官网网址： https://pytorch.org/vision/0.9/models.html#semantic-segmentation

(1)、ImageNet

  1. train_data = torchvision.datasets.ImageNet("../dataset", split='train', transform=torchvision.transforms.ToTensor())

　　ImageNet数据集是一个计算机视觉数据集。 ImageNet数据集一直是评估图像分类算法性能的基准。 ImageNet 数据集是为了促进计算机图像识别技术的发展而设立的一个大型图像数据集。ImageNet详细介绍： https://blog.51cto.com/yunyaniu/5245552

(2) 、神经网络VGG16

  1. weights参数表示是否使用已训练的模型参数。

    代码：
  1. # file     : model_pretrained.py
  2. # time     : 2022/8/5 下午4:19
  3. # function : VGG16
  4. import torchvision.datasets
  5.  
  6. # ImageNet need to be downloaded manually
  7. # train_data = torchvision.datasets.ImageNet("../dataset", split='train', transform=torchvision.transforms.ToTensor())
  8.  
  9. vgg16_false = torchvision.models.vgg16(weights=False)
  10. vgg16_true = torchvision.models.vgg16(weights=True)
  11. print(vgg16_true)

结果：

  1. VGG(
  2.   (features): Sequential(
  3.     (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  4.     (1): ReLU(inplace=True)
  5.     (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  6.     (3): ReLU(inplace=True)
  7.     (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  8.     (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  9.     (6): ReLU(inplace=True)
  10.     (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  11.     (8): ReLU(inplace=True)
  12.     (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  13.     (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  14.     (11): ReLU(inplace=True)
  15.     (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  16.     (13): ReLU(inplace=True)
  17.     (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  18.     (15): ReLU(inplace=True)
  19.     (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  20.     (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  21.     (18): ReLU(inplace=True)
  22.     (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  23.     (20): ReLU(inplace=True)
  24.     (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  25.     (22): ReLU(inplace=True)
  26.     (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  27.     (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  28.     (25): ReLU(inplace=True)
  29.     (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  30.     (27): ReLU(inplace=True)
  31.     (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  32.     (29): ReLU(inplace=True)
  33.     (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  34.   )
  35.   (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  36.   (classifier): Sequential(
  37.     (0): Linear(in_features=25088, out_features=4096, bias=True)
  38.     (1): ReLU(inplace=True)
  39.     (2): Dropout(p=0.5, inplace=False)
  40.     (3): Linear(in_features=4096, out_features=4096, bias=True)
  41.     (4): ReLU(inplace=True)
  42.     (5): Dropout(p=0.5, inplace=False)
  43.     (6): Linear(in_features=4096, out_features=1000, bias=True)
  44.   )
  45. )

结果显示：VGG16网络是由13层卷积层和3层全连接层组成，最后网络输出一共有1000个分类结果。

（3）、修改现有VGG16模型的结构

    在VGG16模型后增加一个线性层，实现将VGG16的1000个类别输出为CIFAR10的10个类别，代码如下：

  1. # file     : model_pretrained.py
  2. # time     : 2022/8/5 下午4:19
  3. # function :
  4. import torchvision.datasets
  5. from torch import nn
  6.  
  7. vgg16_false = torchvision.models.vgg16(weights=False)
  8. vgg16_true = torchvision.models.vgg16(weights=True)
  9. print(vgg16_true)
  10.  
  11. train_data = torchvision.datasets.CIFAR10("../dataset", train=True, transform=torchvision.transforms.ToTensor(), download=False)
  12. vgg16_true.add_module('add_linear', nn.Linear(1000, 10))
  13. print(vgg16_true)

结果:

  1. VGG(
  2.   (features): Sequential(
  3.     (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  4.     (1): ReLU(inplace=True)
  5.     (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  6.     (3): ReLU(inplace=True)
  7.     (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  8.     (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  9.     (6): ReLU(inplace=True)
  10.     (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  11.     (8): ReLU(inplace=True)
  12.     (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  13.     (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  14.     (11): ReLU(inplace=True)
  15.     (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  16.     (13): ReLU(inplace=True)
  17.     (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  18.     (15): ReLU(inplace=True)
  19.     (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  20.     (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  21.     (18): ReLU(inplace=True)
  22.     (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  23.     (20): ReLU(inplace=True)
  24.     (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  25.     (22): ReLU(inplace=True)
  26.     (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  27.     (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  28.     (25): ReLU(inplace=True)
  29.     (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  30.     (27): ReLU(inplace=True)
  31.     (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  32.     (29): ReLU(inplace=True)
  33.     (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  34.   )
  35.   (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  36.   (classifier): Sequential(
  37.     (0): Linear(in_features=25088, out_features=4096, bias=True)
  38.     (1): ReLU(inplace=True)
  39.     (2): Dropout(p=0.5, inplace=False)
  40.     (3): Linear(in_features=4096, out_features=4096, bias=True)
  41.     (4): ReLU(inplace=True)
  42.     (5): Dropout(p=0.5, inplace=False)
  43.     (6): Linear(in_features=4096, out_features=1000, bias=True)
  44.   )
  45. )
  46. VGG(
  47.   (features): Sequential(
  48.     (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  49.     (1): ReLU(inplace=True)
  50.     (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  51.     (3): ReLU(inplace=True)
  52.     (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  53.     (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  54.     (6): ReLU(inplace=True)
  55.     (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  56.     (8): ReLU(inplace=True)
  57.     (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  58.     (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  59.     (11): ReLU(inplace=True)
  60.     (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  61.     (13): ReLU(inplace=True)
  62.     (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  63.     (15): ReLU(inplace=True)
  64.     (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  65.     (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  66.     (18): ReLU(inplace=True)
  67.     (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  68.     (20): ReLU(inplace=True)
  69.     (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  70.     (22): ReLU(inplace=True)
  71.     (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  72.     (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  73.     (25): ReLU(inplace=True)
  74.     (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  75.     (27): ReLU(inplace=True)
  76.     (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  77.     (29): ReLU(inplace=True)
  78.     (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  79.   )
  80.   (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  81.   (classifier): Sequential(
  82.     (0): Linear(in_features=25088, out_features=4096, bias=True)
  83.     (1): ReLU(inplace=True)
  84.     (2): Dropout(p=0.5, inplace=False)
  85.     (3): Linear(in_features=4096, out_features=4096, bias=True)
  86.     (4): ReLU(inplace=True)
  87.     (5): Dropout(p=0.5, inplace=False)
  88.     (6): Linear(in_features=4096, out_features=1000, bias=True)
  89.   )
  90.   (add_linear): Linear(in_features=1000, out_features=10, bias=True)
  91. )

如果想将最后的线性层加在classifier中，则将代码修改如下：

  1. vgg16_true.classifier.add_module('add_linear', nn.Linear(1000, 10))

结果:

  1. VGG(
  2.   (features): Sequential(
  3.     (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  4.     (1): ReLU(inplace=True)
  5.     (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  6.     (3): ReLU(inplace=True)
  7.     (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  8.     (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  9.     (6): ReLU(inplace=True)
  10.     (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  11.     (8): ReLU(inplace=True)
  12.     (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  13.     (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  14.     (11): ReLU(inplace=True)
  15.     (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  16.     (13): ReLU(inplace=True)
  17.     (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  18.     (15): ReLU(inplace=True)
  19.     (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  20.     (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  21.     (18): ReLU(inplace=True)
  22.     (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  23.     (20): ReLU(inplace=True)
  24.     (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  25.     (22): ReLU(inplace=True)
  26.     (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  27.     (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  28.     (25): ReLU(inplace=True)
  29.     (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  30.     (27): ReLU(inplace=True)
  31.     (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  32.     (29): ReLU(inplace=True)
  33.     (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  34.   )
  35.   (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  36.   (classifier): Sequential(
  37.     (0): Linear(in_features=25088, out_features=4096, bias=True)
  38.     (1): ReLU(inplace=True)
  39.     (2): Dropout(p=0.5, inplace=False)
  40.     (3): Linear(in_features=4096, out_features=4096, bias=True)
  41.     (4): ReLU(inplace=True)
  42.     (5): Dropout(p=0.5, inplace=False)
  43.     (6): Linear(in_features=4096, out_features=1000, bias=True)
  44.     (add_linear): Linear(in_features=1000, out_features=10, bias=True)
  45.   )

如果想将在classifier最后一层输出改成10中，则将代码修改如下：

  1. # file     : model_pretrained.py
  2. # time     : 2022/8/5 下午4:19
  3. # function :
  4. import torchvision.datasets
  5. from torch import nn
  6.  
  7. vgg16_false = torchvision.models.vgg16(weights=False)
  8. print(vgg16_false)
  9.  
  10. vgg16_false.classifier[6] = nn.Linear(4096, 10)
  11. print(vgg16_false)
  1. VGG(
  2.   (features): Sequential(
  3.     (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  4.     (1): ReLU(inplace=True)
  5.     (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  6.     (3): ReLU(inplace=True)
  7.     (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  8.     (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  9.     (6): ReLU(inplace=True)
  10.     (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  11.     (8): ReLU(inplace=True)
  12.     (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  13.     (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  14.     (11): ReLU(inplace=True)
  15.     (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  16.     (13): ReLU(inplace=True)
  17.     (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  18.     (15): ReLU(inplace=True)
  19.     (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  20.     (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  21.     (18): ReLU(inplace=True)
  22.     (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  23.     (20): ReLU(inplace=True)
  24.     (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  25.     (22): ReLU(inplace=True)
  26.     (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  27.     (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  28.     (25): ReLU(inplace=True)
  29.     (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  30.     (27): ReLU(inplace=True)
  31.     (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  32.     (29): ReLU(inplace=True)
  33.     (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  34.   )
  35.   (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  36.   (classifier): Sequential(
  37.     (0): Linear(in_features=25088, out_features=4096, bias=True)
  38.     (1): ReLU(inplace=True)
  39.     (2): Dropout(p=0.5, inplace=False)
  40.     (3): Linear(in_features=4096, out_features=4096, bias=True)
  41.     (4): ReLU(inplace=True)
  42.     (5): Dropout(p=0.5, inplace=False)
  43.     (6): Linear(in_features=4096, out_features=1000, bias=True)
  44.   )
  45. )
  46. VGG(
  47.   (features): Sequential(
  48.     (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  49.     (1): ReLU(inplace=True)
  50.     (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  51.     (3): ReLU(inplace=True)
  52.     (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  53.     (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  54.     (6): ReLU(inplace=True)
  55.     (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  56.     (8): ReLU(inplace=True)
  57.     (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  58.     (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  59.     (11): ReLU(inplace=True)
  60.     (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  61.     (13): ReLU(inplace=True)
  62.     (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  63.     (15): ReLU(inplace=True)
  64.     (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  65.     (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  66.     (18): ReLU(inplace=True)
  67.     (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  68.     (20): ReLU(inplace=True)
  69.     (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  70.     (22): ReLU(inplace=True)
  71.     (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  72.     (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  73.     (25): ReLU(inplace=True)
  74.     (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  75.     (27): ReLU(inplace=True)
  76.     (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  77.     (29): ReLU(inplace=True)
  78.     (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  79.   )
  80.   (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  81.   (classifier): Sequential(
  82.     (0): Linear(in_features=25088, out_features=4096, bias=True)
  83.     (1): ReLU(inplace=True)
  84.     (2): Dropout(p=0.5, inplace=False)
  85.     (3): Linear(in_features=4096, out_features=4096, bias=True)
  86.     (4): ReLU(inplace=True)
  87.     (5): Dropout(p=0.5, inplace=False)
  88.     (6): Linear(in_features=4096, out_features=10, bias=True)
  89.   )
  90. )

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