early draft of the training without resnet

mask_optim_recon.py

@@ -3,6 +3,7 @@
 from simca.CassiSystem import CassiSystem
 from data_handler import CubesDataModule
 from MST.simulation.train_code.architecture import *
+from MST.simulation.train_code.utils import *
 import numpy as np
 import snoop
 import matplotlib.pyplot as plt
@@ -41,6 +42,12 @@
 optimizer = torch.optim.Adam(model.parameters(), lr=lr, betas=(0.9, 0.999))
 mse = torch.nn.MSELoss()
 
+def expand_mask_3d(mask):
+    mask3d = np.tile(mask[:, :, np.newaxis], (1, 1, 28))
+    mask3d = np.transpose(mask3d, [2, 0, 1])
+    mask3d = torch.from_numpy(mask3d)
+    return mask3d
+
 def train(model_name):
 
     optimizer.zero_grad()
@@ -50,10 +57,13 @@ def train(model_name):
     cassi_system.pattern = input_mask
     cassi_system.generate_filtering_cube()
 
-    input_acq = cassi_system.image_acquisition(use_psf=False)
+    input_acq = cassi_system.image_acquisition(use_psf=False) # H x (W + d*(28-1))
+    d=2
+    input_acq = shift_back(input_acq, step=d)
 
     model.train()
-    output = model(input_acq, input_mask)
+    input_mask_3d = expand_mask_3d(input_mask) #TODO, like in train_method/utils.py
+    output = model(input_acq, input_mask_3d)
     loss = torch.sqrt(mse(output, cassi_system.dataset))
 
     loss.backward()
@@ -66,7 +76,8 @@ def train(model_name):
     # cassi_system.dataset = datamodule.test_dataloader[i][0]
     # cassi_system.wavelengths = datamodule.test_dataloader[i][1]
     input_acq = cassi_system.image_acquisition(use_psf=False)
+    input_acq = shift_back(input_acq, step=d)
 
-    output = model(input_acq, input_mask)
+    output = model(input_acq, input_mask_3d)
     loss = torch.sqrt(mse(output, cassi_system.dataset))
     model.train()