fix staggered training

main.py

@@ -201,6 +201,7 @@ def main_train(param=None):
                       loss_weights={"client": 0.0, "server": 1.0},
                       metrics=['accuracy'] + custom_metrics)
 
+        model.summary()
         model.fit(features, labels,
                   batch_size=args.batch_size,
                   epochs=args.epochs,
@@ -208,6 +209,7 @@ def main_train(param=None):
 
         logger.info("fix server model")
         model.get_layer("domain_cnn").trainable = False
+        model.get_layer("domain_cnn").layer.trainable = False
         model.get_layer("dense_server").trainable = False
         model.get_layer("server").trainable = False
         loss_weights = {"client": 1.0, "server": 0.0}
@@ -649,6 +651,33 @@ def train_server_only():
               callbacks=callbacks)
 
 
+def test_server_only():
+    logger.info("start test: load data")
+    domain_val, flow_val, _, _, _, _ = dataset.load_or_generate_raw_h5data(args.data,
+                                                                           args.data,
+                                                                           args.domain_length,
+                                                                           args.window)
+    domain_val = domain_val.value.reshape(-1, 40)
+    flow_val = flow_val.value.reshape(-1, 3)
+    domain_encs, _ = dataset.load_or_generate_domains(args.data, args.domain_length)
+    
+    for model_args in get_model_args(args):
+        results = {}
+        logger.info(f"process model {model_args['model_path']}")
+        embd_model, clf_model = load_model(model_args["clf_model"], custom_objects=models.get_custom_objects())
+        
+        pred = clf_model.predict([domain_val, flow_val],
+                                 batch_size=args.batch_size,
+                                 verbose=1)
+        
+        results["server_pred"] = pred
+        
+        domain_embeddings = embd_model.predict(domain_encs, batch_size=args.batch_size, verbose=1)
+        results["domain_embds"] = domain_embeddings
+        
+        dataset.save_predictions(model_args["model_path"], results)
+
+
 def main():
     if "train" == args.mode:
         main_train()
@@ -668,6 +697,8 @@ def main():
         plot_overall_result()
     if "server" == args.mode:
         train_server_only()
+    if "server_test" == args.mode:
+        test_server_only()
 
 
 if __name__ == "__main__":

utils.py

@@ -37,6 +37,10 @@ def load_model(path, custom_objects=None):
     except Exception:
         # in some version i forgot to specify domain_cnn
         # this bug fix is for certain compatibility
+        try:
             embd = clf.layers[1].layer
+        except Exception:
+            embd = clf.get_layer("domain_cnn")
+        
     
     return embd, clf