partially converted to M2S (multi inputs)

experiments/configs/Stocks/96M2S.gin

@@ -0,0 +1,37 @@
+build.experiment_name = 'Stocks/96M2S'
+build.module = 'experiments.forecast'
+build.repeat = 1
+build.variables_dict = {
+}
+
+instance.model_type = 'deeptime3'
+instance.save_vals = False
+
+get_optimizer.lr = 1e-3
+get_optimizer.lambda_lr = 1.
+get_optimizer.weight_decay = 0.
+
+get_scheduler.warmup_epochs = 5
+
+get_data.batch_size = 256
+
+train.loss_name = 'mse'
+train.epochs = 50
+train.clip = 10.
+
+Checkpoint.patience = 7
+
+deeptime3.layer_size = 32
+deeptime3.inr_layers = 5
+deeptime3.n_fourier_feats = 4096
+deeptime3.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
+
+ForecastDataset.data_path = 'stocks/OXY_2019.csv.gz'
+ForecastDataset.target = 'RSMKs_18_144_72'
+ForecastDataset.scale = True
+ForecastDataset.cross_learn = False
+ForecastDataset.time_features = []
+ForecastDataset.normalise_time_features = True
+ForecastDataset.features = 'M2S'
+ForecastDataset.horizon_len = 96
+ForecastDataset.lookback_mult = 1

experiments/forecast.py

@@ -139,16 +139,16 @@ def train(model: nn.Module,
         model.train()
         for it, data in enumerate(train_loader):
             optimizer.zero_grad()
-
-            x, y, x_time, y_time = map(to_tensor, data)
-            forecast = model(x, x_time, y_time)
+            data2 = list(map(to_tensor, data))
+            context_past_x, context_y, query_past_x, query_y, context_time, query_time = data2
+            forecast = model(*data2)
 
             if isinstance(forecast, tuple):
                 # for models which require reconstruction + forecast loss
-                loss = training_loss_fn(forecast[0], x) + \
-                       training_loss_fn(forecast[1], y)
+                loss = training_loss_fn(forecast[0], context_y) + \
+                       training_loss_fn(forecast[1], query_y)
             else:
-                loss = training_loss_fn(forecast, y)
+                loss = training_loss_fn(forecast, query_y)
             loss.backward()
             nn.utils.clip_grad_norm_(model.parameters(), clip)
             optimizer.step()
@@ -188,22 +188,23 @@ def validate(model: nn.Module,
     inps = []
     total_loss = []
     for it, data in enumerate(loader):
-        x, y, x_time, y_time = map(to_tensor, data)
+        data2 = list(map(to_tensor, data))
+        context_past_x, context_y, query_past_x, query_y, context_time, query_time  = data2
 
-        if x.shape[0] == 1:
+        if context_past_x.shape[0] == 1:
             # skip final batch if batch_size == 1
             # due to bug in torch.linalg.solve which raises error when batch_size == 1
             continue
 
-        forecast = model(x, x_time, y_time)
+        forecast = model(*data2)
 
         if report_metrics:
             preds.append(forecast)
-            trues.append(y)
+            trues.append(query_y)
             if save_path is not None:
-                inps.append(x)
+                inps.append(context_y)
         else:
-            loss = loss_fn(forecast, y, reduction='none')
+            loss = loss_fn(forecast, query_y, reduction='none')
             total_loss.append(loss)
 
     if report_metrics: