There are two things to do: get the intermediate features, display them.
First, uou will need to use register_forward_hook 13 on the concerned convolution.
To create it you have to write:
def hook(module, input, output):
    # output is the [N, 64, 30, 30] Tensor containing your features

model.concerned_conv.register_forward_hook(hook)

Then depending on your display method, you will have. You can use torchvision and Tensorboard to do so. The hook body would be something like:
def hook(module, input, output):
    grid = torchvision.utils.make_grid(output)
    writer.add_image('conv_features', grid, 0)

Please find more info on using Tensorboard with PyTorch here 3 .