Add new configs.

configs/benchmark_digicam_celeba.yaml

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+# python scripts/eval/benchmark_recon.py -cn benchmark_digicam_celeba
+defaults:
+  - benchmark
+  - _self_
+
+
+dataset: HFDataset   # DiffuserCam, DigiCamCelebA, HFDataset
+batchsize: 10
+device: "cuda:2"
+
+algorithms: [
+  "ADMM",
+  # "hf:digicam:celeba_26k:U5+Unet8M_wave",
+  # "hf:digicam:celeba_26k:TrainInv+Unet8M_wave",
+  # "hf:digicam:celeba_26k:MWDN8M_wave",
+  # "hf:digicam:celeba_26k:MMCN4M+Unet4M_wave",
+  # "hf:digicam:celeba_26k:Unet2M+MWDN6M_wave",
+  # "hf:digicam:celeba_26k:Unet4M+TrainInv+Unet4M_wave",
+  # "hf:digicam:celeba_26k:Unet2M+MMCN+Unet2M_wave",
+  # "hf:digicam:celeba_26k:Unet4M+U5+Unet4M_wave",
+  # "hf:digicam:celeba_26k:Unet4M+U10+Unet4M_wave",
+
+  # #-- reconstructions trained on other datasets/systems
+  # "hf:diffusercam:mirflickr:Unet4M+U10+Unet4M",
+  # "hf:tapecam:mirflickr:Unet4M+U5+Unet4M",
+  # "hf:diffusercam:mirflickr:Unet4M+U5+Unet4M",
+  # "hf:tapecam:mirflickr:Unet4M+U10+Unet4M",
+  # "hf:digicam:mirflickr_single_25k:Unet4M+U5+Unet4M_wave",
+  # "hf:digicam:mirflickr_single_25k:Unet4M+U10+Unet4M_wave",
+]
+
+
+# ## -- reconstructions trained on other datasets/systems
+# algorithms: [
+#   "hf:diffusercam:mirflickr:Unet4M+U10+Unet4M",
+#   "hf:tapecam:mirflickr:Unet4M+U10+Unet4M",
+#   "hf:digicam:mirflickr_single_25k:Unet4M+U5+Unet4M_wave",
+#   # "hf:digicam:celeba_26k:Unet4M+U5+Unet4M_wave",
+# ]
+
+# # algorithm configuration
+# hf:digicam:mirflickr_single_25k:Unet4M+U10+Unet4M_wave:
+#   skip_post: True
+# hf:digicam:mirflickr_single_25k:Unet4M+U10+Unet4M:
+#   skip_post: True
+# hf:digicam:mirflickr_multi_25k:Unet4M+U10+Unet4M:
+#   skip_post: True
+# hf:digicam:mirflickr_multi_25k:Unet4M+U10+Unet4M_wave:
+#   skip_post: True
+
+save_idx: [0, 2, 3, 4, 9]
+n_iter_range: [100]    # for ADMM
+
+huggingface:
+  repo: bezzam/DigiCam-CelebA-26K
+  # cache_dir: /dev/shm
+  psf: psf_measured.png
+  # psf: psf_simulated_waveprop.png    # psf_simulated_waveprop.png, psf_simulated.png, psf_measured.png
+  split_seed: 0
+  test_size: 0.15
+  downsample: 2
+  image_res: null
+
+  alignment:
+    top_left: null
+    height: null
+
+    # cropping when there is no downsampling
+    crop:
+      vertical: [0, 525]
+      horizontal: [265, 695]
+
+    # for prepping ground truth data
+    simulation:
+      scene2mask: 0.25   # [m]
+      mask2sensor: 0.002   # [m]
+      object_height: 0.33   # [m]
+      sensor: "rpi_hq"
+      # shifting when there is no files to downsample
+      vertical_shift: -117
+      horizontal_shift: -25

configs/benchmark_digicam_mirflickr_multi.yaml

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+# python scripts/eval/benchmark_recon.py -cn benchmark_digicam_mirflickr_multi
+defaults:
+  - benchmark
+  - _self_
+
+
+dataset: HFDataset
+batchsize: 4
+device: "cuda:0"
+
+huggingface:
+  repo: "bezzam/DigiCam-Mirflickr-MultiMask-25K"
+  cache_dir: /dev/shm
+  psf: null   # null for simulating PSF
+  image_res: [900, 1200]  # used during measurement
+  rotate: True   # if measurement is upside-down
+  flipud: False
+  flip_lensed: False   # if rotate or flipud is True, apply to lensed
+  alignment:
+    top_left: [80, 100]  # height, width
+    height: 200
+  downsample: 1
+
+algorithms: [
+  # "ADMM",
+  ## -- reconstructions trained on other datasets/systems
+  # "hf:diffusercam:mirflickr:Unet4M+U10+Unet4M",
+  # "hf:tapecam:mirflickr:Unet4M+U10+Unet4M",
+  # "hf:digicam:mirflickr_single_25k:Unet4M+U10+Unet4M_wave",
+  # "hf:digicam:celeba_26k:Unet4M+U5+Unet4M_wave",
+  # "hf:digicam:mirflickr_single_25k:Unet4M+U5+Unet4M_wave",
+  # "hf:digicam:mirflickr_multi_25k:Unet4M+U5+Unet4M_wave",
+  # "hf:digicam:mirflickr_multi_25k:Unet4M+U10+Unet4M_wave",
+  # "hf:digicam:mirflickr_single_25k:Unet4M+U5+Unet4M_wave_aux1",
+  # "hf:digicam:mirflickr_single_25k:Unet4M+U5+Unet4M_wave_flips",
+  # "hf:digicam:mirflickr_single_25k:Unet4M+U5+Unet4M_wave_flips_rotate10",
+  # "hf:digicam:mirflickr_multi_25k:Unet4M+U5+Unet4M_wave_aux1",
+  # "hf:digicam:mirflickr_multi_25k:Unet4M+U5+Unet4M_wave_flips",
+  # "hf:tapecam:mirflickr:Unet4M+U5+Unet4M",
+  # "hf:diffusercam:mirflickr:Unet4M+U5+Unet4M",
+  "hf:digicam:mirflickr_single_25k:Unet4M+U5+Unet4M_ft_flips",
+  "hf:digicam:mirflickr_single_25k:Unet4M+U5+Unet4M_ft_flips_rotate10",
+]
+
+
+# -- to only use output from unrolled
+hf:digicam:mirflickr_single_25k:Unet4M+U5+Unet4M_wave_aux1:
+  skip_post: True
+  skip_pre: True
+
+# save_idx: [1, 2, 4, 5, 9]
+save_idx: [24, 33, 61]
+n_iter_range: [100]    # for ADMM
+
+# simulating PSF
+simulation:
+  use_waveprop: True
+  deadspace: True
+  scene2mask: 0.3
+  mask2sensor: 0.002

configs/benchmark_tapecam_mirflickr.yaml

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+# python scripts/eval/benchmark_recon.py -cn benchmark_tapecam_mirflickr
+defaults:
+  - benchmark
+  - _self_
+
+dataset: HFDataset
+batchsize: 4
+device: "cuda:0"
+
+huggingface:
+  repo: "bezzam/TapeCam-Mirflickr-25K"
+  cache_dir: /dev/shm
+  psf: psf.png
+  image_res: [900, 1200]  # used during measurement
+  rotate: False   # if measurement is upside-down
+  flipud: False
+  flip_lensed: False   # if rotate or flipud is True, apply to lensed
+  alignment:
+    top_left: [45, 95]  # height, width
+    height: 250
+  downsample: 1
+  single_channel_psf: False
+
+## -- reconstructions trained with same dataset/system
+algorithms: [
+#   "ADMM",
+#   "hf:tapecam:mirflickr:U5+Unet8M",
+#   "hf:tapecam:mirflickr:TrainInv+Unet8M",
+#   "hf:tapecam:mirflickr:MMCN4M+Unet4M",
+#   "hf:tapecam:mirflickr:Unet4M+U5+Unet4M",
+#   "hf:tapecam:mirflickr:Unet4M+TrainInv+Unet4M",
+#   "hf:tapecam:mirflickr:Unet2M+MMCN+Unet2M",
+#   "hf:tapecam:mirflickr:Unet4M+U10+Unet4M",
+
+  # "hf:tapecam:mirflickr:Unet4M+U5+Unet4M_flips_rotate10",
+  # "hf:tapecam:mirflickr:Unet4M+U5+Unet4M_aux1",
+
+    # single_channel_psf = True
+#   "hf:tapecam:mirflickr:MWDN8M",    
+#   "hf:tapecam:mirflickr:Unet2M+MWDN6M",
+
+  # # -- generalization
+  # "hf:tapecam:mirflickr:Unet4M+U5+Unet4M_flips",
+  # "hf:tapecam:mirflickr:Unet4M+U5+Unet4M_flips_rotate10",
+  # "hf:diffusercam:mirflickr_sim:Unet4M+U5+Unet4M",
+  # "hf:diffusercam:mirflickr_sim:Unet4M+U5+Unet4M_ft_tapecam",
+  # "hf:diffusercam:mirflickr_sim:Unet4M+U5+Unet4M_ft_tapecam_post",
+  # "hf:diffusercam:mirflickr_sim:Unet4M+U5+Unet4M_ft_tapecam_pre",
+  # "hf:diffusercam:mirflickr_sim:Unet4M+U5+Unet4M_ft_digicam_multi_post",
+  # "hf:diffusercam:mirflickr:Unet4M+U5+Unet4M_ft_tapecam",
+  # "hf:diffusercam:mirflickr:Unet4M+U5+Unet4M_ft_tapecam_post",
+  # "hf:diffusercam:mirflickr:Unet4M+U5+Unet4M_ft_tapecam_pre",
+  "hf:diffusercam:mirflickr_sim:Unet4M+U5+Unet4M_ft_digicam_multi_pre",
+  "hf:diffusercam:mirflickr_sim:Unet4M+U5+Unet4M_ft_digicam_multi",
+
+## -- reconstructions trained on other datasets/systems
+  # "hf:diffusercam:mirflickr:Unet4M+U10+Unet4M",
+  # "hf:diffusercam:mirflickr:Unet4M+U5+Unet4M",
+  # "hf:digicam:mirflickr_single_25k:Unet4M+U5+Unet4M_wave",
+  # "hf:digicam:celeba_26k:Unet4M+U5+Unet4M_wave",
+  # "hf:digicam:mirflickr_single_25k:Unet4M+U10+Unet4M_wave",
+]
+
+save_idx: [1, 2, 4, 5, 9]
+n_iter_range: [100]    # for ADMM
+

configs/demo_iccp2024.yaml

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+defaults:
+  - demo
+  - _self_
+
+
+gamma: 1  # for plotting PSF
+
+display:
+  image_res: [700, 700]
+  vshift: -20
+
+capture:
+  legacy: False
+  bayer: False
+  rgb: False
+  down: 4
+  awb_gains: [2.0, 1.1]
+
+recon:
+  gamma: null
+  downsample: 4
+  dtype: float32
+  use_torch: True
+  torch_device: cuda:3
+  algo: admm
+
+  fista:
+    n_iter: 300
+
+  admm:
+    n_iter: 10
+
+postproc:
+  # crop in percent to extract region of interest
+  # set to null to skip
+  crop_hor: [0.3, 0.7]
+  crop_vert: [0.22, 0.67]

configs/finetune_tape_for_diffuser.yaml

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+# python scripts/recon/train_learning_based.py -cn finetune_tape_for_diffuser
+defaults:
+  - train_unrolledADMM
+  - _self_
+
+torch_device: 'cuda:0'
+device_ids: [0, 1, 2, 3]
+eval_disp_idx: [0, 1, 3, 4, 8]
+
+# Dataset
+files:
+  dataset: bezzam/DiffuserCam-Lensless-Mirflickr-Dataset-NORM
+  huggingface_dataset: True
+  huggingface_psf: psf.tiff
+  single_channel_psf: True
+  downsample: 2    # factor by which to downsample the PSF, note that for DiffuserCam the PSF has 4x the resolution
+  flipud: True
+  flip_lensed: True   # for measure data
+
+  hf_simulated: True
+
+training:
+  batch_size: 4
+  epoch: 25
+  eval_batch_size: 4
+
+reconstruction:
+  init: hf:tapecam:mirflickr:Unet4M+U5+Unet4M
+  # init: hf:diffusercam:mirflickr:Unet4M+U5+Unet4M
+
+optimizer:
+  lr: 1e-5

configs/telegram_demo_iccp2024.yaml

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+defaults:
+  - telegram_demo
+  - _self_
+
+# for Telegram
+token: null
+whitelist: [360264201]
+setup_fp: voronoi_setup.jpeg
+
+# usernames and IP address
+rpi_username: null
+rpi_hostname: null
+
+config_name: demo_iccp2024
+psf:
+  fp: data/psf/voronoi_rgb.png
+  downsample: 4
+
+files:
+  mnist:
+    fp: data/original/mnist_3.png
+    exposure: 0.05
+    brightness: 100
+  thumb: 
+    fp: data/original/thumbs_up.png
+    exposure: 0.02
+    brightness: 80
+  face:
+    fp: data/original/face.jpg
+    exposure: 0.03
+    brightness: 80
+  tree:
+    fp: data/original/tree.png
+    exposure: 0.08
+    brightness: 100
+
+overlay:
+  alpha: 60
+
+  img1:
+    fp: data/original/epfl_white.png
+    scaling: 0.3
+    position: [10,10]
+
+  img2:
+    fp: data/original/iccp.png
+    scaling: 0.4
+    position: [220,15]
+
+  img3:
+    fp: data/original/lpc.png
+    scaling: 0.4
+    position: [110,300]
+

configs/train_mirflickr_tape.yaml

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+# python scripts/recon/train_learning_based.py -cn train_mirflickr_tape
+defaults:
+  - train_unrolledADMM
+  - _self_
+
+torch_device: 'cuda:0'
+device_ids: [0, 1, 2, 3]
+eval_disp_idx: [1, 2, 4, 5, 9]
+
+# Dataset
+files:
+  dataset: bezzam/TapeCam-Mirflickr-25K
+  huggingface_dataset: True
+  huggingface_psf: psf.png
+  downsample: 1
+  # TODO: these parameters should be in the dataset?
+  image_res: [900, 1200]  # used during measurement
+  rotate: False   # if measurement is upside-down
+  save_psf: True
+
+# TODO: these parameters should be in the dataset?
+alignment:
+  # when there is no downsampling
+  top_left: [45, 95]  # height, width
+  height: 250
+
+training:
+  batch_size: 4
+  epoch: 25
+  eval_batch_size: 4
+
+reconstruction:
+  method: unrolled_admm
+  unrolled_admm:
+    # Number of iterations
+    n_iter: 5
+    # Hyperparameters
+    mu1: 1e-4
+    mu2: 1e-4
+    mu3: 1e-4
+    tau: 2e-4
+  pre_process: 
+    network : UnetRes  # UnetRes or DruNet or null
+    depth : 4 # depth of each up/downsampling layer. Ignore if network is DruNet
+    nc: [32,64,116,128]
+  post_process: 
+    network : UnetRes  # UnetRes or DruNet or null
+    depth : 4 # depth of each up/downsampling layer. Ignore if network is DruNet
+    nc: [32,64,116,128]