num_ftrs = resnet18.fc.in_features resnet18.fc = nn.Linear(num_ftrs, 2) # 加载模型参数 checkpoint = torch.load(m_path) resnet18.load_state_dict(checkpoint['model_state_dict']) if vis_model: from torchsummary import summary summary(resnet18, input_size=(3, 224, 224), device="...
fc.in_features # 输入特征矩阵的深度 net.fc = nn.Linear(in_channel, 5) # 五分类(花分类数据集) # define loss function loss_function = nn.CrossEntropyLoss() # construct an optimizer optimizer = optim.Adam(net.parameters(), lr=0.0001) best_acc = 0.0 save_path = './resNet34.pth' ...
num_ftrs = resnet18.fc.in_features resnet18.fc = nn.Linear(num_ftrs, 2) # 加载模型参数 checkpoint = torch.load(m_path) resnet18.load_state_dict(checkpoint['model_state_dict']) if vis_model: fromtorchsummaryimport summary summary(resnet18, input_size=(3, 224, 224), device="cpu...
num_ftrs = model_ft.fc.in_features # Here the size of each output sample is set to 2. # Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)). model_ft.fc = nn.Linear(num_ftrs, 2) model_ft = model_ft.to(device) criterion = nn.CrossEntropyLoss() #...
fc.in_features, num_classes) # 替换分类层以适应新任务 三、准备数据集 1. 数据集下载与预处理 根据你的任务需求,下载相应的数据集并进行预处理。通常包括图像的大小调整、归一化等操作。确保数据集的格式与预训练模型的输入要求相匹配。 2. 数据加载 使用框架提供的数据加载器(如TensorFlow的tf.data或PyTorch...
resnet18 = models.resnet18()# 修改全连接层的输出num_ftrs = resnet18.fc.in_features resnet18.fc = nn.Linear(num_ftrs, 2)# 加载模型参数checkpoint = torch.load(m_path) resnet18.load_state_dict(checkpoint['model_state_dict']) ...
将全连接层换为torch.nn.Conv2d( in_channels = self.fc.in_features, out_channels = num_classes, kernel_size = 1),即卷积核为 1 的、输入输出维度不变的 二维卷积层 classFullyConvolutionalResnet18(models.ResNet):def__init__(self, num_classes=1000, pretrained=False, **kwargs):# Start with...
(in_channels=self.fc.in_features,out_channels=num_classes,kernel_size=1)self.last_conv.weight.data.copy_(self.fc.weight.data.view(*self.fc.weight.data.shape,1,1))self.last_conv.bias.data.copy_(self.fc.bias.data)# Reimplementing forward pass.def_forward_impl(self,x):# Standard ...
fc.in_features,2,bias=False) save_path="/content/drive/My Drive/colab notebooks/output/resnet18_best.t7" checkpoint = torch.load(save_path,map_location=lambda storage, loc: storage) model.load_state_dict(checkpoint['model']) return model def draw_features(width,height,x,savename): tic=...
model=models.resnet18(pretrained=False)num_ftrs=model.fc.in_features model.fc=nn.Linear(num_ftrs,10)#10类用于MNIST分类 model=model.cuda()# 将模型移至GPU# 定义数据预处理 transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,),(0.5,))])# 加载训练数据集 ...