wavelet and fft fix

eval/step_configs/config_celeba_wavelet.ini

@@ -34,5 +34,5 @@ sharing_class = Wavelet
 change_based_selection = True
 alpha = 0.1
 wavelet=sym2
-level= None
+level= 4
 accumulation = True

eval/step_configs/config_femnist.ini

@@ -31,3 +31,4 @@ addresses_filepath = ip_addr_6Machines.json
 [SHARING]
 sharing_package = decentralizepy.sharing.PartialModel
 sharing_class = PartialModel
+alpha=0.1

eval/step_configs/config_femnist_wavelet.ini

@@ -35,5 +35,5 @@ sharing_class = Wavelet
 change_based_selection = True
 alpha = 0.1
 wavelet=sym2
-level= None
+level= 4
 accumulation = True

src/decentralizepy/sharing/FFT.py

@@ -224,8 +224,11 @@ class FFT(PartialModel):
         with torch.no_grad():
             total = None
             weight_total = 0
-
-            flat_fft = self.change_transformer(self.init_model)
+            tensors_to_cat = [
+                v.data.flatten() for _, v in self.model.state_dict().items()
+            ]
+            pre_share_model = torch.cat(tensors_to_cat, dim=0)
+            flat_fft = self.change_transformer(pre_share_model)
 
             for i, n in enumerate(self.peer_deques):
                 degree, iteration, data = self.peer_deques[n].popleft()

src/decentralizepy/sharing/PartialModel.py

@@ -155,7 +155,7 @@ class PartialModel(Sharing):
             Model converted to a dict
 
         """
-        if self.alpha > self.metadata_cap:  # Share fully
+        if self.alpha >= self.metadata_cap:  # Share fully
             return super().serialized_model()
 
         with torch.no_grad():

src/decentralizepy/sharing/Wavelet.py

@@ -257,7 +257,11 @@ class Wavelet(PartialModel):
         with torch.no_grad():
             total = None
             weight_total = 0
-            wt_params = self.change_transformer(self.init_model)
+            tensors_to_cat = [
+                v.data.flatten() for _, v in self.model.state_dict().items()
+            ]
+            pre_share_model = torch.cat(tensors_to_cat, dim=0)
+            wt_params = self.change_transformer(pre_share_model)
             for i, n in enumerate(self.peer_deques):
                 degree, iteration, data = self.peer_deques[n].popleft()
                 logging.debug(