4
features = out
for i in range(features.shape[1]):
    feature = features[:, i, :, :]
    feature = feature.view(feature.shape[1], feature.shape[2])
    feature = feature.data.numpy()
    # use sigmod to [0,1]
    feature = 1.0 / (1 + np.exp(-1 * feature))
    # to [0,255]
    feature = np.round(feature * 255)
    fg_mask = cv2.merge((feature, feature, feature))
    pt = './' + str(i) + '.jpg'
    cv2.imwrite(pt, fg_mask)

3
import matplotlib.pyplot as plt
feature_map = out.detach().cpu()
for i in range(feature_map.size(1)):
    plt.matshow(feature_map[0, i, :, :], cmap="viridis")
    plt.draw()
    plt.pause(0.1)
    plt.close()

2
transform1 = transforms.ToPILImage(mode='L')
#img = torch.cpu().clone()
for i in range(21):
    image = out[0][i]
    print(image.size())
    image = transform1(np.uint8(image.detach().numpy()))
    image.show()

1
for i in range(21):
    c = i
    feature = out[0, c, :, :]
    img = feature.detach().numpy()
    fg_mask = cv2.merge((img, img, img))
    pt = './' + str(c) + '.jpg'
    cv2.imwrite(pt, fg_mask)