散称训练数据前置处理

configs/scatter.yml

@@ -18,9 +18,9 @@ models:
 
 # 训练参数
 training:
-  epochs: 600                         # 总训练轮次
+  epochs: 800                         # 总训练轮次
   batch_size: 64                     # 批次大小
-  lr: 0.0004                           # 初始学习率
+  lr: 0.01                           # 初始学习率
   optimizer: "sgd"                    # 优化器类型
   metric: 'arcface'                   # 损失函数类型（可选：arcface/cosface/sphereface/softmax）
   loss: "cross_entropy"               # 损失函数类型（可选：cross_entropy/cross_entropy_smooth/center_loss/center_loss_smooth/arcface/cosface/sphereface/softmax）
@@ -29,8 +29,8 @@ training:
   weight_decay: 0.0005                  # 权重衰减
   scheduler: "step"       # 学习率调度器（可选：cosine_annealing/step/none）
   num_workers: 32                     # 数据加载线程数
-  checkpoints: "./checkpoints/resnet18_scatter_6.26/" # 模型保存目录
+  checkpoints: "./checkpoints/resnet18_scatter_7.4/" # 模型保存目录
-  restore: True
+  restore: false
   restore_model: "checkpoints/resnet18_scatter_6.25/best.pth" # 模型恢复路径
 
 
@@ -46,8 +46,8 @@ data:
   train_batch_size: 128               # 训练批次大小
   val_batch_size: 100                 # 验证批次大小
   num_workers: 32                      # 数据加载线程数
-  data_train_dir: "../data_center/scatter/v2/train"   # 训练数据集根目录
+  data_train_dir: "../data_center/scatter/v4/train"   # 训练数据集根目录
-  data_val_dir: "../data_center/scatter/v2/val"   # 验证数据集根目录
+  data_val_dir: "../data_center/scatter/v4/val"   # 验证数据集根目录
 
 transform:
   img_size: 224                     # 图像尺寸
@@ -59,7 +59,7 @@ transform:
 
 # 日志与监控
 logging:
-  logging_dir: "./log/2025.6.25-scatter.txt"                  # 日志保存目录
+  logging_dir: "./log/2025.7.4-scatter.txt"                  # 日志保存目录
   tensorboard: true                   # 是否启用TensorBoard
   checkpoint_interval: 30             # 检查点保存间隔（epoch）
 

configs/scatter_data.yml

@@ -0,0 +1,19 @@
+# configs/scatter_data.yml
+# 专为散称前处理的配置文件
+
+# 数据配置
+data:
+  dataset: "imagenet"                 # 数据集名称（示例用，可替换为实际数据集）
+  source_dir: "../../data_center/scatter/v5/source"                      # 原始数据
+  train_dir: "../../data_center/scatter/v5/train"   # 训练数据集根目录
+  val_dir: "../../data_center/scatter/v5/val"   # 验证数据集根目录
+  extra_dir: "../../data_center/scatter/v5/extra"   # 验证数据集根目录
+  split_ratio: 0.9
+  max_files: 10                     # 数据集小于该阈值则归纳至extra
+
+
+# 日志与监控
+logging:
+  logging_dir: "./log/2025.7.4-scatter.txt"                  # 日志保存目录
+  log_level: "info"                   # 日志级别（debug/info/warning/error）
+

data_preprocessing/create_extra.py

@@ -0,0 +1,92 @@
+import os
+import shutil
+from pathlib import Path
+
+def count_files(directory):
+    """统计目录中的文件数量"""
+    try:
+        return len([f for f in os.listdir(directory)
+                   if os.path.isfile(os.path.join(directory, f))])
+    except Exception as e:
+        print(f"无法统计目录 {directory}: {e}")
+        return 0
+
+def clear_empty_dirs(path):
+    """
+    删除空目录
+    :param path: 目录路径
+    """
+    for root, dirs, files in os.walk(path, topdown=False):
+        for dir_name in dirs:
+            dir_path = os.path.join(root, dir_name)
+            try:
+                if not os.listdir(dir_path):
+                    os.rmdir(dir_path)
+                    print(f"Deleted empty directory: {dir_path}")
+            except Exception as e:
+                print(f"Error: {e.strerror}")
+
+def megre_subdirs(pth):
+    for roots, dir_names, files in os.walk(pth):
+        print(f"image  {dir_names}")
+        for image in dir_names:
+            inner_dir_path = os.path.join(pth, image)
+            for inner_roots, inner_dirs, inner_files in os.walk(inner_dir_path):
+                for inner_dir in inner_dirs:
+                    src_dir = os.path.join(inner_roots, inner_dir)
+                    dest_dir = os.path.join(pth, inner_dir)
+                    # shutil.copytree(src_dir, dest_dir)
+                    shutil.move(src_dir, dest_dir)
+                    print(f"Copied {inner_dir} to {pth}")
+    clear_empty_dirs(pth)
+
+
+def split_subdirs(source_dir, target_dir, max_files=10):
+    """
+    复制文件数≤max_files的子目录到目标目录
+    :param source_dir: 源目录路径
+    :param target_dir: 目标目录路径
+    :param max_files: 最大文件数阈值
+    """
+    megre_subdirs(source_dir)  # 合并子目录,删除上级目录
+    # 创建目标目录
+    Path(target_dir).mkdir(parents=True, exist_ok=True)
+
+    print(f"开始处理目录: {source_dir}")
+    print(f"目标目录: {target_dir}")
+    print(f"筛选条件: 文件数 ≤ {max_files}\n")
+
+    # 遍历源目录
+    for subdir in os.listdir(source_dir):
+        subdir_path = os.path.join(source_dir, subdir)
+
+        if not os.path.isdir(subdir_path):
+            continue
+
+        try:
+            file_count = count_files(subdir_path)
+
+            if file_count <= max_files:
+                print(f"复制 {subdir} (包含 {file_count} 个文件)")
+                dest_path = os.path.join(target_dir, subdir)
+
+                # 如果目标目录已存在则跳过
+                if os.path.exists(dest_path):
+                    print(f"目录已存在，跳过: {dest_path}")
+                    continue
+
+                # shutil.copytree(subdir_path, dest_path)
+                shutil.move(subdir_path, dest_path)
+
+        except Exception as e:
+            print(f"处理目录 {subdir} 时出错: {e}")
+
+    print("\n处理完成")
+
+if __name__ == "__main__":
+    # 配置路径
+    SOURCE_DIR = r"C:\Users\123\Desktop\test1\scatter_sub_class"
+    TARGET_DIR = "scatter_mini"
+
+    # 执行复制操作
+    split_subdirs(SOURCE_DIR, TARGET_DIR)

data_preprocessing/data_split.py

@@ -0,0 +1,73 @@
+import os
+import shutil
+import random
+from pathlib import Path
+
+def is_image_file(filename):
+    """检查文件是否为图像文件"""
+    image_extensions = ('.jpg', '.jpeg', '.png', '.bmp', '.gif', '.tiff')
+    return filename.lower().endswith(image_extensions)
+
+def split_directory(src_dir, train_dir, val_dir, split_ratio=0.9):
+    """
+    分割目录中的图像文件到train和val目录
+    :param src_dir: 源目录路径
+    :param train_dir: 训练集目录路径
+    :param val_dir: 验证集目录路径
+    :param split_ratio: 训练集比例(默认0.9)
+    """
+    # 创建目标目录
+    Path(train_dir).mkdir(parents=True, exist_ok=True)
+    Path(val_dir).mkdir(parents=True, exist_ok=True)
+
+    # 遍历源目录
+    for root, dirs, files in os.walk(src_dir):
+        # 获取相对路径(相对于src_dir)
+        rel_path = os.path.relpath(root, src_dir)
+
+        # 跳过当前目录(.)
+        if rel_path == '.':
+            continue
+
+        # 创建对应的目标子目录
+        train_subdir = os.path.join(train_dir, rel_path)
+        val_subdir = os.path.join(val_dir, rel_path)
+        os.makedirs(train_subdir, exist_ok=True)
+        os.makedirs(val_subdir, exist_ok=True)
+
+        # 筛选图像文件
+        image_files = [f for f in files if is_image_file(f)]
+        if not image_files:
+            continue
+
+        # 随机打乱文件列表
+        random.shuffle(image_files)
+
+        # 计算分割点
+        split_point = int(len(image_files) * split_ratio)
+
+        # 复制文件到训练集
+        for file in image_files[:split_point]:
+            src = os.path.join(root, file)
+            dst = os.path.join(train_subdir, file)
+            # shutil.copy2(src, dst)
+            shutil.move(src, dst)
+
+        # 复制文件到验证集
+        for file in image_files[split_point:]:
+            src = os.path.join(root, file)
+            dst = os.path.join(val_subdir, file)
+            # shutil.copy2(src, dst)
+            shutil.move(src, dst)
+
+        print(f"处理完成: {rel_path} (共 {len(image_files)} 个图像, 训练集: {split_point}, 验证集: {len(image_files)-split_point})")
+
+if __name__ == "__main__":
+    # 设置目录路径
+    SOURCE_DIR = "scatter_add"
+    TRAIN_DIR = "scatter_data/train"
+    VAL_DIR = "scatter_data/val"
+
+    print("开始分割数据集...")
+    split_directory(SOURCE_DIR, TRAIN_DIR, VAL_DIR)
+    print("数据集分割完成")

data_preprocessing/extend.py

@@ -0,0 +1,131 @@
+import os
+import random
+import shutil
+from PIL import Image, ImageEnhance
+
+
+class ImageExtendProcessor:
+    def is_image_file(self, filename):
+        """检查文件是否为图像文件"""
+        image_extensions = ('.jpg', '.jpeg', '.png', '.bmp', '.gif', '.tiff')
+        return filename.lower().endswith(image_extensions)
+
+    def random_cute_image(self, image_path, output_path, ratio=0.8):
+        """
+        对图像进行随机裁剪
+        :param image_path: 输入图像路径
+        :param output_path: 输出图像路径
+        :param ratio: 裁剪比例，决定裁剪区域的大小，默认为0.8
+        """
+        try:
+            with Image.open(image_path) as img:
+                # 获取图像尺寸
+                width, height = img.size
+
+                # 计算裁剪后的尺寸
+                new_width = int(width * ratio)
+                new_height = int(height * ratio)
+
+                # 随机生成裁剪起始点
+                left = random.randint(0, width - new_width)
+                top = random.randint(0, height - new_height)
+                right = left + new_width
+                bottom = top + new_height
+
+                # 执行裁剪
+                cropped_img = img.crop((left, top, right, bottom))
+
+                # 保存裁剪后的图像
+                cropped_img.save(output_path)
+                return True
+        except Exception as e:
+            print(f"处理图像 {image_path} 时出错: {e}")
+            return False
+
+    def random_brightness(self, image_path, output_path, brightness_factor=None):
+        """
+        对图像进行随机亮度调整
+        :param image_path: 输入图像路径
+        :param output_path: 输出图像路径
+        :param brightness_factor: 亮度调整因子，默认为随机值
+        """
+        try:
+            with Image.open(image_path) as img:
+                # 创建一个ImageEnhance.Brightness对象
+                enhancer = ImageEnhance.Brightness(img)
+
+                # 如果没有指定亮度因子，则随机生成
+                if brightness_factor is None:
+                    brightness_factor = random.uniform(0.5, 1.5)
+
+                # 应用亮度调整
+                brightened_img = enhancer.enhance(brightness_factor)
+
+                # 保存调整后的图像
+                brightened_img.save(output_path)
+                return True
+        except Exception as e:
+            print(f"处理图像 {image_path} 时出错: {e}")
+            return False
+
+    def rotate_image(self, image_path, output_path, degrees):
+        """旋转图像并保存到指定路径"""
+        try:
+            with Image.open(image_path) as img:
+                # 旋转图像并自动调整画布大小
+                rotated = img.rotate(degrees, expand=True)
+                rotated.save(output_path)
+                return True
+        except Exception as e:
+            print(f"处理图像 {image_path} 时出错: {e}")
+            return False
+
+    def process_image_directory(self, src_dir, dst_dir, same_directory, **kwargs):
+        """
+        处理单个目录中的图像文件
+        :param src_dir: 源目录路径
+        :param dst_dir: 目标目录路径
+        """
+        if not os.path.exists(dst_dir):
+            os.makedirs(dst_dir)
+        # 获取目录中所有图像文件
+        image_files = [f for f in os.listdir(src_dir)
+                       if self.is_image_file(f) and os.path.isfile(os.path.join(src_dir, f))]
+
+        # 处理每个图像文件
+        for img_file in image_files:
+            src_path = os.path.join(src_dir, img_file)
+            base_name, ext = os.path.splitext(img_file)
+            if not same_directory:
+                # 复制原始文件  （另存文件夹时启用）
+                shutil.copy2(src_path, os.path.join(dst_dir, img_file))
+
+            # 生成并保存旋转后的图像
+            for angle in [90, 180, 270]:
+                dst_path = os.path.join(dst_dir, f"{base_name}_rotated_{angle}{ext}")
+                self.rotate_image(src_path, dst_path, angle)
+            for ratio in [0.8, 0.85, 0.9]:
+                dst_path = os.path.join(dst_dir, f"{base_name}_cute_{ratio}{ext}")
+                self.random_cute_image(src_path, dst_path, ratio)
+            for brightness_factor in [0.8, 0.9, 1.0]:
+                dst_path = os.path.join(dst_dir, f"{base_name}_brightness_{brightness_factor}{ext}")
+                self.random_brightness(src_path, dst_path, brightness_factor)
+
+    def image_extend(self, src_dir, dst_dir, same_directory=False, **kwargs):
+        if same_directory:
+            n_dst_dir = src_dir
+            print(f"处理目录 {src_dir} 中的图像文件  保存至同一目录下")
+        else:
+            n_dst_dir = dst_dir
+            print(f"处理目录 {src_dir} 中的图像文件  保存至不同目录下")
+        for src_subdir in os.listdir(src_dir):
+            src_subdir_path = os.path.join(src_dir, src_subdir)
+            dst_subdir_path = os.path.join(n_dst_dir, src_subdir)
+            self.process_image_directory(src_subdir_path, dst_subdir_path, same_directory)
+
+
+if __name__ == "__main__":
+    src_dir = "./scatter_mini"
+    dst_dir = "./scatter_add"
+    image_ex = ImageExtendProcessor()
+    image_ex.image_extend(src_dir, dst_dir, same_directory=False)

data_preprocessing/scatter_data_preprocessing.py

@@ -0,0 +1,20 @@
+from create_extra import split_subdirs
+from data_split import split_directory
+from extend import ImageExtendProcessor
+import yaml
+
+
+def data_preprocessing(conf):
+    split_subdirs(conf['data']['source_dir'], conf['data']['data_extra_dir'], conf['data']['max_files'])
+    split_directory(conf['data']['source_dir'], conf['data']['train_dir'],
+                    conf['data']['val_dir'], conf['data']['split_ratio'])
+    image_ex = ImageExtendProcessor()
+    image_ex.image_extend(conf['data']['extra_dir'],
+                          '',
+                          same_directory=True)
+
+
+if __name__ == '__main__':
+    with open('../configs/scatter_data.yml', 'r') as f:
+        conf = yaml.load(f, Loader=yaml.FullLoader)
+    data_preprocessing(conf)