other changes

src/policies/policy_executer.py

@@ -0,0 +1,53 @@
+import argparse
+from clearml import Task, Model
+from src.data import DataProcessor, DataConfig
+import torch
+
+# argparse to parse task id and model type
+parser = argparse.ArgumentParser()
+parser.add_argument('--task_id', type=int, default=None)
+parser.add_argument('--model_type', type=str, default=None)
+args = parser.parse_args()
+
+assert args.task_id is not None, "Please specify task id"
+assert args.model_type is not None, "Please specify model type"
+
+def load_model(task_id: str):
+    """
+    Load model from task id
+    """
+    task = Task.get_task(task_id=task_id)
+
+    configuration = task.get_parameters_as_dict()
+    data_features = configuration['data_features']
+
+    ### Data Config ###
+    data_config = DataConfig()
+    for key, value in data_features.items():
+        setattr(data_config, key, bool(value))
+    data_config.PV_FORECAST = False
+    data_config.PV_HISTORY = False
+    data_config.QUARTER = False
+    data_config.DAY_OF_WEEK = False
+
+    ### Data Processor ###
+    data_processor = DataProcessor(data_config, path="../../", lstm=False)
+    data_processor.set_batch_size(8192)
+    data_processor.set_full_day_skip(True)
+
+    ### Model ###
+    output_model_id = task.output_models_id["checkpoint"]
+    clearml_model = Model(model_id=output_model_id)
+    filename = clearml_model.get_weights()
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = torch.load(filename)
+
+    model.to(device)
+    model.eval()
+
+    _, test_loader = data_processor.get_dataloaders(
+        predict_sequence_length=96
+    )
+
+    return configuration, model, test_loader