Add full training steps retraining

run/modeling.py

@@ -151,5 +151,58 @@ def run_vnn_from_classical_2(id=0, gpu_capacity=1, total_devices=4, raise_on_inf
         i += gpu_capacity * total_devices
 
 
+def run_vnn_from_classical_2_full_training_steps(id=0, gpu_capacity=1, total_devices=4, raise_on_infer_error=True):
+
+    device_id = id % total_devices
+    i = id
+
+    training_samples = [2, 4]
+    eval_samples_list = [1, 2, 3, 4]
+
+    def create_models():
+        result = []
+        for init_vnn_name, init_vnn_weights in [
+        ("fill0001", "fill:stds:0.001:0.001"),
+        ("fill00001", "fill:stds:0.0001:0.0001"),
+        ("xavier_uniform001", "xavier_uniform:stds:0.01:0.01"),
+        ("xavier_normal001", "xavier_normal:stds:0.01:0.01"),
+        ]:
+            for single_training_samples in training_samples:
+                name = f"vnnclass1-2{init_vnn_name}_tanet_car"
+                result.append(
+                        Model(
+                            name,
+                            samples_list=[single_training_samples],
+                            eval_samples_list=eval_samples_list,
+                            init_vnn_weights=init_vnn_weights,
+                            classical_name="tanet_car_2",
+                            config="vnn_xyres_16.proto",
+                        ),
+                )
+
+        return result
+
+    models = create_models()
+
+    while i < len(models):
+        model = models[i]
+
+        try:
+            result = model.train(
+                device="cuda:" + str(device_id)
+            )
+            print(result)
+
+        except Exception as e:
+            if raise_on_infer_error:
+                raise e
+            else:
+                print(e)
+
+        i += gpu_capacity * total_devices
+
+
+
+
 if __name__ == "__main__":
     fire.Fire()