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Deep Learning-PyTorch:Chapter2 Pretrained Networks

from torchvision import models

dir(models)

['AlexNet',
 'AlexNet_Weights',
 'ConvNeXt',
 'ConvNeXt_Base_Weights',
 'ConvNeXt_Large_Weights',
 'ConvNeXt_Small_Weights',
 'ConvNeXt_Tiny_Weights',
 'DenseNet',
 'DenseNet121_Weights',
 'DenseNet161_Weights',
 'DenseNet169_Weights',
 'DenseNet201_Weights',
 'EfficientNet',
 'EfficientNet_B0_Weights',
 'EfficientNet_B1_Weights',
 'EfficientNet_B2_Weights',
 'EfficientNet_B3_Weights',
 'EfficientNet_B4_Weights',
 'EfficientNet_B5_Weights',
 'EfficientNet_B6_Weights',
 'EfficientNet_B7_Weights',
 'EfficientNet_V2_L_Weights',
 'EfficientNet_V2_M_Weights',
 'EfficientNet_V2_S_Weights',
 'GoogLeNet',
 'GoogLeNetOutputs',
 'GoogLeNet_Weights',
 'Inception3',
 'InceptionOutputs',
 'Inception_V3_Weights',
 'MNASNet',
 'MNASNet0_5_Weights',
 'MNASNet0_75_Weights',
 'MNASNet1_0_Weights',
 'MNASNet1_3_Weights',
 'MobileNetV2',
 'MobileNetV3',
 'MobileNet_V2_Weights',
 'MobileNet_V3_Large_Weights',
 'MobileNet_V3_Small_Weights',
 'RegNet',
 'RegNet_X_16GF_Weights',
 'RegNet_X_1_6GF_Weights',
 'RegNet_X_32GF_Weights',
 'RegNet_X_3_2GF_Weights',
 'RegNet_X_400MF_Weights',
 'RegNet_X_800MF_Weights',
 'RegNet_X_8GF_Weights',
 'RegNet_Y_128GF_Weights',
 'RegNet_Y_16GF_Weights',
 'RegNet_Y_1_6GF_Weights',
 'RegNet_Y_32GF_Weights',
 'RegNet_Y_3_2GF_Weights',
 'RegNet_Y_400MF_Weights',
 'RegNet_Y_800MF_Weights',
 'RegNet_Y_8GF_Weights',
 'ResNeXt101_32X8D_Weights',
 'ResNeXt101_64X4D_Weights',
 'ResNeXt50_32X4D_Weights',
 'ResNet',
 'ResNet101_Weights',
 'ResNet152_Weights',
 'ResNet18_Weights',
 'ResNet34_Weights',
 'ResNet50_Weights',
 'ShuffleNetV2',
 'ShuffleNet_V2_X0_5_Weights',
 'ShuffleNet_V2_X1_0_Weights',
 'ShuffleNet_V2_X1_5_Weights',
 'ShuffleNet_V2_X2_0_Weights',
 'SqueezeNet',
 'SqueezeNet1_0_Weights',
 'SqueezeNet1_1_Weights',
 'SwinTransformer',
 'Swin_B_Weights',
 'Swin_S_Weights',
 'Swin_T_Weights',
 'VGG',
 'VGG11_BN_Weights',
 'VGG11_Weights',
 'VGG13_BN_Weights',
 'VGG13_Weights',
 'VGG16_BN_Weights',
 'VGG16_Weights',
 'VGG19_BN_Weights',
 'VGG19_Weights',
 'ViT_B_16_Weights',
 'ViT_B_32_Weights',
 'ViT_H_14_Weights',
 'ViT_L_16_Weights',
 'ViT_L_32_Weights',
 'VisionTransformer',
 'Wide_ResNet101_2_Weights',
 'Wide_ResNet50_2_Weights',
 '_GoogLeNetOutputs',
 '_InceptionOutputs',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 '_api',
 '_meta',
 '_utils',
 'alexnet',
 'convnext',
 'convnext_base',
 'convnext_large',
 'convnext_small',
 'convnext_tiny',
 'densenet',
 'densenet121',
 'densenet161',
 'densenet169',
 'densenet201',
 'detection',
 'efficientnet',
 'efficientnet_b0',
 'efficientnet_b1',
 'efficientnet_b2',
 'efficientnet_b3',
 'efficientnet_b4',
 'efficientnet_b5',
 'efficientnet_b6',
 'efficientnet_b7',
 'efficientnet_v2_l',
 'efficientnet_v2_m',
 'efficientnet_v2_s',
 'get_weight',
 'googlenet',
 'inception',
 'inception_v3',
 'mnasnet',
 'mnasnet0_5',
 'mnasnet0_75',
 'mnasnet1_0',
 'mnasnet1_3',
 'mobilenet',
 'mobilenet_v2',
 'mobilenet_v3_large',
 'mobilenet_v3_small',
 'mobilenetv2',
 'mobilenetv3',
 'optical_flow',
 'quantization',
 'regnet',
 'regnet_x_16gf',
 'regnet_x_1_6gf',
 'regnet_x_32gf',
 'regnet_x_3_2gf',
 'regnet_x_400mf',
 'regnet_x_800mf',
 'regnet_x_8gf',
 'regnet_y_128gf',
 'regnet_y_16gf',
 'regnet_y_1_6gf',
 'regnet_y_32gf',
 'regnet_y_3_2gf',
 'regnet_y_400mf',
 'regnet_y_800mf',
 'regnet_y_8gf',
 'resnet',
 'resnet101',
 'resnet152',
 'resnet18',
 'resnet34',
 'resnet50',
 'resnext101_32x8d',
 'resnext101_64x4d',
 'resnext50_32x4d',
 'segmentation',
 'shufflenet_v2_x0_5',
 'shufflenet_v2_x1_0',
 'shufflenet_v2_x1_5',
 'shufflenet_v2_x2_0',
 'shufflenetv2',
 'squeezenet',
 'squeezenet1_0',
 'squeezenet1_1',
 'swin_b',
 'swin_s',
 'swin_t',
 'swin_transformer',
 'vgg',
 'vgg11',
 'vgg11_bn',
 'vgg13',
 'vgg13_bn',
 'vgg16',
 'vgg16_bn',
 'vgg19',
 'vgg19_bn',
 'video',
 'vision_transformer',
 'vit_b_16',
 'vit_b_32',
 'vit_h_14',
 'vit_l_16',
 'vit_l_32',
 'wide_resnet101_2',
 'wide_resnet50_2']

2.1.2 AlexNet

#create an instance fo the Alexnet class
alexnet = models.AlexNet()
#Alexnet() is function
#forward pass
#output = alexnet(input)

2.1.3 ResNet

resnet = models.resnet101(pretrained = True)
#这段代码使用 PyTorch 深度学习框架中的 models 模块来加载预训练的 ResNet-101 模型，并将其存储在 resent 变量中。 
#ResNet-101 是一个经过预训练的卷积神经网络模型，它使用 ImageNet 数据集上的数百万张图像进行了训练，可以用于图像分类、目标检测等计算机视觉任务。
#pretrained=True 的参数意味着加载的是预训练的模型，而不是一个随机初始化的模型。

print(resent)

ResNet(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(inplace=True)
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (layer1): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer2): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer3): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (4): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (5): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (6): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (7): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (8): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (9): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (10): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (11): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (12): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (13): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (14): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (15): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (16): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (17): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (18): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (19): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (20): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (21): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (22): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer4): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  (fc): Linear(in_features=2048, out_features=1000, bias=True)
)

from torchvision import transforms

preprocess = transforms.Compose([
    transforms.Resize(256), #image size 256*256
    transforms.CenterCrop(224),#crop image to 224*224,around the center
    transforms.ToTensor(),#transform image into a tensor
    transforms.Normalize(
    mean = [0.485,0.456,0.406],
    std = [0.229, 0.224,0.225]) #normolize RGB,define means and std
])

from PIL import Image
#Pillow, website

img= Image.open(r"D:\research\dlwpt-code-master\data\p1ch2\bobby.jpg")

print(img)

#img.show()

img_t = preprocess(img)

import torch
batch_t = torch.unsqueeze(img_t,0)

这段代码使用 PyTorch 的 unsqueeze 函数将 img_t 张量的维度从 (C, H, W) 扩展到 (N, C, H, W)，其中 N=1 表示批次大小为 1。
这是因为在推断单个图像时，模型需要一个批次大小为 1 的张量。
unsqueeze 函数会添加一个新的维度，该维度的索引为 0，并将原始张量作为新张量的子张量插入到该维度中。

resnet.eval()
# 将模型设置为评估模式

ResNet(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(inplace=True)
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (layer1): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer2): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer3): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (4): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (5): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (6): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (7): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (8): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (9): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (10): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (11): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (12): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (13): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (14): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (15): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (16): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (17): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (18): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (19): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (20): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (21): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (22): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer4): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  (fc): Linear(in_features=2048, out_features=1000, bias=True)
)

out = resnet(batch_t)

print(out)

tensor([[-3.4997e+00, -1.6490e+00, -2.4391e+00, -3.2243e+00, -3.2465e+00,
         -1.3218e+00, -2.0395e+00, -2.5405e+00, -1.3043e+00, -2.8827e+00,
         -1.6696e+00, -1.2838e+00, -2.6184e+00, -2.9750e+00, -2.4380e+00,
         -2.8256e+00, -3.3083e+00, -7.9667e-01, -6.7075e-01, -1.2162e+00,
         -3.0311e+00, -3.9593e+00, -2.2631e+00, -1.0843e+00, -9.7915e-01,
         -1.0742e+00, -3.0908e+00, -2.4751e+00, -2.2153e+00, -3.1932e+00,
         -3.2964e+00, -1.8507e+00, -2.0642e+00, -2.1202e+00, -1.8665e+00,
         -3.2375e+00, -1.1210e+00, -1.1321e+00, -1.1657e+00, -9.0362e-01,
         -4.5209e-01, -1.4986e+00,  1.4366e+00,  1.2994e-01, -1.8379e+00,
         -1.4815e+00,  9.7278e-01, -9.3662e-01, -3.0276e+00, -2.7341e+00,
         -2.5960e+00, -2.0591e+00, -1.8170e+00, -1.9437e+00, -1.7875e+00,
         -1.3029e+00, -4.5200e-01, -2.0560e+00, -3.2882e+00, -4.7583e-01,
         -3.6261e-01, -1.1650e+00, -7.3942e-01, -1.4489e+00, -1.5039e+00,
         -2.1096e+00, -1.7095e+00, -4.1315e-01, -1.9146e+00, -1.5095e+00,
         -1.2012e+00, -1.3111e+00, -1.0662e+00,  1.2968e-01, -3.8648e-01,
         -2.4670e-01, -8.7028e-01,  5.9567e-01, -8.8329e-01,  1.4535e+00,
         -2.6384e+00, -3.6589e+00, -2.3378e-01, -4.9410e-02, -2.2602e+00,
         -2.3605e+00, -1.4038e+00,  2.4098e-01, -1.0531e+00, -2.9106e+00,
         -2.5321e+00, -2.2253e+00,  4.4062e-01, -1.3269e+00, -2.0447e-02,
         -2.8696e+00, -5.5842e-01, -1.3959e+00, -2.9274e+00, -1.9038e+00,
         -4.2433e+00, -2.9701e+00, -2.0552e+00, -2.4290e+00, -2.7691e+00,
         -4.0207e+00, -3.6511e+00, -4.8903e-01, -9.8899e-01, -1.8995e+00,
         -3.5459e+00, -1.4606e+00,  1.1054e+00, -6.9230e-01, -7.4877e-01,
         -2.1745e+00, -2.2471e+00, -5.3973e-01, -1.5121e+00, -2.5998e+00,
         -3.9889e+00, -9.7558e-01, -5.5630e-01, -8.6530e-01, -1.2110e+00,
         -7.3269e-01, -1.0847e+00, -2.0944e+00, -4.0080e+00, -3.3433e-01,
         -2.6919e+00, -2.9908e+00, -1.8793e+00, -1.8126e+00, -1.2953e+00,
         -2.2093e+00, -2.0246e+00, -3.1878e+00, -3.2739e+00, -2.7969e+00,
         -4.1270e-01, -3.7697e+00, -2.4744e+00, -2.6133e+00, -2.7514e+00,
         -2.6512e+00, -3.2751e+00, -4.3150e+00, -4.2214e+00, -3.5920e+00,
         -1.2186e+00,  2.3577e+00,  1.9648e+00,  2.2958e+00,  5.0050e+00,
          1.0229e+00,  2.9914e+00,  8.8863e-01,  2.2502e+00,  6.1697e+00,
          4.2833e+00,  4.5931e+00,  7.4686e+00,  4.3448e+00,  4.8805e+00,
          5.8076e+00,  3.9792e+00,  3.5555e+00,  9.5253e+00,  1.1172e+00,
          3.3158e+00,  1.9011e+00, -5.1524e-01,  1.3985e+00,  1.8150e+00,
          5.6135e+00,  5.7316e+00,  2.1489e+00,  2.4986e+00,  5.5508e-01,
          1.8658e+00,  1.4871e+00,  3.4364e+00,  4.4125e-01,  4.2860e+00,
          4.3832e+00,  1.3132e+00,  1.6830e+00,  1.1566e+00,  2.1403e+00,
          9.1246e-01,  3.0220e+00,  3.5398e+00,  2.8193e+00,  1.9450e+00,
          8.4404e-02,  2.0383e+00,  2.7105e+00,  1.0817e+00,  1.9388e+00,
          3.5678e+00,  2.3330e+00,  2.5193e+00,  1.3185e+00,  3.6052e+00,
          7.7896e+00,  4.4019e+00,  1.5813e+01,  1.2201e+01,  5.1687e+00,
          1.9371e+00,  5.5298e+00,  6.2411e+00,  7.5443e+00,  5.9197e+00,
          7.0106e+00,  5.7829e+00,  2.6940e+00,  5.3224e+00,  9.9329e+00,
          6.4925e+00,  2.4706e+00,  5.6329e+00,  3.4480e+00,  2.0878e+00,
          1.2667e+00,  2.5974e+00,  5.6932e+00,  7.1867e-01,  1.0250e+00,
          4.8133e+00,  6.0352e+00,  3.9970e+00, -1.7816e+00,  3.6096e+00,
          2.8417e+00,  2.8397e+00,  1.2274e+00,  4.2220e+00,  4.0891e+00,
          3.3476e+00,  2.3510e+00,  2.6175e-01,  8.3823e-01,  5.1252e+00,
         -7.6088e-01,  1.9809e+00,  2.8381e+00,  2.4766e+00,  1.8687e+00,
          5.8744e-01,  3.4939e+00, -5.0017e-01,  1.8486e+00,  4.4583e-01,
          2.4148e+00,  3.4823e+00,  5.1915e+00,  1.8092e+00,  3.9559e+00,
          4.3692e+00,  3.1688e-01, -4.8611e-01,  5.6470e+00,  3.6534e+00,
          2.4436e+00,  3.6937e+00,  3.3958e+00, -9.6209e-01, -2.2416e-01,
          1.2344e-01,  1.1682e+00, -5.7350e-02,  4.4999e+00, -1.8748e+00,
         -2.6818e+00, -1.6847e+00, -6.7984e-01, -1.5588e+00, -2.1798e+00,
         -1.0431e+00,  1.5370e+00,  7.6586e-01, -1.7835e+00,  6.3795e-01,
          8.8728e-01, -2.1297e+00, -2.1428e+00, -1.5652e+00, -4.8034e+00,
         -1.0672e+00, -9.5017e-01, -2.0649e+00,  2.0019e+00, -6.4177e-01,
         -2.5378e+00, -1.5008e+00, -3.1975e+00, -1.9334e+00, -5.8521e-01,
         -1.0363e+00,  1.3329e+00, -1.4872e+00, -2.0102e+00,  1.3183e+00,
          4.2174e-01, -3.5856e-01,  7.4732e-01, -2.7741e-01, -7.5281e-01,
         -5.5096e-01,  1.0833e+00,  1.3690e-01,  5.3100e-02,  1.2991e+00,
         -2.0118e+00, -1.7402e+00,  1.1788e+00, -4.2505e-01,  3.0964e-01,
         -1.4589e+00, -2.6225e+00, -2.4953e+00, -2.1100e-01, -2.3407e+00,
         -1.5377e+00, -2.4802e+00, -8.0411e-01,  2.1876e-01, -2.8007e+00,
         -1.9041e+00, -9.3844e-01, -3.4383e-01, -4.4379e-01, -7.5068e-01,
         -2.5060e+00, -1.9128e+00, -2.1313e+00, -5.4917e-01,  1.7571e-01,
         -2.0437e+00, -1.7883e+00, -2.4830e+00, -3.8768e+00, -4.4253e+00,
         -2.1819e+00, -2.0485e+00, -3.7339e+00, -1.9185e+00, -3.4523e+00,
         -2.0103e+00, -2.2686e+00, -7.9842e-01, -5.0350e-01, -2.9496e+00,
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         -1.1609e+00, -2.7743e+00,  2.5781e-01, -1.7330e-01, -3.0291e-01,
          1.6415e-01,  1.7316e+00,  1.8974e+00, -3.1201e+00, -3.7529e+00,
         -2.6532e+00, -6.6325e-01, -8.5835e-01, -1.3166e+00,  4.9872e-01,
         -2.3256e+00,  4.7837e+00,  1.4947e+00, -1.6190e+00,  3.3725e+00,
          3.0711e+00,  1.5550e+00,  7.3323e-01,  1.2925e+00,  1.6615e+00,
          9.5884e-02,  1.3348e+00,  2.9194e-02,  9.8533e-01, -5.1703e-01,
         -1.3542e+00,  5.3265e-01,  2.7115e-01,  2.8676e+00,  2.2308e+00,
          1.8468e+00,  1.3861e+00,  1.1031e+00,  2.3657e+00,  6.4129e+00,
          1.8896e+00,  5.8361e-01, -1.5098e+00, -1.8949e+00, -2.6363e+00,
          1.5000e+00, -5.3598e-01, -6.9934e-02,  2.0223e+00, -1.7648e+00,
          4.1311e-01,  1.6696e+00,  7.3901e-01,  2.1839e+00, -1.4280e+00,
         -2.4708e+00,  4.8153e-01,  3.3817e+00, -2.9650e-01,  6.8332e-02,
          1.1068e+00, -1.3973e+00,  1.5518e+00, -2.2464e+00, -3.6723e-01,
         -1.5056e+00, -2.1660e+00,  2.7747e+00,  2.5229e+00,  1.8796e-01,
          4.6500e-01, -1.9905e+00, -1.6224e+00, -7.7251e-01, -1.1583e+00,
         -3.0849e-01, -1.4386e+00,  2.2714e+00, -4.5280e-01, -8.7969e-01,
          4.1363e-01, -1.4095e+00, -2.8906e-01,  3.4809e+00,  4.3324e-01,
          4.2387e-01, -1.1204e+00,  1.9781e-01, -2.6528e+00, -2.8554e+00,
         -6.6075e-01, -3.1026e-01,  7.8641e-01,  1.8965e-01, -4.5897e-01,
         -5.5900e-01, -2.8286e-01,  1.6559e+00,  7.1156e-02, -4.2351e-01,
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          3.3237e+00,  7.4655e-01, -7.6160e-01,  1.6335e+00,  1.9438e+00,
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         -1.3969e+00,  1.6010e+00,  2.9322e-02,  8.8535e-01,  9.6981e-01,
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         -3.3062e+00,  1.3640e+00,  1.7516e+00,  3.7723e+00, -1.1675e+00,
          5.5042e-01, -1.8410e+00, -1.3717e+00,  4.1994e-01,  2.6614e+00,
          1.4409e+00,  8.2754e-01,  4.9228e+00,  1.5346e+00, -3.6179e+00,
         -2.2328e+00, -1.3824e+00, -8.8731e-01,  2.2056e+00,  2.0819e+00,
         -9.1448e-01, -1.7883e+00, -1.4364e+00,  1.0453e+00, -7.3781e-01,
         -1.4398e+00,  4.6885e-01,  8.8505e-01,  6.7727e-01, -1.1915e+00,
         -1.4081e+00, -8.5758e-01,  1.1674e+00, -1.8481e+00,  2.5000e+00,
          3.4197e-01, -1.7550e+00, -8.9500e-01,  2.9997e+00,  2.2094e+00,
         -8.3813e-01, -2.0126e+00,  9.3872e-01, -2.1452e+00,  3.8367e-02,
          2.4887e-01, -1.0520e-01, -6.5314e-01,  8.0483e-01,  4.0207e+00,
         -1.2178e+00,  2.6536e+00, -1.8085e+00,  7.5238e-01,  5.0293e-01,
          1.1507e-02, -7.7166e-01,  4.5107e-01,  1.3278e+00,  8.2390e-01,
         -6.1212e-01,  3.0907e-02,  2.6237e-01, -2.1468e+00, -1.4071e+00,
         -9.9406e-02, -4.3217e-01,  4.8178e-01,  1.2984e+00,  7.5574e-01,
          8.5152e-02, -1.3871e-01, -8.5174e-01,  8.1902e-01,  7.0772e-01,
          2.1356e+00,  7.1527e-01, -1.9824e-01,  2.4378e+00,  1.8625e+00,
          1.0683e+00,  1.0118e+00,  1.4432e+00, -1.9516e-02, -1.6767e-01,
         -1.6930e+00, -1.2930e+00, -1.3439e+00, -3.4781e+00,  5.9320e-01,
          1.0796e+00, -1.8918e-02, -3.9900e-01, -4.6973e-01, -3.7134e-01,
         -1.5430e+00,  3.1286e-01, -1.8760e-01,  3.2949e+00,  1.8577e+00,
         -1.5585e+00,  2.9257e+00, -7.3537e-01,  6.2360e-02, -4.3240e-02,
         -1.0193e+00,  1.1614e+00,  6.1004e-01,  1.4297e-01, -3.4956e+00,
          1.3337e+00,  3.8960e-01, -1.2900e+00, -1.2823e+00, -1.6482e+00,
          1.9285e+00,  2.7967e-02, -9.3298e-01,  6.6934e-01,  2.9179e+00,
          1.7228e+00,  2.1640e-01, -8.5777e-01, -2.0391e+00,  1.6510e+00,
         -2.3326e-01,  7.4310e-01,  1.5400e+00, -1.4460e+00, -1.2114e+00,
          5.2273e-01, -6.4955e-01,  4.8662e-02,  2.6569e+00, -1.6518e+00,
         -3.3896e+00,  2.1350e+00,  9.0318e-01,  1.3904e+00, -3.6350e-01,
          2.0223e-01,  1.4554e+00, -1.1211e+00,  1.8399e+00, -1.2923e+00,
         -2.1249e+00, -8.6377e-01, -5.1445e-01, -1.2872e+00,  1.1431e+00,
         -1.4240e+00, -7.6690e-01, -3.5563e-01,  2.1923e+00, -1.9831e-01,
         -1.8438e+00,  9.6278e-02,  1.7458e+00,  1.7894e-01,  8.0167e-01,
          4.7613e+00, -7.3425e-01, -1.6478e+00, -8.1854e-01, -1.5260e+00,
          3.4332e+00,  4.9594e-01, -3.4153e-01, -5.1844e-01,  3.1711e-01,
          1.8911e+00, -1.2033e+00, -2.0005e+00, -1.2113e-01, -7.0711e-01,
         -2.5450e+00, -6.7446e-02,  8.0279e-01, -1.8256e+00,  2.9197e-01,
         -1.1033e+00,  1.5367e+00,  2.9367e+00,  5.3937e-01, -7.6932e-01,
         -9.2092e-01, -1.7367e+00, -1.0012e+00,  9.3510e-01, -4.1549e-02,
         -2.3282e+00,  2.2907e+00,  9.3231e-01,  2.0218e+00, -1.7313e+00,
          2.6369e+00,  3.8262e+00,  6.9461e-01, -2.5070e-03, -9.9190e-01,
          2.1992e+00,  1.4213e+00,  4.3468e-01, -1.3524e+00, -8.5725e-01,
          5.7249e-01, -6.5436e-01,  5.3128e-01,  1.1928e+00,  9.9297e-01,
          2.8021e+00, -3.0989e+00,  3.5913e+00, -1.7487e-01, -7.0675e-02,
         -2.0895e+00, -1.4625e+00,  3.0381e+00, -4.9794e-01,  2.0560e-01,
         -1.4914e-01,  2.2701e+00,  3.2218e-01,  9.6720e-01, -1.1802e+00,
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          6.0959e-01,  4.1543e-01, -7.9037e-01,  3.6867e-01,  1.9525e+00,
          4.2260e-01,  2.4431e+00, -1.9236e+00,  1.2817e-02,  2.2662e+00,
         -1.6735e+00, -7.1517e-01,  1.3959e+00, -2.5440e-02,  7.7251e-01,
          3.0006e+00, -9.7998e-01, -1.3561e+00,  2.3763e-01,  2.3640e+00,
          1.9144e+00,  1.8060e+00,  8.6761e-01,  4.2975e+00,  5.3407e-01,
         -1.4330e+00, -5.0711e-01,  3.0123e+00, -8.6910e-01,  3.2884e+00,
         -6.6958e-01, -2.6305e+00, -3.1957e+00, -2.9512e+00,  1.2077e+00,
          5.6051e+00,  2.7645e-01, -3.1766e+00,  2.5924e+00, -3.3277e-01,
          1.2678e-01,  2.3057e+00, -1.4350e+00,  3.3605e+00, -3.3686e+00,
         -6.4941e-01,  1.1768e+00, -2.6352e+00,  1.6955e+00, -1.4452e+00,
         -2.6586e+00, -3.6629e+00, -1.7268e+00,  1.1226e+00,  1.9893e+00,
         -1.6089e-01,  1.2605e+00,  1.2145e+00,  2.6831e+00, -1.8129e+00,
         -1.8627e+00,  1.5719e+00, -2.0970e+00, -2.3202e+00,  9.8825e-01,
         -3.8493e+00, -1.1166e+00,  8.9900e-01, -3.4761e-01, -3.5048e+00,
          2.0611e+00,  5.2646e-01,  6.1806e-01, -1.1572e-01,  8.0079e-01,
          8.0897e-01,  1.0604e+00, -1.8260e+00,  2.6638e-01, -1.1470e+00,
          8.3008e-01,  1.0902e+00,  9.0343e+00, -9.1355e-01, -9.0900e-01,
          1.1300e+00, -3.8219e-01, -1.7321e+00,  1.0250e-01,  2.0110e-01,
         -1.4002e+00,  8.6910e-01, -4.4545e-01, -7.9011e-01, -3.1371e+00,
         -3.6089e-01,  1.9827e-01, -2.1941e+00,  2.8923e+00,  4.7277e-01,
          1.4522e+00, -3.4306e+00,  1.4684e+00, -2.5527e+00, -2.9418e+00,
         -7.8795e-01,  2.8671e+00, -1.2269e+00, -6.5231e-01,  3.6328e+00,
          1.1509e+00,  1.1695e+00,  3.3913e+00,  9.2258e-01, -1.1167e+00,
          1.0877e+00,  1.9697e-01, -7.4067e-01, -2.9546e+00, -1.7774e-01,
          1.5686e+00, -4.4219e-01,  1.6109e+00,  1.0286e+00, -1.2968e+00,
         -1.7997e+00, -4.9459e-01,  5.5377e-01,  2.3711e+00,  5.1920e-01,
         -1.3707e+00, -2.3571e+00, -7.6039e-01,  1.3218e+00,  2.9741e+00,
         -2.6011e-01, -6.0607e-01, -4.2340e-02, -7.4892e-01, -7.3591e-02,
          2.7876e+00,  3.2539e-01, -1.1395e+00, -1.4875e+00, -4.3684e+00,
         -3.6234e-01, -3.4596e-01, -2.0751e+00,  3.6143e-01, -1.4748e+00,
         -6.3914e-01, -2.0747e+00, -9.1273e-01, -4.0377e-01, -3.3568e-01,
         -1.8499e+00, -1.7760e+00, -6.6116e-01,  6.3051e-01,  3.7449e+00,
          2.1652e+00,  4.1343e+00,  1.6970e+00,  8.3944e-01,  1.2930e+00,
          1.0517e+00,  1.4064e+00,  1.3186e+00, -7.6195e-01,  2.2661e+00,
          1.4491e-01, -7.7404e-01,  1.0131e+00,  8.7297e-01, -6.4795e-01,
         -3.4213e-01, -1.5297e+00,  2.0686e+00,  2.5062e+00,  2.1082e-01,
          7.5025e-01,  1.8742e-02, -1.7822e+00,  1.9568e+00,  4.3805e-01,
          1.2335e+00,  7.7087e-01, -1.0329e+00,  1.5594e-01, -3.6626e-01,
          6.3099e-02,  3.2490e+00,  6.4807e-01, -1.5173e-01,  9.3229e-01,
         -6.0559e-02, -1.1985e+00,  1.4659e-01,  3.9304e-01,  8.8496e-01,
         -1.9458e+00,  1.2217e-01, -1.5016e+00, -1.8235e+00, -3.8595e+00,
          1.8626e-01, -2.9675e+00,  5.4217e-01, -7.8127e-01, -2.6196e+00,
         -4.4958e+00,  5.7119e-01, -1.3398e+00, -3.8117e+00, -7.8630e-01,
         -5.6788e-01, -2.5453e+00,  2.4054e+00,  6.5602e-01,  4.7648e-01,
          2.8749e+00, -3.7451e+00,  1.5124e+00, -3.2777e+00, -2.4971e+00,
         -3.2263e-01,  1.2816e-01, -1.1752e+00,  3.4434e+00,  4.4534e+00]],
       grad_fn=)

print("最大值：", out.max().item())
print("最小值：", out.min().item())
_, index = torch.max(out, 1)
print("最大值所代表的类别：", index.item())

最大值： 15.812638282775879
最小值： -4.85807466506958
最大值所代表的类别： 207

# imagenet 网站找不到分类

Gan模型

import torch
import torch.nn as nn
class ResNetBlock(nn.Module): # <1>

    def __init__(self, dim):
        super(ResNetBlock, self).__init__()
        self.conv_block = self.build_conv_block(dim)

    def build_conv_block(self, dim):
        conv_block = []

        conv_block += [nn.ReflectionPad2d(1)]

        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=0, bias=True),
                       nn.InstanceNorm2d(dim),
                       nn.ReLU(True)]

        conv_block += [nn.ReflectionPad2d(1)]

        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=0, bias=True),
                       nn.InstanceNorm2d(dim)]

        return nn.Sequential(*conv_block)

    def forward(self, x):
        out = x + self.conv_block(x) # <2>
        return out


class ResNetGenerator(nn.Module):

    def __init__(self, input_nc=3, output_nc=3, ngf=64, n_blocks=9): # <3> 

        assert(n_blocks >= 0)
        super(ResNetGenerator, self).__init__()

        self.input_nc = input_nc
        self.output_nc = output_nc
        self.ngf = ngf

        model = [nn.ReflectionPad2d(3),
                 nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=True),
                 nn.InstanceNorm2d(ngf),
                 nn.ReLU(True)]

        n_downsampling = 2
        for i in range(n_downsampling):
            mult = 2**i
            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3,
                                stride=2, padding=1, bias=True),
                      nn.InstanceNorm2d(ngf * mult * 2),
                      nn.ReLU(True)]

        mult = 2**n_downsampling
        for i in range(n_blocks):
            model += [ResNetBlock(ngf * mult)]

        for i in range(n_downsampling):
            mult = 2**(n_downsampling - i)
            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
                                         kernel_size=3, stride=2,
                                         padding=1, output_padding=1,
                                         bias=True),
                      nn.InstanceNorm2d(int(ngf * mult / 2)),
                      nn.ReLU(True)]

        model += [nn.ReflectionPad2d(3)]
        model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
        model += [nn.Tanh()]

        self.model = nn.Sequential(*model)

    def forward(self, input): # <3>
        return self.model(input)

netG = ResNetGenerator()

model_path = r"D:\research\dlwpt-code-master\data\p1ch2\horse2zebra_0.4.0.pth"
model_data = torch.load(model_path)
netG.load_state_dict(model_data)

netG.load_state_dict(model_data) 函数用于将模型的状态从给定的字典加载到模型中。具体来说，它将模型的权重和偏置从字典中加载到模型的对应参数中。

在这个函数中，netG 是要加载状态的模型，model_data 是包含模型状态的字典。一般来说，model_data 是由 torch.save() 函数保存的模型状态字典

netG.eval()

ResNetGenerator(
  (model): Sequential(
    (0): ReflectionPad2d((3, 3, 3, 3))
    (1): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1))
    (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (3): ReLU(inplace=True)
    (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (6): ReLU(inplace=True)
    (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (9): ReLU(inplace=True)
    (10): ResNetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (11): ResNetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (12): ResNetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (13): ResNetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (14): ResNetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (15): ResNetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (16): ResNetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (17): ResNetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (18): ResNetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (19): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (21): ReLU(inplace=True)
    (22): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (24): ReLU(inplace=True)
    (25): ReflectionPad2d((3, 3, 3, 3))
    (26): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
    (27): Tanh()
  )
)

#make a horse to zeber
from PIL import Image
from torchvision import transforms

preprocess = transforms.Compose([transforms.Resize(256),transforms.ToTensor()])

img = Image.open(r"D:\research\dlwpt-code-master\data\p1ch2\horse.jpg")

img_t = preprocess(img)
batch_t = torch.unsqueeze(img_t,0)

batch_out = netG(batch_t)

out_t = (batch_out.data.squeeze()+1.0) / 2.0

在这段代码中，batch_out 表示神经网络输出的张量，通常是一个经过 softmax 函数处理后的概率分布。这个张量的大小为 (batch_size, num_classes)，其中 batch_size 表示批次大小，num_classes 表示分类问题中的分类数目。

为了将输出转换为图像，通常需要对其进行一些处理。具体来说，您提供的代码中使用了以下操作：

batch_out.data：获取 batch_out 张量的数据部分，即去掉梯度信息的部分，得到一个新的张量。
squeeze()：将张量中所有维度大小为 1 的维度去掉。在这里，由于 batch_size 的大小为 1，因此 squeeze() 操作会将第一个维度去掉，返回一个大小为 (num_classes,) 的一维张量。
+1.0：将张量中所有元素加上 1。
/2.0：将张量中所有元素除以 2。

这些操作的目的是将神经网络输出的概率分布转换为图像的像素值。具体来说，将概率分布中的每个元素都加上 1，是为了将所有元素的值都变为非负数。将每个元素都除以 2，则是为了将它们的值缩放到 0 到 1 之间。最终得到的张量可以看作是一个大小为 (num_classes,) 的一维张量，表示图像的像素值。

需要注意的是，这个代码只能处理批次大小为 1 的情况，即只能处理一张图像。如果您要处理多张图像，需要对每个图像分别进行处理，然后将它们组合成一个张量。另外，这个代码还假定输出的概率分布是经过 softmax 函数处理的，如果输出的概率分布没有经过 softmax 函数处理，需要进行额外的处理。

out_img = transforms.ToPILImage()(out_t)

在这段代码中，out_t 表示经过神经网络处理后得到的输出张量，通常是一个大小为 (C, H, W) 的三维张量，其中 C 是通道数，H 和 W 是图像的高度和宽度。

为了将输出张量转换为图像，您使用了 transforms.ToPILImage() 函数。这个函数是 PyTorch 中的一个图像变换函数，用于将一个张量或数组转换为 PIL 图像对象。

具体来说，transforms.ToPILImage() 函数将一个张量或数组转换为 PIL 图像对象，其中张量或数组的数值被映射到 0 到 255 的整数范围内。如果张量或数组中的数值小于 0，则被截断为 0；如果数值大于 255，则被截断为 255。

在这里，transforms.ToPILImage() 函数被调用后返回一个函数对象，这个函数对象可以用于将一个张量或数组转换为 PIL 图像对象。具体来说，transforms.ToPILImage()(out_t) 将输出张量 out_t 转换为 PIL 图像对象 out_img。

需要注意的是，转换为 PIL 图像对象后，您可以使用 PIL 图像库中的各种方法对图像进行处理，例如保存图像、调整图像大小、裁剪图像等。

fn_save = getattr(out_img, 'save')
fn_save(r'D:\research\dlwpt-code-master\data\p1ch2\ganzebra.jpg')

print(out_img)




```python

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