commit 9fea321af39d59b012fe5e740da73c89a10bdf84
Author: tanzk <tanzk@qibebt.ac.cn>
Date:   Tue Jun 18 10:59:34 2024 +0800

    init

diff --git a/README.md b/README.md
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+## DeepLabv3+：Encoder-Decoder with Atrous Separable Convolution语义分割模型在Pytorch当中的实现
+---
+
+### 目录
+1. [仓库更新 Top News](#仓库更新)
+2. [相关仓库 Related code](#相关仓库)
+3. [性能情况 Performance](#性能情况)
+4. [所需环境 Environment](#所需环境)
+5. [文件下载 Download](#文件下载)
+6. [训练步骤 How2train](#训练步骤)
+7. [预测步骤 How2predict](#预测步骤)
+8. [评估步骤 miou](#评估步骤)
+9. [参考资料 Reference](#Reference)
+
+## Top News
+**`2022-04`**:**支持多GPU训练。**  
+
+**`2022-03`**:**进行大幅度更新、支持step、cos学习率下降法、支持adam、sgd优化器选择、支持学习率根据batch_size自适应调整。**  
+BiliBili视频中的原仓库地址为：https://github.com/bubbliiiing/deeplabv3-plus-pytorch/tree/bilibili
+
+**`2020-08`**:**创建仓库、支持多backbone、支持数据miou评估、标注数据处理、大量注释等。**  
+
+## 相关仓库
+| 模型 | 路径 |
+| :----- | :----- |
+Unet | https://github.com/bubbliiiing/unet-pytorch  
+PSPnet | https://github.com/bubbliiiing/pspnet-pytorch
+deeplabv3+ | https://github.com/bubbliiiing/deeplabv3-plus-pytorch
+hrnet | https://github.com/bubbliiiing/hrnet-pytorch
+
+### 性能情况
+| 训练数据集 | 权值文件名称 | 测试数据集 | 输入图片大小 | mIOU | 
+| :-----: | :-----: | :------: | :------: | :------: | 
+| VOC12+SBD | [deeplab_mobilenetv2.pth](https://github.com/bubbliiiing/deeplabv3-plus-pytorch/releases/download/v1.0/deeplab_mobilenetv2.pth) | VOC-Val12 | 512x512| 72.59 | 
+| VOC12+SBD | [deeplab_xception.pth](https://github.com/bubbliiiing/deeplabv3-plus-pytorch/releases/download/v1.0/deeplab_xception.pth) | VOC-Val12 | 512x512| 76.95 | 
+
+### 所需环境
+torch==1.2.0
+
+### 注意事项
+代码中的deeplab_mobilenetv2.pth和deeplab_xception.pth是基于VOC拓展数据集训练的。训练和预测时注意修改backbone。    
+
+### 文件下载
+训练所需的deeplab_mobilenetv2.pth和deeplab_xception.pth可在百度网盘中下载。     
+链接: https://pan.baidu.com/s/1IQ3XYW-yRWQAy7jxCUHq8Q 提取码: qqq4   
+
+VOC拓展数据集的百度网盘如下：  
+链接: https://pan.baidu.com/s/1vkk3lMheUm6IjTXznlg7Ng 提取码: 44mk   
+
+### 训练步骤
+#### a、训练voc数据集
+1、将我提供的voc数据集放入VOCdevkit中（无需运行voc_annotation.py）。  
+2、在train.py中设置对应参数，默认参数已经对应voc数据集所需要的参数了，所以只要修改backbone和model_path即可。  
+3、运行train.py进行训练。  
+
+#### b、训练自己的数据集
+1、本文使用VOC格式进行训练。  
+2、训练前将标签文件放在VOCdevkit文件夹下的VOC2007文件夹下的SegmentationClass中。    
+3、训练前将图片文件放在VOCdevkit文件夹下的VOC2007文件夹下的JPEGImages中。    
+4、在训练前利用voc_annotation.py文件生成对应的txt。    
+5、在train.py文件夹下面，选择自己要使用的主干模型和下采样因子。本文提供的主干模型有mobilenet和xception。下采样因子可以在8和16中选择。需要注意的是，预训练模型需要和主干模型相对应。   
+6、注意修改train.py的num_classes为分类个数+1。    
+7、运行train.py即可开始训练。  
+
+### 预测步骤
+#### a、使用预训练权重
+1、下载完库后解压，如果想用backbone为mobilenet的进行预测，直接运行predict.py就可以了；如果想要利用backbone为xception的进行预测，在百度网盘下载deeplab_xception.pth，放入model_data，修改deeplab.py的backbone和model_path之后再运行predict.py，输入。  
+```python
+img/street.jpg
+```
+可完成预测。    
+2、在predict.py里面进行设置可以进行fps测试、整个文件夹的测试和video视频检测。       
+
+#### b、使用自己训练的权重
+1、按照训练步骤训练。    
+2、在deeplab.py文件里面，在如下部分修改model_path、num_classes、backbone使其对应训练好的文件；**model_path对应logs文件夹下面的权值文件，num_classes代表要预测的类的数量加1，backbone是所使用的主干特征提取网络**。    
+```python
+_defaults = {
+    #----------------------------------------#
+    #   model_path指向logs文件夹下的权值文件
+    #----------------------------------------#
+    "model_path"        : 'model_data/deeplab_mobilenetv2.pth',
+    #----------------------------------------#
+    #   所需要区分的类的个数+1
+    #----------------------------------------#
+    "num_classes"       : 21,
+    #----------------------------------------#
+    #   所使用的的主干网络
+    #----------------------------------------#
+    "backbone"          : "mobilenet",
+    #----------------------------------------#
+    #   输入图片的大小
+    #----------------------------------------#
+    "input_shape"       : [512, 512],
+    #----------------------------------------#
+    #   下采样的倍数，一般可选的为8和16
+    #   与训练时设置的一样即可
+    #----------------------------------------#
+    "downsample_factor" : 16,
+    #--------------------------------#
+    #   blend参数用于控制是否
+    #   让识别结果和原图混合
+    #--------------------------------#
+    "blend"             : True,
+    #-------------------------------#
+    #   是否使用Cuda
+    #   没有GPU可以设置成False
+    #-------------------------------#
+    "cuda"              : True,
+}
+```
+3、运行predict.py，输入    
+```python
+img/street.jpg
+```
+可完成预测。    
+4、在predict.py里面进行设置可以进行fps测试、整个文件夹的测试和video视频检测。   
+
+### 评估步骤
+1、设置get_miou.py里面的num_classes为预测的类的数量加1。  
+2、设置get_miou.py里面的name_classes为需要去区分的类别。  
+3、运行get_miou.py即可获得miou大小。  
+
+### Reference
+https://github.com/ggyyzm/pytorch_segmentation  
+https://github.com/bonlime/keras-deeplab-v3-plus
diff --git a/VOCdevkit/VOC2007/ImageSets/Segmentation/README.md b/VOCdevkit/VOC2007/ImageSets/Segmentation/README.md
new file mode 100644
index 0000000..9042c5f
--- /dev/null
+++ b/VOCdevkit/VOC2007/ImageSets/Segmentation/README.md
@@ -0,0 +1,2 @@
+存放的是指向文件名称的txt
+
diff --git a/VOCdevkit/VOC2007/ImageSets/Segmentation/test.txt b/VOCdevkit/VOC2007/ImageSets/Segmentation/test.txt
new file mode 100644
index 0000000..e69de29
diff --git a/VOCdevkit/VOC2007/ImageSets/Segmentation/train.txt b/VOCdevkit/VOC2007/ImageSets/Segmentation/train.txt
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diff --git a/VOCdevkit/VOC2007/ImageSets/Segmentation/trainval.txt b/VOCdevkit/VOC2007/ImageSets/Segmentation/trainval.txt
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diff --git a/VOCdevkit/VOC2007/ImageSets/Segmentation/val.txt b/VOCdevkit/VOC2007/ImageSets/Segmentation/val.txt
new file mode 100644
index 0000000..ebf9121
--- /dev/null
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diff --git a/VOCdevkit1/VOC2007/ImageSets/Segmentation/README.md b/VOCdevkit1/VOC2007/ImageSets/Segmentation/README.md
new file mode 100644
index 0000000..9042c5f
--- /dev/null
+++ b/VOCdevkit1/VOC2007/ImageSets/Segmentation/README.md
@@ -0,0 +1,2 @@
+存放的是指向文件名称的txt
+
diff --git a/VOCdevkit1/VOC2007/ImageSets/Segmentation/test.txt b/VOCdevkit1/VOC2007/ImageSets/Segmentation/test.txt
new file mode 100644
index 0000000..e69de29
diff --git a/VOCdevkit1/VOC2007/ImageSets/Segmentation/train.txt b/VOCdevkit1/VOC2007/ImageSets/Segmentation/train.txt
new file mode 100644
index 0000000..6c0e27c
--- /dev/null
+++ b/VOCdevkit1/VOC2007/ImageSets/Segmentation/train.txt
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diff --git a/VOCdevkit1/VOC2007/ImageSets/Segmentation/trainval.txt b/VOCdevkit1/VOC2007/ImageSets/Segmentation/trainval.txt
new file mode 100644
index 0000000..ec1d75b
--- /dev/null
+++ b/VOCdevkit1/VOC2007/ImageSets/Segmentation/trainval.txt
@@ -0,0 +1,2339 @@
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diff --git a/VOCdevkit1/VOC2007/ImageSets/Segmentation/val.txt b/VOCdevkit1/VOC2007/ImageSets/Segmentation/val.txt
new file mode 100644
index 0000000..d206f87
--- /dev/null
+++ b/VOCdevkit1/VOC2007/ImageSets/Segmentation/val.txt
@@ -0,0 +1,234 @@
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diff --git a/deeplab.py b/deeplab.py
new file mode 100644
index 0000000..d0cf30d
--- /dev/null
+++ b/deeplab.py
@@ -0,0 +1,397 @@
+import colorsys
+import copy
+import time
+
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from torch import nn
+
+from nets.deeplabv3_plus import DeepLab
+from utils.utils import cvtColor, preprocess_input, resize_image, show_config
+
+
+#-----------------------------------------------------------------------------------#
+#   使用自己训练好的模型预测需要修改3个参数
+#   model_path、backbone和num_classes都需要修改！
+#   如果出现shape不匹配，一定要注意训练时的model_path、backbone和num_classes的修改
+#-----------------------------------------------------------------------------------#
+class DeeplabV3(object):
+    _defaults = {
+        #-------------------------------------------------------------------#
+        #   model_path指向logs文件夹下的权值文件
+        #   训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
+        #   验证集损失较低不代表miou较高，仅代表该权值在验证集上泛化性能较好。
+        #-------------------------------------------------------------------#
+        "model_path"        : 'model_data/last_epoch_weights1.pth',
+        #----------------------------------------#
+        #   所需要区分的类的个数+1
+        #----------------------------------------#
+        "num_classes"       : 46,
+        #----------------------------------------#
+        #   所使用的的主干网络：
+        #   mobilenet
+        #   xception    
+        #----------------------------------------#
+        "backbone"          : "mobilenet",
+        #----------------------------------------#
+        #   输入图片的大小
+        #----------------------------------------#
+        "input_shape"       : [1024, 1042],
+        #----------------------------------------#
+        #   下采样的倍数，一般可选的为8和16
+        #   与训练时设置的一样即可
+        #----------------------------------------#
+        "downsample_factor" : 16,
+        #-------------------------------------------------#
+        #   mix_type参数用于控制检测结果的可视化方式
+        #
+        #   mix_type = 0的时候代表原图与生成的图进行混合
+        #   mix_type = 1的时候代表仅保留生成的图
+        #   mix_type = 2的时候代表仅扣去背景，仅保留原图中的目标
+        #-------------------------------------------------#
+        "mix_type"          : 0,
+        #-------------------------------#
+        #   是否使用Cuda
+        #   没有GPU可以设置成False
+        #-------------------------------#
+        "cuda"              : True,
+    }
+
+    #---------------------------------------------------#
+    #   初始化Deeplab
+    #---------------------------------------------------#
+    def __init__(self, **kwargs):
+        self.__dict__.update(self._defaults)
+        for name, value in kwargs.items():
+            setattr(self, name, value)
+        #---------------------------------------------------#
+        #   画框设置不同的颜色
+        #---------------------------------------------------#
+        if self.num_classes <= 46:
+            self.colors = [ (0, 0, 0),
+                            (128, 0, 0),
+                            (0, 128, 0),
+                            (128, 128, 0),
+                            (0, 0, 128),
+                            (128, 0, 128),
+                            (0, 128, 128),
+                            (128, 128, 128),
+                            (64, 0, 0),
+                            (192, 0, 0),
+                            (64, 128, 0),
+                            (192, 128, 0),
+                            (64, 0, 128),
+                            (192, 0, 128),
+                            (64, 128, 128),
+                            (192, 128, 128),
+                            (0, 64, 0),
+                            (128, 64, 0),
+                            (0, 192, 0),
+                            (128, 192, 0),
+                            (0, 64, 128),
+                            (128, 64, 12),
+                            (0, 0, 142),
+                            (119, 11, 32),
+                            (244,164,140),
+                            (188,143,143),
+                            (64,224,205),
+                            (127,255,0),
+                            (199,97,20),
+                            (189,252,201),
+                            (0,255,127),
+                            (160,32,240),
+                            (138,42,226),
+                            (255,97,0),
+                            (255,215,0),
+                            (255,128,0),
+                            (189,252,201),
+                            (240,255,240),
+                            (0, 130, 180),
+                            (152, 251, 152),
+                            (107, 142, 35),
+                            (153, 153, 153),
+                            (190, 153, 153),
+                            (250, 170, 30),
+                            (220, 220, 0),
+                            (107, 142, 35),
+                             ]
+
+        else:
+            hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
+            self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
+            self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))
+        #---------------------------------------------------#
+        #   获得模型
+        #---------------------------------------------------#
+        self.generate()
+        
+        show_config(**self._defaults)
+                    
+    #---------------------------------------------------#
+    #   获得所有的分类
+    #---------------------------------------------------#
+    def generate(self, onnx=False):
+        #-------------------------------#
+        #   载入模型与权值
+        #-------------------------------#
+        self.net = DeepLab(num_classes=self.num_classes, backbone=self.backbone, downsample_factor=self.downsample_factor, pretrained=False)
+
+        device      = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.net.load_state_dict(torch.load(self.model_path, map_location=device))
+        self.net    = self.net.eval()
+        print('{} model, and classes loaded.'.format(self.model_path))
+        if not onnx:
+            if self.cuda:
+                self.net = nn.DataParallel(self.net)
+                self.net = self.net.cuda()
+
+    #---------------------------------------------------#
+    #   检测图片
+    #---------------------------------------------------#
+    def detect_image(self, image, count=False, name_classes=None):
+        #---------------------------------------------------------#
+        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
+        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
+        #---------------------------------------------------------#
+        image       = cvtColor(image)
+        #---------------------------------------------------#
+        #   对输入图像进行一个备份，后面用于绘图
+        #---------------------------------------------------#
+        old_img     = copy.deepcopy(image)
+        orininal_h  = np.array(image).shape[0]
+        orininal_w  = np.array(image).shape[1]
+        #---------------------------------------------------------#
+        #   给图像增加灰条，实现不失真的resize
+        #   也可以直接resize进行识别
+        #---------------------------------------------------------#
+        image_data, nw, nh  = resize_image(image, (self.input_shape[1],self.input_shape[0]))
+        #---------------------------------------------------------#
+        #   添加上batch_size维度
+        #---------------------------------------------------------#
+        image_data  = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, np.float32)), (2, 0, 1)), 0)
+
+        with torch.no_grad():
+            images = torch.from_numpy(image_data)
+            if self.cuda:
+                images = images.cuda()
+                
+            #---------------------------------------------------#
+            #   图片传入网络进行预测
+            #---------------------------------------------------#
+            pr = self.net(images)[0]
+            #---------------------------------------------------#
+            #   取出每一个像素点的种类
+            #---------------------------------------------------#
+            pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy()
+            #--------------------------------------#
+            #   将灰条部分截取掉
+            #--------------------------------------#
+            pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), 
+                    int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
+            #---------------------------------------------------#
+            #   进行图片的resize
+            #---------------------------------------------------#
+            pr = cv2.resize(pr, (orininal_w, orininal_h), interpolation = cv2.INTER_LINEAR)
+            #---------------------------------------------------#
+            #   取出每一个像素点的种类
+            #---------------------------------------------------#
+            pr = pr.argmax(axis=-1)
+        
+        #---------------------------------------------------------#
+        #   计数
+        #---------------------------------------------------------#
+        if count:
+            classes_nums        = np.zeros([self.num_classes])
+            total_points_num    = orininal_h * orininal_w
+            print('-' * 63)
+            print("|%25s | %15s | %15s|"%("Key", "Value", "Ratio"))
+            print('-' * 63)
+            for i in range(self.num_classes):
+                num     = np.sum(pr == i)
+                ratio   = num / total_points_num * 100
+                if num > 0:
+                    print("|%25s | %15s | %14.2f%%|"%(str(name_classes[i]), str(num), ratio))
+                    print('-' * 63)
+                classes_nums[i] = num
+            print("classes_nums:", classes_nums)
+    
+        if self.mix_type == 0:
+            # seg_img = np.zeros((np.shape(pr)[0], np.shape(pr)[1], 3))
+            # for c in range(self.num_classes):
+            #     seg_img[:, :, 0] += ((pr[:, :] == c ) * self.colors[c][0]).astype('uint8')
+            #     seg_img[:, :, 1] += ((pr[:, :] == c ) * self.colors[c][1]).astype('uint8')
+            #     seg_img[:, :, 2] += ((pr[:, :] == c ) * self.colors[c][2]).astype('uint8')
+            seg_img = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr, [-1])], [orininal_h, orininal_w, -1])
+            #------------------------------------------------#
+            #   将新图片转换成Image的形式
+            #------------------------------------------------#
+            image   = Image.fromarray(np.uint8(seg_img))
+            #------------------------------------------------#
+            #   将新图与原图及进行混合
+            #------------------------------------------------#
+            image   = Image.blend(old_img, image, 0.7)
+
+        elif self.mix_type == 1:
+            # seg_img = np.zeros((np.shape(pr)[0], np.shape(pr)[1], 3))
+            # for c in range(self.num_classes):
+            #     seg_img[:, :, 0] += ((pr[:, :] == c ) * self.colors[c][0]).astype('uint8')
+            #     seg_img[:, :, 1] += ((pr[:, :] == c ) * self.colors[c][1]).astype('uint8')
+            #     seg_img[:, :, 2] += ((pr[:, :] == c ) * self.colors[c][2]).astype('uint8')
+            seg_img = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr, [-1])], [orininal_h, orininal_w, -1])
+            #------------------------------------------------#
+            #   将新图片转换成Image的形式
+            #------------------------------------------------#
+            image   = Image.fromarray(np.uint8(seg_img))
+
+        elif self.mix_type == 2:
+            seg_img = (np.expand_dims(pr != 0, -1) * np.array(old_img, np.float32)).astype('uint8')
+            #------------------------------------------------#
+            #   将新图片转换成Image的形式
+            #------------------------------------------------#
+            image = Image.fromarray(np.uint8(seg_img))
+        
+        return image
+
+    def get_FPS(self, image, test_interval):
+        #---------------------------------------------------------#
+        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
+        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
+        #---------------------------------------------------------#
+        image       = cvtColor(image)
+        #---------------------------------------------------------#
+        #   给图像增加灰条，实现不失真的resize
+        #   也可以直接resize进行识别
+        #---------------------------------------------------------#
+        image_data, nw, nh  = resize_image(image, (self.input_shape[1],self.input_shape[0]))
+        #---------------------------------------------------------#
+        #   添加上batch_size维度
+        #---------------------------------------------------------#
+        image_data  = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, np.float32)), (2, 0, 1)), 0)
+
+        with torch.no_grad():
+            images = torch.from_numpy(image_data)
+            if self.cuda:
+                images = images.cuda()
+                
+            #---------------------------------------------------#
+            #   图片传入网络进行预测
+            #---------------------------------------------------#
+            pr = self.net(images)[0]
+            #---------------------------------------------------#
+            #   取出每一个像素点的种类
+            #---------------------------------------------------#
+            pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy().argmax(axis=-1)
+            #--------------------------------------#
+            #   将灰条部分截取掉
+            #--------------------------------------#
+            pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), 
+                    int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
+
+        t1 = time.time()
+        for _ in range(test_interval):
+            with torch.no_grad():
+                #---------------------------------------------------#
+                #   图片传入网络进行预测
+                #---------------------------------------------------#
+                pr = self.net(images)[0]
+                #---------------------------------------------------#
+                #   取出每一个像素点的种类
+                #---------------------------------------------------#
+                pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy().argmax(axis=-1)
+                #--------------------------------------#
+                #   将灰条部分截取掉
+                #--------------------------------------#
+                pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), 
+                        int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
+        t2 = time.time()
+        tact_time = (t2 - t1) / test_interval
+        return tact_time
+
+    def convert_to_onnx(self, simplify, model_path):
+        import onnx
+        self.generate(onnx=True)
+
+        im                  = torch.zeros(1, 3, *self.input_shape).to('cpu')  # image size(1, 3, 512, 512) BCHW
+        input_layer_names   = ["images"]
+        output_layer_names  = ["output"]
+        
+        # Export the model
+        print(f'Starting export with onnx {onnx.__version__}.')
+        torch.onnx.export(self.net,
+                        im,
+                        f               = model_path,
+                        verbose         = False,
+                        opset_version   = 12,
+                        training        = torch.onnx.TrainingMode.EVAL,
+                        do_constant_folding = True,
+                        input_names     = input_layer_names,
+                        output_names    = output_layer_names,
+                        dynamic_axes    = None)
+
+        # Checks
+        model_onnx = onnx.load(model_path)  # load onnx model
+        onnx.checker.check_model(model_onnx)  # check onnx model
+
+        # Simplify onnx
+        if simplify:
+            import onnxsim
+            print(f'Simplifying with onnx-simplifier {onnxsim.__version__}.')
+            model_onnx, check = onnxsim.simplify(
+                model_onnx,
+                dynamic_input_shape=False,
+                input_shapes=None)
+            assert check, 'assert check failed'
+            onnx.save(model_onnx, model_path)
+
+        print('Onnx model save as {}'.format(model_path))
+    
+    def get_miou_png(self, image):
+        #---------------------------------------------------------#
+        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
+        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
+        #---------------------------------------------------------#
+        image       = cvtColor(image)
+        orininal_h  = np.array(image).shape[0]
+        orininal_w  = np.array(image).shape[1]
+        #---------------------------------------------------------#
+        #   给图像增加灰条，实现不失真的resize
+        #   也可以直接resize进行识别
+        #---------------------------------------------------------#
+        image_data, nw, nh  = resize_image(image, (self.input_shape[1],self.input_shape[0]))
+        #---------------------------------------------------------#
+        #   添加上batch_size维度
+        #---------------------------------------------------------#
+        image_data  = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, np.float32)), (2, 0, 1)), 0)
+
+        with torch.no_grad():
+            images = torch.from_numpy(image_data)
+            if self.cuda:
+                images = images.cuda()
+                
+            #---------------------------------------------------#
+            #   图片传入网络进行预测
+            #---------------------------------------------------#
+            pr = self.net(images)[0]
+            #---------------------------------------------------#
+            #   取出每一个像素点的种类
+            #---------------------------------------------------#
+            pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy()
+            #--------------------------------------#
+            #   将灰条部分截取掉
+            #--------------------------------------#
+            pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), 
+                    int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
+            #---------------------------------------------------#
+            #   进行图片的resize
+            #---------------------------------------------------#
+            pr = cv2.resize(pr, (orininal_w, orininal_h), interpolation = cv2.INTER_LINEAR)
+            #---------------------------------------------------#
+            #   取出每一个像素点的种类
+            #---------------------------------------------------#
+            pr = pr.argmax(axis=-1)
+    
+        image = Image.fromarray(np.uint8(pr))
+        return image
diff --git a/get_miou.py b/get_miou.py
new file mode 100644
index 0000000..cf476ae
--- /dev/null
+++ b/get_miou.py
@@ -0,0 +1,62 @@
+import os
+
+from PIL import Image
+from tqdm import tqdm
+
+from deeplab import DeeplabV3
+from utils.utils_metrics import compute_mIoU, show_results
+
+'''
+进行指标评估需要注意以下几点：
+1、该文件生成的图为灰度图，因为值比较小，按照PNG形式的图看是没有显示效果的，所以看到近似全黑的图是正常的。
+2、该文件计算的是验证集的miou，当前该库将测试集当作验证集使用，不单独划分测试集
+'''
+if __name__ == "__main__":
+    #---------------------------------------------------------------------------#
+    #   miou_mode用于指定该文件运行时计算的内容
+    #   miou_mode为0代表整个miou计算流程，包括获得预测结果、计算miou。
+    #   miou_mode为1代表仅仅获得预测结果。
+    #   miou_mode为2代表仅仅计算miou。
+    #---------------------------------------------------------------------------#
+    miou_mode       = 0
+    #------------------------------#
+    #   分类个数+1、如2+1
+    #------------------------------#
+    num_classes     = 47
+    #--------------------------------------------#
+    #   区分的种类，和json_to_dataset里面的一样
+    #--------------------------------------------#
+    name_classes    = ["_background_", "pl5", "pl20", "pl30", "pl40", "pl50", "pl60", "pl70", "pl80", "pl100", "pl120",  "pm20", "pm55","pr40","p11", "pn", "pne", "p26", "i2", "i4", "i5", "ip", "il60", "il80", "il100", "p5", "p10", "p23", "p3", "pg", "p19", "p12", "p6", "p27", "ph4", "ph4.5", "ph5", "pm30", "w55", "w59", "w13", "w57", "w32", "wo", "io", "po", "indicative"]
+    # name_classes    = ["_background_","cat","dog"]
+    #-------------------------------------------------------#
+    #   指向VOC数据集所在的文件夹
+    #   默认指向根目录下的VOC数据集
+    #-------------------------------------------------------#
+    VOCdevkit_path  = 'VOCdevkit'
+
+    image_ids       = open(os.path.join(VOCdevkit_path, "VOC2007/ImageSets/Segmentation/val.txt"),'r').read().splitlines() 
+    gt_dir          = os.path.join(VOCdevkit_path, "VOC2007/SegmentationClass/")
+    miou_out_path   = "miou_out"
+    pred_dir        = os.path.join(miou_out_path, 'detection-results')
+
+    if miou_mode == 0 or miou_mode == 1:
+        if not os.path.exists(pred_dir):
+            os.makedirs(pred_dir)
+            
+        print("Load model.")
+        deeplab = DeeplabV3()
+        print("Load model done.")
+
+        print("Get predict result.")
+        for image_id in tqdm(image_ids):
+            image_path  = os.path.join(VOCdevkit_path, "VOC2007/JPEGImages/"+image_id+".jpg")
+            image       = Image.open(image_path)
+            image       = deeplab.get_miou_png(image)
+            image.save(os.path.join(pred_dir, image_id + ".png"))
+        print("Get predict result done.")
+
+    if miou_mode == 0 or miou_mode == 2:
+        print("Get miou.")
+        hist, IoUs, PA_Recall, Precision = compute_mIoU(gt_dir, pred_dir, image_ids, num_classes, name_classes)  # 执行计算mIoU的函数
+        print("Get miou done.")
+        show_results(miou_out_path, hist, IoUs, PA_Recall, Precision, name_classes)
\ No newline at end of file
diff --git a/json_to_dataset.py b/json_to_dataset.py
new file mode 100644
index 0000000..4b84bc0
--- /dev/null
+++ b/json_to_dataset.py
@@ -0,0 +1,73 @@
+import base64
+import json
+import os
+import os.path as osp
+
+import numpy as np
+import PIL.Image
+from labelme import utils
+
+'''
+制作自己的语义分割数据集需要注意以下几点：
+1、我使用的labelme版本是3.16.7，建议使用该版本的labelme，有些版本的labelme会发生错误，
+   具体错误为：Too many dimensions: 3 > 2
+   安装方式为命令行pip install labelme==3.16.7
+2、此处生成的标签图是8位彩色图，与视频中看起来的数据集格式不太一样。
+   虽然看起来是彩图，但事实上只有8位，此时每个像素点的值就是这个像素点所属的种类。
+   所以其实和视频中VOC数据集的格式一样。因此这样制作出来的数据集是可以正常使用的。也是正常的。
+'''
+if __name__ == '__main__':
+    jpgs_path   = "datasets/JPEGImages"
+    pngs_path   = "datasets/SegmentationClass"
+    # classes     = ["_background_", "pl5", "pl20", "pl30", "pl40", "pl50", "pl60", "pl70", "pl80", "pl100", "pl120",  "pm20", "pm55","pr40","p11", "pn", "pne", "p26", "i2", "i4", "i5", "ip", "il60", "il80", "il100", "p5", "p10", "p23", "p3", "pg", "p19", "p12", "p6", "p27", "ph4", "ph4.5", "ph5", "pm30", "w55", "w59", "w13", "w57", "w32", "wo", "io", "po", "indicative"]
+    # classes     = ["_background_","cat","dog"]
+    classes = ["_background_", "Historical Village", "Hydraulic Facilities", "Historical Buildings", "Green", "Sky",
+               "Water", "Bare Land",
+               "Infrastructure", "Park Related", "Enclosure", "Garbage and Debris", "Electric Poles",
+               "Modern Architecture", "Hard Surface",
+               "Human Activities", "Identification", "Water Pollution"]
+    count = os.listdir("./datasets/before/") 
+    for i in range(0, len(count)):
+        path = os.path.join("./datasets/before", count[i])
+
+        if os.path.isfile(path) and path.endswith('json'):
+            data = json.load(open(path))
+            
+            if data['imageData']:
+                imageData = data['imageData']
+            else:
+                imagePath = os.path.join(os.path.dirname(path), data['imagePath'])
+                with open(imagePath, 'rb') as f:
+                    imageData = f.read()
+                    imageData = base64.b64encode(imageData).decode('utf-8')
+
+            img = utils.img_b64_to_arr(imageData)
+            label_name_to_value = {'_background_': 0}
+            for shape in data['shapes']:
+                label_name = shape['label']
+                if label_name in label_name_to_value:
+                    label_value = label_name_to_value[label_name]
+                else:
+                    label_value = len(label_name_to_value)
+                    label_name_to_value[label_name] = label_value
+            
+            # label_values must be dense
+            label_values, label_names = [], []
+            for ln, lv in sorted(label_name_to_value.items(), key=lambda x: x[1]):
+                label_values.append(lv)
+                label_names.append(ln)
+            assert label_values == list(range(len(label_values)))
+            
+            lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
+            
+                
+            PIL.Image.fromarray(img).save(osp.join(jpgs_path, count[i].split(".")[0]+'.jpg'))
+
+            new = np.zeros([np.shape(img)[0],np.shape(img)[1]])
+            for name in label_names:
+                index_json = label_names.index(name)
+                index_all = classes.index(name)
+                new = new + index_all*(np.array(lbl) == index_json)
+
+            utils.lblsave(osp.join(pngs_path, count[i].split(".")[0]+'.png'), new)
+            print('Saved ' + count[i].split(".")[0] + '.jpg and ' + count[i].split(".")[0] + '.png')
diff --git a/logs/loss_2023_04_16_14_40_55/epoch_miou.txt b/logs/loss_2023_04_16_14_40_55/epoch_miou.txt
new file mode 100644
index 0000000..573541a
--- /dev/null
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diff --git a/nets/__init__.py b/nets/__init__.py
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+#
\ No newline at end of file
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diff --git a/nets/deeplabv3_plus.py b/nets/deeplabv3_plus.py
new file mode 100644
index 0000000..adb8608
--- /dev/null
+++ b/nets/deeplabv3_plus.py
@@ -0,0 +1,257 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from nets.xception import xception
+from nets.mobilenetv2 import mobilenetv2
+
+
+class MobileNetV2(nn.Module):
+    def __init__(self, downsample_factor=8, pretrained=True):
+        super(MobileNetV2, self).__init__()
+        from functools import partial
+
+        model = mobilenetv2(pretrained)
+        self.features = model.features[:-1]
+
+        self.total_idx = len(self.features)
+        self.down_idx = [2, 4, 7, 14]
+
+        if downsample_factor == 8:
+            for i in range(self.down_idx[-2], self.down_idx[-1]):
+                self.features[i].apply(partial(self._nostride_dilate,
+                                               dilate=2))
+            for i in range(self.down_idx[-1], self.total_idx):
+                self.features[i].apply(partial(self._nostride_dilate,
+                                               dilate=4))
+        elif downsample_factor == 16:
+            for i in range(self.down_idx[-1], self.total_idx):
+                self.features[i].apply(partial(self._nostride_dilate,
+                                               dilate=2))
+
+    def _nostride_dilate(self, m, dilate):
+        classname = m.__class__.__name__
+        if classname.find('Conv') != -1:
+            if m.stride == (2, 2):
+                m.stride = (1, 1)
+                if m.kernel_size == (3, 3):
+                    m.dilation = (dilate // 2, dilate // 2)
+                    m.padding = (dilate // 2, dilate // 2)
+            else:
+                if m.kernel_size == (3, 3):
+                    m.dilation = (dilate, dilate)
+                    m.padding = (dilate, dilate)
+
+    def forward(self, x):
+        low_level_features = self.features[:4](x)
+        x = self.features[4:](low_level_features)
+        return low_level_features, x
+
+
+#-----------------------------------------#
+#   ASPP特征提取模块
+#   利用不同膨胀率的膨胀卷积进行特征提取
+#-----------------------------------------#
+"""
+卷积操作使用了膨胀率 dilation=6 * rate，这意味着卷积核内的采样点之间有 6 个像素的间隔。
+为了保持输出特征图的尺寸与输入数据相同，填充参数 padding 被设置为 padding=6 * rate。
+这样，卷积操作将在输入数据上以膨胀率为 6 的间隔进行卷积，并且在输出特征图的边缘周围填充 6 个像素的零，以确保输出特征图的尺寸不会缩小。
+
+
+bn_mom 在这段代码中是批量归一化层（Batch Normalization）的动量参数（momentum）。
+在 PyTorch 中的批量归一化层的实现中，动量参数控制了均值和方差的移动平均值的更新速度
+
+dilation 是卷积操作的膨胀率参数
+"""
+class ASPP(nn.Module):
+    def __init__(self, dim_in, dim_out, rate=1, bn_mom=0.1):
+        super(ASPP, self).__init__()
+        self.branch1 = nn.Sequential(
+            nn.Conv2d(dim_in,
+                      dim_out,
+                      1,
+                      1,
+                      padding=0,
+                      dilation=rate,
+                      bias=True),
+            nn.BatchNorm2d(dim_out, momentum=bn_mom),
+            nn.ReLU(inplace=True),
+        )
+        self.branch2 = nn.Sequential(
+            nn.Conv2d(dim_in,
+                      dim_out,
+                      3,
+                      1,
+                      padding=6 * rate,
+                      dilation=6 * rate,
+                      bias=True),
+            nn.BatchNorm2d(dim_out, momentum=bn_mom),
+            nn.ReLU(inplace=True),
+        )
+        self.branch3 = nn.Sequential(
+            nn.Conv2d(dim_in,
+                      dim_out,
+                      3,
+                      1,
+                      padding=12 * rate,
+                      dilation=12 * rate,
+                      bias=True),
+            nn.BatchNorm2d(dim_out, momentum=bn_mom),
+            nn.ReLU(inplace=True),
+        )
+        self.branch4 = nn.Sequential(
+            nn.Conv2d(dim_in,
+                      dim_out,
+                      3,
+                      1,
+                      padding=18 * rate,
+                      dilation=18 * rate,
+                      bias=True),
+            nn.BatchNorm2d(dim_out, momentum=bn_mom),
+            nn.ReLU(inplace=True),
+        )
+        # branch5是一个池化过程
+        self.branch5_conv = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=True)
+        self.branch5_bn = nn.BatchNorm2d(dim_out, momentum=bn_mom)
+        self.branch5_relu = nn.ReLU(inplace=True)
+
+        self.conv_cat = nn.Sequential(
+            nn.Conv2d(dim_out * 5, dim_out, 1, 1, padding=0, bias=True),
+            nn.BatchNorm2d(dim_out, momentum=bn_mom),
+            nn.ReLU(inplace=True),
+        )
+
+    def forward(self, x):
+        [b, c, row, col] = x.size()
+        #-----------------------------------------#
+        #   一共五个分支
+        #-----------------------------------------#
+        conv1x1 = self.branch1(x)
+        conv3x3_1 = self.branch2(x)
+        conv3x3_2 = self.branch3(x)
+        conv3x3_3 = self.branch4(x)
+        #-----------------------------------------#
+        #   第五个分支，全局平均池化+卷积
+        #-----------------------------------------#
+        global_feature = torch.mean(x, 2, True)
+        global_feature = torch.mean(global_feature, 3, True) #两次平均池化
+        global_feature = self.branch5_conv(global_feature)
+        global_feature = self.branch5_bn(global_feature)
+        global_feature = self.branch5_relu(global_feature)# 通道的整合
+        global_feature = F.interpolate(global_feature, (row, col), None,
+                                       'bilinear', True)
+        """
+        这一行代码首先使用 torch.mean 函数计算了 x 沿第 2 维度（通常是垂直方向）的平均值。
+        True 参数表示要保持维度的数量，即在结果中保留该维度。这将生成一个张量
+        其形状为 [batch_size, num_channels, 1, width]，
+        其中 batch_size 是批量大小，num_channels 是通道数，width 是特征图的宽度，高度为 1。
+        global_feature = torch.mean(global_feature, 3, True)：
+        同样使用 torch.mean 函数，计算了上一步结果中的 global_feature 沿第 3 维度（通常是水平方向）的平均值，
+        并再次保持维度的数量。这将生成一个形状为 [batch_size, num_channels, 1, 1] 的张量，
+        其中每个通道的值都是整个特征图在该通道上的平均值。
+        
+        这个过程的结果是对整个特征图的每个通道执行全局平均池化，
+        最终生成一个形状为 [batch_size, num_channels, 1, 1] 的全局平均特征向量。
+        
+        将全局平均特征 global_feature 调整为指定的目标尺寸 (row, col)
+        """
+        #-----------------------------------------#
+        #   将五个分支的内容堆叠起来
+        #   然后1x1卷积整合特征。
+        #-----------------------------------------#
+        feature_cat = torch.cat(
+            [conv1x1, conv3x3_1, conv3x3_2, conv3x3_3, global_feature], dim=1)
+        result = self.conv_cat(feature_cat) #1*1卷积 这个就是哪个绿色的
+        return result
+# ASPP就是使用不同膨胀率的膨胀卷积对特征进行提取
+
+class DeepLab(nn.Module):
+    def __init__(self,
+                 num_classes,
+                 backbone="mobilenet",
+                 pretrained=True,
+                 downsample_factor=16):
+        super(DeepLab, self).__init__()
+        if backbone == "xception":
+            #----------------------------------#
+            #   获得两个特征层
+            #   浅层特征    [128,128,256]
+            #   主干部分    [30,30,2048]
+            #----------------------------------#
+            self.backbone = xception(downsample_factor=downsample_factor,
+                                     pretrained=pretrained)
+            in_channels = 2048
+            low_level_channels = 256
+        elif backbone == "mobilenet":
+            #----------------------------------#
+            #   获得两个特征层
+            #   浅层特征    [128,128,24]
+            #   主干部分    [30,30,320]
+            #----------------------------------#
+            self.backbone = MobileNetV2(downsample_factor=downsample_factor,
+                                        pretrained=pretrained)
+            in_channels = 320
+            low_level_channels = 24
+        else:
+            raise ValueError(
+                'Unsupported backbone - `{}`, Use mobilenet, xception.'.format(
+                    backbone))
+
+        #-----------------------------------------#
+        #   ASPP特征提取模块
+        #   利用不同膨胀率的膨胀卷积进行特征提取
+        #-----------------------------------------#
+        self.aspp = ASPP(dim_in=in_channels,
+                         dim_out=256,
+                         rate=16 // downsample_factor)
+
+        #----------------------------------#
+        #   浅层特征边
+        #----------------------------------#
+        self.shortcut_conv = nn.Sequential(
+            nn.Conv2d(low_level_channels, 48, 1),
+            nn.BatchNorm2d(48),
+            nn.ReLU(inplace=True))
+
+        self.cat_conv = nn.Sequential(
+            nn.Conv2d(48 + 256, 256, 3, stride=1, padding=1),
+            nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True),
+            nn.Dropout(0.5),
+
+            nn.Conv2d(256, 256, 3, stride=1, padding=1),
+            nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True),
+            nn.Dropout(0.1),
+        )
+        self.cls_conv = nn.Conv2d(256, num_classes, 1, stride=1)
+    """
+    在每个训练批次中，以一定的概率（通常在 0.2 到 0.5 之间）随机选择一些神经元，并将它们的输出置零。
+    这表示在每次前向传播中，只有部分神经元的输出会被传递到下一层，而其他神经元的输出被设置为零。
+    
+    在每次反向传播中，只有那些没有被置零的神经元才会更新其权重。这意味着每个神经元都有机会被训练，
+    而不是过度依赖于特定的神经元。
+    """
+    def forward(self, x):
+        H, W = x.size(2), x.size(3)
+        #-----------------------------------------#
+        #   获得两个特征层
+        #   low_level_features: 浅层特征-进行卷积处理
+        #   x : 主干部分-利用ASPP结构进行加强特征提取
+        #-----------------------------------------#
+        low_level_features, x = self.backbone(x)
+        x = self.aspp(x)
+        low_level_features = self.shortcut_conv(low_level_features)
+
+        #-----------------------------------------#
+        #   将加强特征边上采样  绿色的模块
+        #   与浅层特征堆叠后利用卷积进行特征提取
+        #-----------------------------------------#
+        x = F.interpolate(x,
+                          size=(low_level_features.size(2),
+                                low_level_features.size(3)),
+                          mode='bilinear',#使用双线性插值进行的上采样操作
+                          align_corners=True) #上采样
+        x = self.cat_conv(torch.cat((x, low_level_features), dim=1))
+        x = self.cls_conv(x)
+        x = F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True)
+        return x
diff --git a/nets/deeplabv3_training.py b/nets/deeplabv3_training.py
new file mode 100644
index 0000000..26d5cc1
--- /dev/null
+++ b/nets/deeplabv3_training.py
@@ -0,0 +1,113 @@
+import math
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def CE_Loss(inputs, target, cls_weights, num_classes=21):
+    n, c, h, w = inputs.size()
+    nt, ht, wt = target.size()
+    if h != ht and w != wt:
+        inputs = F.interpolate(inputs, size=(ht, wt), mode="bilinear", align_corners=True)
+
+    temp_inputs = inputs.transpose(1, 2).transpose(2, 3).contiguous().view(-1, c)
+    temp_target = target.view(-1)
+
+    CE_loss  = nn.CrossEntropyLoss(weight=cls_weights, ignore_index=num_classes)(temp_inputs, temp_target)
+    return CE_loss
+
+def Focal_Loss(inputs, target, cls_weights, num_classes=21, alpha=0.5, gamma=2):
+    n, c, h, w = inputs.size()
+    nt, ht, wt = target.size()
+    if h != ht and w != wt:
+        inputs = F.interpolate(inputs, size=(ht, wt), mode="bilinear", align_corners=True)
+
+    temp_inputs = inputs.transpose(1, 2).transpose(2, 3).contiguous().view(-1, c)
+    temp_target = target.view(-1)
+
+    logpt  = -nn.CrossEntropyLoss(weight=cls_weights, ignore_index=num_classes, reduction='none')(temp_inputs, temp_target)
+    pt = torch.exp(logpt)
+    if alpha is not None:
+        logpt *= alpha
+    loss = -((1 - pt) ** gamma) * logpt
+    loss = loss.mean()
+    return loss
+
+def Dice_loss(inputs, target, beta=1, smooth = 1e-5):
+    n, c, h, w = inputs.size()
+    nt, ht, wt, ct = target.size()
+    if h != ht and w != wt:
+        inputs = F.interpolate(inputs, size=(ht, wt), mode="bilinear", align_corners=True)
+        
+    temp_inputs = torch.softmax(inputs.transpose(1, 2).transpose(2, 3).contiguous().view(n, -1, c),-1)
+    temp_target = target.view(n, -1, ct)
+
+    #--------------------------------------------#
+    #   计算dice loss
+    #--------------------------------------------#
+    tp = torch.sum(temp_target[...,:-1] * temp_inputs, axis=[0,1])
+    fp = torch.sum(temp_inputs                       , axis=[0,1]) - tp
+    fn = torch.sum(temp_target[...,:-1]              , axis=[0,1]) - tp
+
+    score = ((1 + beta ** 2) * tp + smooth) / ((1 + beta ** 2) * tp + beta ** 2 * fn + fp + smooth)
+    dice_loss = 1 - torch.mean(score)
+    return dice_loss
+
+def weights_init(net, init_type='normal', init_gain=0.02):
+    def init_func(m):
+        classname = m.__class__.__name__
+        if hasattr(m, 'weight') and classname.find('Conv') != -1:
+            if init_type == 'normal':
+                torch.nn.init.normal_(m.weight.data, 0.0, init_gain)
+            elif init_type == 'xavier':
+                torch.nn.init.xavier_normal_(m.weight.data, gain=init_gain)
+            elif init_type == 'kaiming':
+                torch.nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
+            elif init_type == 'orthogonal':
+                torch.nn.init.orthogonal_(m.weight.data, gain=init_gain)
+            else:
+                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
+        elif classname.find('BatchNorm2d') != -1:
+            torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
+            torch.nn.init.constant_(m.bias.data, 0.0)
+    print('initialize network with %s type' % init_type)
+    net.apply(init_func)
+
+def get_lr_scheduler(lr_decay_type, lr, min_lr, total_iters, warmup_iters_ratio = 0.1, warmup_lr_ratio = 0.1, no_aug_iter_ratio = 0.3, step_num = 10):
+    def yolox_warm_cos_lr(lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter, iters):
+        if iters <= warmup_total_iters:
+            # lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start
+            lr = (lr - warmup_lr_start) * pow(iters / float(warmup_total_iters), 2) + warmup_lr_start
+        elif iters >= total_iters - no_aug_iter:
+            lr = min_lr
+        else:
+            lr = min_lr + 0.5 * (lr - min_lr) * (
+                1.0 + math.cos(math.pi* (iters - warmup_total_iters) / (total_iters - warmup_total_iters - no_aug_iter))
+            )
+        return lr
+
+    def step_lr(lr, decay_rate, step_size, iters):
+        if step_size < 1:
+            raise ValueError("step_size must above 1.")
+        n       = iters // step_size
+        out_lr  = lr * decay_rate ** n
+        return out_lr
+
+    if lr_decay_type == "cos":
+        warmup_total_iters  = min(max(warmup_iters_ratio * total_iters, 1), 3)
+        warmup_lr_start     = max(warmup_lr_ratio * lr, 1e-6)
+        no_aug_iter         = min(max(no_aug_iter_ratio * total_iters, 1), 15)
+        func = partial(yolox_warm_cos_lr ,lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter)
+    else:
+        decay_rate  = (min_lr / lr) ** (1 / (step_num - 1))
+        step_size   = total_iters / step_num
+        func = partial(step_lr, lr, decay_rate, step_size)
+
+    return func
+
+def set_optimizer_lr(optimizer, lr_scheduler_func, epoch):
+    lr = lr_scheduler_func(epoch)
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
diff --git a/nets/img.png b/nets/img.png
new file mode 100644
index 0000000..3464b35
Binary files /dev/null and b/nets/img.png differ
diff --git a/nets/img_1.png b/nets/img_1.png
new file mode 100644
index 0000000..8daaef3
Binary files /dev/null and b/nets/img_1.png differ
diff --git a/nets/mobilenetv2.py b/nets/mobilenetv2.py
new file mode 100644
index 0000000..13ae643
--- /dev/null
+++ b/nets/mobilenetv2.py
@@ -0,0 +1,164 @@
+import math
+import os
+
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+BatchNorm2d = nn.BatchNorm2d
+
+def conv_bn(inp, oup, stride):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+        BatchNorm2d(oup),
+        nn.ReLU6(inplace=True)
+    )
+
+def conv_1x1_bn(inp, oup):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
+        BatchNorm2d(oup),
+        nn.ReLU6(inplace=True)
+    )
+
+class InvertedResidual(nn.Module):
+    def __init__(self, inp, oup, stride, expand_ratio):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2]
+
+        hidden_dim = round(inp * expand_ratio)
+        self.use_res_connect = self.stride == 1 and inp == oup
+
+        if expand_ratio == 1:
+            self.conv = nn.Sequential(
+                #--------------------------------------------#
+                #   进行3x3的逐层卷积，进行跨特征点的特征提取
+                #--------------------------------------------#
+                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
+                BatchNorm2d(hidden_dim),
+                nn.ReLU6(inplace=True),
+                #-----------------------------------#
+                #   利用1x1卷积进行通道数的调整
+                #-----------------------------------#
+                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+                BatchNorm2d(oup),
+            )
+        else:
+            self.conv = nn.Sequential(
+                #-----------------------------------#
+                #   利用1x1卷积进行通道数的上升
+                #-----------------------------------#
+                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
+                BatchNorm2d(hidden_dim),
+                nn.ReLU6(inplace=True),
+                #--------------------------------------------#
+                #   进行3x3的逐层卷积，进行跨特征点的特征提取
+                #--------------------------------------------#
+                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
+                BatchNorm2d(hidden_dim),
+                nn.ReLU6(inplace=True),
+                #-----------------------------------#
+                #   利用1x1卷积进行通道数的下降
+                #-----------------------------------#
+                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+                BatchNorm2d(oup),
+            )
+
+    def forward(self, x):
+        if self.use_res_connect:
+            return x + self.conv(x)
+        else:
+            return self.conv(x)
+
+class MobileNetV2(nn.Module):
+    def __init__(self, n_class=1000, input_size=224, width_mult=1.):
+        super(MobileNetV2, self).__init__()
+        block = InvertedResidual
+        input_channel = 32
+        last_channel = 1280
+        interverted_residual_setting = [
+            # t, c, n, s   t:expand_ratio 1*1卷积通道数上升,c:output_channel,n 是range(n)n次循环，s是步长
+            [1, 16, 1, 1], 
+            # 256, 256, 32 -> 256, 256, 16
+            [6, 24, 2, 2], 
+            # 256, 256, 16 -> 128, 128, 24   2
+            [6, 32, 3, 2], 
+            # 128, 128, 24 -> 64, 64, 32     4
+            [6, 64, 4, 2], 
+            # 64, 64, 32 -> 32, 32, 64       7
+            [6, 96, 3, 1], 
+            # 32, 32, 64 -> 32, 32, 96
+            [6, 160, 3, 2], 
+            # 32, 32, 96 -> 16, 16, 160     14
+            [6, 320, 1, 1], 
+            # 16, 16, 160 -> 16, 16, 320
+        ]
+
+        assert input_size % 32 == 0
+        input_channel = int(input_channel * width_mult)
+        self.last_channel = int(last_channel * width_mult) if width_mult > 1.0 else last_channel
+        # 512, 512, 3 -> 256, 256, 32
+        self.features = [conv_bn(3, input_channel, 2)]
+
+        for t, c, n, s in interverted_residual_setting:
+            output_channel = int(c * width_mult)
+            for i in range(n):
+                if i == 0:
+                    self.features.append(block(input_channel, output_channel, s, expand_ratio=t))
+                else:
+                    self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))
+                input_channel = output_channel
+
+        self.features.append(conv_1x1_bn(input_channel, self.last_channel))
+        self.features = nn.Sequential(*self.features)
+
+        self.classifier = nn.Sequential(
+            nn.Dropout(0.2),
+            nn.Linear(self.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):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                n = m.weight.size(1)
+                m.weight.data.normal_(0, 0.01)
+                m.bias.data.zero_()
+
+
+def load_url(url, model_dir='./model_data', map_location=None):
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    filename = url.split('/')[-1]
+    cached_file = os.path.join(model_dir, filename)
+    if os.path.exists(cached_file):
+        return torch.load(cached_file, map_location=map_location)
+    else:
+        return model_zoo.load_url(url,model_dir=model_dir)
+
+def mobilenetv2(pretrained=False, **kwargs):
+    model = MobileNetV2(n_class=1000, **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url('https://github.com/bubbliiiing/deeplabv3-plus-pytorch/releases/download/v1.0/mobilenet_v2.pth.tar'), strict=False)
+    return model
+
+if __name__ == "__main__":
+    model = mobilenetv2()
+    for i, layer in enumerate(model.features):
+        print(i, layer)
diff --git a/nets/test_net.py b/nets/test_net.py
new file mode 100644
index 0000000..f1c641b
--- /dev/null
+++ b/nets/test_net.py
@@ -0,0 +1,30 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from nets.xception import xception
+from nets.mobilenetv2 import mobilenetv2
+
+class ASPP(nn.Module):
+    def __init__(self, dim_in, dim_out, rate=1, bn_mom=0.1):
+        super(ASPP, self).__init__()
+        self.branch1 = nn.Sequential(
+            nn.Conv2d(dim_in, dim_out, 1, 1, padding=0, dilation=rate,bias=True),
+            nn.BatchNorm2d(dim_out, momentum=bn_mom),
+            nn.ReLU(inplace=True),
+        )
+        self.branch2 = nn.Sequential(
+            nn.Conv2d(dim_in, dim_out, 3, 1, padding=6*rate, dilation=6*rate, bias=True),
+            nn.BatchNorm2d(dim_out, momentum=bn_mom),
+            nn.ReLU(inplace=True),
+        )
+        self.branch3 = nn.Sequential(
+            nn.Conv2d(dim_in, dim_out, 3, 1, padding=12 * rate, dilation=12 * rate, bias=True),
+            nn.BatchNorm2d(dim_out, momentum=bn_mom),
+            nn.ReLU(inplace=True),
+        )
+        self.branch4 = nn.Sequential(
+            nn.Conv2d(dim_in, dim_out, 3, 1, padding=18 * rate, dilation=18 * rate, bias=True),
+            nn.BatchNorm2d(dim_out, momentum=bn_mom),
+            nn.ReLU(inplace=True),
+        )
+        self.branch5_conv = nn.Conv2d(dim_in, dim_out,1, 1, 0, bias=True),
diff --git a/nets/xception.py b/nets/xception.py
new file mode 100644
index 0000000..3f536c7
--- /dev/null
+++ b/nets/xception.py
@@ -0,0 +1,298 @@
+import math
+import os
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+
+bn_mom = 0.0003
+
+
+class SeparableConv2d(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=1,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 bias=False,
+                 activate_first=True,
+                 inplace=True):
+        super(SeparableConv2d, self).__init__()
+        self.relu0 = nn.ReLU(inplace=inplace)
+        self.depthwise = nn.Conv2d(in_channels,
+                                   in_channels,
+                                   kernel_size,
+                                   stride,
+                                   padding,
+                                   dilation,
+                                   groups=in_channels,
+                                   bias=bias)
+        self.bn1 = nn.BatchNorm2d(in_channels, momentum=bn_mom)
+        self.relu1 = nn.ReLU(inplace=True)
+        self.pointwise = nn.Conv2d(in_channels,
+                                   out_channels,
+                                   1,
+                                   1,
+                                   0,
+                                   1,
+                                   1,
+                                   bias=bias)
+        self.bn2 = nn.BatchNorm2d(out_channels, momentum=bn_mom)
+        self.relu2 = nn.ReLU(inplace=True)
+        self.activate_first = activate_first
+
+    def forward(self, x):
+        if self.activate_first:
+            x = self.relu0(x)
+        x = self.depthwise(x)
+        x = self.bn1(x)
+        if not self.activate_first:
+            x = self.relu1(x)
+        x = self.pointwise(x)
+        x = self.bn2(x)
+        if not self.activate_first:
+            x = self.relu2(x)
+        return x
+
+
+class Block(nn.Module):
+    def __init__(self,
+                 in_filters,
+                 out_filters,
+                 strides=1,
+                 atrous=None,
+                 grow_first=True,
+                 activate_first=True,
+                 inplace=True):
+        super(Block, self).__init__()
+        if atrous is None:
+            atrous = [1] * 3
+        elif isinstance(atrous, int):
+            atrous_list = [atrous] * 3
+            atrous = atrous_list
+        idx = 0
+        self.head_relu = True
+        if out_filters != in_filters or strides != 1:
+            self.skip = nn.Conv2d(in_filters,
+                                  out_filters,
+                                  1,
+                                  stride=strides,
+                                  bias=False)
+            self.skipbn = nn.BatchNorm2d(out_filters, momentum=bn_mom)
+            self.head_relu = False
+        else:
+            self.skip = None
+
+        self.hook_layer = None
+        if grow_first:
+            filters = out_filters
+        else:
+            filters = in_filters
+        self.sepconv1 = SeparableConv2d(in_filters,
+                                        filters,
+                                        3,
+                                        stride=1,
+                                        padding=1 * atrous[0],
+                                        dilation=atrous[0],
+                                        bias=False,
+                                        activate_first=activate_first,
+                                        inplace=self.head_relu)
+        self.sepconv2 = SeparableConv2d(filters,
+                                        out_filters,
+                                        3,
+                                        stride=1,
+                                        padding=1 * atrous[1],
+                                        dilation=atrous[1],
+                                        bias=False,
+                                        activate_first=activate_first)
+        self.sepconv3 = SeparableConv2d(out_filters,
+                                        out_filters,
+                                        3,
+                                        stride=strides,
+                                        padding=1 * atrous[2],
+                                        dilation=atrous[2],
+                                        bias=False,
+                                        activate_first=activate_first,
+                                        inplace=inplace)
+
+    def forward(self, inp):
+
+        if self.skip is not None:
+            skip = self.skip(inp)
+            skip = self.skipbn(skip)
+        else:
+            skip = inp
+
+        x = self.sepconv1(inp)
+        x = self.sepconv2(x)
+        self.hook_layer = x
+        x = self.sepconv3(x)
+
+        x += skip
+        return x
+
+
+class Xception(nn.Module):
+    """
+    Xception optimized for the ImageNet dataset, as specified in
+    https://arxiv.org/pdf/1610.02357.pdf
+    """
+    def __init__(self, downsample_factor):
+        """ Constructor
+        Args:
+            num_classes: number of classes
+        """
+        super(Xception, self).__init__()
+
+        stride_list = None
+        if downsample_factor == 8:
+            stride_list = [2, 1, 1]
+        elif downsample_factor == 16:
+            stride_list = [2, 2, 1]
+        else:
+            raise ValueError(
+                'xception.py: output stride=%d is not supported.' % os)
+        self.conv1 = nn.Conv2d(3, 32, 3, 2, 1, bias=False)
+        self.bn1 = nn.BatchNorm2d(32, momentum=bn_mom)
+        self.relu = nn.ReLU(inplace=True)
+
+        self.conv2 = nn.Conv2d(32, 64, 3, 1, 1, bias=False)
+        self.bn2 = nn.BatchNorm2d(64, momentum=bn_mom)
+        #do relu here
+
+        self.block1 = Block(64, 128, 2)
+        self.block2 = Block(128, 256, stride_list[0], inplace=False)
+        self.block3 = Block(256, 728, stride_list[1])
+
+        rate = 16 // downsample_factor
+        self.block4 = Block(728, 728, 1, atrous=rate)
+        self.block5 = Block(728, 728, 1, atrous=rate)
+        self.block6 = Block(728, 728, 1, atrous=rate)
+        self.block7 = Block(728, 728, 1, atrous=rate)
+
+        self.block8 = Block(728, 728, 1, atrous=rate)
+        self.block9 = Block(728, 728, 1, atrous=rate)
+        self.block10 = Block(728, 728, 1, atrous=rate)
+        self.block11 = Block(728, 728, 1, atrous=rate)
+
+        self.block12 = Block(728, 728, 1, atrous=rate)
+        self.block13 = Block(728, 728, 1, atrous=rate)
+        self.block14 = Block(728, 728, 1, atrous=rate)
+        self.block15 = Block(728, 728, 1, atrous=rate)
+
+        self.block16 = Block(728,
+                             728,
+                             1,
+                             atrous=[1 * rate, 1 * rate, 1 * rate])
+        self.block17 = Block(728,
+                             728,
+                             1,
+                             atrous=[1 * rate, 1 * rate, 1 * rate])
+        self.block18 = Block(728,
+                             728,
+                             1,
+                             atrous=[1 * rate, 1 * rate, 1 * rate])
+        self.block19 = Block(728,
+                             728,
+                             1,
+                             atrous=[1 * rate, 1 * rate, 1 * rate])
+
+        self.block20 = Block(728,
+                             1024,
+                             stride_list[2],
+                             atrous=rate,
+                             grow_first=False)
+        self.conv3 = SeparableConv2d(1024,
+                                     1536,
+                                     3,
+                                     1,
+                                     1 * rate,
+                                     dilation=rate,
+                                     activate_first=False)
+
+        self.conv4 = SeparableConv2d(1536,
+                                     1536,
+                                     3,
+                                     1,
+                                     1 * rate,
+                                     dilation=rate,
+                                     activate_first=False)
+
+        self.conv5 = SeparableConv2d(1536,
+                                     2048,
+                                     3,
+                                     1,
+                                     1 * rate,
+                                     dilation=rate,
+                                     activate_first=False)
+        self.layers = []
+
+        #------- init weights --------
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+        #-----------------------------
+
+    def forward(self, input):
+        self.layers = []
+        x = self.conv1(input)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x = self.relu(x)
+
+        x = self.block1(x)
+        x = self.block2(x)
+        low_featrue_layer = self.block2.hook_layer
+        x = self.block3(x)
+        x = self.block4(x)
+        x = self.block5(x)
+        x = self.block6(x)
+        x = self.block7(x)
+        x = self.block8(x)
+        x = self.block9(x)
+        x = self.block10(x)
+        x = self.block11(x)
+        x = self.block12(x)
+        x = self.block13(x)
+        x = self.block14(x)
+        x = self.block15(x)
+        x = self.block16(x)
+        x = self.block17(x)
+        x = self.block18(x)
+        x = self.block19(x)
+        x = self.block20(x)
+
+        x = self.conv3(x)
+
+        x = self.conv4(x)
+
+        x = self.conv5(x)
+        return low_featrue_layer, x
+
+
+def load_url(url, model_dir='./model_data', map_location=None):
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    filename = url.split('/')[-1]
+    cached_file = os.path.join(model_dir, filename)
+    if os.path.exists(cached_file):
+        return torch.load(cached_file, map_location=map_location)
+    else:
+        return model_zoo.load_url(url, model_dir=model_dir)
+
+
+def xception(pretrained=True, downsample_factor=16):
+    model = Xception(downsample_factor=downsample_factor)
+    if pretrained:
+        model.load_state_dict(load_url(
+            'https://github.com/bubbliiiing/deeplabv3-plus-pytorch/releases/download/v1.0/xception_pytorch_imagenet.pth'
+        ),
+                              strict=False)
+    return model
diff --git a/new_predict.py b/new_predict.py
new file mode 100644
index 0000000..001311a
--- /dev/null
+++ b/new_predict.py
@@ -0,0 +1,82 @@
+import os
+import time
+import json
+
+import torch
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+
+from src import deeplabv3_resnet50
+
+
+def time_synchronized():
+    torch.cuda.synchronize() if torch.cuda.is_available() else None
+    return time.time()
+
+
+def main():
+    aux = False  # inference time not need aux_classifier
+    classes = 20
+    weights_path = "./save_weights/model_29.pth"
+    img_path = "./test.jpg"
+    palette_path = "./palette.json"
+    assert os.path.exists(weights_path), f"weights {weights_path} not found."
+    assert os.path.exists(img_path), f"image {img_path} not found."
+    assert os.path.exists(palette_path), f"palette {palette_path} not found."
+    with open(palette_path, "rb") as f:
+        pallette_dict = json.load(f)
+        pallette = []
+        for v in pallette_dict.values():
+            pallette += v
+
+    # get devices
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    print("using {} device.".format(device))
+
+    # create model
+    model = deeplabv3_resnet50(aux=aux, num_classes=classes+1)
+
+    # delete weights about aux_classifier
+    weights_dict = torch.load(weights_path, map_location='cpu')['model']
+    for k in list(weights_dict.keys()):
+        if "aux" in k:
+            del weights_dict[k]
+
+    # load weights
+    model.load_state_dict(weights_dict)
+    model.to(device)
+
+    # load image
+    original_img = Image.open(img_path)
+
+    # from pil image to tensor and normalize
+    data_transform = transforms.Compose([transforms.Resize(520),
+                                         transforms.ToTensor(),
+                                         transforms.Normalize(mean=(0.485, 0.456, 0.406),
+                                                              std=(0.229, 0.224, 0.225))])
+    img = data_transform(original_img)
+    # expand batch dimension
+    img = torch.unsqueeze(img, dim=0)
+
+    model.eval()  # 进入验证模式
+    with torch.no_grad():
+        # init model
+        img_height, img_width = img.shape[-2:]
+        init_img = torch.zeros((1, 3, img_height, img_width), device=device)
+        model(init_img)
+
+        t_start = time_synchronized()
+        output = model(img.to(device))
+        t_end = time_synchronized()
+        print("inference time: {}".format(t_end - t_start))
+
+        prediction = output['out'].argmax(1).squeeze(0)
+        prediction = prediction.to("cpu").numpy().astype(np.uint8)
+        mask = Image.fromarray(prediction)
+        mask.putpalette(pallette)
+        mask.save("test_result.png")
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/predict.py b/predict.py
new file mode 100644
index 0000000..70d92dc
--- /dev/null
+++ b/predict.py
@@ -0,0 +1,165 @@
+#----------------------------------------------------#
+#   将单张图片预测、摄像头检测和FPS测试功能
+#   整合到了一个py文件中，通过指定mode进行模式的修改。
+#----------------------------------------------------#
+import time
+
+import cv2
+import numpy as np
+from PIL import Image
+
+from deeplab import DeeplabV3
+
+if __name__ == "__main__":
+    #-------------------------------------------------------------------------#
+    #   如果想要修改对应种类的颜色，到__init__函数里修改self.colors即可
+    #-------------------------------------------------------------------------#
+    deeplab = DeeplabV3()
+    #----------------------------------------------------------------------------------------------------------#
+    #   mode用于指定测试的模式：
+    #   'predict'           表示单张图片预测，如果想对预测过程进行修改，如保存图片，截取对象等，可以先看下方详细的注释
+    #   'video'             表示视频检测，可调用摄像头或者视频进行检测，详情查看下方注释。
+    #   'fps'               表示测试fps，使用的图片是img里面的street.jpg，详情查看下方注释。
+    #   'dir_predict'       表示遍历文件夹进行检测并保存。默认遍历img文件夹，保存img_out文件夹，详情查看下方注释。
+    #   'export_onnx'       表示将模型导出为onnx，需要pytorch1.7.1以上。
+    #----------------------------------------------------------------------------------------------------------#
+    mode = "predict"
+    #-------------------------------------------------------------------------#
+    #   count               指定了是否进行目标的像素点计数（即面积）与比例计算
+    #   name_classes        区分的种类，和json_to_dataset里面的一样，用于打印种类和数量
+    #
+    #   count、name_classes仅在mode='predict'时有效
+    #-------------------------------------------------------------------------#
+    count           = False
+    name_classes    = ["background", "pl5", "pl20", "pl30", "pl40", "pl50", "pl60", "pl70", "pl80", "pl100", "pl120",  "pm20", "pm55","pr40","p11", "pn", "pne", "p26", "i2", "i4", "i5", "ip", "il60", "il80", "il100", "p5", "p10", "p23", "p3", "pg", "p19", "p12", "p6", "p27", "ph4", "ph4.5", "ph5", "pm30", "w55", "w59", "w13", "w57", "w32", "wo", "io", "po", "indicative"]
+    # name_classes    = ["background","cat","dog"]
+    #----------------------------------------------------------------------------------------------------------#
+    #   video_path          用于指定视频的路径，当video_path=0时表示检测摄像头
+    #                       想要检测视频，则设置如video_path = "xxx.mp4"即可，代表读取出根目录下的xxx.mp4文件。
+    #   video_save_path     表示视频保存的路径，当video_save_path=""时表示不保存
+    #                       想要保存视频，则设置如video_save_path = "yyy.mp4"即可，代表保存为根目录下的yyy.mp4文件。
+    #   video_fps           用于保存的视频的fps
+    #
+    #   video_path、video_save_path和video_fps仅在mode='video'时有效
+    #   保存视频时需要ctrl+c退出或者运行到最后一帧才会完成完整的保存步骤。
+    #----------------------------------------------------------------------------------------------------------#
+    video_path      = 0
+    video_save_path = ""
+    video_fps       = 25.0
+    #----------------------------------------------------------------------------------------------------------#
+    #   test_interval       用于指定测量fps的时候，图片检测的次数。理论上test_interval越大，fps越准确。
+    #   fps_image_path      用于指定测试的fps图片
+    #   
+    #   test_interval和fps_image_path仅在mode='fps'有效
+    #----------------------------------------------------------------------------------------------------------#
+    test_interval = 100
+    fps_image_path  = "img/73473.jpg"
+    #-------------------------------------------------------------------------#
+    #   dir_origin_path     指定了用于检测的图片的文件夹路径
+    #   dir_save_path       指定了检测完图片的保存路径
+    #   
+    #   dir_origin_path和dir_save_path仅在mode='dir_predict'时有效
+    #-------------------------------------------------------------------------#
+    dir_origin_path = "imgs/"
+    dir_save_path   = "img_out/"
+    #-------------------------------------------------------------------------#
+    #   simplify            使用Simplify onnx
+    #   onnx_save_path      指定了onnx的保存路径
+    #-------------------------------------------------------------------------#
+    simplify        = True
+    onnx_save_path  = "model_data/models.onnx"
+
+    if mode == "predict":
+        '''
+        predict.py有几个注意点
+        1、该代码无法直接进行批量预测，如果想要批量预测，可以利用os.listdir()遍历文件夹，利用Image.open打开图片文件进行预测。
+        具体流程可以参考get_miou_prediction.py，在get_miou_prediction.py即实现了遍历。
+        2、如果想要保存，利用r_image.save("img.jpg")即可保存。
+        3、如果想要原图和分割图不混合，可以把blend参数设置成False。
+        4、如果想根据mask获取对应的区域，可以参考detect_image函数中，利用预测结果绘图的部分，判断每一个像素点的种类，然后根据种类获取对应的部分。
+        seg_img = np.zeros((np.shape(pr)[0],np.shape(pr)[1],3))
+        for c in range(self.num_classes):
+            seg_img[:, :, 0] += ((pr == c)*( self.colors[c][0] )).astype('uint8')
+            seg_img[:, :, 1] += ((pr == c)*( self.colors[c][1] )).astype('uint8')
+            seg_img[:, :, 2] += ((pr == c)*( self.colors[c][2] )).astype('uint8')
+        '''
+        while True:
+            img = input('Input image filename:')
+            try:
+                image = Image.open(img)
+            except:
+                print('Open Error! Try again!')
+                continue
+            else:
+                r_image = deeplab.detect_image(image, count=count, name_classes=name_classes)
+                r_image.show()
+
+    elif mode == "video":
+        capture=cv2.VideoCapture(video_path)
+        if video_save_path!="":
+            fourcc = cv2.VideoWriter_fourcc(*'XVID')
+            size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+            out = cv2.VideoWriter(video_save_path, fourcc, video_fps, size)
+
+        ref, frame = capture.read()
+        if not ref:
+            raise ValueError("未能正确读取摄像头（视频），请注意是否正确安装摄像头（是否正确填写视频路径）。")
+
+        fps = 0.0
+        while(True):
+            t1 = time.time()
+            # 读取某一帧
+            ref, frame = capture.read()
+            if not ref:
+                break
+            # 格式转变，BGRtoRGB
+            frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
+            # 转变成Image
+            frame = Image.fromarray(np.uint8(frame))
+            # 进行检测
+            frame = np.array(deeplab.detect_image(frame))
+            # RGBtoBGR满足opencv显示格式
+            frame = cv2.cvtColor(frame,cv2.COLOR_RGB2BGR)
+            
+            fps  = ( fps + (1./(time.time()-t1)) ) / 2
+            print("fps= %.2f"%(fps))
+            frame = cv2.putText(frame, "fps= %.2f"%(fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+            
+            cv2.imshow("video",frame)
+            c= cv2.waitKey(1) & 0xff 
+            if video_save_path!="":
+                out.write(frame)
+
+            if c==27:
+                capture.release()
+                break
+        print("Video Detection Done!")
+        capture.release()
+        if video_save_path!="":
+            print("Save processed video to the path :" + video_save_path)
+            out.release()
+        cv2.destroyAllWindows()
+
+    elif mode == "fps":
+        img = Image.open(fps_image_path)
+        tact_time = deeplab.get_FPS(img, test_interval)
+        print(str(tact_time) + ' seconds, ' + str(1/tact_time) + 'FPS, @batch_size 1')
+        
+    elif mode == "dir_predict":
+        import os
+        from tqdm import tqdm
+
+        img_names = os.listdir(dir_origin_path)
+        for img_name in tqdm(img_names):
+            if img_name.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')):
+                image_path  = os.path.join(dir_origin_path, img_name)
+                image       = Image.open(image_path)
+                r_image     = deeplab.detect_image(image)
+                if not os.path.exists(dir_save_path):
+                    os.makedirs(dir_save_path)
+                r_image.save(os.path.join(dir_save_path, img_name))
+    elif mode == "export_onnx":
+        deeplab.convert_to_onnx(simplify, onnx_save_path)
+        
+    else:
+        raise AssertionError("Please specify the correct mode: 'predict', 'video', 'fps' or 'dir_predict'.")
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..a4e6b7d
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,9 @@
+scipy==1.2.1
+numpy==1.17.0
+matplotlib==3.1.2
+opencv_python==4.1.2.30
+torch==1.2.0
+torchvision==0.4.0
+tqdm==4.60.0
+Pillow==8.2.0
+h5py==2.10.0
diff --git a/summary.py b/summary.py
new file mode 100644
index 0000000..3c2cb7c
--- /dev/null
+++ b/summary.py
@@ -0,0 +1,30 @@
+#--------------------------------------------#
+#   该部分代码用于看网络结构
+#--------------------------------------------#
+import torch
+from thop import clever_format, profile
+from torchsummary import summary
+
+from nets.deeplabv3_plus import DeepLab
+
+if __name__ == "__main__":
+    input_shape     = [512, 512]
+    num_classes     = 47
+    backbone        = 'mobilenet'
+    
+    device  = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    model   = DeepLab(num_classes=num_classes, backbone=backbone, downsample_factor=16, pretrained=False).to(device)
+    summary(model, (3, input_shape[0], input_shape[1]))
+    
+    dummy_input     = torch.randn(1, 3, input_shape[0], input_shape[1]).to(device)
+    flops, params   = profile(model.to(device), (dummy_input, ), verbose=False)
+    #--------------------------------------------------------#
+    #   flops * 2是因为profile没有将卷积作为两个operations
+    #   有些论文将卷积算乘法、加法两个operations。此时乘2
+    #   有些论文只考虑乘法的运算次数，忽略加法。此时不乘2
+    #   本代码选择乘2，参考YOLOX。
+    #--------------------------------------------------------#
+    flops           = flops * 2
+    flops, params   = clever_format([flops, params], "%.3f")
+    print('Total GFLOPS: %s' % (flops))
+    print('Total params: %s' % (params))
diff --git a/train.py b/train.py
new file mode 100644
index 0000000..9df77b8
--- /dev/null
+++ b/train.py
@@ -0,0 +1,522 @@
+import os
+import datetime
+
+import numpy as np
+import torch
+import torch.backends.cudnn as cudnn
+import torch.distributed as dist
+import torch.optim as optim
+from torch.utils.data import DataLoader
+
+from nets.deeplabv3_plus import DeepLab
+from nets.deeplabv3_training import (get_lr_scheduler, set_optimizer_lr,
+                                     weights_init)
+from utils.callbacks import LossHistory, EvalCallback
+from utils.dataloader import DeeplabDataset, deeplab_dataset_collate
+from utils.utils import download_weights, show_config
+from utils.utils_fit import fit_one_epoch
+
+'''
+训练自己的语义分割模型一定需要注意以下几点：
+1、训练前仔细检查自己的格式是否满足要求，该库要求数据集格式为VOC格式，需要准备好的内容有输入图片和标签
+   输入图片为.jpg图片，无需固定大小，传入训练前会自动进行resize。
+   灰度图会自动转成RGB图片进行训练，无需自己修改。
+   输入图片如果后缀非jpg，需要自己批量转成jpg后再开始训练。
+
+   标签为png图片，无需固定大小，传入训练前会自动进行resize。
+   由于许多同学的数据集是网络上下载的，标签格式并不符合，需要再度处理。一定要注意！标签的每个像素点的值就是这个像素点所属的种类。
+   网上常见的数据集总共对输入图片分两类，背景的像素点值为0，目标的像素点值为255。这样的数据集可以正常运行但是预测是没有效果的！
+   需要改成，背景的像素点值为0，目标的像素点值为1。
+   如果格式有误，参考：https://github.com/bubbliiiing/segmentation-format-fix
+
+2、损失值的大小用于判断是否收敛，比较重要的是有收敛的趋势，即验证集损失不断下降，如果验证集损失基本上不改变的话，模型基本上就收敛了。
+   损失值的具体大小并没有什么意义，大和小只在于损失的计算方式，并不是接近于0才好。如果想要让损失好看点，可以直接到对应的损失函数里面除上10000。
+   训练过程中的损失值会保存在logs文件夹下的loss_%Y_%m_%d_%H_%M_%S文件夹中
+   
+3、训练好的权值文件保存在logs文件夹中，每个训练世代（Epoch）包含若干训练步长（Step），每个训练步长（Step）进行一次梯度下降。
+   如果只是训练了几个Step是不会保存的，Epoch和Step的概念要捋清楚一下。
+'''
+if __name__ == "__main__":
+    #---------------------------------#
+    #   Cuda    是否使用Cuda
+    #           没有GPU可以设置成False
+    #---------------------------------#
+    Cuda            = True
+    #---------------------------------------------------------------------#
+    #   distributed     用于指定是否使用单机多卡分布式运行
+    #                   终端指令仅支持Ubuntu。CUDA_VISIBLE_DEVICES用于在Ubuntu下指定显卡。
+    #                   Windows系统下默认使用DP模式调用所有显卡，不支持DDP。
+    #   DP模式：
+    #       设置            distributed = False
+    #       在终端中输入    CUDA_VISIBLE_DEVICES=0,1 python train.py
+    #   DDP模式：
+    #       设置            distributed = True
+    #       在终端中输入    CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train.py
+    #---------------------------------------------------------------------#
+    distributed     = False
+    #---------------------------------------------------------------------#
+    #   sync_bn     是否使用sync_bn，DDP模式多卡可用
+    #---------------------------------------------------------------------#
+    sync_bn         = False
+    #---------------------------------------------------------------------#
+    #   fp16        是否使用混合精度训练
+    #               可减少约一半的显存、需要pytorch1.7.1以上
+    #---------------------------------------------------------------------#
+    fp16            = False
+    #-----------------------------------------------------#
+    #   num_classes     训练自己的数据集必须要修改的
+    #                   自己需要的分类个数+1，如2+1
+    #-----------------------------------------------------#
+    num_classes     = 46
+    #---------------------------------#
+    #   所使用的的主干网络：
+    #   mobilenet
+    #   xception
+    #---------------------------------#
+    backbone        = "mobilenet"
+    #----------------------------------------------------------------------------------------------------------------------------#
+    #   pretrained      是否使用主干网络的预训练权重，此处使用的是主干的权重，因此是在模型构建的时候进行加载的。
+    #                   如果设置了model_path，则主干的权值无需加载，pretrained的值无意义。
+    #                   如果不设置model_path，pretrained = True，此时仅加载主干开始训练。
+    #                   如果不设置model_path，pretrained = False，Freeze_Train = Fasle，此时从0开始训练，且没有冻结主干的过程。
+    #----------------------------------------------------------------------------------------------------------------------------#
+    pretrained      = False
+    #----------------------------------------------------------------------------------------------------------------------------#
+    #   权值文件的下载请看README，可以通过网盘下载。模型的 预训练权重 对不同数据集是通用的，因为特征是通用的。
+    #   模型的 预训练权重 比较重要的部分是 主干特征提取网络的权值部分，用于进行特征提取。
+    #   预训练权重对于99%的情况都必须要用，不用的话主干部分的权值太过随机，特征提取效果不明显，网络训练的结果也不会好
+    #   训练自己的数据集时提示维度不匹配正常，预测的东西都不一样了自然维度不匹配
+    #
+    #   如果训练过程中存在中断训练的操作，可以将model_path设置成logs文件夹下的权值文件，将已经训练了一部分的权值再次载入。
+    #   同时修改下方的 冻结阶段 或者 解冻阶段 的参数，来保证模型epoch的连续性。
+    #   
+    #   当model_path = ''的时候不加载整个模型的权值。
+    #
+    #   此处使用的是整个模型的权重，因此是在train.py进行加载的，pretrain不影响此处的权值加载。
+    #   如果想要让模型从主干的预训练权值开始训练，则设置model_path = ''，pretrain = True，此时仅加载主干。
+    #   如果想要让模型从0开始训练，则设置model_path = ''，pretrain = Fasle，Freeze_Train = Fasle，此时从0开始训练，且没有冻结主干的过程。
+    #   
+    #   一般来讲，网络从0开始的训练效果会很差，因为权值太过随机，特征提取效果不明显，因此非常、非常、非常不建议大家从0开始训练！
+    #   如果一定要从0开始，可以了解imagenet数据集，首先训练分类模型，获得网络的主干部分权值，分类模型的 主干部分 和该模型通用，基于此进行训练。
+    #----------------------------------------------------------------------------------------------------------------------------#
+    model_path      = "model_data/deeplab_mobilenetv2.pth"
+    #---------------------------------------------------------#
+    #   downsample_factor   下采样的倍数8、16 
+    #                       8下采样的倍数较小、理论上效果更好。
+    #                       但也要求更大的显存
+    #---------------------------------------------------------#
+    downsample_factor   = 16
+    #------------------------------#
+    #   输入图片的大小
+    #------------------------------#
+    input_shape         = [1024, 1024]
+    
+    #----------------------------------------------------------------------------------------------------------------------------#
+    #   训练分为两个阶段，分别是冻结阶段和解冻阶段。设置冻结阶段是为了满足机器性能不足的同学的训练需求。
+    #   冻结训练需要的显存较小，显卡非常差的情况下，可设置Freeze_Epoch等于UnFreeze_Epoch，此时仅仅进行冻结训练。
+    #      
+    #   在此提供若干参数设置建议，各位训练者根据自己的需求进行灵活调整：
+    #   （一）从整个模型的预训练权重开始训练： 
+    #       Adam：
+    #           Init_Epoch = 0，Freeze_Epoch = 50，UnFreeze_Epoch = 100，Freeze_Train = True，optimizer_type = 'adam'，Init_lr = 5e-4，weight_decay = 0。（冻结）
+    #           Init_Epoch = 0，UnFreeze_Epoch = 100，Freeze_Train = False，optimizer_type = 'adam'，Init_lr = 5e-4，weight_decay = 0。（不冻结）
+    #       SGD：
+    #           Init_Epoch = 0，Freeze_Epoch = 50，UnFreeze_Epoch = 100，Freeze_Train = True，optimizer_type = 'sgd'，Init_lr = 7e-3，weight_decay = 1e-4。（冻结）
+    #           Init_Epoch = 0，UnFreeze_Epoch = 100，Freeze_Train = False，optimizer_type = 'sgd'，Init_lr = 7e-3，weight_decay = 1e-4。（不冻结）
+    #       其中：UnFreeze_Epoch可以在100-300之间调整。
+    #   （二）从主干网络的预训练权重开始训练：
+    #       Adam：
+    #           Init_Epoch = 0，Freeze_Epoch = 50，UnFreeze_Epoch = 100，Freeze_Train = True，optimizer_type = 'adam'，Init_lr = 5e-4，weight_decay = 0。（冻结）
+    #           Init_Epoch = 0，UnFreeze_Epoch = 100，Freeze_Train = False，optimizer_type = 'adam'，Init_lr = 5e-4，weight_decay = 0。（不冻结）
+    #       SGD：
+    #           Init_Epoch = 0，Freeze_Epoch = 50，UnFreeze_Epoch = 120，Freeze_Train = True，optimizer_type = 'sgd'，Init_lr = 7e-3，weight_decay = 1e-4。（冻结）
+    #           Init_Epoch = 0，UnFreeze_Epoch = 120，Freeze_Train = False，optimizer_type = 'sgd'，Init_lr = 7e-3，weight_decay = 1e-4。（不冻结）
+    #       其中：由于从主干网络的预训练权重开始训练，主干的权值不一定适合语义分割，需要更多的训练跳出局部最优解。
+    #             UnFreeze_Epoch可以在120-300之间调整。
+    #             Adam相较于SGD收敛的快一些。因此UnFreeze_Epoch理论上可以小一点，但依然推荐更多的Epoch。
+    #   （三）batch_size的设置：
+    #       在显卡能够接受的范围内，以大为好。显存不足与数据集大小无关，提示显存不足（OOM或者CUDA out of memory）请调小batch_size。
+    #       受到BatchNorm层影响，batch_size最小为2，不能为1。
+    #       正常情况下Freeze_batch_size建议为Unfreeze_batch_size的1-2倍。不建议设置的差距过大，因为关系到学习率的自动调整。
+    #----------------------------------------------------------------------------------------------------------------------------#
+    #------------------------------------------------------------------#
+    #   冻结阶段训练参数
+    #   此时模型的主干被冻结了，特征提取网络不发生改变
+    #   占用的显存较小，仅对网络进行微调
+    #   Init_Epoch          模型当前开始的训练世代，其值可以大于Freeze_Epoch，如设置：
+    #                       Init_Epoch = 60、Freeze_Epoch = 50、UnFreeze_Epoch = 100
+    #                       会跳过冻结阶段，直接从60代开始，并调整对应的学习率。
+    #                       （断点续练时使用）
+    #   Freeze_Epoch        模型冻结训练的Freeze_Epoch
+    #                       (当Freeze_Train=False时失效)
+    #   Freeze_batch_size   模型冻结训练的batch_size
+    #                       (当Freeze_Train=False时失效)
+    #------------------------------------------------------------------#
+    Init_Epoch          = 0
+    Freeze_Epoch        = 400
+    Freeze_batch_size   = 8
+    #------------------------------------------------------------------#
+    #   解冻阶段训练参数
+    #   此时模型的主干不被冻结了，特征提取网络会发生改变
+    #   占用的显存较大，网络所有的参数都会发生改变
+    #   UnFreeze_Epoch          模型总共训练的epoch
+    #   Unfreeze_batch_size     模型在解冻后的batch_size
+    #------------------------------------------------------------------#
+    UnFreeze_Epoch      = 200
+    Unfreeze_batch_size = 4
+    #------------------------------------------------------------------#
+    #   Freeze_Train    是否进行冻结训练
+    #                   默认先冻结主干训练后解冻训练。
+    #------------------------------------------------------------------#
+    Freeze_Train        = True
+
+    #------------------------------------------------------------------#
+    #   其它训练参数：学习率、优化器、学习率下降有关
+    #------------------------------------------------------------------#
+    #------------------------------------------------------------------#
+    #   Init_lr         模型的最大学习率
+    #                   当使用Adam优化器时建议设置  Init_lr=5e-4
+    #                   当使用SGD优化器时建议设置   Init_lr=7e-3
+    #   Min_lr          模型的最小学习率，默认为最大学习率的0.01
+    #------------------------------------------------------------------#
+    Init_lr             = 7e-3
+    Min_lr              = Init_lr * 0.01
+    #------------------------------------------------------------------#
+    #   optimizer_type  使用到的优化器种类，可选的有adam、sgd
+    #                   当使用Adam优化器时建议设置  Init_lr=5e-4
+    #                   当使用SGD优化器时建议设置   Init_lr=7e-3
+    #   momentum        优化器内部使用到的momentum参数
+    #   weight_decay    权值衰减，可防止过拟合
+    #                   adam会导致weight_decay错误，使用adam时建议设置为0。
+    #------------------------------------------------------------------#
+    optimizer_type      = "sgd"
+    momentum            = 0.9
+    weight_decay        = 1e-4
+    #------------------------------------------------------------------#
+    #   lr_decay_type   使用到的学习率下降方式，可选的有'step'、'cos'
+    #------------------------------------------------------------------#
+    lr_decay_type       = 'cos'
+    #------------------------------------------------------------------#
+    #   save_period     多少个epoch保存一次权值
+    #------------------------------------------------------------------#
+    save_period         = 5
+    #------------------------------------------------------------------#
+    #   save_dir        权值与日志文件保存的文件夹
+    #------------------------------------------------------------------#
+    save_dir            = 'logs'
+    #------------------------------------------------------------------#
+    #   eval_flag       是否在训练时进行评估，评估对象为验证集
+    #   eval_period     代表多少个epoch评估一次，不建议频繁的评估
+    #                   评估需要消耗较多的时间，频繁评估会导致训练非常慢
+    #   此处获得的mAP会与get_map.py获得的会有所不同，原因有二：
+    #   （一）此处获得的mAP为验证集的mAP。
+    #   （二）此处设置评估参数较为保守，目的是加快评估速度。
+    #------------------------------------------------------------------#
+    eval_flag           = True
+    eval_period         = 5
+
+    #------------------------------------------------------------------#
+    #   VOCdevkit_path  数据集路径
+    #------------------------------------------------------------------#
+    VOCdevkit_path  = 'VOCdevkit'
+    #------------------------------------------------------------------#
+    #   建议选项：
+    #   种类少（几类）时，设置为True
+    #   种类多（十几类）时，如果batch_size比较大（10以上），那么设置为True
+    #   种类多（十几类）时，如果batch_size比较小（10以下），那么设置为False
+    #------------------------------------------------------------------#
+    dice_loss       = False
+    #------------------------------------------------------------------#
+    #   是否使用focal loss来防止正负样本不平衡
+    #------------------------------------------------------------------#
+    focal_loss      = False
+    #------------------------------------------------------------------#
+    #   是否给不同种类赋予不同的损失权值，默认是平衡的。
+    #   设置的话，注意设置成numpy形式的，长度和num_classes一样。
+    #   如：
+    #   num_classes = 3
+    #   cls_weights = np.array([1, 2, 3], np.float32)
+    #------------------------------------------------------------------#
+    cls_weights     = np.ones([num_classes], np.float32)
+    #------------------------------------------------------------------#
+    #   num_workers     用于设置是否使用多线程读取数据，1代表关闭多线程
+    #                   开启后会加快数据读取速度，但是会占用更多内存
+    #                   keras里开启多线程有些时候速度反而慢了许多
+    #                   在IO为瓶颈的时候再开启多线程，即GPU运算速度远大于读取图片的速度。
+    #------------------------------------------------------------------#
+    num_workers         = 4
+
+    #------------------------------------------------------#
+    #   设置用到的显卡
+    #------------------------------------------------------#
+    ngpus_per_node  = torch.cuda.device_count()
+    if distributed:
+        dist.init_process_group(backend="nccl")
+        local_rank  = int(os.environ["LOCAL_RANK"])
+        rank        = int(os.environ["RANK"])
+        device      = torch.device("cuda", local_rank)
+        if local_rank == 0:
+            print(f"[{os.getpid()}] (rank = {rank}, local_rank = {local_rank}) training...")
+            print("Gpu Device Count : ", ngpus_per_node)
+    else:
+        device          = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        local_rank      = 0
+
+    #----------------------------------------------------#
+    #   下载预训练权重
+    #----------------------------------------------------#
+    if pretrained:
+        if distributed:
+            if local_rank == 0:
+                download_weights(backbone)  
+            dist.barrier()
+        else:
+            download_weights(backbone)
+
+    model   = DeepLab(num_classes=num_classes, backbone=backbone, downsample_factor=downsample_factor, pretrained=pretrained)
+    if not pretrained:
+        weights_init(model)
+    if model_path != '':
+        #------------------------------------------------------#
+        #   权值文件请看README，百度网盘下载
+        #------------------------------------------------------#
+        if local_rank == 0:
+            print('Load weights {}.'.format(model_path))
+        
+        #------------------------------------------------------#
+        #   根据预训练权重的Key和模型的Key进行加载
+        #------------------------------------------------------#
+        model_dict      = model.state_dict()
+        pretrained_dict = torch.load(model_path, map_location = device)
+        load_key, no_load_key, temp_dict = [], [], {}
+        for k, v in pretrained_dict.items():
+            if k in model_dict.keys() and np.shape(model_dict[k]) == np.shape(v):
+                temp_dict[k] = v
+                load_key.append(k)
+            else:
+                no_load_key.append(k)
+        model_dict.update(temp_dict)
+        model.load_state_dict(model_dict)
+        #------------------------------------------------------#
+        #   显示没有匹配上的Key
+        #------------------------------------------------------#
+        if local_rank == 0:
+            print("\nSuccessful Load Key:", str(load_key)[:500], "……\nSuccessful Load Key Num:", len(load_key))
+            print("\nFail To Load Key:", str(no_load_key)[:500], "……\nFail To Load Key num:", len(no_load_key))
+            print("\n\033[1;33;44m温馨提示，head部分没有载入是正常现象，Backbone部分没有载入是错误的。\033[0m")
+
+    #----------------------#
+    #   记录Loss
+    #----------------------#
+    if local_rank == 0:
+        time_str        = datetime.datetime.strftime(datetime.datetime.now(),'%Y_%m_%d_%H_%M_%S')
+        log_dir         = os.path.join(save_dir, "loss_" + str(time_str))
+        loss_history    = LossHistory(log_dir, model, input_shape=input_shape)
+    else:
+        loss_history    = None
+
+    #------------------------------------------------------------------#
+    #   torch 1.2不支持amp，建议使用torch 1.7.1及以上正确使用fp16
+    #   因此torch1.2这里显示"could not be resolve"
+    #------------------------------------------------------------------#
+    if fp16:
+        from torch.cuda.amp import GradScaler as GradScaler
+        scaler = GradScaler()
+    else:
+        scaler = None
+
+    model_train     = model.train()
+    #----------------------------#
+    #   多卡同步Bn
+    #----------------------------#
+    if sync_bn and ngpus_per_node > 1 and distributed:
+        model_train = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model_train)
+    elif sync_bn:
+        print("Sync_bn is not support in one gpu or not distributed.")
+
+    if Cuda:
+        if distributed:
+            #----------------------------#
+            #   多卡平行运行
+            #----------------------------#
+            model_train = model_train.cuda(local_rank)
+            model_train = torch.nn.parallel.DistributedDataParallel(model_train, device_ids=[local_rank], find_unused_parameters=True)
+        else:
+            model_train = torch.nn.DataParallel(model)
+            cudnn.benchmark = True
+            model_train = model_train.cuda()
+    
+    #---------------------------#
+    #   读取数据集对应的txt
+    #---------------------------#
+    with open(os.path.join(VOCdevkit_path, "VOC2007/ImageSets/Segmentation/train.txt"),"r") as f:
+        train_lines = f.readlines()
+    with open(os.path.join(VOCdevkit_path, "VOC2007/ImageSets/Segmentation/val.txt"),"r") as f:
+        val_lines = f.readlines()
+    num_train   = len(train_lines)
+    num_val     = len(val_lines)
+
+    if local_rank == 0:
+        show_config(
+            num_classes = num_classes, backbone = backbone, model_path = model_path, input_shape = input_shape, 
+            Init_Epoch = Init_Epoch, Freeze_Epoch = Freeze_Epoch, UnFreeze_Epoch = UnFreeze_Epoch, Freeze_batch_size = Freeze_batch_size, Unfreeze_batch_size = Unfreeze_batch_size, Freeze_Train = Freeze_Train, 
+            Init_lr = Init_lr, Min_lr = Min_lr, optimizer_type = optimizer_type, momentum = momentum, lr_decay_type = lr_decay_type, 
+            save_period = save_period, save_dir = save_dir, num_workers = num_workers, num_train = num_train, num_val = num_val
+        )
+        #---------------------------------------------------------#
+        #   总训练世代指的是遍历全部数据的总次数
+        #   总训练步长指的是梯度下降的总次数 
+        #   每个训练世代包含若干训练步长，每个训练步长进行一次梯度下降。
+        #   此处仅建议最低训练世代，上不封顶，计算时只考虑了解冻部分
+        #----------------------------------------------------------#
+        wanted_step = 1.5e4 if optimizer_type == "sgd" else 0.5e4
+        total_step  = num_train // Unfreeze_batch_size * UnFreeze_Epoch
+        if total_step <= wanted_step:
+            if num_train // Unfreeze_batch_size == 0:
+                raise ValueError('数据集过小，无法进行训练，请扩充数据集。')
+            wanted_epoch = wanted_step // (num_train // Unfreeze_batch_size) + 1
+            print("\n\033[1;33;44m[Warning] 使用%s优化器时，建议将训练总步长设置到%d以上。\033[0m"%(optimizer_type, wanted_step))
+            print("\033[1;33;44m[Warning] 本次运行的总训练数据量为%d，Unfreeze_batch_size为%d，共训练%d个Epoch，计算出总训练步长为%d。\033[0m"%(num_train, Unfreeze_batch_size, UnFreeze_Epoch, total_step))
+            print("\033[1;33;44m[Warning] 由于总训练步长为%d，小于建议总步长%d，建议设置总世代为%d。\033[0m"%(total_step, wanted_step, wanted_epoch))
+        
+    #------------------------------------------------------#
+    #   主干特征提取网络特征通用，冻结训练可以加快训练速度
+    #   也可以在训练初期防止权值被破坏。
+    #   Init_Epoch为起始世代
+    #   Interval_Epoch为冻结训练的世代
+    #   Epoch总训练世代
+    #   提示OOM或者显存不足请调小Batch_size
+    #------------------------------------------------------#
+    if True:
+        UnFreeze_flag = False
+        #------------------------------------#
+        #   冻结一定部分训练
+        #------------------------------------#
+        if Freeze_Train:
+            for param in model.backbone.parameters():
+                param.requires_grad = False
+
+        #-------------------------------------------------------------------#
+        #   如果不冻结训练的话，直接设置batch_size为Unfreeze_batch_size
+        #-------------------------------------------------------------------#
+        batch_size = Freeze_batch_size if Freeze_Train else Unfreeze_batch_size
+
+        #-------------------------------------------------------------------#
+        #   判断当前batch_size，自适应调整学习率
+        #-------------------------------------------------------------------#
+        nbs             = 16
+        lr_limit_max    = 5e-4 if optimizer_type == 'adam' else 1e-1
+        lr_limit_min    = 3e-4 if optimizer_type == 'adam' else 5e-4
+        if backbone == "xception":
+            lr_limit_max    = 1e-4 if optimizer_type == 'adam' else 1e-1
+            lr_limit_min    = 1e-4 if optimizer_type == 'adam' else 5e-4
+        Init_lr_fit     = min(max(batch_size / nbs * Init_lr, lr_limit_min), lr_limit_max)
+        Min_lr_fit      = min(max(batch_size / nbs * Min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2)
+
+        #---------------------------------------#
+        #   根据optimizer_type选择优化器
+        #---------------------------------------#
+        optimizer = {
+            'adam'  : optim.Adam(model.parameters(), Init_lr_fit, betas = (momentum, 0.999), weight_decay = weight_decay),
+            'sgd'   : optim.SGD(model.parameters(), Init_lr_fit, momentum = momentum, nesterov=True, weight_decay = weight_decay)
+        }[optimizer_type]
+
+        #---------------------------------------#
+        #   获得学习率下降的公式
+        #---------------------------------------#
+        lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch)
+        
+        #---------------------------------------#
+        #   判断每一个世代的长度
+        #---------------------------------------#
+        epoch_step      = num_train // batch_size
+        epoch_step_val  = num_val // batch_size
+        
+        if epoch_step == 0 or epoch_step_val == 0:
+            raise ValueError("数据集过小，无法继续进行训练，请扩充数据集。")
+
+        train_dataset   = DeeplabDataset(train_lines, input_shape, num_classes, True, VOCdevkit_path)
+        val_dataset     = DeeplabDataset(val_lines, input_shape, num_classes, False, VOCdevkit_path)
+
+        if distributed:
+            train_sampler   = torch.utils.data.distributed.DistributedSampler(train_dataset, shuffle=True,)
+            val_sampler     = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False,)
+            batch_size      = batch_size // ngpus_per_node
+            shuffle         = False
+        else:
+            train_sampler   = None
+            val_sampler     = None
+            shuffle         = True
+
+        gen             = DataLoader(train_dataset, shuffle = shuffle, batch_size = batch_size, num_workers = num_workers, pin_memory=True,
+                                    drop_last = True, collate_fn = deeplab_dataset_collate, sampler=train_sampler)
+        gen_val         = DataLoader(val_dataset  , shuffle = shuffle, batch_size = batch_size, num_workers = num_workers, pin_memory=True, 
+                                    drop_last = True, collate_fn = deeplab_dataset_collate, sampler=val_sampler)
+
+        #----------------------#
+        #   记录eval的map曲线
+        #----------------------#
+        if local_rank == 0:
+            eval_callback   = EvalCallback(model, input_shape, num_classes, val_lines, VOCdevkit_path, log_dir, Cuda, eval_flag=eval_flag, period=eval_period)
+        else:
+            eval_callback   = None
+        
+        #---------------------------------------#
+        #   开始模型训练
+        #---------------------------------------#
+        for epoch in range(Init_Epoch, UnFreeze_Epoch):
+            #---------------------------------------#
+            #   如果模型有冻结学习部分
+            #   则解冻，并设置参数
+            #---------------------------------------#
+            if epoch >= Freeze_Epoch and not UnFreeze_flag and Freeze_Train:
+                batch_size = Unfreeze_batch_size
+
+                #-------------------------------------------------------------------#
+                #   判断当前batch_size，自适应调整学习率
+                #-------------------------------------------------------------------#
+                nbs             = 16
+                lr_limit_max    = 5e-4 if optimizer_type == 'adam' else 1e-1
+                lr_limit_min    = 3e-4 if optimizer_type == 'adam' else 5e-4
+                if backbone == "xception":
+                    lr_limit_max    = 1e-4 if optimizer_type == 'adam' else 1e-1
+                    lr_limit_min    = 1e-4 if optimizer_type == 'adam' else 5e-4
+                Init_lr_fit     = min(max(batch_size / nbs * Init_lr, lr_limit_min), lr_limit_max)
+                Min_lr_fit      = min(max(batch_size / nbs * Min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2)
+                #---------------------------------------#
+                #   获得学习率下降的公式
+                #---------------------------------------#
+                lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch)
+                    
+                for param in model.backbone.parameters():
+                    param.requires_grad = True
+                            
+                epoch_step      = num_train // batch_size
+                epoch_step_val  = num_val // batch_size
+
+                if epoch_step == 0 or epoch_step_val == 0:
+                    raise ValueError("数据集过小，无法继续进行训练，请扩充数据集。")
+
+                if distributed:
+                    batch_size = batch_size // ngpus_per_node
+
+                gen             = DataLoader(train_dataset, shuffle = shuffle, batch_size = batch_size, num_workers = num_workers, pin_memory=True,
+                                            drop_last = True, collate_fn = deeplab_dataset_collate, sampler=train_sampler)
+                gen_val         = DataLoader(val_dataset  , shuffle = shuffle, batch_size = batch_size, num_workers = num_workers, pin_memory=True, 
+                                            drop_last = True, collate_fn = deeplab_dataset_collate, sampler=val_sampler)
+
+                UnFreeze_flag = True
+
+            if distributed:
+                train_sampler.set_epoch(epoch)
+
+            set_optimizer_lr(optimizer, lr_scheduler_func, epoch)
+
+            fit_one_epoch(model_train, model, loss_history, eval_callback, optimizer, epoch, 
+                    epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, dice_loss, focal_loss, cls_weights, num_classes, fp16, scaler, save_period, save_dir, local_rank)
+
+            if distributed:
+                dist.barrier()
+
+        if local_rank == 0:
+            loss_history.writer.close()
diff --git a/utils/__init__.py b/utils/__init__.py
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+#
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diff --git a/utils/callbacks.py b/utils/callbacks.py
new file mode 100644
index 0000000..16ca6e5
--- /dev/null
+++ b/utils/callbacks.py
@@ -0,0 +1,200 @@
+import os
+
+import matplotlib
+import torch
+import torch.nn.functional as F
+
+matplotlib.use('Agg')
+from matplotlib import pyplot as plt
+import scipy.signal
+
+import cv2
+import shutil
+import numpy as np
+
+from PIL import Image
+from tqdm import tqdm
+from torch.utils.tensorboard import SummaryWriter
+from .utils import cvtColor, preprocess_input, resize_image
+from .utils_metrics import compute_mIoU
+
+
+class LossHistory():
+    def __init__(self, log_dir, model, input_shape):
+        self.log_dir    = log_dir
+        self.losses     = []
+        self.val_loss   = []
+        
+        os.makedirs(self.log_dir)
+        self.writer     = SummaryWriter(self.log_dir)
+        try:
+            dummy_input     = torch.randn(2, 3, input_shape[0], input_shape[1])
+            self.writer.add_graph(model, dummy_input)
+        except:
+            pass
+
+    def append_loss(self, epoch, loss, val_loss):
+        if not os.path.exists(self.log_dir):
+            os.makedirs(self.log_dir)
+
+        self.losses.append(loss)
+        self.val_loss.append(val_loss)
+
+        with open(os.path.join(self.log_dir, "epoch_loss.txt"), 'a') as f:
+            f.write(str(loss))
+            f.write("\n")
+        with open(os.path.join(self.log_dir, "epoch_val_loss.txt"), 'a') as f:
+            f.write(str(val_loss))
+            f.write("\n")
+
+        self.writer.add_scalar('loss', loss, epoch)
+        self.writer.add_scalar('val_loss', val_loss, epoch)
+        self.loss_plot()
+
+    def loss_plot(self):
+        iters = range(len(self.losses))
+
+        plt.figure()
+        plt.plot(iters, self.losses, 'red', linewidth = 2, label='train loss')
+        plt.plot(iters, self.val_loss, 'coral', linewidth = 2, label='val loss')
+        try:
+            if len(self.losses) < 25:
+                num = 5
+            else:
+                num = 15
+            
+            plt.plot(iters, scipy.signal.savgol_filter(self.losses, num, 3), 'green', linestyle = '--', linewidth = 2, label='smooth train loss')
+            plt.plot(iters, scipy.signal.savgol_filter(self.val_loss, num, 3), '#8B4513', linestyle = '--', linewidth = 2, label='smooth val loss')
+        except:
+            pass
+
+        plt.grid(True)
+        plt.xlabel('Epoch')
+        plt.ylabel('Loss')
+        plt.legend(loc="upper right")
+
+        plt.savefig(os.path.join(self.log_dir, "epoch_loss.png"))
+
+        plt.cla()
+        plt.close("all")
+
+class EvalCallback():
+    def __init__(self, net, input_shape, num_classes, image_ids, dataset_path, log_dir, cuda, 
+            miou_out_path=".temp_miou_out", eval_flag=True, period=1):
+        super(EvalCallback, self).__init__()
+        
+        self.net                = net
+        self.input_shape        = input_shape
+        self.num_classes        = num_classes
+        self.image_ids          = image_ids
+        self.dataset_path       = dataset_path
+        self.log_dir            = log_dir
+        self.cuda               = cuda
+        self.miou_out_path      = miou_out_path
+        self.eval_flag          = eval_flag
+        self.period             = period
+        
+        self.image_ids          = [image_id.split()[0] for image_id in image_ids]
+        self.mious      = [0]
+        self.epoches    = [0]
+        if self.eval_flag:
+            with open(os.path.join(self.log_dir, "epoch_miou.txt"), 'a') as f:
+                f.write(str(0))
+                f.write("\n")
+
+    def get_miou_png(self, image):
+        #---------------------------------------------------------#
+        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
+        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
+        #---------------------------------------------------------#
+        image       = cvtColor(image)
+        orininal_h  = np.array(image).shape[0]
+        orininal_w  = np.array(image).shape[1]
+        #---------------------------------------------------------#
+        #   给图像增加灰条，实现不失真的resize
+        #   也可以直接resize进行识别
+        #---------------------------------------------------------#
+        image_data, nw, nh  = resize_image(image, (self.input_shape[1],self.input_shape[0]))
+        #---------------------------------------------------------#
+        #   添加上batch_size维度
+        #---------------------------------------------------------#
+        image_data  = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, np.float32)), (2, 0, 1)), 0)
+
+        with torch.no_grad():
+            images = torch.from_numpy(image_data)
+            if self.cuda:
+                images = images.cuda()
+                
+            #---------------------------------------------------#
+            #   图片传入网络进行预测
+            #---------------------------------------------------#
+            pr = self.net(images)[0]
+            #---------------------------------------------------#
+            #   取出每一个像素点的种类
+            #---------------------------------------------------#
+            pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy()
+            #--------------------------------------#
+            #   将灰条部分截取掉
+            #--------------------------------------#
+            pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), 
+                    int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
+            #---------------------------------------------------#
+            #   进行图片的resize
+            #---------------------------------------------------#
+            pr = cv2.resize(pr, (orininal_w, orininal_h), interpolation = cv2.INTER_LINEAR)
+            #---------------------------------------------------#
+            #   取出每一个像素点的种类
+            #---------------------------------------------------#
+            pr = pr.argmax(axis=-1)
+    
+        image = Image.fromarray(np.uint8(pr))
+        return image
+    
+    def on_epoch_end(self, epoch, model_eval):
+        if epoch % self.period == 0 and self.eval_flag:
+            self.net    = model_eval
+            gt_dir      = os.path.join(self.dataset_path, "VOC2007/SegmentationClass/")
+            pred_dir    = os.path.join(self.miou_out_path, 'detection-results')
+            if not os.path.exists(self.miou_out_path):
+                os.makedirs(self.miou_out_path)
+            if not os.path.exists(pred_dir):
+                os.makedirs(pred_dir)
+            print("Get miou.")
+            for image_id in tqdm(self.image_ids):
+                #-------------------------------#
+                #   从文件中读取图像
+                #-------------------------------#
+                image_path  = os.path.join(self.dataset_path, "VOC2007/JPEGImages/"+image_id+".jpg")
+                image       = Image.open(image_path)
+                #------------------------------#
+                #   获得预测txt
+                #------------------------------#
+                image       = self.get_miou_png(image)
+                image.save(os.path.join(pred_dir, image_id + ".png"))
+                        
+            print("Calculate miou.")
+            _, IoUs, _, _ = compute_mIoU(gt_dir, pred_dir, self.image_ids, self.num_classes, None)  # 执行计算mIoU的函数
+            temp_miou = np.nanmean(IoUs) * 100
+
+            self.mious.append(temp_miou)
+            self.epoches.append(epoch)
+
+            with open(os.path.join(self.log_dir, "epoch_miou.txt"), 'a') as f:
+                f.write(str(temp_miou))
+                f.write("\n")
+            
+            plt.figure()
+            plt.plot(self.epoches, self.mious, 'red', linewidth = 2, label='train miou')
+
+            plt.grid(True)
+            plt.xlabel('Epoch')
+            plt.ylabel('Miou')
+            plt.title('A Miou Curve')
+            plt.legend(loc="upper right")
+
+            plt.savefig(os.path.join(self.log_dir, "epoch_miou.png"))
+            plt.cla()
+            plt.close("all")
+
+            print("Get miou done.")
+            shutil.rmtree(self.miou_out_path)
diff --git a/utils/dataloader.py b/utils/dataloader.py
new file mode 100644
index 0000000..9ab78b3
--- /dev/null
+++ b/utils/dataloader.py
@@ -0,0 +1,169 @@
+import os
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data.dataset import Dataset
+
+from utils.utils import cvtColor, preprocess_input
+
+
+class DeeplabDataset(Dataset):
+    def __init__(self, annotation_lines, input_shape, num_classes, train, dataset_path):
+        super(DeeplabDataset, self).__init__()
+        self.annotation_lines   = annotation_lines
+        self.length             = len(annotation_lines)
+        self.input_shape        = input_shape
+        self.num_classes        = num_classes
+        self.train              = train
+        self.dataset_path       = dataset_path
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, index):
+        annotation_line = self.annotation_lines[index]
+        name            = annotation_line.split()[0]
+
+        #-------------------------------#
+        #   从文件中读取图像
+        #-------------------------------#
+        jpg         = Image.open(os.path.join(os.path.join(self.dataset_path, "VOC2007/JPEGImages"), name + ".jpg"))
+        png         = Image.open(os.path.join(os.path.join(self.dataset_path, "VOC2007/SegmentationClass"), name + ".png"))
+        #-------------------------------#
+        #   数据增强
+        #-------------------------------#
+        jpg, png    = self.get_random_data(jpg, png, self.input_shape, random = self.train)
+
+        jpg         = np.transpose(preprocess_input(np.array(jpg, np.float64)), [2,0,1])
+        png         = np.array(png)
+        png[png >= self.num_classes] = self.num_classes
+        #-------------------------------------------------------#
+        #   转化成one_hot的形式
+        #   在这里需要+1是因为voc数据集有些标签具有白边部分
+        #   我们需要将白边部分进行忽略，+1的目的是方便忽略。
+        #-------------------------------------------------------#
+        seg_labels  = np.eye(self.num_classes + 1)[png.reshape([-1])]
+        seg_labels  = seg_labels.reshape((int(self.input_shape[0]), int(self.input_shape[1]), self.num_classes + 1))
+
+        return jpg, png, seg_labels
+
+    def rand(self, a=0, b=1):
+        return np.random.rand() * (b - a) + a
+
+    def get_random_data(self, image, label, input_shape, jitter=.3, hue=.1, sat=0.7, val=0.3, random=True):
+        image   = cvtColor(image)
+        label   = Image.fromarray(np.array(label))
+        #------------------------------#
+        #   获得图像的高宽与目标高宽
+        #------------------------------#
+        iw, ih  = image.size
+        h, w    = input_shape
+
+        if not random:
+            iw, ih  = image.size
+            scale   = min(w/iw, h/ih)
+            nw      = int(iw*scale)
+            nh      = int(ih*scale)
+
+            image       = image.resize((nw,nh), Image.BICUBIC)
+            new_image   = Image.new('RGB', [w, h], (128,128,128))
+            new_image.paste(image, ((w-nw)//2, (h-nh)//2))
+
+            label       = label.resize((nw,nh), Image.NEAREST)
+            new_label   = Image.new('L', [w, h], (0))
+            new_label.paste(label, ((w-nw)//2, (h-nh)//2))
+            return new_image, new_label
+
+        #------------------------------------------#
+        #   对图像进行缩放并且进行长和宽的扭曲
+        #------------------------------------------#
+        new_ar = iw/ih * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter)
+        scale = self.rand(0.25, 2)
+        if new_ar < 1:
+            nh = int(scale*h)
+            nw = int(nh*new_ar)
+        else:
+            nw = int(scale*w)
+            nh = int(nw/new_ar)
+        image = image.resize((nw,nh), Image.BICUBIC)
+        label = label.resize((nw,nh), Image.NEAREST)
+        
+        #------------------------------------------#
+        #   翻转图像
+        #------------------------------------------#
+        flip = self.rand()<.5
+        if flip: 
+            image = image.transpose(Image.FLIP_LEFT_RIGHT)
+            label = label.transpose(Image.FLIP_LEFT_RIGHT)
+        
+        #------------------------------------------#
+        #   将图像多余的部分加上灰条
+        #------------------------------------------#
+        dx = int(self.rand(0, w-nw))
+        dy = int(self.rand(0, h-nh))
+        new_image = Image.new('RGB', (w,h), (128,128,128))
+        new_label = Image.new('L', (w,h), (0))
+        new_image.paste(image, (dx, dy))
+        new_label.paste(label, (dx, dy))
+        image = new_image
+        label = new_label
+
+        image_data      = np.array(image, np.uint8)
+
+        #------------------------------------------#
+        #   高斯模糊
+        #------------------------------------------#
+        blur = self.rand() < 0.25
+        if blur: 
+            image_data = cv2.GaussianBlur(image_data, (5, 5), 0)
+
+        #------------------------------------------#
+        #   旋转
+        #------------------------------------------#
+        rotate = self.rand() < 0.25
+        if rotate: 
+            center      = (w // 2, h // 2)
+            rotation    = np.random.randint(-10, 11)
+            M           = cv2.getRotationMatrix2D(center, -rotation, scale=1)
+            image_data  = cv2.warpAffine(image_data, M, (w, h), flags=cv2.INTER_CUBIC, borderValue=(128,128,128))
+            label       = cv2.warpAffine(np.array(label, np.uint8), M, (w, h), flags=cv2.INTER_NEAREST, borderValue=(0))
+
+        #---------------------------------#
+        #   对图像进行色域变换
+        #   计算色域变换的参数
+        #---------------------------------#
+        r               = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1
+        #---------------------------------#
+        #   将图像转到HSV上
+        #---------------------------------#
+        hue, sat, val   = cv2.split(cv2.cvtColor(image_data, cv2.COLOR_RGB2HSV))
+        dtype           = image_data.dtype
+        #---------------------------------#
+        #   应用变换
+        #---------------------------------#
+        x       = np.arange(0, 256, dtype=r.dtype)
+        lut_hue = ((x * r[0]) % 180).astype(dtype)
+        lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
+        lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
+
+        image_data = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
+        image_data = cv2.cvtColor(image_data, cv2.COLOR_HSV2RGB)
+        
+        return image_data, label
+
+
+# DataLoader中collate_fn使用
+def deeplab_dataset_collate(batch):
+    images      = []
+    pngs        = []
+    seg_labels  = []
+    for img, png, labels in batch:
+        images.append(img)
+        pngs.append(png)
+        seg_labels.append(labels)
+    images      = torch.from_numpy(np.array(images)).type(torch.FloatTensor)
+    pngs        = torch.from_numpy(np.array(pngs)).long()
+    seg_labels  = torch.from_numpy(np.array(seg_labels)).type(torch.FloatTensor)
+    return images, pngs, seg_labels
diff --git a/utils/utils.py b/utils/utils.py
new file mode 100644
index 0000000..63f2be1
--- /dev/null
+++ b/utils/utils.py
@@ -0,0 +1,64 @@
+import numpy as np
+from PIL import Image
+
+#---------------------------------------------------------#
+#   将图像转换成RGB图像，防止灰度图在预测时报错。
+#   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
+#---------------------------------------------------------#
+def cvtColor(image):
+    if len(np.shape(image)) == 3 and np.shape(image)[2] == 3:
+        return image 
+    else:
+        image = image.convert('RGB')
+        return image 
+
+#---------------------------------------------------#
+#   对输入图像进行resize
+#---------------------------------------------------#
+def resize_image(image, size):
+    iw, ih  = image.size
+    w, h    = size
+
+    scale   = min(w/iw, h/ih)
+    nw      = int(iw*scale)
+    nh      = int(ih*scale)
+
+    image   = image.resize((nw,nh), Image.BICUBIC)
+    new_image = Image.new('RGB', size, (128,128,128))
+    new_image.paste(image, ((w-nw)//2, (h-nh)//2))
+
+    return new_image, nw, nh
+    
+#---------------------------------------------------#
+#   获得学习率
+#---------------------------------------------------#
+def get_lr(optimizer):
+    for param_group in optimizer.param_groups:
+        return param_group['lr']
+
+def preprocess_input(image):
+    image /= 255.0
+    return image
+
+def show_config(**kwargs):
+    print('Configurations:')
+    print('-' * 70)
+    print('|%25s | %40s|' % ('keys', 'values'))
+    print('-' * 70)
+    for key, value in kwargs.items():
+        print('|%25s | %40s|' % (str(key), str(value)))
+    print('-' * 70)
+
+def download_weights(backbone, model_dir="./model_data"):
+    import os
+    from torch.hub import load_state_dict_from_url
+    
+    download_urls = {
+        'mobilenet' : 'https://github.com/bubbliiiing/deeplabv3-plus-pytorch/releases/download/v1.0/mobilenet_v2.pth.tar',
+        'xception'  : 'https://github.com/bubbliiiing/deeplabv3-plus-pytorch/releases/download/v1.0/xception_pytorch_imagenet.pth',
+    }
+    url = download_urls[backbone]
+    
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    load_state_dict_from_url(url, model_dir)
\ No newline at end of file
diff --git a/utils/utils_fit.py b/utils/utils_fit.py
new file mode 100644
index 0000000..f113705
--- /dev/null
+++ b/utils/utils_fit.py
@@ -0,0 +1,174 @@
+import os
+
+import torch
+from nets.deeplabv3_training import (CE_Loss, Dice_loss, Focal_Loss,
+                                     weights_init)
+from tqdm import tqdm
+
+from utils.utils import get_lr
+from utils.utils_metrics import f_score
+
+
+def fit_one_epoch(model_train, model, loss_history, eval_callback, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, Epoch, cuda, dice_loss, focal_loss, cls_weights, num_classes, fp16, scaler, save_period, save_dir, local_rank=0):
+    total_loss      = 0
+    total_f_score   = 0
+
+    val_loss        = 0
+    val_f_score     = 0
+
+    if local_rank == 0:
+        print('Start Train')
+        pbar = tqdm(total=epoch_step,desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3)
+    model_train.train()
+    for iteration, batch in enumerate(gen):
+        if iteration >= epoch_step: 
+            break
+        imgs, pngs, labels = batch
+
+        with torch.no_grad():
+            weights = torch.from_numpy(cls_weights)
+            if cuda:
+                imgs    = imgs.cuda(local_rank)
+                pngs    = pngs.cuda(local_rank)
+                labels  = labels.cuda(local_rank)
+                weights = weights.cuda(local_rank)
+        #----------------------#
+        #   清零梯度
+        #----------------------#
+        optimizer.zero_grad()
+        if not fp16:
+            #----------------------#
+            #   前向传播
+            #----------------------#
+            outputs = model_train(imgs)
+            #----------------------#
+            #   计算损失
+            #----------------------#
+            if focal_loss:
+                loss = Focal_Loss(outputs, pngs, weights, num_classes = num_classes)
+            else:
+                loss = CE_Loss(outputs, pngs, weights, num_classes = num_classes)
+
+            if dice_loss:
+                main_dice = Dice_loss(outputs, labels)
+                loss      = loss + main_dice
+
+            with torch.no_grad():
+                #-------------------------------#
+                #   计算f_score
+                #-------------------------------#
+                _f_score = f_score(outputs, labels)
+
+            #----------------------#
+            #   反向传播
+            #----------------------#
+            loss.backward()
+            optimizer.step()
+        else:
+            from torch.cuda.amp import autocast
+            with autocast():
+                #----------------------#
+                #   前向传播
+                #----------------------#
+                outputs = model_train(imgs)
+                #----------------------#
+                #   计算损失
+                #----------------------#
+                if focal_loss:
+                    loss = Focal_Loss(outputs, pngs, weights, num_classes = num_classes)
+                else:
+                    loss = CE_Loss(outputs, pngs, weights, num_classes = num_classes)
+
+                if dice_loss:
+                    main_dice = Dice_loss(outputs, labels)
+                    loss      = loss + main_dice
+
+                with torch.no_grad():
+                    #-------------------------------#
+                    #   计算f_score
+                    #-------------------------------#
+                    _f_score = f_score(outputs, labels)
+                    
+            #----------------------#
+            #   反向传播
+            #----------------------#
+            scaler.scale(loss).backward()
+            scaler.step(optimizer)
+            scaler.update()
+
+        total_loss      += loss.item()
+        total_f_score   += _f_score.item()
+            
+        if local_rank == 0:
+            pbar.set_postfix(**{'total_loss': total_loss / (iteration + 1), 
+                                'f_score'   : total_f_score / (iteration + 1),
+                                'lr'        : get_lr(optimizer)})
+            pbar.update(1)
+
+    if local_rank == 0:
+        pbar.close()
+        print('Finish Train')
+        print('Start Validation')
+        pbar = tqdm(total=epoch_step_val, desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3)
+
+    model_train.eval()
+    for iteration, batch in enumerate(gen_val):
+        if iteration >= epoch_step_val:
+            break
+        imgs, pngs, labels = batch
+        with torch.no_grad():
+            weights = torch.from_numpy(cls_weights)
+            if cuda:
+                imgs    = imgs.cuda(local_rank)
+                pngs    = pngs.cuda(local_rank)
+                labels  = labels.cuda(local_rank)
+                weights = weights.cuda(local_rank)
+
+            #----------------------#
+            #   前向传播
+            #----------------------#
+            outputs     = model_train(imgs)
+            #----------------------#
+            #   计算损失
+            #----------------------#
+            if focal_loss:
+                loss = Focal_Loss(outputs, pngs, weights, num_classes = num_classes)
+            else:
+                loss = CE_Loss(outputs, pngs, weights, num_classes = num_classes)
+
+            if dice_loss:
+                main_dice = Dice_loss(outputs, labels)
+                loss  = loss + main_dice
+            #-------------------------------#
+            #   计算f_score
+            #-------------------------------#
+            _f_score    = f_score(outputs, labels)
+
+            val_loss    += loss.item()
+            val_f_score += _f_score.item()
+            
+            if local_rank == 0:
+                pbar.set_postfix(**{'val_loss'  : val_loss / (iteration + 1),
+                                    'f_score'   : val_f_score / (iteration + 1),
+                                    'lr'        : get_lr(optimizer)})
+                pbar.update(1)
+            
+    if local_rank == 0:
+        pbar.close()
+        print('Finish Validation')
+        loss_history.append_loss(epoch + 1, total_loss / epoch_step, val_loss / epoch_step_val)
+        eval_callback.on_epoch_end(epoch + 1, model_train)
+        print('Epoch:'+ str(epoch + 1) + '/' + str(Epoch))
+        print('Total Loss: %.3f || Val Loss: %.3f ' % (total_loss / epoch_step, val_loss / epoch_step_val))
+        
+        #-----------------------------------------------#
+        #   保存权值
+        #-----------------------------------------------#
+        if (epoch + 1) % save_period == 0 or epoch + 1 == Epoch:
+            torch.save(model.state_dict(), os.path.join(save_dir, 'ep%03d-loss%.3f-val_loss%.3f.pth' % (epoch + 1, total_loss / epoch_step, val_loss / epoch_step_val)))
+
+        if len(loss_history.val_loss) <= 1 or (val_loss / epoch_step_val) <= min(loss_history.val_loss):
+            print('Save best model to best_epoch_weights.pth')
+            torch.save(model.state_dict(), os.path.join(save_dir, "best_epoch_weights.pth"))
+            
+        torch.save(model.state_dict(), os.path.join(save_dir, "last_epoch_weights.pth"))
\ No newline at end of file
diff --git a/utils/utils_metrics.py b/utils/utils_metrics.py
new file mode 100644
index 0000000..84a22a2
--- /dev/null
+++ b/utils/utils_metrics.py
@@ -0,0 +1,182 @@
+import csv
+import os
+from os.path import join
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+
+
+def f_score(inputs, target, beta=1, smooth = 1e-5, threhold = 0.5):
+    n, c, h, w = inputs.size()
+    nt, ht, wt, ct = target.size()
+    if h != ht and w != wt:
+        inputs = F.interpolate(inputs, size=(ht, wt), mode="bilinear", align_corners=True)
+        
+    temp_inputs = torch.softmax(inputs.transpose(1, 2).transpose(2, 3).contiguous().view(n, -1, c),-1)
+    temp_target = target.view(n, -1, ct)
+
+    #--------------------------------------------#
+    #   计算dice系数
+    #--------------------------------------------#
+    temp_inputs = torch.gt(temp_inputs, threhold).float()
+    tp = torch.sum(temp_target[...,:-1] * temp_inputs, axis=[0,1])
+    fp = torch.sum(temp_inputs                       , axis=[0,1]) - tp
+    fn = torch.sum(temp_target[...,:-1]              , axis=[0,1]) - tp
+
+    score = ((1 + beta ** 2) * tp + smooth) / ((1 + beta ** 2) * tp + beta ** 2 * fn + fp + smooth)
+    score = torch.mean(score)
+    return score
+
+# 设标签宽W，长H
+def fast_hist(a, b, n):
+    #--------------------------------------------------------------------------------#
+    #   a是转化成一维数组的标签，形状(H×W,)；b是转化成一维数组的预测结果，形状(H×W,)
+    #--------------------------------------------------------------------------------#
+    k = (a >= 0) & (a < n)
+    #--------------------------------------------------------------------------------#
+    #   np.bincount计算了从0到n**2-1这n**2个数中每个数出现的次数，返回值形状(n, n)
+    #   返回中，写对角线上的为分类正确的像素点
+    #--------------------------------------------------------------------------------#
+    return np.bincount(n * a[k].astype(int) + b[k], minlength=n ** 2).reshape(n, n)  
+
+def per_class_iu(hist):
+    return np.diag(hist) / np.maximum((hist.sum(1) + hist.sum(0) - np.diag(hist)), 1) 
+
+def per_class_PA_Recall(hist):
+    return np.diag(hist) / np.maximum(hist.sum(1), 1) 
+
+def per_class_Precision(hist):
+    return np.diag(hist) / np.maximum(hist.sum(0), 1) 
+
+def per_Accuracy(hist):
+    return np.sum(np.diag(hist)) / np.maximum(np.sum(hist), 1) 
+
+def compute_mIoU(gt_dir, pred_dir, png_name_list, num_classes, name_classes=None):  
+    print('Num classes', num_classes)  
+    #-----------------------------------------#
+    #   创建一个全是0的矩阵，是一个混淆矩阵
+    #-----------------------------------------#
+    hist = np.zeros((num_classes, num_classes))
+    
+    #------------------------------------------------#
+    #   获得验证集标签路径列表，方便直接读取
+    #   获得验证集图像分割结果路径列表，方便直接读取
+    #------------------------------------------------#
+    gt_imgs     = [join(gt_dir, x + ".png") for x in png_name_list]  
+    pred_imgs   = [join(pred_dir, x + ".png") for x in png_name_list]  
+
+    #------------------------------------------------#
+    #   读取每一个（图片-标签）对
+    #------------------------------------------------#
+    for ind in range(len(gt_imgs)): 
+        #------------------------------------------------#
+        #   读取一张图像分割结果，转化成numpy数组
+        #------------------------------------------------#
+        pred = np.array(Image.open(pred_imgs[ind]))  
+        #------------------------------------------------#
+        #   读取一张对应的标签，转化成numpy数组
+        #------------------------------------------------#
+        label = np.array(Image.open(gt_imgs[ind]))  
+
+        # 如果图像分割结果与标签的大小不一样，这张图片就不计算
+        if len(label.flatten()) != len(pred.flatten()):  
+            print(
+                'Skipping: len(gt) = {:d}, len(pred) = {:d}, {:s}, {:s}'.format(
+                    len(label.flatten()), len(pred.flatten()), gt_imgs[ind],
+                    pred_imgs[ind]))
+            continue
+
+        #------------------------------------------------#
+        #   对一张图片计算21×21的hist矩阵，并累加
+        #------------------------------------------------#
+        hist += fast_hist(label.flatten(), pred.flatten(), num_classes)  
+        # 每计算10张就输出一下目前已计算的图片中所有类别平均的mIoU值
+        if name_classes is not None and ind > 0 and ind % 10 == 0: 
+            print('{:d} / {:d}: mIou-{:0.2f}%; mPA-{:0.2f}%; Accuracy-{:0.2f}%'.format(
+                    ind, 
+                    len(gt_imgs),
+                    100 * np.nanmean(per_class_iu(hist)),
+                    100 * np.nanmean(per_class_PA_Recall(hist)),
+                    100 * per_Accuracy(hist)
+                )
+            )
+    #------------------------------------------------#
+    #   计算所有验证集图片的逐类别mIoU值
+    #------------------------------------------------#
+    IoUs        = per_class_iu(hist)
+    PA_Recall   = per_class_PA_Recall(hist)
+    Precision   = per_class_Precision(hist)
+    #------------------------------------------------#
+    #   逐类别输出一下mIoU值
+    #------------------------------------------------#
+    if name_classes is not None:
+        for ind_class in range(num_classes):
+            print('===>' + name_classes[ind_class] + ':\tIou-' + str(round(IoUs[ind_class] * 100, 2)) 
+                + '; Recall (equal to the PA)-' + str(round(PA_Recall[ind_class] * 100, 2))+ '; Precision-' + str(round(Precision[ind_class] * 100, 2)))
+
+    #-----------------------------------------------------------------#
+    #   在所有验证集图像上求所有类别平均的mIoU值，计算时忽略NaN值
+    #-----------------------------------------------------------------#
+    print('===> mIoU: ' + str(round(np.nanmean(IoUs) * 100, 2)) + '; mPA: ' + str(round(np.nanmean(PA_Recall) * 100, 2)) + '; Accuracy: ' + str(round(per_Accuracy(hist) * 100, 2)))  
+    return np.array(hist, np.int), IoUs, PA_Recall, Precision
+
+def adjust_axes(r, t, fig, axes):
+    bb                  = t.get_window_extent(renderer=r)
+    text_width_inches   = bb.width / fig.dpi
+    current_fig_width   = fig.get_figwidth()
+    new_fig_width       = current_fig_width + text_width_inches
+    propotion           = new_fig_width / current_fig_width
+    x_lim               = axes.get_xlim()
+    axes.set_xlim([x_lim[0], x_lim[1] * propotion])
+
+def draw_plot_func(values, name_classes, plot_title, x_label, output_path, tick_font_size = 12, plt_show = True):
+    fig     = plt.gcf() 
+    axes    = plt.gca()
+    plt.barh(range(len(values)), values, color='royalblue')
+    plt.title(plot_title, fontsize=tick_font_size + 2)
+    plt.xlabel(x_label, fontsize=tick_font_size)
+    plt.yticks(range(len(values)), name_classes, fontsize=tick_font_size)
+    r = fig.canvas.get_renderer()
+    for i, val in enumerate(values):
+        str_val = " " + str(val) 
+        if val < 1.0:
+            str_val = " {0:.2f}".format(val)
+        t = plt.text(val, i, str_val, color='royalblue', va='center', fontweight='bold')
+        if i == (len(values)-1):
+            adjust_axes(r, t, fig, axes)
+
+    fig.tight_layout()
+    fig.savefig(output_path)
+    if plt_show:
+        plt.show()
+    plt.close()
+
+def show_results(miou_out_path, hist, IoUs, PA_Recall, Precision, name_classes, tick_font_size = 12):
+    draw_plot_func(IoUs, name_classes, "mIoU = {0:.2f}%".format(np.nanmean(IoUs)*100), "Intersection over Union", 
+        os.path.join(miou_out_path, "mIoU.png"), tick_font_size = tick_font_size, plt_show = True)
+    print("Save mIoU out to " + os.path.join(miou_out_path, "mIoU.png"))
+
+    draw_plot_func(PA_Recall, name_classes, "mPA = {0:.2f}%".format(np.nanmean(PA_Recall)*100), "Pixel Accuracy", 
+        os.path.join(miou_out_path, "mPA.png"), tick_font_size = tick_font_size, plt_show = False)
+    print("Save mPA out to " + os.path.join(miou_out_path, "mPA.png"))
+    
+    draw_plot_func(PA_Recall, name_classes, "mRecall = {0:.2f}%".format(np.nanmean(PA_Recall)*100), "Recall", 
+        os.path.join(miou_out_path, "Recall.png"), tick_font_size = tick_font_size, plt_show = False)
+    print("Save Recall out to " + os.path.join(miou_out_path, "Recall.png"))
+
+    draw_plot_func(Precision, name_classes, "mPrecision = {0:.2f}%".format(np.nanmean(Precision)*100), "Precision", 
+        os.path.join(miou_out_path, "Precision.png"), tick_font_size = tick_font_size, plt_show = False)
+    print("Save Precision out to " + os.path.join(miou_out_path, "Precision.png"))
+
+    with open(os.path.join(miou_out_path, "confusion_matrix.csv"), 'w', newline='') as f:
+        writer          = csv.writer(f)
+        writer_list     = []
+        writer_list.append([' '] + [str(c) for c in name_classes])
+        for i in range(len(hist)):
+            writer_list.append([name_classes[i]] + [str(x) for x in hist[i]])
+        writer.writerows(writer_list)
+    print("Save confusion_matrix out to " + os.path.join(miou_out_path, "confusion_matrix.csv"))
+            
\ No newline at end of file
diff --git a/voc_annotation.py b/voc_annotation.py
new file mode 100644
index 0000000..c04a46d
--- /dev/null
+++ b/voc_annotation.py
@@ -0,0 +1,100 @@
+import os
+import random
+
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+#-------------------------------------------------------#
+#   想要增加测试集修改trainval_percent 
+#   修改train_percent用于改变验证集的比例 9:1
+#   
+#   当前该库将测试集当作验证集使用，不单独划分测试集
+#-------------------------------------------------------#
+trainval_percent    = 1
+train_percent       = 0.9
+#-------------------------------------------------------#
+#   指向VOC数据集所在的文件夹
+#   默认指向根目录下的VOC数据集
+#-------------------------------------------------------#
+VOCdevkit_path      = 'VOCdevkit'
+
+if __name__ == "__main__":
+    random.seed(0)
+    print("Generate txt in ImageSets.")
+    segfilepath     = os.path.join(VOCdevkit_path, 'VOC2007/SegmentationClass')
+    saveBasePath    = os.path.join(VOCdevkit_path, 'VOC2007/ImageSets/Segmentation')
+    
+    temp_seg = os.listdir(segfilepath)
+    total_seg = []
+    for seg in temp_seg:
+        if seg.endswith(".png"):
+            total_seg.append(seg)
+
+    num     = len(total_seg)  
+    list    = range(num)  
+    tv      = int(num*trainval_percent)  
+    tr      = int(tv*train_percent)  
+    trainval= random.sample(list,tv)  
+    train   = random.sample(trainval,tr)  
+    
+    print("train and val size",tv)
+    print("traub suze",tr)
+    ftrainval   = open(os.path.join(saveBasePath,'trainval.txt'), 'w')  
+    ftest       = open(os.path.join(saveBasePath,'test.txt'), 'w')  
+    ftrain      = open(os.path.join(saveBasePath,'train.txt'), 'w')  
+    fval        = open(os.path.join(saveBasePath,'val.txt'), 'w')  
+    
+    for i in list:  
+        name = total_seg[i][:-4]+'\n'  
+        if i in trainval:  
+            ftrainval.write(name)  
+            if i in train:  
+                ftrain.write(name)  
+            else:  
+                fval.write(name)  
+        else:  
+            ftest.write(name)  
+    
+    ftrainval.close()  
+    ftrain.close()  
+    fval.close()  
+    ftest.close()
+    print("Generate txt in ImageSets done.")
+
+    print("Check datasets format, this may take a while.")
+    print("检查数据集格式是否符合要求，这可能需要一段时间。")
+    # classes_nums        = np.zeros([256], np.int)
+    classes_nums = np.zeros([256], dtype=int)  # 使用内置int
+
+    for i in tqdm(list):
+        name            = total_seg[i]
+        png_file_name   = os.path.join(segfilepath, name)
+        if not os.path.exists(png_file_name):
+            raise ValueError("未检测到标签图片%s，请查看具体路径下文件是否存在以及后缀是否为png。"%(png_file_name))
+        
+        png             = np.array(Image.open(png_file_name), np.uint8)
+        if len(np.shape(png)) > 2:
+            print("标签图片%s的shape为%s，不属于灰度图或者八位彩图，请仔细检查数据集格式。"%(name, str(np.shape(png))))
+            #print("标签图片需要为灰度图或者八位彩图，标签的每个像素点的值就是这个像素点所属的种类。"%(name, str(np.shape(png))))
+
+        classes_nums += np.bincount(np.reshape(png, [-1]), minlength=256)
+            
+    print("打印像素点的值与数量。")
+    print('-' * 37)
+    print("| %15s | %15s |"%("Key", "Value"))
+    print('-' * 37)
+    for i in range(256):
+        if classes_nums[i] > 0:
+            print("| %15s | %15s |"%(str(i), str(classes_nums[i])))
+            print('-' * 37)
+    
+    if classes_nums[255] > 0 and classes_nums[0] > 0 and np.sum(classes_nums[1:255]) == 0:
+        print("检测到标签中像素点的值仅包含0与255，数据格式有误。")
+        print("二分类问题需要将标签修改为背景的像素点值为0，目标的像素点值为1。")
+    elif classes_nums[0] > 0 and np.sum(classes_nums[1:]) == 0:
+        print("检测到标签中仅仅包含背景像素点，数据格式有误，请仔细检查数据集格式。")
+
+    print("JPEGImages中的图片应当为.jpg文件、SegmentationClass中的图片应当为.png文件。")
+    print("如果格式有误，参考:")
+    print("https://github.com/bubbliiiing/segmentation-format-fix")
\ No newline at end of file
diff --git a/常见问题汇总.md b/常见问题汇总.md
new file mode 100644
index 0000000..7586fe3
--- /dev/null
+++ b/常见问题汇总.md
@@ -0,0 +1,554 @@
+问题汇总的博客地址为[https://blog.csdn.net/weixin_44791964/article/details/107517428](https://blog.csdn.net/weixin_44791964/article/details/107517428)。
+
+# 问题汇总
+## 1、下载问题
+### a、代码下载
+**问：up主，可以给我发一份代码吗，代码在哪里下载啊？ 
+答：Github上的地址就在视频简介里。复制一下就能进去下载了。**
+
+**问：up主，为什么我下载的代码提示压缩包损坏？
+答：重新去Github下载。**
+
+**问：up主，为什么我下载的代码和你在视频以及博客上的代码不一样？
+答：我常常会对代码进行更新，最终以实际的代码为准。**
+
+### b、 权值下载
+**问：up主，为什么我下载的代码里面，model_data下面没有.pth或者.h5文件？ 
+答：我一般会把权值上传到Github和百度网盘，在GITHUB的README里面就能找到。**
+
+### c、 数据集下载
+**问：up主，XXXX数据集在哪里下载啊？
+答：一般数据集的下载地址我会放在README里面，基本上都有，没有的话请及时联系我添加，直接发github的issue即可**。
+
+## 2、环境配置问题
+### a、现在库中所用的环境
+**pytorch代码对应的pytorch版本为1.2，博客地址对应**[https://blog.csdn.net/weixin_44791964/article/details/106037141](https://blog.csdn.net/weixin_44791964/article/details/106037141)。
+
+**keras代码对应的tensorflow版本为1.13.2，keras版本是2.1.5，博客地址对应**[https://blog.csdn.net/weixin_44791964/article/details/104702142](https://blog.csdn.net/weixin_44791964/article/details/104702142)。
+
+**tf2代码对应的tensorflow版本为2.2.0，无需安装keras，博客地址对应**[https://blog.csdn.net/weixin_44791964/article/details/109161493](https://blog.csdn.net/weixin_44791964/article/details/109161493)。
+
+**问：你的代码某某某版本的tensorflow和pytorch能用嘛？
+答：最好按照我推荐的配置，配置教程也有！其它版本的我没有试过！可能出现问题但是一般问题不大。仅需要改少量代码即可。**
+
+### b、30系列显卡环境配置
+30系显卡由于框架更新不可使用上述环境配置教程。
+当前我已经测试的可以用的30显卡配置如下：
+**pytorch代码对应的pytorch版本为1.7.0，cuda为11.0，cudnn为8.0.5**。
+
+**keras代码无法在win10下配置cuda11，在ubuntu下可以百度查询一下，配置tensorflow版本为1.15.4，keras版本是2.1.5或者2.3.1（少量函数接口不同，代码可能还需要少量调整。）**
+
+**tf2代码对应的tensorflow版本为2.4.0，cuda为11.0，cudnn为8.0.5**。
+
+### c、GPU利用问题与环境使用问题
+**问：为什么我安装了tensorflow-gpu但是却没用利用GPU进行训练呢？
+答：确认tensorflow-gpu已经装好，利用pip list查看tensorflow版本，然后查看任务管理器或者利用nvidia命令看看是否使用了gpu进行训练，任务管理器的话要看显存使用情况。**
+
+**问：up主，我好像没有在用gpu进行训练啊，怎么看是不是用了GPU进行训练？
+答：查看是否使用GPU进行训练一般使用NVIDIA在命令行的查看命令，如果要看任务管理器的话，请看性能部分GPU的显存是否利用，或者查看任务管理器的Cuda，而非Copy。**
+![在这里插入图片描述](https://img-blog.csdnimg.cn/20201013234241524.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center)
+
+**问：up主，为什么我按照你的环境配置后还是不能使用？
+答：请把你的GPU、CUDA、CUDNN、TF版本以及PYTORCH版本B站私聊告诉我。**
+
+**问：出现如下错误**
+```python
+Traceback (most recent call last):
+  File "C:\Users\focus\Anaconda3\ana\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in <module>
+ from tensorflow.python.pywrap_tensorflow_internal import *
+File "C:\Users\focus\Anaconda3\ana\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in <module>
+pywrap_tensorflow_internal = swig_import_helper()
+  File "C:\Users\focus\Anaconda3\ana\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper
+    _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
+File "C:\Users\focus\Anaconda3\ana\envs\tensorflow-gpu\lib\imp.py", line 243, in load_modulereturn load_dynamic(name, filename, file)
+File "C:\Users\focus\Anaconda3\ana\envs\tensorflow-gpu\lib\imp.py", line 343, in load_dynamic
+    return _load(spec)
+ImportError: DLL load failed: 找不到指定的模块。
+```
+**答：如果没重启过就重启一下，否则重新按照步骤安装，还无法解决则把你的GPU、CUDA、CUDNN、TF版本以及PYTORCH版本私聊告诉我。**
+
+### d、no module问题
+**问：为什么提示说no module name utils.utils（no module name nets.yolo、no module name nets.ssd等一系列问题）啊？
+答：utils并不需要用pip装，它就在我上传的仓库的根目录，出现这个问题的原因是根目录不对，查查相对目录和根目录的概念。查了基本上就明白了。**
+
+**问：为什么提示说no module name matplotlib（no module name PIL，no module name cv2等等）？
+答：这个库没安装打开命令行安装就好。pip install matplotlib**
+
+**问：为什么我已经用pip装了opencv（pillow、matplotlib等），还是提示no module name cv2？
+答：没有激活环境装，要激活对应的conda环境进行安装才可以正常使用**
+
+**问：为什么提示说No module named 'torch' ？
+答：其实我也真的很想知道为什么会有这个问题……这个pytorch没装是什么情况？一般就俩情况，一个是真的没装，还有一个是装到其它环境了，当前激活的环境不是自己装的环境。**
+
+**问：为什么提示说No module named 'tensorflow' ？
+答：同上。**
+
+### e、cuda安装失败问题
+一般cuda安装前需要安装Visual Studio，装个2017版本即可。
+
+### f、Ubuntu系统问题
+**所有代码在Ubuntu下可以使用，我两个系统都试过。**
+
+### g、VSCODE提示错误的问题
+**问：为什么在VSCODE里面提示一大堆的错误啊？
+答：我也提示一大堆的错误，但是不影响，是VSCODE的问题，如果不想看错误的话就装Pycharm。**
+
+### h、使用cpu进行训练与预测的问题
+**对于keras和tf2的代码而言，如果想用cpu进行训练和预测，直接装cpu版本的tensorflow就可以了。**
+
+**对于pytorch的代码而言，如果想用cpu进行训练和预测，需要将cuda=True修改成cuda=False。**
+
+### i、tqdm没有pos参数问题
+**问：运行代码提示'tqdm' object has no attribute 'pos'。
+答：重装tqdm，换个版本就可以了。**
+
+### j、提示decode(“utf-8”)的问题
+**由于h5py库的更新，安装过程中会自动安装h5py=3.0.0以上的版本，会导致decode("utf-8")的错误！
+各位一定要在安装完tensorflow后利用命令装h5py=2.10.0！**
+```
+pip install h5py==2.10.0
+```
+
+### k、提示TypeError: __array__() takes 1 positional argument but 2 were given错误
+可以修改pillow版本解决。
+```
+pip install pillow==8.2.0
+```
+
+### l、其它问题
+**问：为什么提示TypeError: cat() got an unexpected keyword argument 'axis'，Traceback (most recent call last)，AttributeError: 'Tensor' object has no attribute 'bool'？
+答：这是版本问题，建议使用torch1.2以上版本**
+**其它有很多稀奇古怪的问题，很多是版本问题，建议按照我的视频教程安装Keras和tensorflow。比如装的是tensorflow2，就不用问我说为什么我没法运行Keras-yolo啥的。那是必然不行的。**
+
+## 3、目标检测库问题汇总（人脸检测和分类库也可参考）
+### a、shape不匹配问题
+#### 1）、训练时shape不匹配问题
+**问：up主，为什么运行train.py会提示shape不匹配啊？
+答：在keras环境中，因为你训练的种类和原始的种类不同，网络结构会变化，所以最尾部的shape会有少量不匹配。**
+
+#### 2）、预测时shape不匹配问题
+**问：为什么我运行predict.py会提示我说shape不匹配呀。
+在Pytorch里面是这样的：**
+![在这里插入图片描述](https://img-blog.csdnimg.cn/20200722171631901.png)
+在Keras里面是这样的：
+![在这里插入图片描述](https://img-blog.csdnimg.cn/20200722171523380.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70)
+**答：原因主要有仨：
+1、在ssd、FasterRCNN里面，可能是train.py里面的num_classes没改。
+2、model_path没改。
+3、classes_path没改。
+请检查清楚了！确定自己所用的model_path和classes_path是对应的！训练的时候用到的num_classes或者classes_path也需要检查！**
+
+### b、显存不足问题
+**问：为什么我运行train.py下面的命令行闪的贼快，还提示OOM啥的？ 
+答：这是在keras中出现的，爆显存了，可以改小batch_size，SSD的显存占用率是最小的，建议用SSD；
+2G显存：SSD、YOLOV4-TINY
+4G显存：YOLOV3
+6G显存：YOLOV4、Retinanet、M2det、Efficientdet、Faster RCNN等
+8G+显存：随便选吧。**
+**需要注意的是，受到BatchNorm2d影响，batch_size不可为1，至少为2。**
+
+**问：为什么提示 RuntimeError: CUDA out of memory. Tried to allocate 52.00 MiB (GPU 0; 15.90 GiB total capacity; 14.85 GiB already allocated; 51.88 MiB free; 15.07 GiB reserved in total by PyTorch)？ 
+答：这是pytorch中出现的，爆显存了，同上。**
+
+**问：为什么我显存都没利用，就直接爆显存了？ 
+答：都爆显存了，自然就不利用了，模型没有开始训练。**
+### c、训练问题（冻结训练，LOSS问题、训练效果问题等）
+**问：为什么要冻结训练和解冻训练呀？
+答：这是迁移学习的思想，因为神经网络主干特征提取部分所提取到的特征是通用的，我们冻结起来训练可以加快训练效率，也可以防止权值被破坏。**
+在冻结阶段，模型的主干被冻结了，特征提取网络不发生改变。占用的显存较小，仅对网络进行微调。
+在解冻阶段，模型的主干不被冻结了，特征提取网络会发生改变。占用的显存较大，网络所有的参数都会发生改变。
+
+**问：为什么我的网络不收敛啊，LOSS是XXXX。
+答：不同网络的LOSS不同，LOSS只是一个参考指标，用于查看网络是否收敛，而非评价网络好坏，我的yolo代码都没有归一化，所以LOSS值看起来比较高，LOSS的值不重要，重要的是是否在变小，预测是否有效果。**
+
+**问：为什么我的训练效果不好？预测了没有框（框不准）。
+答：**
+
+考虑几个问题：
+1、目标信息问题，查看2007_train.txt文件是否有目标信息，没有的话请修改voc_annotation.py。
+2、数据集问题，小于500的自行考虑增加数据集，同时测试不同的模型，确认数据集是好的。
+3、是否解冻训练，如果数据集分布与常规画面差距过大需要进一步解冻训练，调整主干，加强特征提取能力。
+4、网络问题，比如SSD不适合小目标，因为先验框固定了。
+5、训练时长问题，有些同学只训练了几代表示没有效果，按默认参数训练完。
+6、确认自己是否按照步骤去做了，如果比如voc_annotation.py里面的classes是否修改了等。
+7、不同网络的LOSS不同，LOSS只是一个参考指标，用于查看网络是否收敛，而非评价网络好坏，LOSS的值不重要，重要的是是否收敛。
+
+**问：我怎么出现了gbk什么的编码错误啊：**
+```python
+UnicodeDecodeError: 'gbk' codec can't decode byte 0xa6 in position 446: illegal multibyte sequence
+```
+**答：标签和路径不要使用中文，如果一定要使用中文，请注意处理的时候编码的问题，改成打开文件的encoding方式改为utf-8。**
+
+**问：我的图片是xxx*xxx的分辨率的，可以用吗！**
+**答：可以用，代码里面会自动进行resize或者数据增强。**
+
+**问：怎么进行多GPU训练？
+答：pytorch的大多数代码可以直接使用gpu训练，keras的话直接百度就好了，实现并不复杂，我没有多卡没法详细测试，还需要各位同学自己努力了。**
+### d、灰度图问题
+**问：能不能训练灰度图（预测灰度图）啊？
+答：我的大多数库会将灰度图转化成RGB进行训练和预测，如果遇到代码不能训练或者预测灰度图的情况，可以尝试一下在get_random_data里面将Image.open后的结果转换成RGB，预测的时候也这样试试。（仅供参考）**
+
+### e、断点续练问题
+**问：我已经训练过几个世代了，能不能从这个基础上继续开始训练
+答：可以，你在训练前，和载入预训练权重一样载入训练过的权重就行了。一般训练好的权重会保存在logs文件夹里面，将model_path修改成你要开始的权值的路径即可。**
+
+### f、预训练权重的问题
+**问：如果我要训练其它的数据集，预训练权重要怎么办啊？**
+**答：数据的预训练权重对不同数据集是通用的，因为特征是通用的，预训练权重对于99%的情况都必须要用，不用的话权值太过随机，特征提取效果不明显，网络训练的结果也不会好。**
+
+**问：up，我修改了网络，预训练权重还能用吗？
+答：修改了主干的话，如果不是用的现有的网络，基本上预训练权重是不能用的，要么就自己判断权值里卷积核的shape然后自己匹配，要么只能自己预训练去了；修改了后半部分的话，前半部分的主干部分的预训练权重还是可以用的，如果是pytorch代码的话，需要自己修改一下载入权值的方式，判断shape后载入，如果是keras代码，直接by_name=True,skip_mismatch=True即可。**
+权值匹配的方式可以参考如下：
+```python
+# 加快模型训练的效率
+print('Loading weights into state dict...')
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model_dict = model.state_dict()
+pretrained_dict = torch.load(model_path, map_location=device)
+a = {}
+for k, v in pretrained_dict.items():
+    try:    
+        if np.shape(model_dict[k]) ==  np.shape(v):
+            a[k]=v
+    except:
+        pass
+model_dict.update(a)
+model.load_state_dict(model_dict)
+print('Finished!')
+```
+
+**问：我要怎么不使用预训练权重啊？
+答：把载入预训练权重的代码注释了就行。**
+
+**问：为什么我不使用预训练权重效果这么差啊？
+答：因为随机初始化的权值不好，提取的特征不好，也就导致了模型训练的效果不好，voc07+12、coco+voc07+12效果都不一样，预训练权重还是非常重要的。**
+
+### g、视频检测问题与摄像头检测问题
+**问：怎么用摄像头检测呀？
+答：predict.py修改参数可以进行摄像头检测，也有视频详细解释了摄像头检测的思路。**
+
+**问：怎么用视频检测呀？
+答：同上**
+### h、从0开始训练问题
+**问：怎么在模型上从0开始训练？
+答：在算力不足与调参能力不足的情况下从0开始训练毫无意义。模型特征提取能力在随机初始化参数的情况下非常差。没有好的参数调节能力和算力，无法使得网络正常收敛。**
+如果一定要从0开始，那么训练的时候请注意几点：
+ - 不载入预训练权重。 
+ - 不要进行冻结训练，注释冻结模型的代码。
+
+**问：为什么我不使用预训练权重效果这么差啊？
+答：因为随机初始化的权值不好，提取的特征不好，也就导致了模型训练的效果不好，voc07+12、coco+voc07+12效果都不一样，预训练权重还是非常重要的。**
+
+### i、保存问题
+**问：检测完的图片怎么保存？
+答：一般目标检测用的是Image，所以查询一下PIL库的Image如何进行保存。详细看看predict.py文件的注释。**
+
+**问：怎么用视频保存呀？
+答：详细看看predict.py文件的注释。**
+
+### j、遍历问题
+**问：如何对一个文件夹的图片进行遍历？
+答：一般使用os.listdir先找出文件夹里面的所有图片，然后根据predict.py文件里面的执行思路检测图片就行了，详细看看predict.py文件的注释。**
+
+**问：如何对一个文件夹的图片进行遍历？并且保存。
+答：遍历的话一般使用os.listdir先找出文件夹里面的所有图片，然后根据predict.py文件里面的执行思路检测图片就行了。保存的话一般目标检测用的是Image，所以查询一下PIL库的Image如何进行保存。如果有些库用的是cv2，那就是查一下cv2怎么保存图片。详细看看predict.py文件的注释。**
+
+### k、路径问题（No such file or directory）
+**问：我怎么出现了这样的错误呀：**
+```python
+FileNotFoundError: 【Errno 2】 No such file or directory
+……………………………………
+……………………………………
+```
+**答：去检查一下文件夹路径，查看是否有对应文件；并且检查一下2007_train.txt，其中文件路径是否有错。**
+关于路径有几个重要的点：
+**文件夹名称中一定不要有空格。
+注意相对路径和绝对路径。
+多百度路径相关的知识。**
+
+**所有的路径问题基本上都是根目录问题，好好查一下相对目录的概念！**
+### l、和原版比较问题
+**问：你这个代码和原版比怎么样，可以达到原版的效果么？
+答：基本上可以达到，我都用voc数据测过，我没有好显卡，没有能力在coco上测试与训练。**
+
+**问：你有没有实现yolov4所有的tricks，和原版差距多少？
+答：并没有实现全部的改进部分，由于YOLOV4使用的改进实在太多了，很难完全实现与列出来，这里只列出来了一些我比较感兴趣，而且非常有效的改进。论文中提到的SAM（注意力机制模块），作者自己的源码也没有使用。还有其它很多的tricks，不是所有的tricks都有提升，我也没法实现全部的tricks。至于和原版的比较，我没有能力训练coco数据集，根据使用过的同学反应差距不大。**
+
+### m、FPS问题（检测速度问题）
+**问：你这个FPS可以到达多少，可以到 XX FPS么？
+答：FPS和机子的配置有关，配置高就快，配置低就慢。**
+
+**问：为什么我用服务器去测试yolov4（or others）的FPS只有十几？
+答：检查是否正确安装了tensorflow-gpu或者pytorch的gpu版本，如果已经正确安装，可以去利用time.time()的方法查看detect_image里面，哪一段代码耗时更长（不仅只有网络耗时长，其它处理部分也会耗时，如绘图等）。**
+
+**问：为什么论文中说速度可以达到XX，但是这里却没有？
+答：检查是否正确安装了tensorflow-gpu或者pytorch的gpu版本，如果已经正确安装，可以去利用time.time()的方法查看detect_image里面，哪一段代码耗时更长（不仅只有网络耗时长，其它处理部分也会耗时，如绘图等）。有些论文还会使用多batch进行预测，我并没有去实现这个部分。**
+
+### n、预测图片不显示问题
+**问：为什么你的代码在预测完成后不显示图片？只是在命令行告诉我有什么目标。
+答：给系统安装一个图片查看器就行了。**
+
+### o、算法评价问题（目标检测的map、PR曲线、Recall、Precision等）
+**问：怎么计算map？
+答：看map视频，都一个流程。**
+
+**问：计算map的时候，get_map.py里面有一个MINOVERLAP是什么用的，是iou吗？
+答：是iou，它的作用是判断预测框和真实框的重合成度，如果重合程度大于MINOVERLAP，则预测正确。**
+
+**问：为什么get_map.py里面的self.confidence（self.score）要设置的那么小？
+答：看一下map的视频的原理部分，要知道所有的结果然后再进行pr曲线的绘制。**
+
+**问：能不能说说怎么绘制PR曲线啥的呀。
+答：可以看mAP视频，结果里面有PR曲线。**
+
+**问：怎么计算Recall、Precision指标。
+答：这俩指标应该是相对于特定的置信度的，计算map的时候也会获得。**
+
+### p、coco数据集训练问题
+**问：目标检测怎么训练COCO数据集啊？。
+答：coco数据训练所需要的txt文件可以参考qqwweee的yolo3的库，格式都是一样的。**
+
+### q、模型优化（模型修改）问题
+**问：up，YOLO系列使用Focal LOSS的代码你有吗，有提升吗？
+答：很多人试过，提升效果也不大（甚至变的更Low），它自己有自己的正负样本的平衡方式。**
+
+**问：up，我修改了网络，预训练权重还能用吗？
+答：修改了主干的话，如果不是用的现有的网络，基本上预训练权重是不能用的，要么就自己判断权值里卷积核的shape然后自己匹配，要么只能自己预训练去了；修改了后半部分的话，前半部分的主干部分的预训练权重还是可以用的，如果是pytorch代码的话，需要自己修改一下载入权值的方式，判断shape后载入，如果是keras代码，直接by_name=True,skip_mismatch=True即可。**
+权值匹配的方式可以参考如下：
+```python
+# 加快模型训练的效率
+print('Loading weights into state dict...')
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model_dict = model.state_dict()
+pretrained_dict = torch.load(model_path, map_location=device)
+a = {}
+for k, v in pretrained_dict.items():
+    try:    
+        if np.shape(model_dict[k]) ==  np.shape(v):
+            a[k]=v
+    except:
+        pass
+model_dict.update(a)
+model.load_state_dict(model_dict)
+print('Finished!')
+```
+
+**问：up，怎么修改模型啊，我想发个小论文！
+答：建议看看yolov3和yolov4的区别，然后看看yolov4的论文，作为一个大型调参现场非常有参考意义，使用了很多tricks。我能给的建议就是多看一些经典模型，然后拆解里面的亮点结构并使用。**
+
+### r、部署问题
+我没有具体部署到手机等设备上过，所以很多部署问题我并不了解……
+
+## 4、语义分割库问题汇总
+### a、shape不匹配问题
+#### 1）、训练时shape不匹配问题
+**问：up主，为什么运行train.py会提示shape不匹配啊？
+答：在keras环境中，因为你训练的种类和原始的种类不同，网络结构会变化，所以最尾部的shape会有少量不匹配。**
+
+#### 2）、预测时shape不匹配问题
+**问：为什么我运行predict.py会提示我说shape不匹配呀。
+在Pytorch里面是这样的：**
+![在这里插入图片描述](https://img-blog.csdnimg.cn/20200722171631901.png)
+在Keras里面是这样的：
+![在这里插入图片描述](https://img-blog.csdnimg.cn/20200722171523380.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70)
+**答：原因主要有二：
+1、train.py里面的num_classes没改。
+2、预测时num_classes没改。
+请检查清楚！训练和预测的时候用到的num_classes都需要检查！**
+
+### b、显存不足问题
+**问：为什么我运行train.py下面的命令行闪的贼快，还提示OOM啥的？ 
+答：这是在keras中出现的，爆显存了，可以改小batch_size。**
+
+**需要注意的是，受到BatchNorm2d影响，batch_size不可为1，至少为2。**
+
+**问：为什么提示 RuntimeError: CUDA out of memory. Tried to allocate 52.00 MiB (GPU 0; 15.90 GiB total capacity; 14.85 GiB already allocated; 51.88 MiB free; 15.07 GiB reserved in total by PyTorch)？ 
+答：这是pytorch中出现的，爆显存了，同上。**
+
+**问：为什么我显存都没利用，就直接爆显存了？ 
+答：都爆显存了，自然就不利用了，模型没有开始训练。**
+
+### c、训练问题（冻结训练，LOSS问题、训练效果问题等）
+**问：为什么要冻结训练和解冻训练呀？
+答：这是迁移学习的思想，因为神经网络主干特征提取部分所提取到的特征是通用的，我们冻结起来训练可以加快训练效率，也可以防止权值被破坏。**
+**在冻结阶段，模型的主干被冻结了，特征提取网络不发生改变。占用的显存较小，仅对网络进行微调。**
+**在解冻阶段，模型的主干不被冻结了，特征提取网络会发生改变。占用的显存较大，网络所有的参数都会发生改变。**
+
+**问：为什么我的网络不收敛啊，LOSS是XXXX。
+答：不同网络的LOSS不同，LOSS只是一个参考指标，用于查看网络是否收敛，而非评价网络好坏，我的yolo代码都没有归一化，所以LOSS值看起来比较高，LOSS的值不重要，重要的是是否在变小，预测是否有效果。**
+
+**问：为什么我的训练效果不好？预测了没有目标，结果是一片黑。
+答：**
+**考虑几个问题：
+1、数据集问题，这是最重要的问题。小于500的自行考虑增加数据集；一定要检查数据集的标签，视频中详细解析了VOC数据集的格式，但并不是有输入图片有输出标签即可，还需要确认标签的每一个像素值是否为它对应的种类。很多同学的标签格式不对，最常见的错误格式就是标签的背景为黑，目标为白，此时目标的像素点值为255，无法正常训练，目标需要为1才行。
+2、是否解冻训练，如果数据集分布与常规画面差距过大需要进一步解冻训练，调整主干，加强特征提取能力。
+3、网络问题，可以尝试不同的网络。
+4、训练时长问题，有些同学只训练了几代表示没有效果，按默认参数训练完。
+5、确认自己是否按照步骤去做了。
+6、不同网络的LOSS不同，LOSS只是一个参考指标，用于查看网络是否收敛，而非评价网络好坏，LOSS的值不重要，重要的是是否收敛。**
+
+
+
+**问：为什么我的训练效果不好？对小目标预测不准确。
+答：对于deeplab和pspnet而言，可以修改一下downsample_factor，当downsample_factor为16的时候下采样倍数过多，效果不太好，可以修改为8。**
+
+**问：我怎么出现了gbk什么的编码错误啊：**
+```python
+UnicodeDecodeError: 'gbk' codec can't decode byte 0xa6 in position 446: illegal multibyte sequence
+```
+**答：标签和路径不要使用中文，如果一定要使用中文，请注意处理的时候编码的问题，改成打开文件的encoding方式改为utf-8。**
+
+**问：我的图片是xxx*xxx的分辨率的，可以用吗！**
+**答：可以用，代码里面会自动进行resize或者数据增强。**
+
+**问：怎么进行多GPU训练？
+答：pytorch的大多数代码可以直接使用gpu训练，keras的话直接百度就好了，实现并不复杂，我没有多卡没法详细测试，还需要各位同学自己努力了。**
+
+### d、灰度图问题
+**问：能不能训练灰度图（预测灰度图）啊？
+答：我的大多数库会将灰度图转化成RGB进行训练和预测，如果遇到代码不能训练或者预测灰度图的情况，可以尝试一下在get_random_data里面将Image.open后的结果转换成RGB，预测的时候也这样试试。（仅供参考）**
+
+### e、断点续练问题
+**问：我已经训练过几个世代了，能不能从这个基础上继续开始训练
+答：可以，你在训练前，和载入预训练权重一样载入训练过的权重就行了。一般训练好的权重会保存在logs文件夹里面，将model_path修改成你要开始的权值的路径即可。**
+
+### f、预训练权重的问题
+
+**问：如果我要训练其它的数据集，预训练权重要怎么办啊？**
+**答：数据的预训练权重对不同数据集是通用的，因为特征是通用的，预训练权重对于99%的情况都必须要用，不用的话权值太过随机，特征提取效果不明显，网络训练的结果也不会好。**
+
+**问：up，我修改了网络，预训练权重还能用吗？
+答：修改了主干的话，如果不是用的现有的网络，基本上预训练权重是不能用的，要么就自己判断权值里卷积核的shape然后自己匹配，要么只能自己预训练去了；修改了后半部分的话，前半部分的主干部分的预训练权重还是可以用的，如果是pytorch代码的话，需要自己修改一下载入权值的方式，判断shape后载入，如果是keras代码，直接by_name=True,skip_mismatch=True即可。**
+权值匹配的方式可以参考如下：
+
+```python
+# 加快模型训练的效率
+print('Loading weights into state dict...')
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model_dict = model.state_dict()
+pretrained_dict = torch.load(model_path, map_location=device)
+a = {}
+for k, v in pretrained_dict.items():
+    try:    
+        if np.shape(model_dict[k]) ==  np.shape(v):
+            a[k]=v
+    except:
+        pass
+model_dict.update(a)
+model.load_state_dict(model_dict)
+print('Finished!')
+```
+
+**问：我要怎么不使用预训练权重啊？
+答：把载入预训练权重的代码注释了就行。**
+
+**问：为什么我不使用预训练权重效果这么差啊？
+答：因为随机初始化的权值不好，提取的特征不好，也就导致了模型训练的效果不好，预训练权重还是非常重要的。**
+
+### g、视频检测问题与摄像头检测问题
+**问：怎么用摄像头检测呀？
+答：predict.py修改参数可以进行摄像头检测，也有视频详细解释了摄像头检测的思路。**
+
+**问：怎么用视频检测呀？
+答：同上**
+
+### h、从0开始训练问题
+**问：怎么在模型上从0开始训练？
+答：在算力不足与调参能力不足的情况下从0开始训练毫无意义。模型特征提取能力在随机初始化参数的情况下非常差。没有好的参数调节能力和算力，无法使得网络正常收敛。**
+如果一定要从0开始，那么训练的时候请注意几点：
+ - 不载入预训练权重。
+ - 不要进行冻结训练，注释冻结模型的代码。
+
+**问：为什么我不使用预训练权重效果这么差啊？
+答：因为随机初始化的权值不好，提取的特征不好，也就导致了模型训练的效果不好，预训练权重还是非常重要的。**
+
+### i、保存问题
+**问：检测完的图片怎么保存？
+答：一般目标检测用的是Image，所以查询一下PIL库的Image如何进行保存。详细看看predict.py文件的注释。**
+
+**问：怎么用视频保存呀？
+答：详细看看predict.py文件的注释。**
+
+### j、遍历问题
+**问：如何对一个文件夹的图片进行遍历？
+答：一般使用os.listdir先找出文件夹里面的所有图片，然后根据predict.py文件里面的执行思路检测图片就行了，详细看看predict.py文件的注释。**
+
+**问：如何对一个文件夹的图片进行遍历？并且保存。
+答：遍历的话一般使用os.listdir先找出文件夹里面的所有图片，然后根据predict.py文件里面的执行思路检测图片就行了。保存的话一般目标检测用的是Image，所以查询一下PIL库的Image如何进行保存。如果有些库用的是cv2，那就是查一下cv2怎么保存图片。详细看看predict.py文件的注释。**
+
+### k、路径问题（No such file or directory）
+**问：我怎么出现了这样的错误呀：**
+```python
+FileNotFoundError: 【Errno 2】 No such file or directory
+……………………………………
+……………………………………
+```
+
+**答：去检查一下文件夹路径，查看是否有对应文件；并且检查一下2007_train.txt，其中文件路径是否有错。**
+关于路径有几个重要的点：
+**文件夹名称中一定不要有空格。
+注意相对路径和绝对路径。
+多百度路径相关的知识。**
+
+**所有的路径问题基本上都是根目录问题，好好查一下相对目录的概念！**
+
+### l、FPS问题（检测速度问题）
+**问：你这个FPS可以到达多少，可以到 XX FPS么？
+答：FPS和机子的配置有关，配置高就快，配置低就慢。**
+
+**问：为什么论文中说速度可以达到XX，但是这里却没有？
+答：检查是否正确安装了tensorflow-gpu或者pytorch的gpu版本，如果已经正确安装，可以去利用time.time()的方法查看detect_image里面，哪一段代码耗时更长（不仅只有网络耗时长，其它处理部分也会耗时，如绘图等）。有些论文还会使用多batch进行预测，我并没有去实现这个部分。**
+
+### m、预测图片不显示问题
+**问：为什么你的代码在预测完成后不显示图片？只是在命令行告诉我有什么目标。
+答：给系统安装一个图片查看器就行了。**
+
+### n、算法评价问题（miou）
+**问：怎么计算miou？
+答：参考视频里的miou测量部分。**
+
+**问：怎么计算Recall、Precision指标。
+答：现有的代码还无法获得，需要各位同学理解一下混淆矩阵的概念，然后自行计算一下。**
+
+### o、模型优化（模型修改）问题
+**问：up，我修改了网络，预训练权重还能用吗？
+答：修改了主干的话，如果不是用的现有的网络，基本上预训练权重是不能用的，要么就自己判断权值里卷积核的shape然后自己匹配，要么只能自己预训练去了；修改了后半部分的话，前半部分的主干部分的预训练权重还是可以用的，如果是pytorch代码的话，需要自己修改一下载入权值的方式，判断shape后载入，如果是keras代码，直接by_name=True,skip_mismatch=True即可。**
+权值匹配的方式可以参考如下：
+
+```python
+# 加快模型训练的效率
+print('Loading weights into state dict...')
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model_dict = model.state_dict()
+pretrained_dict = torch.load(model_path, map_location=device)
+a = {}
+for k, v in pretrained_dict.items():
+    try:    
+        if np.shape(model_dict[k]) ==  np.shape(v):
+            a[k]=v
+    except:
+        pass
+model_dict.update(a)
+model.load_state_dict(model_dict)
+print('Finished!')
+```
+
+
+
+**问：up，怎么修改模型啊，我想发个小论文！
+答：建议看看目标检测中yolov4的论文，作为一个大型调参现场非常有参考意义，使用了很多tricks。我能给的建议就是多看一些经典模型，然后拆解里面的亮点结构并使用。常用的tricks如注意力机制什么的，可以试试。**
+
+### p、部署问题
+我没有具体部署到手机等设备上过，所以很多部署问题我并不了解……
+
+## 5、交流群问题
+**问：up，有没有QQ群啥的呢？
+答：没有没有，我没有时间管理QQ群……**
+
+## 6、怎么学习的问题
+**问：up，你的学习路线怎么样的？我是个小白我要怎么学？
+答：这里有几点需要注意哈
+1、我不是高手，很多东西我也不会，我的学习路线也不一定适用所有人。
+2、我实验室不做深度学习，所以我很多东西都是自学，自己摸索，正确与否我也不知道。
+3、我个人觉得学习更靠自学**
+学习路线的话，我是先学习了莫烦的python教程，从tensorflow、keras、pytorch入门，入门完之后学的SSD，YOLO，然后了解了很多经典的卷积网，后面就开始学很多不同的代码了，我的学习方法就是一行一行的看，了解整个代码的执行流程，特征层的shape变化等，花了很多时间也没有什么捷径，就是要花时间吧。
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