You can replace the first conv layer. Then train ooooon 
import torchvision.models as models
model = models.resnet50(pretrained=True)
n_image_channels = 4
model.conv1 = nn.Conv2d(n_image_channels,
                               model.conv1.out_channels,
                               kernel_size=7,
                               stride=2,
                               padding=3,
                               bias=False)