Print spectrograms anywhere in the model

torch.save(input_target.to(torch.device('cpu')), "inputTensor.pth")

import torch
import matplotlib.pyplot as plt
import librosa.display


def phased_visualization():
    data = torch.load("/home3/weiwb/code/Multi-Channel-Model-master/x9_LA1.pth")
    print(data.shape)
    # 这里需要将data从tensor转换为numpy格式
    dpi = 300
    random_c = data[0][1]
    # random_c = torch.squeeze(torch.sum(data, dim=1))
    random_c = random_c.permute(1, 0)
    random_c = random_c.detach().numpy()
    fig = plt.figure(figsize=(random_c.shape[1]/dpi, random_c.shape[0]/dpi), dpi=dpi)
    axes = fig.add_axes([0, 0, 1, 1])
    axes.set_axis_off()

    plt.gca().xaxis.set_major_locator(plt.NullLocator())
    plt.gca().yaxis.set_major_locator(plt.NullLocator())
    plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace&#