如何把imagenet预训练的模型，输入层的通道数随心所欲的修改，从而来适应自己的任务

#增加一个通道
w = layers[0].weight
layers[0] = nn.Conv2d(4, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
layers[0].weight = torch.nn.Parameter(torch.cat((w, w[:, :1, :, :]), dim=1))
 
#方式2
w = layers[0].weight
layers[0] = nn.Conv2d(4, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
layers[0].weight = torch.nn.Parameter(torch.cat((w, torch.zeros(64, 1, 7, 7)), dim=1))
 
 
#单通道输入
layers[0] = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
layers[0].weight = torch.nn.Parameter(w[:, :1, :, :])