f"RMSE {value[1]:6.4f}\n"
f"PSNR {value[2]:6.2f}\n"
f"SSIM {value[3]:6.4f}\n"
f"LPIPS {value[4]:6.4f}\n"
f"GMSD {value[5]:6.4f}\n")
else:
images = torch.cat([bicubic, sr], dim=-1)
vutils.save_image(lr, os.path.join("tests", f"lr_{filename}"))
vutils.save_image(bicubic, os.path.join("tests", f"bicubic_{filename}"))
vutils.save_image(sr, os.path.join("tests", f"sr_{filename}"))
vutils.save_image(images,
os.path.join("tests", f"compare_{filename}"),
padding=10)
if __name__ == "__main__":
print("##################################################\n")
print("Run Testing Engine.\n")
create_folder("tests")
logger.info("TestingEngine:")
print("\tAPI version .......... 0.2.0")
print("\tBuild ................ 2021.04.15")
print("##################################################\n")
main()
logger.info(
"Test single image performance evaluation completed successfully.\n")
"url used to set up distributed training. (Default: `tcp://59.110.31.55:12345`)"
)
parser.add_argument("--dist-backend",
default="nccl",
type=str,
help="Distributed backend. (Default: `nccl`)")
parser.add_argument("--seed",
default=None,
type=int,
help="Seed for initializing training.")
parser.add_argument("--gpu", default=None, type=int, help="GPU id to use.")
parser.add_argument(
"--multiprocessing-distributed",
action="store_true",
help="Use multi-processing distributed training to launch "
"N processes per node, which has N GPUs. This is the "
"fastest way to use PyTorch for either single node or "
"multi node data parallel training.")
args = parser.parse_args()
create_folder("runs")
create_folder("weights")
logger.info("TrainEngine:")
logger.info("\tAPI version .......... 0.3.0")
logger.info("\tBuild ................ 2021.06.15")
main(args)
logger.info("All training has been completed successfully.\n")
writer.add_scalar("Train/G Loss", g_loss.item(), iters)
writer.add_scalar("Train/Pixel Loss", pixel_loss.item(), iters)
writer.add_scalar("Train/Content Loss", content_loss.item(), iters)
writer.add_scalar("Train/Adversarial Loss", adversarial_loss.item(), iters)
# Output results every 100 batches.
if i % 100 == 0:
progress.display(i)
# Save image every 300 batches.
if iters % 300 == 0:
vutils.save_image(hr.detach(), os.path.join("runs", "hr", f"GAN_{iters}.bmp"))
vutils.save_image(sr.detach(), os.path.join("runs", "sr", f"GAN_{iters}.bmp"))
if __name__ == "__main__":
print("##################################################\n")
print("Run Training Engine.\n")
create_folder("runs")
create_folder("runs/hr")
create_folder("runs/sr")
create_folder("weights")
logger.info("TrainingEngine:")
print("\tAPI version .......... 0.2.0")
print("\tBuild ................ 2021.04.15")
print("##################################################\n")
main()
logger.info("All training has been completed successfully.\n")
bottom_image_width))(tensor2pil(bottom_image)))
# 4. Combine the bottom zone with the upper zone.
final_image = np.concatenate((top_image, bottom_image))
# save compare video
compare_writer.write(final_image)
if args.view:
# display video
cv2.imshow("LR video convert SR video ", final_image)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# next frame
success, raw_frame = video_capture.read()
if __name__ == "__main__":
print("##################################################\n")
print("Run SR Engine.\n")
create_folder("videos")
logger.info("SREngine:")
print("\tAPI version .......... 0.2.0")
print("\tBuild ................ 2021.04.15")
print("##################################################\n")
main()
logger.info("Super-resolution video completed successfully.\n")
images = torch.cat([bicubic, sr, hr], -1)
vutils.save_image(images,
os.path.join("benchmarks", f"{i + 1}.bmp"),
padding=10)
print(f"Performance average results:\n")
print(f"indicator Score\n")
print(f"--------- -----\n")
print(f"MSE {total_mse_value / len(dataloader):6.4f}\n"
f"RMSE {total_rmse_value / len(dataloader):6.4f}\n"
f"PSNR {total_psnr_value / len(dataloader):6.2f}\n"
f"SSIM {total_ssim_value / len(dataloader):6.4f}\n"
f"LPIPS {total_lpips_value / len(dataloader):6.4f}\n"
f"GMSD {total_gmsd_value / len(dataloader):6.4f}")
if __name__ == "__main__":
print("##################################################\n")
print("Run Testing Engine.\n")
create_folder("benchmarks")
logger.info("TestingEngine:")
print("\tAPI version .......... 0.2.0")
print("\tBuild ................ 2021.04.15")
print("##################################################\n")
main()
logger.info("Test dataset performance evaluation completed successfully.")