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,
-1.6959e+00, -2.9048e-01, -2.8050e+00, -1.8296e+00, 9.0891e-02,
-2.1747e+00, -1.5452e+00, -1.2558e+00, -9.5389e-01, -2.5265e+00,
-1.3665e+00, -3.8682e+00, -4.3242e+00, -1.5148e+00, -1.9113e+00,
-2.9872e+00, -2.9385e+00, -4.8581e+00, -2.4930e+00, -1.3708e+00,
-2.8970e+00, -1.2814e+00, -5.6305e-01, -2.8289e+00, -1.8666e+00,
-2.3429e+00, -3.4331e+00, -2.5701e+00, -3.8991e+00, -4.4677e+00,
-3.5612e+00, -9.3197e-01, -2.3963e+00, -2.0673e+00, -1.8581e+00,
-3.2819e+00, -3.5219e+00, -2.8122e+00, -1.9661e+00, -3.7761e+00,
-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,
1.1153e+00, 2.2649e+00, -1.0259e+00, 9.7924e-01, 1.0194e+00,
-2.6048e+00, -5.5008e-01, 2.1353e+00, -9.3641e-01, 4.3959e-02,
-3.9618e-01, 1.5395e+00, -1.0598e+00, 1.8685e+00, 1.4945e+00,
-4.4445e+00, -1.7147e-01, -1.6854e-01, -7.5243e-01, 1.8241e+00,
3.3237e+00, 7.4655e-01, -7.6160e-01, 1.6335e+00, 1.9438e+00,
1.2983e+00, -1.5951e+00, 2.2410e+00, 9.3765e-01, -6.2485e-01,
-1.3969e+00, 1.6010e+00, 2.9322e-02, 8.8535e-01, 9.6981e-01,
5.0879e-01, 3.9120e-01, 1.0257e+00, 6.5950e-01, 3.3687e+00,
-1.0271e+00, -2.9353e+00, -7.5909e-01, -7.9742e-01, 7.0959e+00,
-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,
3.3260e+00, -1.8322e+00, 3.2782e+00, -1.7115e-01, 1.3883e+00,
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