Claude/finetune flood detection zq leg

8to2.py

@@ -0,0 +1,170 @@
+import os
+import numpy as np
+import rasterio
+from shutil import copy2
+from tqdm import tqdm
+
+
+def keep_last_two_bands(folder_path, backup=True, backup_suffix='_8band_backup'):
+    """
+    将所有TIF文件只保留最后两个波段，覆盖原文件
+
+    参数:
+        folder_path: 要处理的根目录路径
+        backup: 是否备份原文件
+        backup_suffix: 备份文件后缀
+    """
+    total_files = 0
+    files_processed = 0
+    files_skipped = 0
+    tif_extensions = ['.tif', '.tiff', '.TIF', '.TIFF']
+
+    # 收集所有tif文件
+    print(f"扫描路径: {folder_path}")
+    tif_files = []
+    for root, dirs, files in os.walk(folder_path):
+        for file in files:
+            if any(file.endswith(ext) for ext in tif_extensions):
+                tif_files.append(os.path.join(root, file))
+
+    total_files = len(tif_files)
+    print(f"找到 {total_files} 个TIF文件\n")
+
+    # 第一步：检查文件波段数
+    print("步骤1: 检查文件波段数...")
+    print("=" * 80)
+
+    band_info = {}
+    files_to_process = []
+
+    try:
+        iterator = tqdm(tif_files, desc="扫描文件")
+    except:
+        iterator = tif_files
+
+    for file_path in iterator:
+        try:
+            with rasterio.open(file_path) as src:
+                num_bands = src.count
+                rel_path = os.path.relpath(file_path, folder_path)
+
+                if num_bands not in band_info:
+                    band_info[num_bands] = []
+                band_info[num_bands].append(rel_path)
+
+                if num_bands >= 2:  # 只处理波段数>=2的文件
+                    files_to_process.append((file_path, num_bands))
+                else:
+                    files_skipped += 1
+
+        except Exception as e:
+            print(f"\n读取错误: {os.path.basename(file_path)}")
+            print(f"  错误: {e}")
+
+    # 显示波段数统计
+    print("\n波段数统计:")
+    for num_bands in sorted(band_info.keys()):
+        print(f"  {num_bands}波段: {len(band_info[num_bands])} 个文件")
+
+    print(f"\n将处理 {len(files_to_process)} 个文件（波段数>=2）")
+    print(f"跳过 {files_skipped} 个文件（波段数<2）")
+
+    if len(files_to_process) == 0:
+        print("没有需要处理的文件！")
+        return
+
+    # 第二步：确认并处理
+    print("\n" + "=" * 80)
+    print("步骤2: 处理文件")
+    print("操作: 只保留最后两个波段，覆盖原文件")
+
+    if backup:
+        print(f"将创建备份文件（后缀: {backup_suffix}）")
+    else:
+        print("警告: 不会创建备份！")
+
+    user_input = input("\n是否继续？(y/n): ")
+    if user_input.lower() != 'y':
+        print("操作已取消")
+        return
+
+    print("\n开始处理...")
+
+    try:
+        iterator = tqdm(files_to_process, desc="处理文件")
+    except:
+        iterator = files_to_process
+
+    for file_path, original_bands in iterator:
+        try:
+            # 备份原文件
+            if backup:
+                backup_path = file_path + backup_suffix
+                copy2(file_path, backup_path)
+
+            # 读取文件
+            with rasterio.open(file_path) as src:
+                # 保存元数据并修改波段数
+                meta = src.meta.copy()
+                meta['count'] = 2  # 修改为2波段
+
+                # 读取最后两个波段
+                band_n_1 = src.read(original_bands - 1)  # 倒数第二个波段
+                band_n = src.read(original_bands)  # 最后一个波段
+
+                # 创建临时文件
+                temp_path = file_path + '.tmp'
+
+                # 写入新文件（只有2个波段）
+                with rasterio.open(temp_path, 'w', **meta) as dst:
+                    dst.write(band_n_1, 1)  # 写入第1个波段
+                    dst.write(band_n, 2)  # 写入第2个波段
+
+            # 替换原文件
+            os.replace(temp_path, file_path)
+            files_processed += 1
+
+        except Exception as e:
+            print(f"\n处理错误: {os.path.basename(file_path)}")
+            print(f"  原波段数: {original_bands}")
+            print(f"  错误: {e}")
+            # 清理临时文件
+            temp_path = file_path + '.tmp'
+            if os.path.exists(temp_path):
+                os.remove(temp_path)
+
+    # 输出结果
+    print("\n" + "=" * 80)
+    print("处理完成!")
+    print(f"总文件数: {total_files}")
+    print(f"成功处理的文件: {files_processed}")
+    print(f"跳过的文件: {files_skipped}")
+
+    if backup:
+        print(f"\n原始文件已备份（后缀: {backup_suffix}）")
+        print("如果确认无误，可以使用以下命令删除备份文件:")
+        print(f"  find {folder_path} -name '*{backup_suffix}' -delete")
+
+    # 验证处理结果
+    print("\n步骤3: 验证处理结果...")
+    print("检查前3个文件的波段数...")
+
+    for i, (file_path, _) in enumerate(files_to_process[:3]):
+        try:
+            with rasterio.open(file_path) as src:
+                rel_path = os.path.relpath(file_path, folder_path)
+                print(f"  ✓ {rel_path}: {src.count} 波段")
+        except Exception as e:
+            print(f"  ✗ 验证失败: {e}")
+
+        if i >= 2:  # 只检查前3个
+            break
+
+
+if __name__ == "__main__":
+    folder_path = "/mnt/d/Project/Code/Floodnet/data/mixed_dataset/SAR/"
+
+    # 执行处理
+    # backup=True 会备份原文件（推荐）
+    # backup=False 直接覆盖（不推荐）
+    keep_last_two_bands(folder_path, backup=False, backup_suffix='_8band_backup')

command

@@ -0,0 +1,148 @@
+git clone https://VIncentmuyi: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@github.com/VIncentmuyi/Floodnet.git
+
+/解决python不搜索本文件夹包的问题
+ModuleNotFoundError: No module named 'mmseg'
+export PYTHONPATH=.:$PYTHONPATH
+
+ps -ef | grep python
+pkill -9 python
+git fetch origin
+git reset --hard origin/main
+git clean -fd  # 删除未跟踪的文件
+
+python tools/train.py ./configs/deeplabv3plus/Deeplabv3+UAVflood.py --work-dir work_dirs/SAR/Deeplabv3+
+python tools/test_full_metrics.py ./configs/deeplabv3plus/Deeplabv3+UAVflood.py work_dirs/SAR/Deeplabv3+/best_mIoU_epoch_100.pth  --work-dir ./Result/SAR/Deeplabv3+ --show-dir ./Result/SAR/Deeplabv3+/vis --cfg-options visualizer.alpha=1.0
+python tools/analysis_tools/benchmark.py \
+    ./configs/deeplabv3plus/Deeplabv3+UAVflood.py \
+    work_dirs/UAVflood/Deeplabv3+/best_val_mIoU_iter_20000.pth \
+    --repeat-times 3
+python tools/analysis_tools/get_flops.py \
+    ./configs/deeplabv3plus/Deeplabv3+UAVflood.py \
+    --shape 8 256 256
+
+python tools/train.py ./configs/segformer/segformer_mit-b0_8xb1-160k_UAVflood-256x256.py --work-dir work_dirs/SAR/segformer
+python tools/test_full_metrics.py ./configs/segformer/segformer_mit-b0_8xb1-160k_UAVflood-256x256.py  work_dirs/SAR/segformer/best_mIoU_epoch_100.pth --work-dir ./Result/SAR/segformer/ --show-dir ./Result/SAR/segformer/vis --cfg-options visualizer.alpha=1.0
+python tools/analysis_tools/benchmark.py \
+    ./configs/segformer/segformer_mit-b0_8xb1-160k_UAVflood-256x256.py\
+    work_dirs/UAVflood/segformer/best_val_mIoU_iter_40000.pth \
+    --repeat-times 3
+python tools/analysis_tools/get_flops.py \
+    ./configs/segformer/segformer_mit-b0_8xb1-160k_UAVflood-256x256.py \
+    --shape 5 256 256
+
+python tools/train.py ./configs/unet/Unet-Uavflood.py --work-dir work_dirs/SARflood/unet
+python tools/test_full_metrics.py ./configs/unet/Unet-Uavflood.py  work_dirs/SAR/unet/best_mIoU_epoch_90.pth --work-dir ./Result/SAR/unet/  --show-dir ./Result/SAR/unet/vis --cfg-options visualizer.alpha=1.0
+python tools/analysis_tools/benchmark.py \
+    ./configs/unet/Unet-Uavflood.py \
+    work_dirs/UAVflood/unet/best_val_mIoU_iter_40000.pth \
+    --repeat-times 3
+python tools/analysis_tools/get_flops.py \
+    ./configs/unet/Unet-Uavflood.py\
+    --shape 8 256 256
+
+python tools/train.py ./configs/mae/mae-base-Uavflood.py --work-dir work_dirs/UAVflood/mae
+python tools/test_full_metrics.py ./configs/mae/mae-base-Uavflood.py   work_dirs/UAVflood/mae/best_val_mIoU_iter_20000.pth --work-dir ./Result/UAV/mae/ --show-dir ./Result/UAV/mae/vis --cfg-options visualizer.alpha=1.0
+python tools/analysis_tools/benchmark.py \
+    ./configs/mae/mae-base-Uavflood.py\
+    work_dirs/UAVflood/mae/best_val_mIoU_iter_28000.pth \
+    --repeat-times 3
+python tools/analysis_tools/get_flops.py \
+    ./configs/mae/mae-base-Uavflood.py\
+    --shape 5 256 256
+
+python tools/train.py ./configs/vit/vit-Uavflood.py --work-dir work_dirs/SAR/vit
+python tools/test_full_metrics.py ./configs/vit/vit-Uavflood.py  work_dirs/SAR/vit/best_mIoU_epoch_100.pth --work-dir ./Result/SAR/vit/  --show-dir ./Result/SAR/vit/vis --cfg-options visualizer.alpha=1.0
+python tools/analysis_tools/benchmark.py \
+    ./configs/vit/vit-Uavflood.py\
+    work_dirs/UAVflood/vit/best_val_mIoU_iter_36000.pth \
+    --repeat-times 3
+python tools/analysis_tools/get_flops.py \
+    ./configs/vit/vit-Uavflood.py\
+    --shape 3 256 256
+
+python tools/train.py ./configs/beit/beit-Uavflood.py --work-dir work_dirs/SARflood/beit
+python tools/test_full_metrics.py ./configs/beit/beit-Uavflood.py  work_dirs/UAVflood/beit/best_val_mIoU_iter_16000.pth --work-dir ./Result/UAV/beit/  --show-dir ./Result/UAV/beit/vis --cfg-options visualizer.alpha=1.0
+python tools/analysis_tools/benchmark.py \
+    ./configs/beit/beit-Uavflood.py\
+    work_dirs/UAVflood/beit/best_val_mIoU_iter_40000.pth\
+    --repeat-times 3
+python tools/analysis_tools/get_flops.py \
+    ./configs/beit/beit-Uavflood.py\
+    --shape 3 256 256
+
+python tools/train.py ./configs/convnext/convnext-base-uavflood.py --work-dir work_dirs/SAR/convnext
+python tools/test_full_metrics.py ./configs/convnext/convnext-base-uavflood.py  work_dirs/SAR/convnext/best_mIoU_epoch_100.pth --work-dir ./Result/SAR/convnext/  --show-dir ./Result/SAR/convnext/vis --cfg-options visualizer.alpha=1.0
+python tools/analysis_tools/benchmark.py \
+    ./configs/convnext/convnext-base-uavflood.py\
+    work_dirs/GFflood/convnext/best_val_mIoU_iter_20000.pth \
+    --repeat-times 3
+python tools/analysis_tools/get_flops.py \
+    ./configs/convnext/convnext-base-uavflood.py\
+    --shape 5 256 256
+
+python tools/train.py ./configs/swin/Swin-uavflood-256x256.py --work-dir work_dirs/SAR/Swin --cfg-options seed=42
+python tools/test_full_metrics.py ./configs/swin/Swin-uavflood-256x256.py  work_dirs/GFflood/Swin/best_val_mIoU_iter_16000.pth --work-dir ./Result/GF/swin/  --show-dir ./Result/GF/swin/vis --cfg-options visualizer.alpha=1.0
+python tools/analysis_tools/benchmark.py \
+    ./configs/swin/Swin-uavflood-256x256.py\
+    work_dirs/GFflood/Swin/best_val_mIoU_iter_16000.pth\
+    --repeat-times 3
+python tools/analysis_tools/get_flops.py \
+    ./configs/swin/Swin-uavflood-256x256.py\
+    --shape 5 256 256
+
+python tools/train.py ./configs/floodnet/multimodal_floodnet_sar_boost_swinbase_moe_config.py --work-dir work_dirs/floodnet/SwinmoeB/655 --cfg-options seed=42
+python tools/test_full_metrics.py ./configs/floodnet/multimodal_floodnet_sar_boost_swin_moe_config.py  work_dirs/floodnet/SwinmoeB/best_mIoU_epoch_100.pth --work-dir ./Result/Floodnet/SAR/  --show-dir ./Result/Floodnet/SAR/vis --cfg-options visualizer.alpha=1.0
+
+python tools/test_full_metrics.py \
+  configs/floodnet/multimodal_floodnet_sar_boost_swinbase_moe_config.py \
+  work_dirs/floodnet/SwinmoeB/655/best_mIoU_epoch_100.pth \
+  --cfg-options test_dataloader.dataset.filter_modality=sar \
+  --work-dir ./Result/Floodnet/SAR/  \
+  --show-dir ./Result/Floodnet/SAR/vis --cfg-options visualizer.alpha=1.0
+
+
+python tools/train.py \
+  configs/floodnet/multimodal_floodnet_sar_only_swinbase_moe_config.py \
+  --work-dir work_dirs/floodnet/SwinmoeB_sar_only \
+  --cfg-options seed=42
+
+python tools/test_full_metrics.py \
+    configs/floodnet/multimodal_floodnet_sar_only_swinbase_moe_config.py \
+    work_dirs/floodnet/SwinmoeB_sar_only/best_mIoU_epoch_100.pth \
+
+
+ python tools/train.py configs/floodnet/continue_train_150ep.py --work-dir work_dirs/floodnet/SwinmoeB/655 --resume --cfg-options load_from="work_dirs/floodnet/SwinmoeB/655/best_mIoU_epoch_100.pth"
+
+ python tools/analysis_tools/visualize_expert_routing.py \
+    configs/floodnet/multimodal_floodnet_sar_boost_swinbase_moe_config.py \
+    work_dirs/floodnet/SwinmoeB/655/best_mIoU_epoch_100.pth \
+    --output-dir work_dirs/figures/expert_routing \
+    --num-samples 50
+
+python tools/train.py configs/floodnet/finetune_single_modal.py \
+    --work-dir work_dirs/generalization/LY-train-station/ \
+    --cfg-options \
+        train_dataloader.dataset.data_root="data/LY-train-station/" \
+        val_dataloader.dataset.data_root="data/LY-train-station/" \
+        test_dataloader.dataset.data_root="data/LY-train-station/"
+
+python tools/test.py \
+    configs/floodnet/finetune_single_modal.py \
+    work_dirs/generalization/LY-train-station/best_mIoU_epoch_50.pth \
+    --work-dir work_dirs/generalization/LY-train-station/test_results \
+    --cfg-options \
+        test_dataloader.dataset.data_root="data/LY-train-station/" \
+        "test_evaluator.iou_metrics=['mIoU','mDice','mFscore']" \
+    --show-dir work_dirs/generalization/LY-train-station/test_results/vis \
+    --out work_dirs/generalization/LY-train-station/test_results/predictions
+
+python tools/predict_large_tif.py \
+    configs/floodnet/finetune_single_modal.py \
+    work_dirs/generalization/LY-train-station/best_mIoU_epoch_50.pth \
+    --input data/luoyuan/result.tif \
+    --output data/luoyuan/prediction.tif \
+    --tile-size 512 \
+    --overlap 64 \
+    --modal rgb \
+    --bands 0 1 2 \
+    --batch-size 16