:param model: :return:
(model)
| 70 | return True |
| 71 | |
| 72 | def generagedImage(model): |
| 73 | """ |
| 74 | :param model: |
| 75 | :return: |
| 76 | """ |
| 77 | random_noise = torch.randn(size=(16, 100,1,1), device='cpu') |
| 78 | genImg = model(random_noise).detach().cpu() |
| 79 | # -------------------------------------------------------------- |
| 80 | # vutilsImg = vutils.make_grid(result, padding=2, normalize=True) |
| 81 | # fig = plt.figure(figsize=(4, 4)) |
| 82 | # plt.imshow(np.transpose(vutilsImg, (1, 2, 0))) |
| 83 | # plt.axis('off') |
| 84 | # plt.show() |
| 85 | # -------------------------------------------------------------- |
| 86 | result = np.squeeze(genImg.numpy()) |
| 87 | fig = plt.figure(figsize=(4, 4)) |
| 88 | for i in range(16): |
| 89 | plt.subplot(4, 4, i + 1) |
| 90 | plt.imshow(np.transpose(result[i], (1, 2, 0))) |
| 91 | plt.axis('off') |
| 92 | plt.savefig('cur.png') |
| 93 | |
| 94 | def loadModel(model,root): |
| 95 | """ |