Fix class balancing weights not being taken into account (#15)

Co-authored-by: Ayush Pandey <[email protected]>

main.py

@@ -275,11 +275,11 @@ def convert_from_color(x):
         prediction = clf.predict(img.reshape(-1, N_BANDS))
         prediction = prediction.reshape(img.shape[:2])
     else:
-        # Neural network
-        model, optimizer, loss, hyperparams = get_model(MODEL, **hyperparams)
         if CLASS_BALANCING:
             weights = compute_imf_weights(train_gt, N_CLASSES, IGNORED_LABELS)
             hyperparams['weights'] = torch.from_numpy(weights)
+        # Neural network
+        model, optimizer, loss, hyperparams = get_model(MODEL, **hyperparams)
         # Split train set in train/val
         train_gt, val_gt = sample_gt(train_gt, 0.95, mode='random')
         # Generate the dataset