Support for dataset with measured background

CHANGELOG.rst

@@ -13,7 +13,6 @@ Unreleased
 Added
 ~~~~~
 
-- Option to pass background image to ``utils.io.load_data``.
 - Option to set image resolution with ``hardware.utils.display`` function.
 - Add utility for mask adapter generation in ``lenseless.hardware.fabrication``
 - Option to add simulated background in ``util.dataset``
@@ -28,11 +27,19 @@ Added
 - HFSimulated object for simulating lensless data from ground-truth and PSF.
 - Option to set cache directory for Hugging Face datasets.
 - Option to initialize training with another model.
+- Option to pass background image to ``utils.io.load_data``.
+- Option to use background in ``lensless.eval.benchmark``. 
+- Different techniques to use measured background: direct subtraction, learned subtraction, integrated subtraction, concatenated to input.
+- Learnable background subtraction for classes that derive from ``lensless.recon.trainable_recon.TrainableReconstructionAlgorithm``.
+- Integrated background subtraction object ``lensless.recon.integrated_background.IntegratedBackgroundSub``.
+- Option to concatenate background to input to pre-processor.
+- Add support for datasets with measured background to ``lensless.utils.dataset.HFDataset``.
+
 
 Changed
 ~~~~~~~
 
-- Nothing
+- ``lensless.utils.dataset.HFDataset`` no longer inherits from ``lensless.utils.dataset.DualDataset``.
 
 Bugfix
 ~~~~~~

configs/benchmark.yaml

@@ -26,6 +26,7 @@ huggingface:
   downsample_lensed: 1
   split_seed: null
   single_channel_psf: False
+  use_background: True
 
 device: "cuda"
 # numbers of iterations to benchmark

configs/benchmark_multilens_mirflickr_ambient.yaml

@@ -0,0 +1,45 @@
+# python scripts/eval/benchmark_recon.py -cn benchmark_multilens_mirflickr_ambient
+defaults:
+  - benchmark
+  - _self_
+
+dataset: HFDataset
+batchsize: 8
+device: "cuda:0"
+
+huggingface:
+  repo: Lensless/MultiLens-Mirflickr-Ambient
+  cache_dir: /dev/shm
+  psf: psf.png
+  image_res: [600, 600]  # used during measurement
+  rotate: False   # if measurement is upside-down
+  alignment:
+    top_left: [118, 220]  # height, width
+    height: 123
+  use_background: True
+
+## -- reconstructions trained with same dataset/system
+algorithms: [
+  "ADMM",
+  "hf:multilens:mirflickr_ambient:U5+Unet8M",
+  "hf:multilens:mirflickr_ambient:U5+Unet8M_direct_sub",
+  "hf:multilens:mirflickr_ambient:U5+Unet8M_learned_sub",
+  "hf:multilens:mirflickr_ambient:Unet4M+U5+Unet4M",
+  "hf:multilens:mirflickr_ambient:Unet4M+U5+Unet4M_direct_sub",
+  "hf:multilens:mirflickr_ambient:Unet4M+U5+Unet4M_learned_sub",
+  "hf:multilens:mirflickr_ambient:Unet4M+U5+Unet4M_concat",
+  "hf:multilens:mirflickr_ambient:TrainInv+Unet8M",
+  "hf:multilens:mirflickr_ambient:TrainInv+Unet8M_learned_sub",
+  "hf:multilens:mirflickr_ambient:Unet4M+TrainInv+Unet4M",
+  "hf:multilens:mirflickr_ambient:Unet4M+TrainInv+Unet4M_learned_sub",
+  "hf:multilens:mirflickr_ambient:Unet4M+TrainInv+Unet4M_concat",
+  "hf:multilens:mirflickr_ambient:TrainInv+Unet8M_direct_sub",
+  "hf:multilens:mirflickr_ambient:Unet4M+TrainInv+Unet4M_direct_sub",
+]
+
+save_idx: [
+  1, 2, 4, 5, 9, 64, # bottom right
+  2141, 2155, 2162, 2225, 2502, 2602,   # top right (door, flower, cookies, wolf, plush, sky)
+  3262, 3304, 3438, 3451, 3644, 3667    # bottom left (pancakes, flower, grapes, pencils, bird, sign)
+]
+n_iter_range: [100]    # for ADMM

configs/recon_multilens_ambient_mirflickr.yaml

@@ -0,0 +1,51 @@
+# python scripts/recon/multilens_ambient_mirflickr.py
+defaults:
+  - defaults_recon
+  - _self_
+
+cache_dir: /dev/shm
+
+## - Uncomment to reconstruct from dataset (screen capture)
+idx: 1    # index from test set to reconstruct
+fn: null  # if not null, set local path or download this file from https://huggingface.co/datasets/Lensless/MultiLens-Mirflickr-Ambient/tree/main
+background_fn: null    
+
+## - Uncomment to reconstruct plush parrot (direct capture)
+# fn: parrot_raw.png 
+# background_fn: parrot_background.png
+# rotate: False
+# alignment:
+#   dim: [160, 160]
+#   top_left: [110, 200]
+
+## - Uncomment to reconstruct plush monkey (direct capture)
+# fn: monkey_raw.png
+# background_fn: monkey_background.png
+# rotate: False
+# alignment:
+#   dim: [123, 123]
+#   top_left: [118, 220]
+
+## - Uncomment to reconstruct plant (direct capture)
+# fn: plant_raw.png     
+# background_fn: plant_background.png 
+# rotate: False
+# alignment:
+#   dim: [200, 200]
+#   top_left: [60, 186]
+
+## Reconstruction
+background_sub: True  # whether to subtract background
+
+# -- for learning-based methods (uncommment one line)
+model: Unet4M+U5+Unet4M_concat
+# model: U5+Unet8M
+# model: Unet4M+U5+Unet4M_learned_sub
+
+# # -- for ADMM with fixed parameters (uncomment and comment learning-based methods)
+# model: admm
+# n_iter: 100
+
+device: cuda:0
+n_trials: 1   # to get average inference time
+save: True

configs/train_mirflickr_multilens_ambient.yaml

@@ -0,0 +1,24 @@
+# python scripts/recon/train_learning_based.py -cn train_mirflickr_multilens_ambient
+defaults:
+  - train_mirflickr_tape
+  - _self_
+
+wandb_project: multilens_ambient
+
+# Dataset
+files:
+  dataset: Lensless/MultiLens-Mirflickr-Ambient
+  cache_dir: /dev/shm
+  image_res: [600, 600]
+
+reconstruction:
+  direct_background_subtraction: True
+
+alignment:
+  # when there is no downsampling
+  top_left: [118, 220]  # height, width
+  height: 123
+
+optimizer:
+  type: AdamW
+  cosine_decay_warmup: True

configs/train_mirflickr_tape_ambient.yaml

@@ -0,0 +1,24 @@
+# python scripts/recon/train_learning_based.py -cn train_mirflickr_tape_ambient
+defaults:
+  - train_mirflickr_tape
+  - _self_
+
+wandb_project: tapecam_ambient
+device_ids:
+
+# Dataset
+files:
+  dataset: Lensless/TapeCam-Mirflickr-Ambient
+  image_res: [600, 600]
+
+reconstruction:
+  direct_background_subtraction: True
+
+alignment:
+  # when there is no downsampling
+  top_left: [85, 185]  # height, width
+  height: 178
+
+optimizer:
+  type: AdamW
+  cosine_decay_warmup: True

configs/train_mirflickr_tape_ambient_integrated_sub.yaml

@@ -0,0 +1,33 @@
+# python scripts/recon/train_learning_based.py -cn train_mirflickr_tape_ambient_integrated_sub
+defaults:
+  - train_mirflickr_tape
+  - _self_
+
+wandb_project: tapecam_ambient
+device_ids: [0, 1, 2, 3]
+torch_device: cuda:0
+
+# Dataset
+files:
+  dataset: Lensless/TapeCam-Mirflickr-Ambient   # 16K examples
+  cache_dir: /dev/shm
+  #dataset: Lensless/TapeCam-Mirflickr-Ambient-100   # 100 examples
+  image_res: [600, 600]
+
+reconstruction:
+  # one or the other
+  direct_background_subtraction: False
+  learned_background_subtraction: False
+  integrated_background_subtraction: [32, 64, 128, 210, 210]
+  down_subtraction: False
+  pre_process: 
+    network: null  # TODO assert null when integrated_background_subtraction is not False
+
+alignment:
+  # when there is no downsampling
+  top_left: [85, 185]  # height, width
+  height: 178
+
+optimizer:
+  type: AdamW
+  cosine_decay_warmup: True

configs/train_mirflickr_tape_ambient_learned_sub.yaml

@@ -0,0 +1,36 @@
+# python scripts/recon/train_learning_based.py -cn train_mirflickr_tape_ambient_learned_sub
+defaults:
+  - train_mirflickr_tape
+  - _self_
+
+wandb_project: tapecam_ambient
+device_ids: [0, 1 ,2, 3]
+torch_device: cuda:0
+
+# Dataset
+files:
+  #n_files: 10
+  dataset: Lensless/TapeCam-Mirflickr-Ambient   # 16K examples
+  #dataset: Lensless/TapeCam-Mirflickr-Ambient-100   # 100 examples
+  cache_dir: /dev/shm
+  image_res: [600, 600]
+
+reconstruction:
+  # one or the other
+  direct_background_subtraction: False
+  learned_background_subtraction: [4, 8, 16, 32]   # 127740 parameters, False to turn off
+  integrated_background_subtraction: False
+
+  pre_process:   ## Targeting 3923428 parameters
+    network : UnetRes  # UnetRes or DruNet or null
+    depth : 4 # depth of each up/downsampling layer. Ignore if network is DruNet
+    nc: [32,64,112,128]
+
+alignment:
+  # when there is no downsampling
+  top_left: [85, 185]  # height, width
+  height: 178
+
+optimizer:
+  type: AdamW
+  cosine_decay_warmup: True

configs/train_tapecam_simulated_background.yaml

@@ -1,4 +1,4 @@
-# python scripts/recon/train_learning_based.py -cn train_mirflickr_tape
+# python scripts/recon/train_learning_based.py -cn train_tapecam_simulated_background
 defaults:
   - train_mirflickr_tape
   - _self_

configs/train_unrolledADMM.yaml

@@ -90,6 +90,15 @@ reconstruction:
   init_pre: True   # if `init_processors`, set pre-procesor is available
   init_post: True   # if `init_processors`, set post-procesor is available
 
+  # background subtraction (if dataset has corresponding background images)
+  direct_background_subtraction: False    # True or False
+  learned_background_subtraction: False   # False, or set number of channels for UnetRes, e.g. [8,16,32,64]
+  integrated_background_subtraction: False   # False, or set number of channels for UnetRes, e.g. [8,16,32,64]
+  down_subtraction: False  # for integrated_background_subtraction, whether to concatenate background subtraction during downsample or upsample
+  integrated_background_unetres: False   # whether to integrate within UNetRes
+  unetres_input_background: False   # whether to input background to UNetRes
+
+
   # Hyperparameters for each method
   unrolled_fista: # for unrolled_fista
     # Number of iterations
@@ -181,18 +190,17 @@ training:
   crop_preloss: False  # crop region for computing loss, files.crop should be set
 
 optimizer:
-  type: Adam  # Adam, SGD... (Pytorch class)
+  type: AdamW  # Adam, SGD... (Pytorch class)
   lr: 1e-4
   lr_step_epoch: True   # True -> update LR at end of each epoch, False at the end of each mini-batch
   final_lr: False   # if set, exponentially decay *to* this value
   exp_decay: False  # if set, exponentially decay *with* this value
   slow_start: False  #float how much to reduce lr for first epoch
-  cosine_decay_warmup: False  # if set, cosine decay with warmup of 5%
+  cosine_decay_warmup: True  # if set, cosine decay with warmup of 5%
   # Decay LR in step fashion: https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.StepLR.html
   step: False     # int, period of learning rate decay. False to not apply
   gamma: 0.1      # float, factor for learning rate decay 
 
-
 loss: 'l2'
 # set lpips to false to deactivate. Otherwise, give the weigth for the loss (the main loss l2/l1 always having a weigth of 1)
 lpips: 1.0

configs/upload_multilens_mirflickr_ambient.yaml

@@ -6,13 +6,17 @@ defaults:
 repo_id: "Lensless/MultiLens-Mirflickr-Ambient"
 n_files:
 test_size: 0.15
+
+# # -- to match TapeCam dataset content distribution, and same light distribution in train/test
+# split: 100   # "first: first `nfiles*test_size` for test, `int`: test_size*split for test (interleaved) as if multimask with this many masks
+
 lensless:
   dir: /dev/shm/all_measured_20240813-183259
   ambient: True
   ext: ".png"
 
 lensed:
-  dir: data/mirflickr/mirflickr
+  dir: /root/LenslessPiCam/data/mirflickr/mirflickr
   ext: ".jpg"
 
 files:

lensless/eval/benchmark.py

@@ -42,6 +42,7 @@ def benchmark(
     use_wandb=False,
     label=None,
     epoch=None,
+    use_background=True,
     **kwargs,
 ):
     """
@@ -69,6 +70,8 @@ def benchmark(
         If True, return the average value of the metrics, by default True.
     snr : float, optional
         Signal to noise ratio for adding shot noise. If None, no noise is added, by default None.
+    use_background: bool, optional
+        If dataset has background, use it for reconstruction, by default True.
 
     Returns
     -------
@@ -121,8 +124,11 @@ def benchmark(
 
             flip_lr = None
             flip_ud = None
+            background = None
             lensless = batch[0].to(device)
             lensed = batch[1].to(device)
+            if dataset.measured_bg and use_background:
+                background = batch[-1].to(device)
             if dataset.multimask or dataset.random_flip:
                 psfs = batch[2]
                 psfs = psfs.to(device)
@@ -146,11 +152,12 @@ def benchmark(
                     plot=False,
                     save=False,
                     output_intermediate=unrolled_output_factor or pre_process_aux,
+                    background=background,
                     **kwargs,
                 )
 
             else:
-                prediction = model.forward(lensless, psfs, **kwargs)
+                prediction = model.forward(lensless, psfs, background=background, **kwargs)
 
             if unrolled_output_factor or pre_process_aux:
                 pre_process_out = prediction[2]