more accurate sampling

optimization_modules.py

@@ -24,8 +24,8 @@ def __init__(self, model_name):
 
     def on_validation_start(self,stage=None):
         print("---VALIDATION START---")
-        #self.config = "simca/configs/cassi_system_optim_optics_full_triplet_sd_cassi.yml"
-        self.config = "simca/configs/cassi_system_optim_optics_full_triplet_sd_cassi_shifted.yml"
+        self.config = "simca/configs/cassi_system_optim_optics_full_triplet_sd_cassi.yml"
+        #self.config = "simca/configs/cassi_system_optim_optics_full_triplet_sd_cassi_shifted.yml"
         config_system = load_yaml_config(self.config)
         self.config_patterns = load_yaml_config("simca/configs/pattern.yml")
         self.cassi_system = CassiSystemOptim(system_config=config_system)
@@ -46,7 +46,9 @@ def forward(self, x):
         # generate first acquisition with simca
 
         acquired_image1 = self.cassi_system.image_acquisition(hyperspectral_cube, pattern, wavelengths)
-        filtering_cubes = self.cassi_system.filtering_cube.permute(0, 3, 1, 2)[:,:28,:,:].float().to(self.device)
+        filtering_cubes = subsample(self.cassi_system.filtering_cube, np.linspace(450, 650, self.cassi_system.filtering_cube.shape[-1]), np.linspace(450, 650, 28))
+        filtering_cubes = filtering_cubes.permute(0, 3, 1, 2).float().to(self.device)
+        filtering_cubes = torch.flip(filtering_cubes, dims=(2,)) # -1 magnification
         displacement_in_pix = self.cassi_system.get_displacement_in_pixels(dataset_wavelengths=wavelengths)
         #print("displacement_in_pix", displacement_in_pix)
 
@@ -141,6 +143,15 @@ def configure_optimizers(self):
         optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
         return optimizer
 
+def subsample(input, origin_sampling, target_sampling):
+    [bs, row, col, nC] = input.shape
+    output = torch.zeros(bs, row, col, len(target_sampling))
+    for i in range(len(target_sampling)):
+        sample = target_sampling[i]
+        idx = np.abs(origin_sampling-sample).argmin()
+        output[:,:,:,i] = input[:,:,:,idx]
+    return output
+
 def expand_mask_3d(mask_batch):
     if len(mask_batch.shape)==3:
         mask3d = mask_batch.unsqueeze(-1).repeat((1, 1, 1, 28))