Merge branch 'main' into trainable_amplitude_mask

CHANGELOG.rst

@@ -13,6 +13,24 @@ Unreleased
 Added
 ~~~~~
 
+- Script to upload measured datasets to Hugging Face: ``scripts/data/upload_dataset_huggingface.py``
+
+Changed
+~~~~~
+
+- Dataset reconstruction script uses datasets from Hugging Face: ``scripts/recon/dataset.py``
+
+Bugfix
+~~~~~
+
+- Nothing
+
+1.0.6 - (2024-02-21)
+--------------------
+
+Added
+~~~~~
+
 - Trainable reconstruction can return intermediate outputs (between pre- and post-processing).
 - Auto-download for DRUNet model.
 - ``utils.dataset.DiffuserCamMirflickr`` helper class for Mirflickr dataset.
@@ -23,6 +41,13 @@ Added
 - Option to freeze/unfreeze/add pre- and post-processor components during training.
 - Option to skip unrolled training and just use U-Net.
 - Dataset objects for Adafruit LCD: measured CelebA and hardware-in-the-loop.
+- Option to add auxiliary loss from output of camera inversion.
+- Option to specify denoiser to iterative methods for plug-and-play.
+- Model repository of trained models in ``lensless.recon.model_dict``.
+- TrainableInversion component as in FlatNet.
+- ``lensless.recon.utils.get_drunet_function_v2`` which doesn't normalize each color channel.
+- Option to add noise to DiffuserCamMirflickr dataset.
+- Option to initialize pre- and post-processor with components from another model.
 
 Changed
 ~~~~~~~
@@ -37,6 +62,7 @@ Bugfix
 - Fix bad train/test split for DiffuserCamMirflickr in unrolled training.
 - Resize utility.
 - Aperture, index to dimension conversion.
+- Submodule imports.
 
 
 1.0.5 - (2023-09-05)

README.rst

@@ -16,6 +16,15 @@ LenslessPiCam
       :alt: Downloads
 
 
+.. image:: https://colab.research.google.com/assets/colab-badge.svg
+      :target: https://drive.google.com/drive/folders/1nBDsg86RaZIqQM6qD-612k9v8gDrgdwB?usp=drive_link
+      :alt: notebooks
+
+.. image:: https://huggingface.co/datasets/huggingface/badges/resolve/main/powered-by-huggingface-dark.svg
+      :target: https://huggingface.co/bezzam
+      :alt: huggingface
+
+
 *A Hardware and Software Toolkit for Lensless Computational Imaging with a Raspberry Pi*
 -----------------------------------------------------------------------------------------
 

configs/admm_pnp.yaml

@@ -0,0 +1,13 @@
+# python scripts/recon/admm.py -cn admm_pnp
+defaults:
+  - defaults_recon
+  - _self_
+
+torch: True
+torch_device: 'cuda:1'
+
+admm:
+  denoiser:
+    network: DruNet
+    noise_level: 0.05
+    use_dual: False

configs/benchmark.yaml

@@ -14,7 +14,7 @@ device: "cuda"
 # numbers of iterations to benchmark
 n_iter_range: [5, 10, 20, 50, 100, 200, 300]
 # number of files to benchmark
-n_files: 200    # null for all files
+n_files: null    # null for all files
 #How much should the image be downsampled
 downsample: 2
 #algorithm to benchmark

configs/defaults_recon.yaml

@@ -64,6 +64,12 @@ admm:
   mu2: 1e-5
   mu3: 4e-5
   tau: 0.0001
+  # PnP
+  denoiser: null  # set to use PnP
+  # denoiser:
+  #   network: DruNet
+  #   noise_level: 10   # within [0, 255]
+  #   use_dual: False  # just for ADMM
   #Loading unrolled model
   unrolled: false
   checkpoint_fp: null

configs/recon_celeba_digicam.yaml

@@ -0,0 +1,32 @@
+# python scripts/recon/dataset.py -cn recon_celeba_digicam
+defaults:
+  - recon_dataset
+  - _self_
+
+torch: True
+torch_device: 'cuda:0'
+
+repo_id: "bezzam/DigiCam-CelebA-10K"
+split: "test"    # "train", "test", "all"
+psf_fn: "psf_measured.png"    # in repo root
+n_files: 25    # null for all files
+
+preprocess:
+  flip_ud: True 
+  flip_lr: True
+  downsample: 6
+
+  # to have different data shape than PSF
+  data_dim: null
+  # data_dim: [48, 64]       # down 64
+  # data_dim: [506, 676]   # down 6
+
+algo: admm   # "admm", "apgd", "null" to just copy over (resized) raw data
+admm:
+  n_iter: 10
+
+# extraction region of interest
+# roi: null   # top, left, bottom, right
+# roi: [10, 300, 560, 705]  # down 4
+roi: [10, 190, 377, 490]   # down 6
+# roi: [5, 150, 280, 352]   # down 8

configs/recon_dataset.yaml

@@ -6,42 +6,29 @@ defaults:
 torch: True
 torch_device: 'cuda:0'
 
-input:
-  # https://drive.switch.ch/index.php/s/NdgHlcDeHVDH5ww?path=%2Fpsf
-  psf: data/psf/adafruit_random_2mm_20231907.png
-  # https://drive.switch.ch/index.php/s/m89D1tFEfktQueS
-  raw_data: data/celeba_adafruit_random_2mm_20230720_1K
-
+repo_id: "bezzam/DiffuserCam-Lensless-Mirflickr-Dataset"
+split: "test"    # "train", "test", "all"
+psf_fn: "psf.png"    # in repo root
+output_folder: null  # autocreate name if not spe
 n_files: 25    # null for all files
-output_folder: data/celeba_adafruit_recon
-
-# extraction region of interest
-roi: null   # top, left, bottom, right
-# -- values for `data/celeba_adafruit_random_2mm_20230720_1K`
-# roi: [10, 300, 560, 705]  # down 4
-# roi: [6, 200, 373, 470]   # down 6
-# roi: [5, 150, 280, 352]   # down 8
 
 preprocess:
-  flip: True
+  flip_ud: True 
+  flip_lr: False
   downsample: 6
-
   # to have different data shape than PSF
   data_dim: null
-  # data_dim: [48, 64]       # down 64
-  # data_dim: [506, 676]   # down 6
-
-display:
-  disp: -1
-  plot: False
 
 algo: admm   # "admm", "apgd", "null" to just copy over (resized) raw data
-
+admm:
+  n_iter: 100
 apgd:  
   n_jobs: 1    # run in parallel as algo is slow
   max_iter: 500
 
-admm:
-  n_iter: 10
+# extraction region of interest
+roi: null   # top, left, bottom, right
 
-save: False
+display:
+  disp: -1
+  plot: False

configs/recon_diffusercam_mirflickr.yaml

@@ -0,0 +1,36 @@
+# python scripts/recon/diffusercam_mirflickr.py
+# defaults to plug-and-play
+defaults:
+  - defaults_recon
+  - _self_
+
+
+files:
+  dataset: /scratch/bezzam/DiffuserCam_mirflickr/dataset
+  psf: data/psf/diffusercam_psf.tiff
+  diffusercam_psf: True
+  downsample: 2
+
+model_name: null
+legacy_denoiser: True
+
+# defaults to plug-and-play
+admm:
+  # Number of iterations
+  n_iter: 20
+  # Hyperparameters
+  mu1: 1e-6
+  mu2: 1e-5
+  mu3: 4e-5
+  tau: 0.0001
+  # PnP
+  denoiser:
+    network: DruNet
+    noise_level: 0.05
+    use_dual: False
+
+
+device: cuda:0
+n_trials: 1   # more if you want to get average inference time
+idx: 3    # index from test set to reconstruct
+save: True

configs/sim_digicam_psf.yaml

@@ -33,7 +33,7 @@ sim:
   flipud: True
 
   # in practice found waveprop=True or False doesn't make difference
-  waveprop: True
+  waveprop: False
 
   # below are ignored if waveprop=False
   scene2mask: 0.3   # [m]

configs/train_celeba_classifier.yaml

@@ -12,7 +12,7 @@ data:
 
   # -- raw
   # https://drive.switch.ch/index.php/s/m89D1tFEfktQueS
-  measured: data/celeba_adafruit_random_2mm_20230720_10K
+  measured: /scratch/bezzam/celeba_adafruit_random_2mm_20230720_10K
   raw: True
 
   # # -- reconstructed

configs/train_celeba_digicam.yaml

@@ -32,7 +32,6 @@ files:
   celeba_root: /scratch/bezzam
 
 test_idx: [0, 1, 2, 3, 4]
-# test_idx: [1000, 2000, 3000, 4000]
 
 # for prepping ground truth data
 simulation:

configs/train_celeba_digicam_hitl.yaml

@@ -0,0 +1,111 @@
+# Learn mask with HITL training by setting measure configuration (set to null for learning in simulation)
+# 
+# EXAMPLE COMMAND: 
+# python scripts/recon/train_unrolled.py -cn train_celeba_digicam_hitl measure.rpi_username=USERNAME measure.rpi_hostname=HOSTNAME files.vertical_shift=SHIFT
+
+defaults:
+  - train_celeba_digicam
+  - _self_
+
+# Train Dataset
+files:
+
+  dataset: CelebA
+  celeba_root: /scratch/bezzam
+  n_files: 1000
+
+  downsample: 8
+  # TODO: set appropriately to align
+  vertical_shift: -520
+  horizontal_shift: null
+  crop:
+    vertical: [0, 2000]
+    horizontal: [1200, 2800]
+
+measure:
+  # TODO: set for device
+  rpi_username: null
+  rpi_hostname: null
+
+  display:
+    # default to this screen: https://www.dell.com/en-us/work/shop/dell-ultrasharp-usb-c-hub-monitor-u2421e/apd/210-axmg/monitors-monitor-accessories#techspecs_section
+    screen_res: [1920, 1200]   # width, height
+    pad: 10
+    hshift: 0
+    vshift: -18
+    brightness: 100
+    rot90: 3
+
+  capture:
+
+    # NB: not being used
+    max_level: 254
+    min_level: 150
+    max_tries: 4
+    delay: 2
+
+    sensor: rpi_hq
+    gamma: null # for visualization
+    exp: 0.8
+    script: ~/LenslessPiCam/scripts/measure/on_device_capture.py
+    iso: 100
+    config_pause: 1
+    sensor_mode: "0"
+    nbits_out: 8
+    nbits_capture: 12
+    legacy: True
+    gray: False
+    fn: raw_data
+    bayer: True
+    awb_gains: [1.6, 1.2]
+    rgb: True
+    down: 8
+    flip: True
+
+
+# for prepping ground truth data
+simulation:
+  scene2mask: 0.3   # [m]
+  mask2sensor: 0.002   # [m]
+  object_height: 0.38   # [m]
+  snr_db: 5
+  downsample: null
+  random_vflip: False
+  random_hflip: False
+  quantize: False
+  flip: False
+
+#Training
+training:
+  batch_size: 4
+  epoch: 10
+  eval_batch_size: 1
+  crop_preloss: True
+  save_every: 1
+
+#Trainable Mask
+trainable_mask:
+  mask_type: AdafruitLCD #Null or "TrainablePSF" or "AdafruitLCD"
+  # "random" (with shape of config.files.psf) or path to npy file 
+  grayscale: False
+  mask_lr: 1e-3
+  L1_strength: False
+  min_val: 0
+
+  train_mask_vals: True
+  train_color_filter: True
+
+  # -- only for AdafruitLCD 
+  initial_value: data/psf/adafruit_random_pattern_20231107_150902.npy
+  ap_center: [57, 77]
+  ap_shape: [18, 26]
+  rotate: 0   # rotation in degrees
+  # to align with measured PSF (so reconstruction also aligned)
+  vertical_shift: 0 # [px]
+  horizontal_shift: 0  # [px]
+
+  slm: adafruit
+  sensor: rpi_hq
+  flipud: True
+  waveprop: True  # TODO: remove if too slow
+

configs/train_pre-post-processing.yaml

@@ -3,9 +3,6 @@ defaults:
   - train_unrolledADMM
   - _self_
 
-display:
-  disp: 400
-
 reconstruction:
   method: unrolled_admm