first submit

.gitignore

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+__pycache__
+.idea
+*.xml
+data
+test_images_folder
+test_images_result
+*.tar
+runs

README.md

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+
+[Chinese Document](./zh-cn.md)
+# DeeplapV3+_ResNet_Res2Net_semantic_segmentation
+
+Just use VOC2012 as the dataset, and take some school pictures to do the forecast.
+VOC2012 download link:
+[VOC2012 download link](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar)
+## trained DeeplabV3+ using ResNet101 as backbone
+No information
+## trained DeeplabV3+ using Res2Net as backbone
+No information
+## Result
+![](./synthesized/Original_ResNet_Res2Net_0.png)
+![](./synthesized/Original_ResNet_Res2Net_1.png)
+![](./synthesized/Original_ResNet_Res2Net_2.png)
+![](./synthesized/Original_ResNet_Res2Net_3.png)
+![](./synthesized/Original_ResNet_Res2Net_4.png)
+![](./synthesized/Original_ResNet_Res2Net_5.png)
+![](./synthesized/Original_ResNet_Res2Net_6.png)
+![](./synthesized/Original_ResNet_Res2Net_7.png)
+![](./synthesized/Original_ResNet_Res2Net_8.png)

dataset2.py

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+import torch
+import torchvision
+import numpy as np
+from matplotlib import pyplot as plt
+from PIL import Image
+import sys
+from IPython import display
+from tqdm import tqdm
+import warnings
+torch.cuda.empty_cache()
+warnings.filterwarnings("ignore")  # 忽略警告
+
+
+def read_voc_images(
+    root="D:/Projects/Models/data/VOCdevkit/VOC2012", is_train=True, max_num=None
+):
+    txt_fname = "%s/ImageSets/Segmentation/%s" % (
+        root,
+        "train.txt" if is_train else "val.txt",
+    )
+    with open(txt_fname, "r") as f:
+        images = f.read().split()  # 拆分成一个个名字组成list
+    if max_num is not None:
+        images = images[: min(max_num, len(images))]
+    features, labels = [None] * len(images), [None] * len(images)
+    for i, fname in tqdm(enumerate(images)):
+        # 读入数据并且转为RGB的 PIL image
+        features[i] = Image.open("%s/JPEGImages/%s.jpg" % (root, fname)).convert("RGB")
+        labels[i] = Image.open("%s/SegmentationClass/%s.png" % (root, fname)).convert(
+            "RGB"
+        )
+    return features, labels  # PIL image 0-255
+
+
+# 这个函数可以不需要
+def set_figsize(figsize=(3.5, 2.5)):
+    """在jupyter使用svg显示"""
+    display.set_matplotlib_formats("svg")
+    # 设置图的尺寸
+    plt.rcParams["figure.figsize"] = figsize
+
+
+def show_images(imgs, num_rows, num_cols, scale=2):
+    # a_img = np.asarray(imgs)
+    figsize = (num_cols * scale, num_rows * scale)
+    _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
+    for i in range(num_rows):
+        for j in range(num_cols):
+            axes[i][j].imshow(imgs[i * num_cols + j])
+            axes[i][j].axes.get_xaxis().set_visible(False)
+            axes[i][j].axes.get_yaxis().set_visible(False)
+    plt.show()
+    return axes
+
+
+# # 根据自己存放数据集的路径修改voc_dir
+# voc_dir = r"D:\Projects\Models\data\VOCdevkit\VOC2012"
+# train_features, train_labels = read_voc_images(voc_dir, max_num=10)
+# n = 5  # 展示几张图像
+# imgs = train_features[0:n] + train_labels[0:n]  # PIL image
+# show_images(imgs, 2, n)
+
+# 标签中每个RGB颜色的值
+VOC_COLORMAP = [
+    [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],
+]
+# 标签其标注的类别
+VOC_CLASSES = [
+    "background","aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair",
+    "cow","diningtable","dog","horse","motorbike","person","potted plant","sheep","sofa",
+    "train","tv/monitor",
+]
+
+
+colormap2label = torch.zeros(256 ** 3, dtype=torch.uint8)  # torch.Size([16777216])
+for i, colormap in enumerate(VOC_COLORMAP):
+    # 每个通道的进制是256，这样可以保证每个 rgb 对应一个下标 i
+    colormap2label[(colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i
+
+# 构造标签矩阵
+def voc_label_indices(colormap, colormap2label):
+    colormap = np.array(colormap.convert("RGB")).astype("int32")
+    idx = (colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256 + colormap[:, :, 2]
+    return colormap2label[idx]  # colormap 映射 到colormaplabel中计算的下标
+
+
+# y = voc_label_indices(train_labels[0], colormap2label)
+# print(y[100:110, 130:140])  # 打印结果是一个int型tensor，tensor中的每个元素i表示该像素的类别是VOC_CLASSES[i]
+
+
+def voc_rand_crop(feature, label, height, width):
+    """
+    随机裁剪feature(PIL image) 和 label(PIL image).
+    为了使裁剪的区域相同，不能直接使用RandomCrop，而要像下面这样做
+    Get parameters for ``crop`` for a random crop.
+    Args:
+        img (PIL Image): Image to be cropped.
+        output_size (tuple): Expected output size of the crop.
+    Returns:
+        tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
+    """
+    i, j, h, w = torchvision.transforms.RandomCrop.get_params(
+        feature, output_size=(height, width)
+    )
+    feature = torchvision.transforms.functional.crop(feature, i, j, h, w)
+    label = torchvision.transforms.functional.crop(label, i, j, h, w)
+    return feature, label
+
+
+# 显示n张随机裁剪的图像和标签，前面的n是5
+# imgs = []
+# for _ in range(n):
+#     imgs += voc_rand_crop(train_features[0], train_labels[0], 200, 300)
+# show_images(imgs[::2] + imgs[1::2], 2, n)
+
+
+class VOCSegDataset(torch.utils.data.Dataset):
+    def __init__(self, is_train, crop_size, voc_dir, colormap2label, max_num=None):
+        """
+        crop_size: (h, w)
+        """
+        # 对输入图像的RGB三个通道的值分别做标准化
+        self.rgb_mean = np.array([0.485, 0.456, 0.406])
+        self.rgb_std = np.array([0.229, 0.224, 0.225])
+        self.tsf = torchvision.transforms.Compose(
+            [
+                torchvision.transforms.ToTensor(),
+                torchvision.transforms.Normalize(mean=self.rgb_mean, std=self.rgb_std),
+            ]
+        )
+        self.crop_size = crop_size  # (h, w)
+        features, labels = read_voc_images(
+            root=voc_dir, is_train=is_train, max_num=max_num
+        )
+        # 由于数据集中有些图像的尺寸可能小于随机裁剪所指定的输出尺寸，这些样本需要通过自定义的filter函数所移除
+        self.features = self.filter(features)  # PIL image
+        self.labels = self.filter(labels)  # PIL image
+        self.colormap2label = colormap2label
+        # print("read " + str(len(self.features)) + " valid examples")
+        print("read " + str(len(self.features)) + " train examples" if is_train else "read " + str(len(self.features)) + " valid examples")
+
+    def filter(self, imgs):
+        return [
+            img
+            for img in imgs
+            if (img.size[1] >= self.crop_size[0] and img.size[0] >= self.crop_size[1])
+        ]
+
+    def __getitem__(self, idx):
+        feature, label = voc_rand_crop(
+            self.features[idx], self.labels[idx], *self.crop_size
+        )
+        # float32 tensor           uint8 tensor (b,h,w)
+        return (self.tsf(feature), voc_label_indices(label, self.colormap2label))
+
+    def __len__(self):
+        return len(self.features)
+
+
+if __name__ == "__main__":
+    # 根据自己存放数据集的路径修改voc_dir
+    voc_dir = r"D:\Projects\Models\data\VOCdevkit\VOC2012"
+    batch_size = 10  # 实际上我的小笔记本不允许我这么做！哭了（大家根据自己电脑内存改吧）
+    crop_size = (320, 480)  # 指定随机裁剪的输出图像的形状为(320,480)
+    max_num = 20000  # 最多从本地读多少张图片，我指定的这个尺寸过滤完不合适的图像之后也就只有1175张~
+
+    # 创建训练集和测试集的实例
+    voc_train = VOCSegDataset(True, crop_size, voc_dir, colormap2label, max_num)
+    voc_test = VOCSegDataset(False, crop_size, voc_dir, colormap2label, max_num)
+
+    # 设批量大小为32，分别定义【训练集】和【测试集】的数据迭代器
+    num_workers = 0 if sys.platform.startswith("win32") else 4
+    train_iter = torch.utils.data.DataLoader(
+        voc_train, batch_size, shuffle=True, drop_last=True, num_workers=num_workers
+    )
+    test_iter = torch.utils.data.DataLoader(
+        voc_test, batch_size, drop_last=True, num_workers=num_workers
+    )
+
+    # 方便封装，把训练集和验证集保存在dict里
+    dataloaders = {"train": train_iter, "val": test_iter}
+    dataset_sizes = {"train": len(voc_train), "val": len(voc_test)}
+
+    for i,(input,label) in enumerate(train_iter):
+        print(label.shape)
+        print(label)
+
+
+
+
+
+
+
+