Small changes to scripts. (#103)

scripts/recon/train_unrolled.py

@@ -409,15 +409,16 @@ def train_unrolled(config):
         dataset.psf = dataset.psf.to(device)
         log.info(f"Data shape :  {dataset[0][0].shape}")
 
+        if config.files.n_files is not None:
+            dataset = Subset(dataset, np.arange(config.files.n_files))
+            dataset.psf = dataset.dataset.psf
+
         # train-test split
         train_size = int((1 - config.files.test_size) * len(dataset))
         test_size = len(dataset) - train_size
         train_set, test_set = torch.utils.data.random_split(
             dataset, [train_size, test_size], generator=generator
         )
-        if config.files.n_files is not None:
-            train_set = Subset(train_set, np.arange(config.files.n_files))
-            test_set = Subset(test_set, np.arange(config.files.n_files))
 
         # -- if learning mask
         downsample = config.files.downsample * 4  # measured files are 4x downsampled
@@ -470,7 +471,6 @@ def train_unrolled(config):
 
             for i, _idx in enumerate(config.test_idx):
 
-                # lensless, lensed = dataset[_idx]
                 lensless, lensed = test_set[_idx]
                 recon = ADMM(psf)
 

scripts/sim/digicam_psf.py

@@ -8,7 +8,6 @@
 from slm_controller import slm
 from lensless.utils.io import save_image, get_dtype, load_psf
 from lensless.utils.plot import plot_image
-from lensless.utils.image import gamma_correction
 from lensless.hardware.sensor import VirtualSensor
 from lensless.hardware.slm import get_programmable_mask, get_intensity_psf
 from waveprop.devices import slm_dict
@@ -41,6 +40,9 @@ def digicam_psf(config):
     Load pattern
     """
     pattern = np.load(fp)
+    # - make random pattern like original
+    # pattern = np.random.rand(*pattern.shape) * 255
+    # pattern = pattern.astype(np.uint8)
 
     # -- apply aperture
     aperture = np.zeros(pattern.shape, dtype=np.uint8)
@@ -58,6 +60,7 @@ def digicam_psf(config):
         idx_1 : idx_1 + ap_shape[0],
         idx_2 : idx_2 + ap_shape[1],
     ]
+
     print("Controllable region shape: ", pattern_sub.shape)
     print("Total number of pixels: ", np.prod(pattern_sub.shape))