| """ | |
| Plotting utilities to visualize training logs. | |
| """ | |
| import imageio | |
| import os | |
| import torchvision.utils as v_utils | |
| def plot_samples_per_epoch(gen_batch, output_dir, epoch, iteration, nsample): | |
| """ | |
| Plot and save output samples per epoch | |
| """ | |
| fname = "samples_epoch_{:d}_{:d}.jpg".format(epoch, iteration) | |
| fpath = os.path.join(output_dir, fname) | |
| nrow = gen_batch.shape[0] // nsample | |
| image = v_utils.make_grid(gen_batch, nrow=nrow, padding=2, normalize=True) | |
| v_utils.save_image(image, fpath) | |
| return image | |
| def plot_val_samples(gen_batch, output_dir, fname, nrow): | |
| """ | |
| Plot and dsave output samples for validations | |
| """ | |
| fpath = os.path.join(output_dir, fname) | |
| image = v_utils.make_grid(gen_batch, nrow=nrow, padding=2, normalize=True) | |
| v_utils.save_image(image, fpath) | |
| return image | |
| def plot_image(img, output_dir, fname): | |
| """ | |
| img in tensor format | |
| """ | |
| fpath = os.path.join(output_dir, fname) | |
| v_utils.save_image(img, fpath, nrow=4, padding=2, normalize=True) | |
| return imageio.imread(fpath) | |