From 39650786450df7acc136d4e644b0c16039dafcb1 Mon Sep 17 00:00:00 2001
From: Pengcheng Shi <shipc1220@gmail.com>
Date: Fri, 29 Mar 2024 23:04:48 +0800
Subject: [PATCH] Delete nnUNetTrainer/nnUNetTrainer_NexToU_Res.py

---
 nnUNetTrainer/nnUNetTrainer_NexToU_Res.py | 92 -----------------------
 1 file changed, 92 deletions(-)
 delete mode 100644 nnUNetTrainer/nnUNetTrainer_NexToU_Res.py

diff --git a/nnUNetTrainer/nnUNetTrainer_NexToU_Res.py b/nnUNetTrainer/nnUNetTrainer_NexToU_Res.py
deleted file mode 100644
index a6e69de..0000000
--- a/nnUNetTrainer/nnUNetTrainer_NexToU_Res.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import os
-import torch
-from torch import autocast, nn
-from typing import Union, Tuple, List
-from torch import distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-
-from dynamic_network_architectures.architectures.unet import ResidualEncoderUNet, PlainConvUNet
-from nnunetv2.training.nnUNetTrainer.variants.network_architecture.NexToU_Res import NexToU_Res
-from dynamic_network_architectures.building_blocks.helper import convert_dim_to_conv_op, get_matching_batchnorm
-from dynamic_network_architectures.initialization.weight_init import init_last_bn_before_add_to_0, InitWeights_He
-from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
-from nnunetv2.utilities.plans_handling.plans_handler import ConfigurationManager, PlansManager
-from nnunetv2.utilities.get_network_from_plans import get_network_from_plans
-from nnunetv2.utilities.label_handling.label_handling import convert_labelmap_to_one_hot, determine_num_input_channels
-
-class nnUNetTrainer_NexToU_Res(nnUNetTrainer):
-    @staticmethod
-    def build_network_architecture(plans_manager: PlansManager,
-                                   dataset_json,
-                                   configuration_manager: ConfigurationManager,
-                                   num_input_channels,
-                                   enable_deep_supervision: bool = True) -> nn.Module:
-        num_stages = len(configuration_manager.conv_kernel_sizes)
-
-        dim = len(configuration_manager.conv_kernel_sizes[0])
-        conv_op = convert_dim_to_conv_op(dim)
-
-        label_manager = plans_manager.get_label_manager(dataset_json)
-
-        segmentation_network_class_name = 'NexToU_Res' #configuration_manager.UNet_class_name
-        mapping = {
-            'PlainConvUNet': PlainConvUNet,
-            'ResidualEncoderUNet': ResidualEncoderUNet,
-            'NexToU_Res': NexToU_Res
-        }
-        kwargs = {
-            'PlainConvUNet': {
-                'conv_bias': True,
-                'norm_op': get_matching_batchnorm(conv_op),
-                'norm_op_kwargs': {'eps': 1e-5, 'affine': True},
-                'dropout_op': None, 'dropout_op_kwargs': None,
-                'nonlin': nn.LeakyReLU, 'nonlin_kwargs': {'inplace': True},
-            },
-            'ResidualEncoderUNet': {
-                'conv_bias': True,
-                'norm_op': get_matching_batchnorm(conv_op),
-                'norm_op_kwargs': {'eps': 1e-5, 'affine': True},
-                'dropout_op': None, 'dropout_op_kwargs': None,
-                'nonlin': nn.LeakyReLU, 'nonlin_kwargs': {'inplace': True},
-            },
-            'NexToU_Res': {
-                'conv_bias': True,
-                'norm_op': get_matching_batchnorm(conv_op),
-                'norm_op_kwargs': {'eps': 1e-5, 'affine': True},
-                'dropout_op': None, 'dropout_op_kwargs': None,
-                'nonlin': nn.LeakyReLU, 'nonlin_kwargs': {'inplace': True},
-            }
-        }
-        assert segmentation_network_class_name in mapping.keys(), 'The network architecture specified by the plans file ' \
-                                                                  'is non-standard (maybe your own?). Yo\'ll have to dive ' \
-                                                                  'into either this ' \
-                                                                  'function (get_network_from_plans) or ' \
-                                                                  'the init of your nnUNetModule to accomodate that.'
-        network_class = mapping[segmentation_network_class_name]
-
-        conv_or_blocks_per_stage = {
-            'n_blocks_per_stage'
-            if network_class != ResidualEncoderUNet else 'n_blocks_per_stage': configuration_manager.n_conv_per_stage_encoder,
-            'n_blocks_per_stage_decoder': configuration_manager.n_conv_per_stage_decoder
-        }
-
-        # network class name!!
-        model = network_class(
-            input_channels=num_input_channels,
-            patch_size=configuration_manager.patch_size,
-            n_stages=num_stages,
-            features_per_stage=[min(configuration_manager.UNet_base_num_features * 2 ** i,
-                                    configuration_manager.unet_max_num_features) for i in range(num_stages)],
-            conv_op=conv_op,
-            kernel_sizes=configuration_manager.conv_kernel_sizes,
-            strides=configuration_manager.pool_op_kernel_sizes,
-            num_classes=label_manager.num_segmentation_heads,
-            deep_supervision=enable_deep_supervision,
-            **conv_or_blocks_per_stage,
-            **kwargs[segmentation_network_class_name]
-        )
-        model.apply(InitWeights_He(1e-2))
-        if network_class == ResidualEncoderUNet:
-            model.apply(init_last_bn_before_add_to_0)
-        return model
-