类型: <class 'collections.OrderedDict'>
值的结构:
  键: conv_blocks_context.0.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 1, 3, 3, 3])
  键: conv_blocks_context.0.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 32, 3, 3, 3])
  键: conv_blocks_context.0.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 1, 1, 1, 1])
  键: conv_blocks_context.0.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.1.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 32, 3, 3, 3])
  键: conv_blocks_context.1.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 64, 3, 3, 3])
  键: conv_blocks_context.1.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 32, 1, 1, 1])
  键: conv_blocks_context.1.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.2.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 64, 3, 3, 3])
  键: conv_blocks_context.2.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 128, 3, 3, 3])
  键: conv_blocks_context.2.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 64, 1, 1, 1])
  键: conv_blocks_context.2.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.3.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 128, 3, 3, 3])
  键: conv_blocks_context.3.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 256, 3, 3, 3])
  键: conv_blocks_context.3.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 128, 1, 1, 1])
  键: conv_blocks_context.3.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.4.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 256, 3, 3, 3])
  键: conv_blocks_context.4.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 3, 3, 3])
  键: conv_blocks_context.4.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 256, 1, 1, 1])
  键: conv_blocks_context.4.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 3, 3, 3])
  键: conv_blocks_context.5.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 3, 3, 3])
  键: conv_blocks_context.5.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 1, 1, 1])
  键: conv_blocks_context.5.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: upsample_layers.0.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 1, 1, 1])
  键: upsample_layers.0.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: upsample_layers.1.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 512, 1, 1, 1])
  键: upsample_layers.1.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: upsample_layers.2.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 256, 1, 1, 1])
  键: upsample_layers.2.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: upsample_layers.3.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 128, 1, 1, 1])
  键: upsample_layers.3.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: upsample_layers.4.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 64, 1, 1, 1])
  键: upsample_layers.4.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.0.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 1024, 3, 3, 3])
  键: conv_blocks_localization.0.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 3, 3, 3])
  键: conv_blocks_localization.0.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 1024, 1, 1, 1])
  键: conv_blocks_localization.0.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.1.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 512, 3, 3, 3])
  键: conv_blocks_localization.1.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 256, 3, 3, 3])
  键: conv_blocks_localization.1.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 512, 1, 1, 1])
  键: conv_blocks_localization.1.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.2.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 256, 3, 3, 3])
  键: conv_blocks_localization.2.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 128, 3, 3, 3])
  键: conv_blocks_localization.2.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 256, 1, 1, 1])
  键: conv_blocks_localization.2.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.3.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 128, 3, 3, 3])
  键: conv_blocks_localization.3.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 64, 3, 3, 3])
  键: conv_blocks_localization.3.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 128, 1, 1, 1])
  键: conv_blocks_localization.3.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.4.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 64, 3, 3, 3])
  键: conv_blocks_localization.4.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 32, 3, 3, 3])
  键: conv_blocks_localization.4.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 64, 1, 1, 1])
  键: conv_blocks_localization.4.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: seg_outputs.0.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105, 512, 1, 1, 1])
  键: seg_outputs.0.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105])
  键: seg_outputs.1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105, 256, 1, 1, 1])
  键: seg_outputs.1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105])
  键: seg_outputs.2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105, 128, 1, 1, 1])
  键: seg_outputs.2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105])
  键: seg_outputs.3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105, 64, 1, 1, 1])
  键: seg_outputs.3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105])
  键: seg_outputs.4.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105, 32, 1, 1, 1])
  键: seg_outputs.4.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105])

键: optimizer_state_dict
值的类型: <class 'dict'>
值的结构:
  键: state
  值的类型: <class 'dict'>
  键: param_groups
  值的类型: <class 'list'>

键: lr_scheduler_state_dict
值的类型: <class 'NoneType'>
值: None

键: plot_stuff
值的类型: <class 'tuple'>
值: （略）

键: best_stuff
值的类型: <class 'tuple'>
值: (3978, -0.8753797063923451, 0.8816069818132722)

键: amp_grad_scaler
值的类型: <class 'dict'>
值的结构:
  键: scale
  值的类型: <class 'float'>
  键: growth_factor
  值的类型: <class 'float'>
  键: backoff_factor
  值的类型: <class 'float'>
  键: growth_interval
  值的类型: <class 'int'>
  键: _growth_tracker
  值的类型: <class 'int'>

其中，plot_stuff的值放在最后。

正统STUNetv2的模型结构

然后，随便找一个STUNet的模型文件，看看v2版本的STUNet的模型文件是怎么保存的：

checkpoint_best.pth
键: network_weights
值的类型: <class 'collections.OrderedDict'>
值的结构:
  键: conv_blocks_context.0.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 4, 3, 3, 3])
  键: conv_blocks_context.0.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 32, 3, 3, 3])
  键: conv_blocks_context.0.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 4, 1, 1, 1])
  键: conv_blocks_context.0.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.1.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 32, 3, 3, 3])
  键: conv_blocks_context.1.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 64, 3, 3, 3])
  键: conv_blocks_context.1.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 32, 1, 1, 1])
  键: conv_blocks_context.1.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.2.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 64, 3, 3, 3])
  键: conv_blocks_context.2.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 128, 3, 3, 3])
  键: conv_blocks_context.2.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 64, 1, 1, 1])
  键: conv_blocks_context.2.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.3.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 128, 3, 3, 3])
  键: conv_blocks_context.3.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 256, 3, 3, 3])
  键: conv_blocks_context.3.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 128, 1, 1, 1])
  键: conv_blocks_context.3.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.4.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 256, 3, 3, 3])
  键: conv_blocks_context.4.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 3, 3, 3])
  键: conv_blocks_context.4.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 256, 1, 1, 1])
  键: conv_blocks_context.4.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 3, 3, 3])
  键: conv_blocks_context.5.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 3, 3, 3])
  键: conv_blocks_context.5.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 1, 1, 1])
  键: conv_blocks_context.5.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: upsample_layers.0.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 1, 1, 1])
  键: upsample_layers.0.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: upsample_layers.1.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 512, 1, 1, 1])
  键: upsample_layers.1.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: upsample_layers.2.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 256, 1, 1, 1])
  键: upsample_layers.2.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: upsample_layers.3.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 128, 1, 1, 1])
  键: upsample_layers.3.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: upsample_layers.4.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 64, 1, 1, 1])
  键: upsample_layers.4.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.0.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 1024, 3, 3, 3])
  键: conv_blocks_localization.0.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 3, 3, 3])
  键: conv_blocks_localization.0.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 1024, 1, 1, 1])
  键: conv_blocks_localization.0.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.1.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 512, 3, 3, 3])
  键: conv_blocks_localization.1.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 256, 3, 3, 3])
  键: conv_blocks_localization.1.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 512, 1, 1, 1])
  键: conv_blocks_localization.1.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.2.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 256, 3, 3, 3])
  键: conv_blocks_localization.2.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 128, 3, 3, 3])
  键: conv_blocks_localization.2.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 256, 1, 1, 1])
  键: conv_blocks_localization.2.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.3.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 128, 3, 3, 3])
  键: conv_blocks_localization.3.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 64, 3, 3, 3])
  键: conv_blocks_localization.3.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 128, 1, 1, 1])
  键: conv_blocks_localization.3.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.4.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 64, 3, 3, 3])
  键: conv_blocks_localization.4.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 32, 3, 3, 3])
  键: conv_blocks_localization.4.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 64, 1, 1, 1])
  键: conv_blocks_localization.4.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: seg_outputs.0.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([4, 512, 1, 1, 1])
  键: seg_outputs.0.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([4])
  键: seg_outputs.1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([4, 256, 1, 1, 1])
  键: seg_outputs.1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([4])
  键: seg_outputs.2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([4, 128, 1, 1, 1])
  键: seg_outputs.2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([4])
  键: seg_outputs.3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([4, 64, 1, 1, 1])
  键: seg_outputs.3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([4])
  键: seg_outputs.4.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([4, 32, 1, 1, 1])
  键: seg_outputs.4.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([4])

键: optimizer_state
值的类型: <class 'dict'>
值的结构:
  键: state
  值的类型: <class 'dict'>
  键: param_groups
  值的类型: <class 'list'>

键: grad_scaler_state
值的类型: <class 'dict'>
值的结构:
  键: scale
  值的类型: <class 'float'>
  键: growth_factor
  值的类型: <class 'float'>
  键: backoff_factor
  值的类型: <class 'float'>
  键: growth_interval
  值的类型: <class 'int'>
  键: _growth_tracker
  值的类型: <class 'int'>

键: logging
值的类型: <class 'dict'>
值的结构:
  键: mean_fg_dice
  值的类型: <class 'list'>
  键: ema_fg_dice
  值的类型: <class 'list'>
  键: dice_per_class_or_region
  值的类型: <class 'list'>
  键: train_losses
  值的类型: <class 'list'>
  键: val_losses
  值的类型: <class 'list'>
  键: lrs
  值的类型: <class 'list'>
  键: epoch_start_timestamps
  值的类型: <class 'list'>
  键: epoch_end_timestamps
  值的类型: <class 'list'>

键: _best_ema
值的类型: <class 'numpy.float64'>
值: 0.7113046541216426

键: current_epoch
值的类型: <class 'int'>
值: 8

键: init_args
值的类型: <class 'dict'>
值的结构:
  键: plans
  值的类型: <class 'dict'>
  键: configuration
  值的类型: <class 'str'>
  键: fold
  值的类型: <class 'int'>
  键: dataset_json
  值的类型: <class 'dict'>
  键: unpack_dataset
  值的类型: <class 'bool'>
  键: device
  值的类型: <class 'torch.device'>

键: trainer_name
值的类型: <class 'str'>
值: STUNetTrainer_base

键: inference_allowed_mirroring_axes
值的类型: <class 'tuple'>
值: (0, 1, 2)

区别与修改

可以看出，v2版本保存的数据较少，其中最重要的键值一样，只要把主键改一下就行了。其他的不存在的键就先删除了。这样应该就能让v2版本使用v1版本的预训练模型。
又经过和AI的愉快聊天，得到了以下的修改模型键值的文件：

import torch
import numpy as np

# 加载 PyTorch 保存的模型文件
model_path = r"D:\nnUNetWeb\STUNet\Pre-trained Models\base_ep4k.model"
model_data = torch.load(model_path)

# 修改键名
if "epoch" in model_data:
    model_data["current_epoch"] = model_data.pop("epoch")
    print("已将键名 'epoch' 修改为 'current_epoch'")
else:
    print("模型文件中不存在键名 'epoch'")

if "optimizer_state_dict" in model_data:
    model_data["optimizer_state"] = model_data.pop("optimizer_state_dict")
    print("已将键名 'optimizer_state_dict' 修改为 'optimizer_state'")
else:
    print("模型文件中不存在键名 'optimizer_state_dict'")

if "state_dict" in model_data:
    model_data["network_weights"] = model_data.pop("state_dict")
    print("已将键名 'state_dict' 修改为 'network_weights'")
else:
    print("模型文件中不存在键名 'state_dict'")

if "amp_grad_scaler" in model_data:
    model_data["grad_scaler_state"] = model_data.pop("amp_grad_scaler")
    print("已将键名 'amp_grad_scaler' 修改为 'grad_scaler_state'")
else:
    print("模型文件中不存在键名 'state_dict'")

# 删除键
if "lr_scheduler_state_dict" in model_data:
    del model_data["lr_scheduler_state_dict"]
    print("已删除键 'lr_scheduler_state_dict'")
else:
    print("模型文件中不存在键 'lr_scheduler_state_dict'")

if "plot_stuff" in model_data:
    del model_data["plot_stuff"]
    print("已删除键 'plot_stuff'")
else:
    print("模型文件中不存在键 'plot_stuff'")

if "best_stuff" in model_data:
    del model_data["best_stuff"]
    print("已删除键 'best_stuff'")
else:
    print("模型文件中不存在键 'best_stuff'")

# 新建空白键
model_data["logging"] = {
    "mean_fg_dice": [],
    "ema_fg_dice": [],
    "dice_per_class_or_region": [],
    "train_losses": [],
    "val_losses": [],
    "lrs": [],
    "epoch_start_timestamps": [],
    "epoch_end_timestamps": []
}
print("已新建空白键 'logging'")

model_data["_best_ema"] = np.float64(0.7113046541216426)
print("已新建键 '_best_ema'")

model_data["init_args"] = {
    "plans": {},
    "configuration": "",
    "fold": 0,
    "dataset_json": {},
    "unpack_dataset": False,
    "device": torch.device("cpu")
}
print("已新建空白键 'init_args'")

model_data["trainer_name"] = "STUNetTrainer_base"
print("已新建键 'trainer_name'")

model_data["inference_allowed_mirroring_axes"] = (0, 1, 2)
print("已新建键 'inference_allowed_mirroring_axes'")

# 保存修改后的模型文件
new_model_path = r"D:\nnUNetWeb\STUNet\Pre-trained Models\checkpoint_best_modified_2.pth"
torch.save(model_data, new_model_path)
print("已保存修改后的模型文件:", new_model_path)

修改了键名后的模型

修改了之后，看着正常多了：

键: current_epoch
值的类型: <class 'int'>
值: 4000

键: optimizer_state
值的类型: <class 'dict'>
值的结构:
  键: state
  值的类型: <class 'dict'>
  键: param_groups
  值的类型: <class 'list'>

键: network_weights
值的类型: <class 'collections.OrderedDict'>
值的结构:
  键: conv_blocks_context.0.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 1, 3, 3, 3])
  键: conv_blocks_context.0.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 32, 3, 3, 3])
  键: conv_blocks_context.0.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.0.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 1, 1, 1, 1])
  键: conv_blocks_context.0.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_context.1.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 32, 3, 3, 3])
  键: conv_blocks_context.1.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 64, 3, 3, 3])
  键: conv_blocks_context.1.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.1.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 32, 1, 1, 1])
  键: conv_blocks_context.1.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_context.2.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 64, 3, 3, 3])
  键: conv_blocks_context.2.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 128, 3, 3, 3])
  键: conv_blocks_context.2.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.2.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 64, 1, 1, 1])
  键: conv_blocks_context.2.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_context.3.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 128, 3, 3, 3])
  键: conv_blocks_context.3.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 256, 3, 3, 3])
  键: conv_blocks_context.3.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.3.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 128, 1, 1, 1])
  键: conv_blocks_context.3.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_context.4.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 256, 3, 3, 3])
  键: conv_blocks_context.4.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 3, 3, 3])
  键: conv_blocks_context.4.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.4.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 256, 1, 1, 1])
  键: conv_blocks_context.4.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 3, 3, 3])
  键: conv_blocks_context.5.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 3, 3, 3])
  键: conv_blocks_context.5.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_context.5.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 1, 1, 1])
  键: conv_blocks_context.5.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: upsample_layers.0.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 1, 1, 1])
  键: upsample_layers.0.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: upsample_layers.1.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 512, 1, 1, 1])
  键: upsample_layers.1.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: upsample_layers.2.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 256, 1, 1, 1])
  键: upsample_layers.2.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: upsample_layers.3.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 128, 1, 1, 1])
  键: upsample_layers.3.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: upsample_layers.4.conv.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 64, 1, 1, 1])
  键: upsample_layers.4.conv.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.0.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 1024, 3, 3, 3])
  键: conv_blocks_localization.0.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 512, 3, 3, 3])
  键: conv_blocks_localization.0.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.0.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512, 1024, 1, 1, 1])
  键: conv_blocks_localization.0.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([512])
  键: conv_blocks_localization.1.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 512, 3, 3, 3])
  键: conv_blocks_localization.1.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 256, 3, 3, 3])
  键: conv_blocks_localization.1.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.1.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256, 512, 1, 1, 1])
  键: conv_blocks_localization.1.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([256])
  键: conv_blocks_localization.2.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 256, 3, 3, 3])
  键: conv_blocks_localization.2.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 128, 3, 3, 3])
  键: conv_blocks_localization.2.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.2.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128, 256, 1, 1, 1])
  键: conv_blocks_localization.2.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([128])
  键: conv_blocks_localization.3.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 128, 3, 3, 3])
  键: conv_blocks_localization.3.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 64, 3, 3, 3])
  键: conv_blocks_localization.3.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.3.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64, 128, 1, 1, 1])
  键: conv_blocks_localization.3.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([64])
  键: conv_blocks_localization.4.0.conv1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 64, 3, 3, 3])
  键: conv_blocks_localization.4.0.conv1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.norm1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.norm1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.conv2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 32, 3, 3, 3])
  键: conv_blocks_localization.4.0.conv2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.norm2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.norm2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: conv_blocks_localization.4.0.conv3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32, 64, 1, 1, 1])
  键: conv_blocks_localization.4.0.conv3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([32])
  键: seg_outputs.0.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105, 512, 1, 1, 1])
  键: seg_outputs.0.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105])
  键: seg_outputs.1.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105, 256, 1, 1, 1])
  键: seg_outputs.1.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105])
  键: seg_outputs.2.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105, 128, 1, 1, 1])
  键: seg_outputs.2.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105])
  键: seg_outputs.3.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105, 64, 1, 1, 1])
  键: seg_outputs.3.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105])
  键: seg_outputs.4.weight
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105, 32, 1, 1, 1])
  键: seg_outputs.4.bias
  值的类型: <class 'torch.Tensor'>
  值的形状: torch.Size([105])

键: grad_scaler_state
值的类型: <class 'dict'>
值的结构:
  键: scale
  值的类型: <class 'float'>
  键: growth_factor
  值的类型: <class 'float'>
  键: backoff_factor
  值的类型: <class 'float'>
  键: growth_interval
  值的类型: <class 'int'>
  键: _growth_tracker
  值的类型: <class 'int'>

键: logging
值的类型: <class 'dict'>
值的结构:
  键: mean_fg_dice
  值的类型: <class 'list'>
  键: ema_fg_dice
  值的类型: <class 'list'>
  键: dice_per_class_or_region
  值的类型: <class 'list'>
  键: train_losses
  值的类型: <class 'list'>
  键: val_losses
  值的类型: <class 'list'>
  键: lrs
  值的类型: <class 'list'>
  键: epoch_start_timestamps
  值的类型: <class 'list'>
  键: epoch_end_timestamps
  值的类型: <class 'list'>

键: _best_ema
值的类型: <class 'numpy.float64'>
值: 0.7113046541216426

键: init_args
值的类型: <class 'dict'>
值的结构:
  键: plans
  值的类型: <class 'dict'>
  键: configuration
  值的类型: <class 'str'>
  键: fold
  值的类型: <class 'int'>
  键: dataset_json
  值的类型: <class 'dict'>
  键: unpack_dataset
  值的类型: <class 'bool'>
  键: device
  值的类型: <class 'torch.device'>

键: trainer_name
值的类型: <class 'str'>
值: STUNetTrainer_base

键: inference_allowed_mirroring_axes
值的类型: <class 'tuple'>
值: (0, 1, 2)

处理不存在与不匹配的键值

然后，不存在的键值就新建一个新的，维度不匹配的就使用插值或重复进行填充或缩限，让它匹配。

import torch

# 加载checkpoint
checkpoint_path = 'D:\\nnUNetWeb\\STUNet\\Pre-trained Models\\Task043_BraTS2019\\STUNetTrainer_base_ft__nnUNetPlansv2.1\\totalseg\\checkpoint_best_modified_2.pth'
checkpoint = torch.load(checkpoint_path)

# 获取模型的state_dict
state_dict = checkpoint['network_weights']

# 定义目标尺寸
target_conv1_size = torch.Size([32, 4, 3, 3, 3])
target_conv3_size = torch.Size([32, 4, 1, 1, 1])
target_seg_output_sizes = [
    torch.Size([4, 512, 1, 1, 1]),
    torch.Size([4, 256, 1, 1, 1]),
    torch.Size([4, 128, 1, 1, 1]),
    torch.Size([4, 64, 1, 1, 1]),
    torch.Size([4, 32, 1, 1, 1])
]
target_bias_size = torch.Size([4])

# 修改conv1的输入通道数
if 'conv_blocks_context.0.0.conv1.weight' in state_dict:
    param = state_dict['conv_blocks_context.0.0.conv1.weight']
    if param.size(1) != target_conv1_size[1]:
        # 重复通道数以匹配目标通道数
        new_param = param.repeat(1, target_conv1_size[1] // param.size(1), 1, 1, 1) / (target_conv1_size[1] // param.size(1))
        state_dict['conv_blocks_context.0.0.conv1.weight'] = new_param

# 修改conv3的输入通道数
if 'conv_blocks_context.0.0.conv3.weight' in state_dict:
    param = state_dict['conv_blocks_context.0.0.conv3.weight']
    if param.size(1) != target_conv3_size[1]:
        # 重复通道数以匹配目标通道数
        new_param = param.repeat(1, target_conv3_size[1] // param.size(1), 1, 1, 1) / (target_conv3_size[1] // param.size(1))
        state_dict['conv_blocks_context.0.0.conv3.weight'] = new_param

# 修改seg_output的输出类别数
for i, target_size in enumerate(target_seg_output_sizes):
    if f'seg_outputs.{i}.weight' in state_dict:
        param = state_dict[f'seg_outputs.{i}.weight']
        if param.size() != target_size:
            # 创建一个全零张量，大小与目标大小匹配
            new_param = torch.zeros(target_size)
            # 计算原始张量和目标张量在每个维度上的最小尺寸
            min_size = torch.Size([min(param.size(0), target_size[0]), min(param.size(1), target_size[1]), min(param.size(2), target_size[2]), min(param.size(3), target_size[3]), min(param.size(4), target_size[4])])
            # 将原始张量的值复制到新的全零张量中
            new_param[:min_size[0], :min_size[1], :min_size[2], :min_size[3], :min_size[4]] = param[:min_size[0], :min_size[1], :min_size[2], :min_size[3], :min_size[4]]
            state_dict[f'seg_outputs.{i}.weight'] = new_param
    if f'seg_outputs.{i}.bias' in state_dict:
        param = state_dict[f'seg_outputs.{i}.bias']
        if param.size(0) != target_bias_size[0]:
            # 创建一个全零张量，大小与目标大小匹配
            new_param = torch.zeros(target_bias_size)
            # 计算原始张量和目标张量在每个维度上的最小尺寸
            min_size = min(param.size(0), target_bias_size[0])
            # 将原始张量的值复制到新的全零张量中
            new_param[:min_size] = param[:min_size]
            state_dict[f'seg_outputs.{i}.bias'] = new_param

# 保存修改后的checkpoint
checkpoint['network_weights'] = state_dict
torch.save(checkpoint, 'D:\\nnUNetWeb\\STUNet\\Pre-trained Models\\Task043_BraTS2019\\STUNetTrainer_base_ft__nnUNetPlansv2.1\\totalseg\\checkpoint_best_modified_2_fixed.pth')

这样就可以使预训练模型与所需要的模型进行匹配。

Try IT!

2024-06-17 14:58:51,281 - INFO - 127.0.0.1 - - [17/Jun/2024 14:58:51] "POST /start_training_from_pretrained HTTP/1.1" 200 -
2024-06-17 15:00:37,118 - INFO - Using pretrained model path: D:\nnUNetWeb\STUNet/Pre-trained Models\Task043_BraTS2019\STUNetTrainer_base_ft__nnUNetPlansv2.1\totalseg\checkpoint_best_modified_2_fixed.pth
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.
#######################################################################

################### Loading pretrained weights from file  D:\nnUNetWeb\STUNet/Pre-trained Models\Task043_BraTS2019\STUNetTrainer_base_ft__nnUNetPlansv2.1\totalseg\checkpoint_best_modified_2_fixed.pth ###################
Below is the list of overlapping blocks in pretrained model and nnUNet architecture:
conv_blocks_context.0.0.conv1.weight shape torch.Size([32, 4, 3, 3, 3])
conv_blocks_context.0.0.conv1.bias shape torch.Size([32])
conv_blocks_context.0.0.norm1.weight shape torch.Size([32])
conv_blocks_context.0.0.norm1.bias shape torch.Size([32])
conv_blocks_context.0.0.conv2.weight shape torch.Size([32, 32, 3, 3, 3])
conv_blocks_context.0.0.conv2.bias shape torch.Size([32])
conv_blocks_context.0.0.norm2.weight shape torch.Size([32])
conv_blocks_context.0.0.norm2.bias shape torch.Size([32])
conv_blocks_context.0.0.conv3.weight shape torch.Size([32, 4, 1, 1, 1])
conv_blocks_context.0.0.conv3.bias shape torch.Size([32])
conv_blocks_context.1.0.conv1.weight shape torch.Size([64, 32, 3, 3, 3])
conv_blocks_context.1.0.conv1.bias shape torch.Size([64])
conv_blocks_context.1.0.norm1.weight shape torch.Size([64])
conv_blocks_context.1.0.norm1.bias shape torch.Size([64])
conv_blocks_context.1.0.conv2.weight shape torch.Size([64, 64, 3, 3, 3])
conv_blocks_context.1.0.conv2.bias shape torch.Size([64])
conv_blocks_context.1.0.norm2.weight shape torch.Size([64])
conv_blocks_context.1.0.norm2.bias shape torch.Size([64])
conv_blocks_context.1.0.conv3.weight shape torch.Size([64, 32, 1, 1, 1])
conv_blocks_context.1.0.conv3.bias shape torch.Size([64])
conv_blocks_context.2.0.conv1.weight shape torch.Size([128, 64, 3, 3, 3])
conv_blocks_context.2.0.conv1.bias shape torch.Size([128])
conv_blocks_context.2.0.norm1.weight shape torch.Size([128])
conv_blocks_context.2.0.norm1.bias shape torch.Size([128])
conv_blocks_context.2.0.conv2.weight shape torch.Size([128, 128, 3, 3, 3])
conv_blocks_context.2.0.conv2.bias shape torch.Size([128])
conv_blocks_context.2.0.norm2.weight shape torch.Size([128])
conv_blocks_context.2.0.norm2.bias shape torch.Size([128])
conv_blocks_context.2.0.conv3.weight shape torch.Size([128, 64, 1, 1, 1])
conv_blocks_context.2.0.conv3.bias shape torch.Size([128])
conv_blocks_context.3.0.conv1.weight shape torch.Size([256, 128, 3, 3, 3])
conv_blocks_context.3.0.conv1.bias shape torch.Size([256])
conv_blocks_context.3.0.norm1.weight shape torch.Size([256])
conv_blocks_context.3.0.norm1.bias shape torch.Size([256])
conv_blocks_context.3.0.conv2.weight shape torch.Size([256, 256, 3, 3, 3])
conv_blocks_context.3.0.conv2.bias shape torch.Size([256])
conv_blocks_context.3.0.norm2.weight shape torch.Size([256])
conv_blocks_context.3.0.norm2.bias shape torch.Size([256])
conv_blocks_context.3.0.conv3.weight shape torch.Size([256, 128, 1, 1, 1])
conv_blocks_context.3.0.conv3.bias shape torch.Size([256])
conv_blocks_context.4.0.conv1.weight shape torch.Size([512, 256, 3, 3, 3])
conv_blocks_context.4.0.conv1.bias shape torch.Size([512])
conv_blocks_context.4.0.norm1.weight shape torch.Size([512])
conv_blocks_context.4.0.norm1.bias shape torch.Size([512])
conv_blocks_context.4.0.conv2.weight shape torch.Size([512, 512, 3, 3, 3])
conv_blocks_context.4.0.conv2.bias shape torch.Size([512])
conv_blocks_context.4.0.norm2.weight shape torch.Size([512])
conv_blocks_context.4.0.norm2.bias shape torch.Size([512])
conv_blocks_context.4.0.conv3.weight shape torch.Size([512, 256, 1, 1, 1])
conv_blocks_context.4.0.conv3.bias shape torch.Size([512])
conv_blocks_context.5.0.conv1.weight shape torch.Size([512, 512, 3, 3, 3])
conv_blocks_context.5.0.conv1.bias shape torch.Size([512])
conv_blocks_context.5.0.norm1.weight shape torch.Size([512])
conv_blocks_context.5.0.norm1.bias shape torch.Size([512])
conv_blocks_context.5.0.conv2.weight shape torch.Size([512, 512, 3, 3, 3])
conv_blocks_context.5.0.conv2.bias shape torch.Size([512])
conv_blocks_context.5.0.norm2.weight shape torch.Size([512])
conv_blocks_context.5.0.norm2.bias shape torch.Size([512])
conv_blocks_context.5.0.conv3.weight shape torch.Size([512, 512, 1, 1, 1])
conv_blocks_context.5.0.conv3.bias shape torch.Size([512])
upsample_layers.0.conv.weight shape torch.Size([512, 512, 1, 1, 1])
upsample_layers.0.conv.bias shape torch.Size([512])
upsample_layers.1.conv.weight shape torch.Size([256, 512, 1, 1, 1])
upsample_layers.1.conv.bias shape torch.Size([256])
upsample_layers.2.conv.weight shape torch.Size([128, 256, 1, 1, 1])
upsample_layers.2.conv.bias shape torch.Size([128])
upsample_layers.3.conv.weight shape torch.Size([64, 128, 1, 1, 1])
upsample_layers.3.conv.bias shape torch.Size([64])
upsample_layers.4.conv.weight shape torch.Size([32, 64, 1, 1, 1])
upsample_layers.4.conv.bias shape torch.Size([32])
conv_blocks_localization.0.0.conv1.weight shape torch.Size([512, 1024, 3, 3, 3])
conv_blocks_localization.0.0.conv1.bias shape torch.Size([512])
conv_blocks_localization.0.0.norm1.weight shape torch.Size([512])
conv_blocks_localization.0.0.norm1.bias shape torch.Size([512])
conv_blocks_localization.0.0.conv2.weight shape torch.Size([512, 512, 3, 3, 3])
conv_blocks_localization.0.0.conv2.bias shape torch.Size([512])
conv_blocks_localization.0.0.norm2.weight shape torch.Size([512])
conv_blocks_localization.0.0.norm2.bias shape torch.Size([512])
conv_blocks_localization.0.0.conv3.weight shape torch.Size([512, 1024, 1, 1, 1])
conv_blocks_localization.0.0.conv3.bias shape torch.Size([512])
conv_blocks_localization.1.0.conv1.weight shape torch.Size([256, 512, 3, 3, 3])
conv_blocks_localization.1.0.conv1.bias shape torch.Size([256])
conv_blocks_localization.1.0.norm1.weight shape torch.Size([256])
conv_blocks_localization.1.0.norm1.bias shape torch.Size([256])
conv_blocks_localization.1.0.conv2.weight shape torch.Size([256, 256, 3, 3, 3])
conv_blocks_localization.1.0.conv2.bias shape torch.Size([256])
conv_blocks_localization.1.0.norm2.weight shape torch.Size([256])
conv_blocks_localization.1.0.norm2.bias shape torch.Size([256])
conv_blocks_localization.1.0.conv3.weight shape torch.Size([256, 512, 1, 1, 1])
conv_blocks_localization.1.0.conv3.bias shape torch.Size([256])
conv_blocks_localization.2.0.conv1.weight shape torch.Size([128, 256, 3, 3, 3])
conv_blocks_localization.2.0.conv1.bias shape torch.Size([128])
conv_blocks_localization.2.0.norm1.weight shape torch.Size([128])
conv_blocks_localization.2.0.norm1.bias shape torch.Size([128])
conv_blocks_localization.2.0.conv2.weight shape torch.Size([128, 128, 3, 3, 3])
conv_blocks_localization.2.0.conv2.bias shape torch.Size([128])
conv_blocks_localization.2.0.norm2.weight shape torch.Size([128])
conv_blocks_localization.2.0.norm2.bias shape torch.Size([128])
conv_blocks_localization.2.0.conv3.weight shape torch.Size([128, 256, 1, 1, 1])
conv_blocks_localization.2.0.conv3.bias shape torch.Size([128])
conv_blocks_localization.3.0.conv1.weight shape torch.Size([64, 128, 3, 3, 3])
conv_blocks_localization.3.0.conv1.bias shape torch.Size([64])
conv_blocks_localization.3.0.norm1.weight shape torch.Size([64])
conv_blocks_localization.3.0.norm1.bias shape torch.Size([64])
conv_blocks_localization.3.0.conv2.weight shape torch.Size([64, 64, 3, 3, 3])
conv_blocks_localization.3.0.conv2.bias shape torch.Size([64])
conv_blocks_localization.3.0.norm2.weight shape torch.Size([64])
conv_blocks_localization.3.0.norm2.bias shape torch.Size([64])
conv_blocks_localization.3.0.conv3.weight shape torch.Size([64, 128, 1, 1, 1])
conv_blocks_localization.3.0.conv3.bias shape torch.Size([64])
conv_blocks_localization.4.0.conv1.weight shape torch.Size([32, 64, 3, 3, 3])
conv_blocks_localization.4.0.conv1.bias shape torch.Size([32])
conv_blocks_localization.4.0.norm1.weight shape torch.Size([32])
conv_blocks_localization.4.0.norm1.bias shape torch.Size([32])
conv_blocks_localization.4.0.conv2.weight shape torch.Size([32, 32, 3, 3, 3])
conv_blocks_localization.4.0.conv2.bias shape torch.Size([32])
conv_blocks_localization.4.0.norm2.weight shape torch.Size([32])
conv_blocks_localization.4.0.norm2.bias shape torch.Size([32])
conv_blocks_localization.4.0.conv3.weight shape torch.Size([32, 64, 1, 1, 1])
conv_blocks_localization.4.0.conv3.bias shape torch.Size([32])
seg_outputs.0.weight shape torch.Size([4, 512, 1, 1, 1])
seg_outputs.0.bias shape torch.Size([4])
seg_outputs.1.weight shape torch.Size([4, 256, 1, 1, 1])
seg_outputs.1.bias shape torch.Size([4])
seg_outputs.2.weight shape torch.Size([4, 128, 1, 1, 1])
seg_outputs.2.bias shape torch.Size([4])
seg_outputs.3.weight shape torch.Size([4, 64, 1, 1, 1])
seg_outputs.3.bias shape torch.Size([4])
seg_outputs.4.weight shape torch.Size([4, 32, 1, 1, 1])
seg_outputs.4.bias shape torch.Size([4])
################### Done ###################

This is the configuration used by this training:
Configuration name: 3d_fullres
 {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [139.0, 169.0, 138.0], 'spacing': [1.0, 1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [True, True, True, True], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': False} 

These are the global plan.json settings:
 {'dataset_name': 'Dataset043_BraTS2019', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [139, 169, 138], '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': 21113.0, 'mean': 542.999755859375, 'median': 343.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 8810.0, 'std': 929.7076416015625}, '1': {'max': 18011.0, 'mean': 637.3016967773438, 'median': 398.0, 'min': 0.0, 'percentile_00_5': 35.0, 'percentile_99_5': 8077.0, 'std': 934.3526611328125}, '2': {'max': 31404.0, 'mean': 929.93505859375, 'median': 629.0, 'min': 0.0, 'percentile_00_5': 94.0, 'percentile_99_5': 16792.0, 'std': 1901.22119140625}, '3': {'max': 29422.0, 'mean': 655.0728149414062, 'median': 409.0, 'min': 0.0, 'percentile_00_5': 65.0, 'percentile_99_5': 16096.0, 'std': 1809.3948974609375}}} 

2024-06-17 15:00:37.916939: unpacking dataset...
2024-06-17 15:00:51.846472: unpacking done...
2024-06-17 15:00:51.853708: do_dummy_2d_data_aug: False
2024-06-17 15:00:51.862103: Using splits from existing split file: D:\nnUNetWeb\STUNet\nnUNet_preprocessed\Dataset043_BraTS2019\splits_final.json
2024-06-17 15:00:51.876102: The split file contains 5 splits.
2024-06-17 15:00:51.876102: Desired fold for training: 0
2024-06-17 15:00:51.876102: This split has 268 training and 67 validation cases.
2024-06-17 15:01:47.790931: Unable to plot network architecture:
2024-06-17 15:01:47.791931: module 'torch.onnx' has no attribute '_optimize_trace'
2024-06-17 15:01:47.896213: 
2024-06-17 15:01:47.896213: Epoch 0
2024-06-17 15:01:47.898214: Current learning rate: 0.001
using pin_memory on device 0
using pin_memory on device 0
2024-06-17 15:56:13.637645: train_loss -0.2886
2024-06-17 15:56:13.676620: val_loss -0.5557
2024-06-17 15:56:13.676620: Pseudo dice [0.7325, 0.6843, 0.764]
2024-06-17 15:56:13.676620: Epoch time: 3265.74 s
2024-06-17 15:56:13.677621: Yayy! New best EMA pseudo Dice: 0.7269
2024-06-17 15:56:25.247149: 
2024-06-17 15:56:25.249151: Epoch 1
2024-06-17 15:56:25.249151: Current learning rate: 0.001

终于匹配了，那些空白的就让他学习去吧。

附：plot_stuff数据：

1
2
3

键: plot_stuff
值的类型: <class 'tuple'>
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