Chronos: set kernel of Autoformer to odd number (intel-analytics#5534)

* set kernel to odd
ForJadeForest · Sep 20, 2022 · e4aa294 · e4aa294
1 parent f17f3dd
commit e4aa294
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Showing 2 changed files with 20 additions and 3 deletions.
diff --git a/python/chronos/src/bigdl/chronos/model/autoformer/Autoformer.py b/python/chronos/src/bigdl/chronos/model/autoformer/Autoformer.py
@@ -72,7 +72,8 @@ def __init__(self, configs):
         self.loss = loss_creator(configs.loss)
 
         # Decomp
-        kernel_size = configs.moving_avg
+        # change kernei_size to odd
+        kernel_size = int(2 * (configs.moving_avg // 2)) + 1
         self.decomp = series_decomp(kernel_size)
 
         # Embedding
@@ -93,7 +94,7 @@ def __init__(self, configs):
                         configs.d_model, configs.n_heads),
                     configs.d_model,
                     configs.d_ff,
-                    moving_avg=configs.moving_avg,
+                    moving_avg=kernel_size,
                     dropout=configs.dropout,
                     activation=configs.activation
                 ) for l in range(configs.e_layers)
@@ -115,7 +116,7 @@ def __init__(self, configs):
                     configs.d_model,
                     configs.c_out,
                     configs.d_ff,
-                    moving_avg=configs.moving_avg,
+                    moving_avg=kernel_size,
                     dropout=configs.dropout,
                     activation=configs.activation,
                 )

diff --git a/python/chronos/test/bigdl/chronos/forecaster/test_autoformer_forecaster.py b/python/chronos/test/bigdl/chronos/forecaster/test_autoformer_forecaster.py
@@ -224,3 +224,19 @@ def test_autoformer_forecaster_tune_save_load(self):
                 forecaster.load(ckpt_name)
                 evaluate2 = forecaster.evaluate(val_data)
             assert evaluate1[0]['val/loss'] == evaluate2[0]['val_loss']
+
+    def test_autoformer_forecaster_even_kernel(self):
+        train_loader, val_loader, test_loader = create_data(loader=True)
+        evaluate_list = []
+        forecaster = AutoformerForecaster(past_seq_len=24,
+                                            future_seq_len=5,
+                                            input_feature_num=2,
+                                            output_feature_num=2,
+                                            label_len=12,
+                                            freq='s',
+                                            seed=0,
+                                            moving_avg=20) # even
+        forecaster.fit(train_loader, epochs=3, batch_size=32)
+        evaluate = forecaster.evaluate(val_loader)
+        pred = forecaster.predict(test_loader)
+        evaluate_list.append(evaluate)