update code for kd

.gitignore

@@ -1 +1,3 @@
 data
+*.pth
+*.pyc

FED.py

@@ -6,45 +6,28 @@ from torch.utils.data import DataLoader, Subset
 import numpy as np
 import copy
 
+from tqdm import tqdm
+
+from model.repvit import repvit_m1_1
+from model.mobilenetv3 import MobileNetV3
+
 # 配置参数
-NUM_CLIENTS = 10
+NUM_CLIENTS = 4
 NUM_ROUNDS = 3
-CLIENT_EPOCHS = 2
+CLIENT_EPOCHS = 5
 BATCH_SIZE = 32
 TEMP = 2.0  # 蒸馏温度
 
 # 设备配置
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# 定义中心大模型
-class ServerModel(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.fc1 = nn.Linear(784, 512)
-        self.fc2 = nn.Linear(512, 256)
-        self.fc3 = nn.Linear(256, 10)
-
-    def forward(self, x):
-        x = x.view(-1, 784)
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        return self.fc3(x)
-
-# 定义端侧小模型
-class ClientModel(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.fc1 = nn.Linear(784, 64)
-        self.fc2 = nn.Linear(64, 10)
-
-    def forward(self, x):
-        x = x.view(-1, 784)
-        x = F.relu(self.fc1(x))
-        return self.fc2(x)
-
 # 数据准备
 def prepare_data(num_clients):
-    transform = transforms.Compose([transforms.ToTensor()])
+    transform = transforms.Compose([
+            transforms.Resize((224, 224)),  # 将图像调整为 224x224
+            transforms.Grayscale(num_output_channels=3),
+            transforms.ToTensor()
+        ])
     train_set = datasets.MNIST("./data", train=True, download=True, transform=transform)
     
     # 非IID数据划分（每个客户端2个类别）
@@ -67,30 +50,80 @@ def client_train(client_model, server_model, dataset):
     optimizer = torch.optim.SGD(client_model.parameters(), lr=0.1)
     loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
     
-    for _ in range(CLIENT_EPOCHS):
+    # 训练进度条
-        for data, target in loader:
+    progress_bar = tqdm(total=CLIENT_EPOCHS*len(loader), 
+                      desc="Client Training", 
+                      unit="batch")
+    
+    for epoch in range(CLIENT_EPOCHS):
+        epoch_loss = 0.0
+        task_loss = 0.0
+        distill_loss = 0.0
+        correct = 0
+        total = 0
+        
+        for batch_idx, (data, target) in enumerate(loader):
             data, target = data.to(device), target.to(device)
             optimizer.zero_grad()
             
-            # 获取小模型输出
+            # 前向传播
             client_output = client_model(data)
             
-            # 获取大模型输出（知识蒸馏）
+            # 获取教师模型输出
             with torch.no_grad():
                 server_output = server_model(data)
             
-            # 计算联合损失
+            # 计算损失
             loss_task = F.cross_entropy(client_output, target)
             loss_distill = F.kl_div(
                 F.log_softmax(client_output/TEMP, dim=1),
                 F.softmax(server_output/TEMP, dim=1),
                 reduction="batchmean"
             ) * (TEMP**2)
-            
             total_loss = loss_task + loss_distill
+            
+            # 反向传播
             total_loss.backward()
             optimizer.step()
             
+            # 统计指标
+            epoch_loss += total_loss.item()
+            task_loss += loss_task.item()
+            distill_loss += loss_distill.item()
+            _, predicted = torch.max(client_output.data, 1)
+            correct += (predicted == target).sum().item()
+            total += target.size(0)
+            
+            # 实时更新进度条
+            progress_bar.set_postfix({
+                "Epoch": f"{epoch+1}/{CLIENT_EPOCHS}",
+                "Batch": f"{batch_idx+1}/{len(loader)}",
+                "Loss": f"{total_loss.item():.4f}",
+                "Acc": f"{100*correct/total:.2f}%\n",
+            })
+            progress_bar.update(1)
+                        
+            # 每10个batch打印详细信息
+            if (batch_idx + 1) % 10 == 0:
+                progress_bar.write(f"\nEpoch {epoch+1} | Batch {batch_idx+1}")
+                progress_bar.write(f"Task Loss: {loss_task:.4f}")
+                progress_bar.write(f"Distill Loss: {loss_distill:.4f}")
+                progress_bar.write(f"Total Loss: {total_loss:.4f}")
+                progress_bar.write(f"Batch Accuracy: {100*correct/total:.2f}%\n")
+        # 每个epoch结束打印汇总信息
+        avg_loss = epoch_loss / len(loader)
+        avg_task = task_loss / len(loader)
+        avg_distill = distill_loss / len(loader)
+        epoch_acc = 100 * correct / total
+        print(f"\n{'='*40}")
+        print(f"Epoch {epoch+1} Summary:")
+        print(f"Average Loss: {avg_loss:.4f}")
+        print(f"Task Loss: {avg_task:.4f}")
+        print(f"Distill Loss: {avg_distill:.4f}") 
+        print(f"Training Accuracy: {epoch_acc:.2f}%")
+        print(f"{'='*40}\n")
+    
+    progress_bar.close()
     return client_model.state_dict()
 
 # 模型参数聚合（FedAvg）
@@ -105,7 +138,10 @@ def server_update(server_model, client_models, public_loader):
     server_model.train()
     optimizer = torch.optim.Adam(server_model.parameters(), lr=0.001)
     
-    for data, _ in public_loader:
+    total_loss = 0.0
+    progress_bar = tqdm(public_loader, desc="Server Updating", unit="batch")
+    
+    for batch_idx, (data, _) in enumerate(progress_bar):
         data = data.to(device)
         optimizer.zero_grad()
         
@@ -122,9 +158,20 @@ def server_update(server_model, client_models, public_loader):
             reduction="batchmean"
         )
         
+        # 反向传播
         loss.backward()
         optimizer.step()
         
+        # 更新统计信息
+        total_loss += loss.item()
+        progress_bar.set_postfix({
+            "Avg Loss": f"{total_loss/(batch_idx+1):.4f}",
+            "Current Loss": f"{loss.item():.4f}"
+        })
+        
+    print(f"\nServer Update Complete | Average Loss: {total_loss/len(public_loader):.4f}\n")
+
+
 def test_model(model, test_loader):
     model.eval()
     correct = 0
@@ -139,36 +186,54 @@ def test_model(model, test_loader):
     accuracy = 100 * correct / total
     return accuracy
 
+
 # 主训练流程
 def main():
     # 初始化模型
-    global_server_model = ServerModel().to(device)
+    global_server_model = repvit_m1_1(num_classes=10).to(device)
-    client_models = [ClientModel().to(device) for _ in range(NUM_CLIENTS)]
+    client_models = [MobileNetV3(n_class=10).to(device) for _ in range(NUM_CLIENTS)]
+
+    round_progress = tqdm(total=NUM_ROUNDS, desc="Federated Rounds", unit="round")
     
     # 准备数据
     client_datasets = prepare_data(NUM_CLIENTS)
     public_loader = DataLoader(
         datasets.MNIST("./data", train=False, download=True, 
-                      transform=transforms.ToTensor()),
+                      transform= transforms.Compose([
+                        transforms.Resize((224, 224)),  # 将图像调整为 224x224
+                        transforms.Grayscale(num_output_channels=3),
+                        transforms.ToTensor()           # 将图像转换为张量
+                    ])),
         batch_size=100, shuffle=True)
     
+    test_dataset = datasets.MNIST(
+        "./data", 
+        train=False,
+        transform= transforms.Compose([
+                        transforms.Resize((224, 224)),  # 将图像调整为 224x224
+                        transforms.Grayscale(num_output_channels=3),
+                        transforms.ToTensor()           # 将图像转换为张量
+                    ])
+    )
+    test_loader = DataLoader(test_dataset, batch_size=100, shuffle=False)
+    
     for round in range(NUM_ROUNDS):
+        print(f"\n{'#'*50}")
+        print(f"Federated Round {round+1}/{NUM_ROUNDS}")
+        print(f"{'#'*50}")
+        
         # 客户端选择
-        selected_clients = np.random.choice(NUM_CLIENTS, 5, replace=False)
+        selected_clients = np.random.choice(NUM_CLIENTS, 2, replace=False)
+        print(f"Selected Clients: {selected_clients}")
         
         # 客户端本地训练
         client_params = []
         for cid in selected_clients:
-            # 下载全局模型
+            print(f"\nTraining Client {cid}")
             local_model = copy.deepcopy(client_models[cid])
             local_model.load_state_dict(client_models[cid].state_dict())
             
-            # 本地训练
+            updated_params = client_train(local_model, global_server_model, client_datasets[cid])
-            updated_params = client_train(
-                local_model,
-                global_server_model,
-                client_datasets[cid]
-            )
             client_params.append(updated_params)
         
         # 模型聚合
@@ -177,8 +242,18 @@ def main():
             model.load_state_dict(global_client_params)
         
         # 服务器知识更新
+        print("\nServer Updating...")
         server_update(global_server_model, client_models, public_loader)
         
+        # 测试模型性能
+        server_acc = test_model(global_server_model, test_loader)
+        client_acc = test_model(client_models[0], test_loader)
+        print(f"\nRound {round+1} Performance:")
+        print(f"Global Model Accuracy: {server_acc:.2f}%")
+        print(f"Client Model Accuracy: {client_acc:.2f}%")
+        
+        round_progress.update(1)
+        
         print(f"Round {round+1} completed")
     
     print("Training completed!")
@@ -189,21 +264,15 @@ def main():
     print("Models saved successfully.")
 
     # 创建测试数据加载器
-    test_dataset = datasets.MNIST(
-        "./data", 
-        train=False,
-        transform=transforms.ToTensor()
-    )
-    test_loader = DataLoader(test_dataset, batch_size=100, shuffle=False)
 
     # 测试服务器模型
-    server_model = ServerModel().to(device)
+    server_model = repvit_m1_1(num_classes=10).to(device)
     server_model.load_state_dict(torch.load("server_model.pth"))
     server_acc = test_model(server_model, test_loader)
     print(f"Server Model Test Accuracy: {server_acc:.2f}%")
 
     # 测试客户端模型
-    client_model = ClientModel().to(device)
+    client_model = MobileNetV3(n_class=10).to(device)
     client_model.load_state_dict(torch.load("client_model.pth"))
     client_acc = test_model(client_model, test_loader)
     print(f"Client Model Test Accuracy: {client_acc:.2f}%")

dataset/recover.py

@@ -0,0 +1,157 @@
+import random
+import cv2
+import numpy as np
+import os
+
+# 假设我们有一个函数 `remove_background` 使用某种方法去背景，返回前景掩码和前景图像
+def remove_background(image_path):
+    # 加载图像
+    source_image = cv2.imread(image_path)
+    # 这里可以使用一个预训练的模型去背景，比如 U2-Net。为了简化，假设我们得到一个二值掩码
+    # 掩码生成逻辑可以替换为实际的模型推理
+     # 转换为灰度图像
+    GRAY = cv2.cvtColor(source_image, cv2.COLOR_BGR2GRAY)
+
+    # 二值化处理
+    _, mask_threshold = cv2.threshold(GRAY, 0, 1, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
+
+    # 定义结构元素
+    element = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
+    element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
+
+    # 膨胀和腐蚀操作
+    mask_dilate = cv2.dilate(mask_threshold, element)
+    mask_erode = cv2.erode(mask_dilate, element1)
+
+    # 计算非零像素数量
+    count2 = cv2.countNonZero(mask_erode)
+
+    # 查找轮廓
+    contours, hierarchy = cv2.findContours(mask_erode, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)
+
+    # 过滤轮廓
+    contours = [c for c in contours if cv2.contourArea(c) >= count2 * 0.3]
+
+    # 绘制轮廓
+    mask = np.zeros_like(mask_erode)
+    cv2.drawContours(mask, contours, -1, 1, -1)
+
+    # 将掩码转换为3通道
+    mask_cvtColor = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
+
+    # 应用掩码
+    source_image_multiply = cv2.multiply(source_image, mask_cvtColor)
+
+    # 转换为HSV颜色空间
+    imgHSV = cv2.cvtColor(source_image_multiply, cv2.COLOR_BGR2HSV)
+
+    # 定义HSV范围
+    scalarL = np.array([0, 46, 46])
+    scalarH = np.array([45, 255, 255])
+
+    # 根据HSV范围生成掩码
+    mask_inRange = cv2.inRange(imgHSV, scalarL, scalarH)
+
+    # 二值化处理
+    _, mask_tthreshold = cv2.threshold(mask_inRange, 0, 1, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
+
+    # 中值滤波
+    mask_medianBlur = cv2.medianBlur(mask_tthreshold, 7)
+
+    # 将掩码转换为3通道
+    mask_scvtColor = cv2.cvtColor(mask_medianBlur, cv2.COLOR_GRAY2BGR)
+
+    # 应用掩码
+    source_image = cv2.multiply(source_image, mask_scvtColor)
+
+    return source_image
+
+def synthesize_background(foreground, background):
+    
+    # 创建前景掩膜（非黑色区域）
+    gray = cv2.cvtColor(foreground, cv2.COLOR_BGR2GRAY)
+    _, mask = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY)  # 阈值设为1以保留所有非纯黑像素
+    
+    mask = cv2.GaussianBlur(mask, (5,5), 0)  # 高斯模糊柔化边缘
+    _, mask = cv2.threshold(mask, 200, 255, cv2.THRESH_BINARY)  # 重新二值化
+    
+    # 精准形态学处理
+    kernel = np.ones((2,2), np.uint8)
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=1)  # 闭运算填充小孔
+    
+    # 反转掩膜用于获取背景区域
+    mask_inv = cv2.bitwise_not(mask)
+    
+    # 提取背景和前景的ROI区域
+    background_roi = cv2.bitwise_and(background, background, mask=mask_inv)
+    foreground_roi = cv2.bitwise_and(foreground, foreground, mask=mask)
+    
+    # 合成图像
+    result = cv2.add(foreground_roi, background_roi)
+    
+    return result
+
+def edge_fill2(img):
+    (height, width, p) = img.shape
+    H = 2384
+    W = 1560
+    top = bottom=int((W - height) / 2)
+    left= right= int((H - width) / 2)
+    img_result = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=0)
+    return img_result
+
+
+def process_images(input_folder, background_image_path, output_base):
+    """
+    递归处理所有子文件夹并保持目录结构
+    """
+    # 预处理背景路径（只需执行一次）
+    if os.path.isfile(background_image_path):
+        background_paths = [background_image_path]
+    else:
+        valid_ext = ['.jpg', '.jpeg', '.png', '.bmp', '.webp']
+        background_paths = [
+            os.path.join(background_image_path, f) 
+            for f in os.listdir(background_image_path)
+            if os.path.splitext(f)[1].lower() in valid_ext
+        ]
+    
+    # 递归遍历输入目录
+    for root, dirs, files in os.walk(input_folder):
+        # 计算相对路径
+        relative_path = os.path.relpath(root, input_folder)
+        
+        # 创建对应的输出目录
+        output_dir = os.path.join(output_base, relative_path)
+        os.makedirs(output_dir, exist_ok=True)
+
+        # 处理当前目录的文件
+        for filename in files:
+            input_path = os.path.join(root, filename)
+            output_path = os.path.join(output_dir, filename)
+            
+            # 跳过非图像文件
+            if not filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp')):
+                continue
+
+            try:
+                # 去背景处理
+                foreground = remove_background(input_path)
+                
+           
+                result = edge_fill2(foreground)
+                
+                # 保存结果
+                cv2.imwrite(output_path, result)
+                print(f"Processed: {input_path} -> {output_path}")
+                
+            except Exception as e:
+                print(f"Error processing {input_path}: {str(e)}")
+
+
+# 使用示例
+input_directory = 'L:/Tobacco/2023_JY/20230821/SOURCE'
+background_image_path = 'F:/dataset/02.TA_EC/rundata/BACKGROUND/ZY_B'
+output_directory = 'L:/Test'
+
+process_images(input_directory, background_image_path, output_directory)

dataset/split.py

@@ -0,0 +1,60 @@
+import os
+import shutil
+import random
+
+def create_dataset_splits(base_dir, output_dir, train_ratio=0.7, val_ratio=0.15, test_ratio=0.15):
+    # 确保比例总和为1
+    assert train_ratio + val_ratio + test_ratio == 1.0, "Ratios must sum to 1"
+
+    # 创建输出目录
+    os.makedirs(output_dir, exist_ok=True)
+    train_dir = os.path.join(output_dir, 'train')
+    val_dir = os.path.join(output_dir, 'val')
+    test_dir = os.path.join(output_dir, 'test')
+    
+    os.makedirs(train_dir, exist_ok=True)
+    os.makedirs(val_dir, exist_ok=True)
+    os.makedirs(test_dir, exist_ok=True)
+
+    categories = os.listdir(base_dir)
+
+    for category in categories:
+        category_path = os.path.join(base_dir, category)
+
+        if not os.path.isdir(category_path):
+            continue  # 跳过非目录文件
+
+        # 获取类别文件夹中的所有图像文件
+        images = [f for f in os.listdir(category_path) if os.path.isfile(os.path.join(category_path, f))]
+        
+        # 打乱图像文件顺序
+        random.shuffle(images)
+
+        # 计算分割点
+        total_images = len(images)
+        train_end = int(train_ratio * total_images)
+        val_end = train_end + int(val_ratio * total_images)
+
+        # 创建类别文件夹于各个输出目录中
+        os.makedirs(os.path.join(train_dir, category), exist_ok=True)
+        os.makedirs(os.path.join(val_dir, category), exist_ok=True)
+        os.makedirs(os.path.join(test_dir, category), exist_ok=True)
+
+        # 分配图像到训练集
+        for i in range(train_end):
+            shutil.copy(os.path.join(category_path, images[i]), os.path.join(train_dir, category))
+
+        # 分配图像到验证集
+        for i in range(train_end, val_end):
+            shutil.copy(os.path.join(category_path, images[i]), os.path.join(val_dir, category))
+
+        # 分配图像到测试集
+        for i in range(val_end, total_images):
+            shutil.copy(os.path.join(category_path, images[i]), os.path.join(test_dir, category))
+
+    print("Dataset successfully split into train, validation, and test sets.")
+
+# 使用示例
+base_directory = 'F:/dataset/02.TA_EC/EC27/JY_A'
+output_directory = 'F:/dataset/02.TA_EC/datasets/EC27'
+create_dataset_splits(base_directory, output_directory)

dataset/splitbc_compsition.py

@@ -0,0 +1,184 @@
+import random
+import cv2
+import numpy as np
+import os
+
+# 假设我们有一个函数 `remove_background` 使用某种方法去背景，返回前景掩码和前景图像
+def remove_background(image_path):
+    # 加载图像
+    source_image = cv2.imread(image_path)
+    # 这里可以使用一个预训练的模型去背景，比如 U2-Net。为了简化，假设我们得到一个二值掩码
+    # 掩码生成逻辑可以替换为实际的模型推理
+     # 转换为灰度图像
+    GRAY = cv2.cvtColor(source_image, cv2.COLOR_BGR2GRAY)
+
+    # 二值化处理
+    _, mask_threshold = cv2.threshold(GRAY, 0, 1, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
+
+    # 定义结构元素
+    element = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
+    element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
+
+    # 膨胀和腐蚀操作
+    mask_dilate = cv2.dilate(mask_threshold, element)
+    mask_erode = cv2.erode(mask_dilate, element1)
+
+    # 计算非零像素数量
+    count2 = cv2.countNonZero(mask_erode)
+
+    # 查找轮廓
+    contours, hierarchy = cv2.findContours(mask_erode, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)
+
+    # 过滤轮廓
+    contours = [c for c in contours if cv2.contourArea(c) >= count2 * 0.3]
+
+    # 绘制轮廓
+    mask = np.zeros_like(mask_erode)
+    cv2.drawContours(mask, contours, -1, 1, -1)
+
+    # 将掩码转换为3通道
+    mask_cvtColor = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
+
+    # 应用掩码
+    source_image_multiply = cv2.multiply(source_image, mask_cvtColor)
+
+    # 转换为HSV颜色空间
+    imgHSV = cv2.cvtColor(source_image_multiply, cv2.COLOR_BGR2HSV)
+
+    # 定义HSV范围
+    scalarL = np.array([0, 46, 46])
+    scalarH = np.array([45, 255, 255])
+
+    # 根据HSV范围生成掩码
+    mask_inRange = cv2.inRange(imgHSV, scalarL, scalarH)
+
+    # 二值化处理
+    _, mask_tthreshold = cv2.threshold(mask_inRange, 0, 1, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
+
+    # 中值滤波
+    mask_medianBlur = cv2.medianBlur(mask_tthreshold, 7)
+
+    # 将掩码转换为3通道
+    mask_scvtColor = cv2.cvtColor(mask_medianBlur, cv2.COLOR_GRAY2BGR)
+
+    # 应用掩码
+    source_image = cv2.multiply(source_image, mask_scvtColor)
+
+    return source_image
+
+def synthesize_background(foreground, background_image_path):
+   # 验证输入路径有效性
+    if not os.path.exists(background_image_path):
+        raise FileNotFoundError(f"指定的背景路径不存在: {background_image_path}")
+
+    # 获取所有可用背景图像路径
+    background_paths = []
+    if os.path.isfile(background_image_path):
+        # 如果是单个图像文件
+        background_paths = [background_image_path]
+    elif os.path.isdir(background_image_path):
+        # 如果是目录，搜索常见图像格式
+        valid_extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.webp']
+        for filename in os.listdir(background_image_path):
+            if os.path.splitext(filename)[1].lower() in valid_extensions:
+                background_paths.append(os.path.join(background_image_path, filename))
+        
+        # 验证找到的图像文件数量
+        if not background_paths:
+            raise ValueError(f"目录中未找到支持的图像文件: {background_image_path}")
+
+    # 随机选择背景图像
+    selected_bg_path = random.choice(background_paths)
+    
+    # 加载并验证背景图像
+    background = cv2.imread(selected_bg_path)
+    
+    # 调整背景大小与前景一致
+    background = cv2.resize(background, (foreground.shape[1], foreground.shape[0]))
+    
+    # 创建前景掩膜（非黑色区域）
+    gray = cv2.cvtColor(foreground, cv2.COLOR_BGR2GRAY)
+    _, mask = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY)  # 阈值设为1以保留所有非纯黑像素
+    
+    mask = cv2.GaussianBlur(mask, (5,5), 0)  # 高斯模糊柔化边缘
+    _, mask = cv2.threshold(mask, 200, 255, cv2.THRESH_BINARY)  # 重新二值化
+    
+    # 精准形态学处理
+    kernel = np.ones((2,2), np.uint8)
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=1)  # 闭运算填充小孔
+    
+    # 反转掩膜用于获取背景区域
+    mask_inv = cv2.bitwise_not(mask)
+    
+    # 提取背景和前景的ROI区域
+    background_roi = cv2.bitwise_and(background, background, mask=mask_inv)
+    foreground_roi = cv2.bitwise_and(foreground, foreground, mask=mask)
+    
+    # 合成图像
+    result = cv2.add(foreground_roi, background_roi)
+    
+    # 保存结果
+    return result
+
+def process_images(input_folder, background_image_path, output_base):
+    """
+    递归处理所有子文件夹并保持目录结构
+    """
+    # 预处理背景路径（只需执行一次）
+    if os.path.isfile(background_image_path):
+        background_paths = [background_image_path]
+    else:
+        valid_ext = ['.jpg', '.jpeg', '.png', '.bmp', '.webp']
+        background_paths = [
+            os.path.join(background_image_path, f) 
+            for f in os.listdir(background_image_path)
+            if os.path.splitext(f)[1].lower() in valid_ext
+        ]
+    
+    # 递归遍历输入目录
+    for root, dirs, files in os.walk(input_folder):
+        # 计算相对路径
+        relative_path = os.path.relpath(root, input_folder)
+        
+        # 创建对应的输出目录
+        output_dir = os.path.join(output_base, relative_path)
+        os.makedirs(output_dir, exist_ok=True)
+
+        # 处理当前目录的文件
+        for filename in files:
+            input_path = os.path.join(root, filename)
+            output_path = os.path.join(output_dir, filename)
+            
+            # 跳过非图像文件
+            if not filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp')):
+                continue
+
+            try:
+                # 去背景处理
+                foreground = remove_background(input_path)
+                
+                # 随机选择背景（每次处理都重新选择）
+                bg_path = random.choice(background_paths)
+                background = cv2.imread(bg_path)
+                
+                # 调整背景尺寸
+                h, w = foreground.shape[:2]
+                resized_bg = cv2.resize(background, (w, h))
+                
+                # 合成背景
+                result = synthesize_background(foreground, bg_path)
+                
+                # 保存结果
+                cv2.imwrite(output_path, result)
+                print(f"Processed: {input_path} -> {output_path}")
+                
+            except Exception as e:
+                print(f"Error processing {input_path}: {str(e)}")
+
+
+# 使用示例
+input_directory = 'F:/dataset/02.TA_EC/EC27/JY_A/'
+background_image_path = 'F:/dataset/02.TA_EC/rundata/BACKGROUND/ZY_B'
+output_directory = 'F:/dataset/02.TA_EC/rundata/test'
+
+process_images(input_directory, background_image_path, output_directory)

model/mobilenetv3.py

@@ -0,0 +1,248 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+__all__ = ['MobileNetV3', 'mobilenetv3']
+
+
+def conv_bn(inp, oup, stride, conv_layer=nn.Conv2d, norm_layer=nn.BatchNorm2d, nlin_layer=nn.ReLU):
+    return nn.Sequential(
+        conv_layer(inp, oup, 3, stride, 1, bias=False),
+        norm_layer(oup),
+        nlin_layer(inplace=True)
+    )
+
+
+def conv_1x1_bn(inp, oup, conv_layer=nn.Conv2d, norm_layer=nn.BatchNorm2d, nlin_layer=nn.ReLU):
+    return nn.Sequential(
+        conv_layer(inp, oup, 1, 1, 0, bias=False),
+        norm_layer(oup),
+        nlin_layer(inplace=True)
+    )
+
+
+class Hswish(nn.Module):
+    def __init__(self, inplace=True):
+        super(Hswish, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return x * F.relu6(x + 3., inplace=self.inplace) / 6.
+
+
+class Hsigmoid(nn.Module):
+    def __init__(self, inplace=True):
+        super(Hsigmoid, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return F.relu6(x + 3., inplace=self.inplace) / 6.
+
+
+class SEModule(nn.Module):
+    def __init__(self, channel, reduction=4):
+        super(SEModule, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction, bias=False),
+            nn.ReLU(inplace=True),
+            nn.Linear(channel // reduction, channel, bias=False),
+            Hsigmoid()
+            # nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y.expand_as(x)
+
+
+class Identity(nn.Module):
+    def __init__(self, channel):
+        super(Identity, self).__init__()
+
+    def forward(self, x):
+        return x
+
+
+def make_divisible(x, divisible_by=8):
+    import numpy as np
+    return int(np.ceil(x * 1. / divisible_by) * divisible_by)
+
+
+class MobileBottleneck(nn.Module):
+    def __init__(self, inp, oup, kernel, stride, exp, se=False, nl='RE'):
+        super(MobileBottleneck, self).__init__()
+        assert stride in [1, 2]
+        assert kernel in [3, 5]
+        padding = (kernel - 1) // 2
+        self.use_res_connect = stride == 1 and inp == oup
+
+        conv_layer = nn.Conv2d
+        norm_layer = nn.BatchNorm2d
+        if nl == 'RE':
+            nlin_layer = nn.ReLU # or ReLU6
+        elif nl == 'HS':
+            nlin_layer = Hswish
+        else:
+            raise NotImplementedError
+        if se:
+            SELayer = SEModule
+        else:
+            SELayer = Identity
+
+        self.conv = nn.Sequential(
+            # pw
+            conv_layer(inp, exp, 1, 1, 0, bias=False),
+            norm_layer(exp),
+            nlin_layer(inplace=True),
+            # dw
+            conv_layer(exp, exp, kernel, stride, padding, groups=exp, bias=False),
+            norm_layer(exp),
+            SELayer(exp),
+            nlin_layer(inplace=True),
+            # pw-linear
+            conv_layer(exp, oup, 1, 1, 0, bias=False),
+            norm_layer(oup),
+        )
+
+    def forward(self, x):
+        if self.use_res_connect:
+            return x + self.conv(x)
+        else:
+            return self.conv(x)
+
+
+class MobileNetV3(nn.Module):
+    def __init__(self, n_class=1000, input_size=224, dropout=0.8, mode='small', width_mult=1.0):
+        super(MobileNetV3, self).__init__()
+        input_channel = 16
+        last_channel = 1280
+        if mode == 'large':
+            # refer to Table 1 in paper
+            mobile_setting = [
+                # k, exp, c,  se,     nl,  s,
+                [3, 16,  16,  False, 'RE', 1],
+                [3, 64,  24,  False, 'RE', 2],
+                [3, 72,  24,  False, 'RE', 1],
+                [5, 72,  40,  True,  'RE', 2],
+                [5, 120, 40,  True,  'RE', 1],
+                [5, 120, 40,  True,  'RE', 1],
+                [3, 240, 80,  False, 'HS', 2],
+                [3, 200, 80,  False, 'HS', 1],
+                [3, 184, 80,  False, 'HS', 1],
+                [3, 184, 80,  False, 'HS', 1],
+                [3, 480, 112, True,  'HS', 1],
+                [3, 672, 112, True,  'HS', 1],
+                [5, 672, 160, True,  'HS', 2],
+                [5, 960, 160, True,  'HS', 1],
+                [5, 960, 160, True,  'HS', 1],
+            ]
+        elif mode == 'small':
+            # refer to Table 2 in paper
+            mobile_setting = [
+                # k, exp, c,  se,     nl,  s,
+                [3, 16,  16,  True,  'RE', 2],
+                [3, 72,  24,  False, 'RE', 2],
+                [3, 88,  24,  False, 'RE', 1],
+                [5, 96,  40,  True,  'HS', 2],
+                [5, 240, 40,  True,  'HS', 1],
+                [5, 240, 40,  True,  'HS', 1],
+                [5, 120, 48,  True,  'HS', 1],
+                [5, 144, 48,  True,  'HS', 1],
+                [5, 288, 96,  True,  'HS', 2],
+                [5, 576, 96,  True,  'HS', 1],
+                [5, 576, 96,  True,  'HS', 1],
+            ]
+        else:
+            raise NotImplementedError
+
+        # building first layer
+        assert input_size % 32 == 0
+        last_channel = make_divisible(last_channel * width_mult) if width_mult > 1.0 else last_channel
+        self.features = [conv_bn(3, input_channel, 2, nlin_layer=Hswish)]
+        self.classifier = []
+
+        # building mobile blocks
+        for k, exp, c, se, nl, s in mobile_setting:
+            output_channel = make_divisible(c * width_mult)
+            exp_channel = make_divisible(exp * width_mult)
+            self.features.append(MobileBottleneck(input_channel, output_channel, k, s, exp_channel, se, nl))
+            input_channel = output_channel
+
+        # building last several layers
+        if mode == 'large':
+            last_conv = make_divisible(960 * width_mult)
+            self.features.append(conv_1x1_bn(input_channel, last_conv, nlin_layer=Hswish))
+            self.features.append(nn.AdaptiveAvgPool2d(1))
+            self.features.append(nn.Conv2d(last_conv, last_channel, 1, 1, 0))
+            self.features.append(Hswish(inplace=True))
+        elif mode == 'small':
+            last_conv = make_divisible(576 * width_mult)
+            self.features.append(conv_1x1_bn(input_channel, last_conv, nlin_layer=Hswish))
+            # self.features.append(SEModule(last_conv))  # refer to paper Table2, but I think this is a mistake
+            self.features.append(nn.AdaptiveAvgPool2d(1))
+            self.features.append(nn.Conv2d(last_conv, last_channel, 1, 1, 0))
+            self.features.append(Hswish(inplace=True))
+        else:
+            raise NotImplementedError
+
+        # make it nn.Sequential
+        self.features = nn.Sequential(*self.features)
+
+        # building classifier
+        self.classifier = nn.Sequential(
+            nn.Dropout(p=dropout),    # refer to paper section 6
+            nn.Linear(last_channel, n_class),
+        )
+
+        self._initialize_weights()
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.mean(3).mean(2)
+        x = self.classifier(x)
+        return x
+
+    def _initialize_weights(self):
+        # weight initialization
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out')
+                if m.bias is not None:
+                    nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.ones_(m.weight)
+                nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    nn.init.zeros_(m.bias)
+
+
+def mobilenetv3(pretrained=False, **kwargs):
+    model = MobileNetV3(**kwargs)
+    if pretrained:
+        state_dict = torch.load('mobilenetv3_small_67.4.pth.tar')
+        model.load_state_dict(state_dict, strict=True)
+        # raise NotImplementedError
+    return model
+
+
+if __name__ == '__main__':
+    net = mobilenetv3()
+    print('mobilenetv3:\n', net)
+    print('Total params: %.2fM' % (sum(p.numel() for p in net.parameters())/1000000.0))
+    input_size=(1, 3, 224, 224)
+    # pip install --upgrade git+https://github.com/kuan-wang/pytorch-OpCounter.git
+    from thop import profile
+    input_tensor = torch.randn(input_size)
+    flops, params = profile(net, inputs=(input_tensor,))
+    # print(flops)
+    # print(params)
+    print('Total params: %.2fM' % (params/1000000.0))
+    print('Total flops: %.2f GMACs' % ((flops/1000000000.0) / 2.0))
+    x = torch.randn(input_size)
+    out = net(x)

model/repvit.py

@@ -0,0 +1,520 @@
+import torch.nn as nn
+
+def _make_divisible(v, divisor, min_value=None):
+    """
+    This function is taken from the original tf repo.
+    It ensures that all layers have a channel number that is divisible by 8
+    It can be seen here:
+    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+    :param v:
+    :param divisor:
+    :param min_value:
+    :return:
+    """
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+from timm.models.layers import SqueezeExcite
+
+import torch
+
+class Conv2d_BN(torch.nn.Sequential):
+    def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,
+                 groups=1, bn_weight_init=1, resolution=-10000):
+        super().__init__()
+        self.add_module('c', torch.nn.Conv2d(
+            a, b, ks, stride, pad, dilation, groups, bias=False))
+        self.add_module('bn', torch.nn.BatchNorm2d(b))
+        torch.nn.init.constant_(self.bn.weight, bn_weight_init)
+        torch.nn.init.constant_(self.bn.bias, 0)
+
+    @torch.no_grad()
+    def fuse(self):
+        c, bn = self._modules.values()
+        w = bn.weight / (bn.running_var + bn.eps)**0.5
+        w = c.weight * w[:, None, None, None]
+        b = bn.bias - bn.running_mean * bn.weight / \
+            (bn.running_var + bn.eps)**0.5
+        m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(
+            0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups,
+            device=c.weight.device)
+        m.weight.data.copy_(w)
+        m.bias.data.copy_(b)
+        return m
+
+class Residual(torch.nn.Module):
+    def __init__(self, m, drop=0.):
+        super().__init__()
+        self.m = m
+        self.drop = drop
+
+    def forward(self, x):
+        if self.training and self.drop > 0:
+            return x + self.m(x) * torch.rand(x.size(0), 1, 1, 1,
+                                              device=x.device).ge_(self.drop).div(1 - self.drop).detach()
+        else:
+            return x + self.m(x)
+    
+    @torch.no_grad()
+    def fuse(self):
+        if isinstance(self.m, Conv2d_BN):
+            m = self.m.fuse()
+            assert(m.groups == m.in_channels)
+            identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
+            identity = torch.nn.functional.pad(identity, [1,1,1,1])
+            m.weight += identity.to(m.weight.device)
+            return m
+        elif isinstance(self.m, torch.nn.Conv2d):
+            m = self.m
+            assert(m.groups != m.in_channels)
+            identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
+            identity = torch.nn.functional.pad(identity, [1,1,1,1])
+            m.weight += identity.to(m.weight.device)
+            return m
+        else:
+            return self
+
+
+class RepVGGDW(torch.nn.Module):
+    def __init__(self, ed) -> None:
+        super().__init__()
+        self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed)
+        self.conv1 = torch.nn.Conv2d(ed, ed, 1, 1, 0, groups=ed)
+        self.dim = ed
+        self.bn = torch.nn.BatchNorm2d(ed)
+    
+    def forward(self, x):
+        return self.bn((self.conv(x) + self.conv1(x)) + x)
+    
+    @torch.no_grad()
+    def fuse(self):
+        conv = self.conv.fuse()
+        conv1 = self.conv1
+        
+        conv_w = conv.weight
+        conv_b = conv.bias
+        conv1_w = conv1.weight
+        conv1_b = conv1.bias
+        
+        conv1_w = torch.nn.functional.pad(conv1_w, [1,1,1,1])
+
+        identity = torch.nn.functional.pad(torch.ones(conv1_w.shape[0], conv1_w.shape[1], 1, 1, device=conv1_w.device), [1,1,1,1])
+
+        final_conv_w = conv_w + conv1_w + identity
+        final_conv_b = conv_b + conv1_b
+
+        conv.weight.data.copy_(final_conv_w)
+        conv.bias.data.copy_(final_conv_b)
+
+        bn = self.bn
+        w = bn.weight / (bn.running_var + bn.eps)**0.5
+        w = conv.weight * w[:, None, None, None]
+        b = bn.bias + (conv.bias - bn.running_mean) * bn.weight / \
+            (bn.running_var + bn.eps)**0.5
+        conv.weight.data.copy_(w)
+        conv.bias.data.copy_(b)
+        return conv
+
+
+class RepViTBlock(nn.Module):
+    def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs):
+        super(RepViTBlock, self).__init__()
+        assert stride in [1, 2]
+
+        self.identity = stride == 1 and inp == oup
+        assert(hidden_dim == 2 * inp)
+
+        if stride == 2:
+            self.token_mixer = nn.Sequential(
+                Conv2d_BN(inp, inp, kernel_size, stride, (kernel_size - 1) // 2, groups=inp),
+                SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),
+                Conv2d_BN(inp, oup, ks=1, stride=1, pad=0)
+            )
+            self.channel_mixer = Residual(nn.Sequential(
+                    # pw
+                    Conv2d_BN(oup, 2 * oup, 1, 1, 0),
+                    nn.GELU() if use_hs else nn.GELU(),
+                    # pw-linear
+                    Conv2d_BN(2 * oup, oup, 1, 1, 0, bn_weight_init=0),
+                ))
+        else:
+            assert(self.identity)
+            self.token_mixer = nn.Sequential(
+                RepVGGDW(inp),
+                SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),
+            )
+            self.channel_mixer = Residual(nn.Sequential(
+                    # pw
+                    Conv2d_BN(inp, hidden_dim, 1, 1, 0),
+                    nn.GELU() if use_hs else nn.GELU(),
+                    # pw-linear
+                    Conv2d_BN(hidden_dim, oup, 1, 1, 0, bn_weight_init=0),
+                ))
+
+    def forward(self, x):
+        return self.channel_mixer(self.token_mixer(x))
+
+from timm.models.vision_transformer import trunc_normal_
+class BN_Linear(torch.nn.Sequential):
+    def __init__(self, a, b, bias=True, std=0.02):
+        super().__init__()
+        self.add_module('bn', torch.nn.BatchNorm1d(a))
+        self.add_module('l', torch.nn.Linear(a, b, bias=bias))
+        trunc_normal_(self.l.weight, std=std)
+        if bias:
+            torch.nn.init.constant_(self.l.bias, 0)
+
+    @torch.no_grad()
+    def fuse(self):
+        bn, l = self._modules.values()
+        w = bn.weight / (bn.running_var + bn.eps)**0.5
+        b = bn.bias - self.bn.running_mean * \
+            self.bn.weight / (bn.running_var + bn.eps)**0.5
+        w = l.weight * w[None, :]
+        if l.bias is None:
+            b = b @ self.l.weight.T
+        else:
+            b = (l.weight @ b[:, None]).view(-1) + self.l.bias
+        m = torch.nn.Linear(w.size(1), w.size(0), device=l.weight.device)
+        m.weight.data.copy_(w)
+        m.bias.data.copy_(b)
+        return m
+
+class Classfier(nn.Module):
+    def __init__(self, dim, num_classes, distillation=True):
+        super().__init__()
+        self.classifier = BN_Linear(dim, num_classes) if num_classes > 0 else torch.nn.Identity()
+        self.distillation = distillation
+        if distillation:
+            self.classifier_dist = BN_Linear(dim, num_classes) if num_classes > 0 else torch.nn.Identity()
+
+    def forward(self, x):
+        if self.distillation:
+            x = self.classifier(x), self.classifier_dist(x)
+            if not self.training:
+                x = (x[0] + x[1]) / 2
+        else:
+            x = self.classifier(x)
+        return x
+
+    @torch.no_grad()
+    def fuse(self):
+        classifier = self.classifier.fuse()
+        if self.distillation:
+            classifier_dist = self.classifier_dist.fuse()
+            classifier.weight += classifier_dist.weight
+            classifier.bias += classifier_dist.bias
+            classifier.weight /= 2
+            classifier.bias /= 2
+            return classifier
+        else:
+            return classifier
+
+class RepViT(nn.Module):
+    def __init__(self, cfgs, num_classes=1000, distillation=False):
+        super(RepViT, self).__init__()
+        # setting of inverted residual blocks
+        self.cfgs = cfgs
+
+        # building first layer
+        input_channel = self.cfgs[0][2]
+        patch_embed = torch.nn.Sequential(Conv2d_BN(3, input_channel // 2, 3, 2, 1), torch.nn.GELU(),
+                           Conv2d_BN(input_channel // 2, input_channel, 3, 2, 1))
+        layers = [patch_embed]
+        # building inverted residual blocks
+        block = RepViTBlock
+        for k, t, c, use_se, use_hs, s in self.cfgs:
+            output_channel = _make_divisible(c, 8)
+            exp_size = _make_divisible(input_channel * t, 8)
+            layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs))
+            input_channel = output_channel
+        self.features = nn.ModuleList(layers)
+        self.classifier = Classfier(output_channel, num_classes, distillation)
+        
+    def forward(self, x):
+        # x = self.features(x)
+        for f in self.features:
+            x = f(x)
+        x = torch.nn.functional.adaptive_avg_pool2d(x, 1).flatten(1)
+        x = self.classifier(x)
+        return x
+
+from timm.models import register_model
+
+
+
+def repvit_m0_6(pretrained=False, num_classes = 1000, distillation=False):
+    """
+    Constructs a MobileNetV3-Large model
+    """
+    cfgs = [
+        [3,   2,  40, 1, 0, 1],
+        [3,   2,  40, 0, 0, 1],
+        [3,   2,  80, 0, 0, 2],
+        [3,   2,  80, 1, 0, 1],
+        [3,   2,  80, 0, 0, 1],
+        [3,   2,  160, 0, 1, 2],
+        [3,   2, 160, 1, 1, 1],
+        [3,   2, 160, 0, 1, 1],
+        [3,   2, 160, 1, 1, 1],
+        [3,   2, 160, 0, 1, 1],
+        [3,   2, 160, 1, 1, 1],
+        [3,   2, 160, 0, 1, 1],
+        [3,   2, 160, 1, 1, 1],
+        [3,   2, 160, 0, 1, 1],
+        [3,   2, 160, 0, 1, 1],
+        [3,   2, 320, 0, 1, 2],
+        [3,   2, 320, 1, 1, 1],
+    ]
+    return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
+
+
+def repvit_m0_9(pretrained=False, num_classes = 1000, distillation=False):
+    """
+    Constructs a MobileNetV3-Large model
+    """
+    cfgs = [
+        # k, t, c, SE, HS, s 
+        [3,   2,  48, 1, 0, 1],
+        [3,   2,  48, 0, 0, 1],
+        [3,   2,  48, 0, 0, 1],
+        [3,   2,  96, 0, 0, 2],
+        [3,   2,  96, 1, 0, 1],
+        [3,   2,  96, 0, 0, 1],
+        [3,   2,  96, 0, 0, 1],
+        [3,   2,  192, 0, 1, 2],
+        [3,   2,  192, 1, 1, 1],
+        [3,   2,  192, 0, 1, 1],
+        [3,   2,  192, 1, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 192, 1, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 192, 1, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 192, 1, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 192, 1, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 192, 1, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 192, 0, 1, 1],
+        [3,   2, 384, 0, 1, 2],
+        [3,   2, 384, 1, 1, 1],
+        [3,   2, 384, 0, 1, 1]
+    ]
+    return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
+
+
+def repvit_m1_0(pretrained=False, num_classes = 1000, distillation=False):
+    """
+    Constructs a MobileNetV3-Large model
+    """
+    cfgs = [
+        # k, t, c, SE, HS, s 
+        [3,   2,  56, 1, 0, 1],
+        [3,   2,  56, 0, 0, 1],
+        [3,   2,  56, 0, 0, 1],
+        [3,   2,  112, 0, 0, 2],
+        [3,   2,  112, 1, 0, 1],
+        [3,   2,  112, 0, 0, 1],
+        [3,   2,  112, 0, 0, 1],
+        [3,   2,  224, 0, 1, 2],
+        [3,   2,  224, 1, 1, 1],
+        [3,   2,  224, 0, 1, 1],
+        [3,   2,  224, 1, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 224, 1, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 224, 1, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 224, 1, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 224, 1, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 224, 1, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 224, 0, 1, 1],
+        [3,   2, 448, 0, 1, 2],
+        [3,   2, 448, 1, 1, 1],
+        [3,   2, 448, 0, 1, 1]
+    ]
+    return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
+
+
+
+def repvit_m1_1(pretrained=False, num_classes = 1000, distillation=False):
+    """
+    Constructs a MobileNetV3-Large model
+    """
+    cfgs = [
+        # k, t, c, SE, HS, s 
+        [3,   2,  64, 1, 0, 1],
+        [3,   2,  64, 0, 0, 1],
+        [3,   2,  64, 0, 0, 1],
+        [3,   2,  128, 0, 0, 2],
+        [3,   2,  128, 1, 0, 1],
+        [3,   2,  128, 0, 0, 1],
+        [3,   2,  128, 0, 0, 1],
+        [3,   2,  256, 0, 1, 2],
+        [3,   2,  256, 1, 1, 1],
+        [3,   2,  256, 0, 1, 1],
+        [3,   2,  256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 512, 0, 1, 2],
+        [3,   2, 512, 1, 1, 1],
+        [3,   2, 512, 0, 1, 1]
+    ]
+    return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
+
+
+
+def repvit_m1_5(pretrained=False, num_classes = 1000, distillation=False):
+    """
+    Constructs a MobileNetV3-Large model
+    """
+    cfgs = [
+        # k, t, c, SE, HS, s 
+        [3,   2,  64, 1, 0, 1],
+        [3,   2,  64, 0, 0, 1],
+        [3,   2,  64, 1, 0, 1],
+        [3,   2,  64, 0, 0, 1],
+        [3,   2,  64, 0, 0, 1],
+        [3,   2,  128, 0, 0, 2],
+        [3,   2,  128, 1, 0, 1],
+        [3,   2,  128, 0, 0, 1],
+        [3,   2,  128, 1, 0, 1],
+        [3,   2,  128, 0, 0, 1],
+        [3,   2,  128, 0, 0, 1],
+        [3,   2,  256, 0, 1, 2],
+        [3,   2,  256, 1, 1, 1],
+        [3,   2,  256, 0, 1, 1],
+        [3,   2,  256, 1, 1, 1],
+        [3,   2,  256, 0, 1, 1],
+        [3,   2,  256, 1, 1, 1],
+        [3,   2,  256, 0, 1, 1],
+        [3,   2,  256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 1, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 256, 0, 1, 1],
+        [3,   2, 512, 0, 1, 2],
+        [3,   2, 512, 1, 1, 1],
+        [3,   2, 512, 0, 1, 1],
+        [3,   2, 512, 1, 1, 1],
+        [3,   2, 512, 0, 1, 1]
+    ]
+    return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
+
+
+
+
+def repvit_m2_3(pretrained=False, num_classes = 1000, distillation=False):
+    """
+    Constructs a MobileNetV3-Large model
+    """
+    cfgs = [
+        # k, t, c, SE, HS, s 
+        [3,   2,  80, 1, 0, 1],
+        [3,   2,  80, 0, 0, 1],
+        [3,   2,  80, 1, 0, 1],
+        [3,   2,  80, 0, 0, 1],
+        [3,   2,  80, 1, 0, 1],
+        [3,   2,  80, 0, 0, 1],
+        [3,   2,  80, 0, 0, 1],
+        [3,   2,  160, 0, 0, 2],
+        [3,   2,  160, 1, 0, 1],
+        [3,   2,  160, 0, 0, 1],
+        [3,   2,  160, 1, 0, 1],
+        [3,   2,  160, 0, 0, 1],
+        [3,   2,  160, 1, 0, 1],
+        [3,   2,  160, 0, 0, 1],
+        [3,   2,  160, 0, 0, 1],
+        [3,   2,  320, 0, 1, 2],
+        [3,   2,  320, 1, 1, 1],
+        [3,   2,  320, 0, 1, 1],
+        [3,   2,  320, 1, 1, 1],
+        [3,   2,  320, 0, 1, 1],
+        [3,   2,  320, 1, 1, 1],
+        [3,   2,  320, 0, 1, 1],
+        [3,   2,  320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 1, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        # [3,   2, 320, 1, 1, 1],
+        # [3,   2, 320, 0, 1, 1],
+        [3,   2, 320, 0, 1, 1],
+        [3,   2, 640, 0, 1, 2],
+        [3,   2, 640, 1, 1, 1],
+        [3,   2, 640, 0, 1, 1],
+        # [3,   2, 640, 1, 1, 1],
+        # [3,   2, 640, 0, 1, 1]
+    ]    
+    return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
+
+if __name__ == '__main__':
+    net = repvit_m1_1()
+    print('mobilenetv3:\n', net)
+    print('Total params: %.2fM' % (sum(p.numel() for p in net.parameters())/1000000.0))
+    input_size=(4, 3, 224, 224)
+    # pip install --upgrade git+https://github.com/kuan-wang/pytorch-OpCounter.git
+    from thop import profile
+    input_tensor = torch.randn(input_size)
+    flops, params = profile(net, inputs=(input_tensor,))
+    # print(flops)
+    # print(params)
+    print('Total params: %.2fM' % (params/1000000.0))
+    print('Total flops: %.2f GMACs' % ((flops/1000000000.0) / 2.0))
+    x = torch.randn(input_size)
+    out = net(x)