[new] hybrid lens and other important updates

6_hybridlens_design.py

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+"""
+Jointly optimize refractive-diffractive lens with a differentiable ray-wave model. This code can be easily extended to end-to-end refractive-diffractive lens and network design. 
+
+Technical Paper:
+    Xinge Yang, Matheus Souza, Kunyi Wang, Praneeth Chakravarthula, Qiang Fu and Wolfgang Heidrich, "End-to-End Hybrid Refractive-Diffractive Lens Design with Differentiable Ray-Wave Model," Siggraph Asia 2024.
+
+This code and data is released under the Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC.) In a nutshell:
+    # The license is only for non-commercial use (commercial licenses can be obtained from authors).
+    # The material is provided as-is, with no warranties whatsoever.
+    # If you publish any code, data, or scientific work based on this, please cite our work.
+"""
+
+import logging
+import os
+import random
+import string
+from datetime import datetime
+
+import torch
+import yaml
+from torchvision.utils import save_image
+from tqdm import tqdm
+
+from deeplens.hybridlens import HybridLens
+from deeplens.optics.loss import PSFLoss
+from deeplens.utils import set_logger, set_seed
+
+
+def config():
+    # ==> Config
+    args = {"seed": 0, "DEBUG": True}
+
+    # ==> Result folder
+    characters = string.ascii_letters + string.digits
+    random_string = "".join(random.choice(characters) for i in range(4))
+    result_dir = (
+        "./results/"
+        + datetime.now().strftime("%m%d-%H%M%S")
+        + "-HybridLens"
+        + "-"
+        + random_string
+    )
+    args["result_dir"] = result_dir
+    os.makedirs(result_dir, exist_ok=True)
+    print(f"Result folder: {result_dir}")
+
+    if args["seed"] is None:
+        seed = random.randint(0, 100)
+        args["seed"] = seed
+    set_seed(args["seed"])
+
+    # ==> Log
+    set_logger(result_dir)
+    if not args["DEBUG"]:
+        raise Exception("Add your wandb logging config here.")
+
+    # ==> Device
+    num_gpus = torch.cuda.device_count()
+    args["num_gpus"] = num_gpus
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    args["device"] = device
+    logging.info(f"Using {num_gpus} {torch.cuda.get_device_name(0)} GPU(s)")
+
+    # ==> Save config
+    with open(f"{result_dir}/config.yml", "w") as f:
+        yaml.dump(args, f)
+
+    with open(f"{result_dir}/6_hybridlens_design.py", "w") as f:
+        with open("6_hybridlens_design.py", "r") as code:
+            f.write(code.read())
+
+    return args
+
+
+def main(args):
+    # Create a hybrid refractive-diffractive lens
+    lens = HybridLens(filename="./lenses/hybridlens/a489_doe.json")
+    lens.double()
+
+    # PSF optimization loop to focus blue light
+    optimizer = lens.get_optimizer(doe_lr=0.1, lens_lr=[1e-4, 1e-4, 1e-1, 1e-5])
+    loss_fn = PSFLoss()
+    for i in tqdm(range(100 + 1)):
+        psf = lens.psf(point=[0.0, 0.0, -10000.0], ks=101, wvln=0.489)
+
+        optimizer.zero_grad()
+        loss = loss_fn(psf)
+        loss.backward()
+        optimizer.step()
+
+        if i % 25 == 0:
+            lens.write_lens_json(f"{args['result_dir']}/lens_iter{i}.json")
+            lens.analysis(save_name=f"{args['result_dir']}/lens_iter{i}.png")
+            save_image(
+                psf.detach().clone(),
+                f"{args['result_dir']}/psf_iter{i}.png",
+                normalize=True,
+            )
+
+
+if __name__ == "__main__":
+    args = config()
+    main(args)