When using the Inverted Transformer takes multiple feature columns but has only one target it results in an error in the decoding process. ERROR Run oy5tkp61 errored: RuntimeError('The expanded size of the tensor (1) must match the existing size (4) at non-singleton dimension 2. Target sizes: [1, 6, 1]. Tensor sizes [6, 4]).
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RuntimeError Traceback (most recent call last)
Cell In[5], line 4
2 from flood_forecast.trainer import train_function
3 config = make_config_file("", "", "")
----> 4 final_model = train_function("PyTorch", config)
File /kaggle/working/flow-forecast/flood_forecast/trainer.py:172, in train_function(model_type, params)
170 # TODO Move to other func
171 if params["dataset_params"]["class"] != "GeneralClassificationLoader" and params["dataset_params"]["class"] !="VariableSequenceLength":
--> 172 handle_core_eval(trained_model, params, model_type)
174 else:
175 raise Exception("Please supply valid model type for forecasting or classification")
File /kaggle/working/flow-forecast/flood_forecast/trainer.py:84, in handle_core_eval(trained_model, params, model_type)
74 def handle_core_eval(trained_model, params: Dict, model_type: str):
75 """_summary_
76
77 :param trained_model: _description_
(...)
82 :type model_type: str
83 """
---> 84 test_acc = evaluate_model(
85 trained_model,
86 model_type,
87 params["dataset_params"]["target_col"],
88 params["metrics"],
89 params["inference_params"],
90 {})
91 if params["dataset_params"]["class"] == "SeriesIDLoader":
92 data = test_acc[1]
File /kaggle/working/flow-forecast/flood_forecast/evaluator.py:112, in evaluate_model(model, model_type, target_col, evaluation_metrics, inference_params, eval_log)
87 """
88 A function to evaluate a model. Called automatically at end of training.
89 Can be imported for continuing to evaluate a model in other places as well.
(...)
101 '''
102 """
103 if model_type == "PyTorch":
104 (
105 df_train_and_test,
106 end_tensor,
107 forecast_history,
108 forecast_start_idx,
109 test_data,
110 df_predictions,
111 # df_prediction_samples_std_dev,
--> 112 ) = infer_on_torch_model(model, **inference_params)
113 if model.params["dataset_params"]["class"] == "SeriesIDLoader":
114 print(end_tensor[0].shape)
File /kaggle/working/flow-forecast/flood_forecast/evaluator.py:320, in infer_on_torch_model(model, test_csv_path, datetime_start, hours_to_forecast, decoder_params, dataset_params, num_prediction_samples, probabilistic, criterion_params)
314 else:
315 (
316 history,
317 df_train_and_test,
318 forecast_start_idx,
319 ) = csv_test_loader.get_from_start_date(datetime_start)
--> 320 end_tensor = generate_predictions(
321 model,
322 df_train_and_test,
323 csv_test_loader,
324 history,
325 device,
326 forecast_start_idx,
327 forecast_length,
328 hours_to_forecast,
329 decoder_params,
330 multi_params=multi_params,
331 targs=targ
332 )
333 return handle_later_ev(model, df_train_and_test, end_tensor, model.params, csv_test_loader, multi_params,
334 forecast_start_idx, history, datetime_start)
File /kaggle/working/flow-forecast/flood_forecast/evaluator.py:541, in generate_predictions(model, df, test_data, history, device, forecast_start_idx, forecast_length, hours_to_forecast, decoder_params, targs, multi_params)
531 end_tensor = generate_predictions_non_decoded(
532 model, df, test_data, history_dim, forecast_length, hours_to_forecast,
533 )
534 else:
535 # model, src, max_seq_len, real_target, output_len=1, unsqueeze_dim=1
536 # hours_to_forecast 336
(...)
539 # real_target:torch.Tensor, start_symbol:torch.Tensor
540 # unsqueeze_dim=1, device='cpu')
--> 541 end_tensor = generate_decoded_predictions(
542 model,
543 test_data,
544 forecast_start_idx,
545 device,
546 history_dim,
547 hours_to_forecast,
548 decoder_params,
549 multi_targets=multi_params,
550 targs=targs
551 )
552 return end_tensor
File /kaggle/working/flow-forecast/flood_forecast/evaluator.py:656, in generate_decoded_predictions(model, test_data, forecast_start_idx, device, history_dim, hours_to_forecast, decoder_params, multi_targets, targs)
654 else:
655 decoder_seq_len = model.params["model_params"]["label_len"]
--> 656 end_tensor = decoding_function(model.model, src0, trg[1], model.params["dataset_params"]["forecast_length"],
657 src[1], trg[0], 1, decoder_seq_len, hours_to_forecast, device)
658 else:
659 end_tensor = decoding_functions[decoder_params["decoder_function"]](
660 model.model,
661 history_dim,
(...)
669 scaler=scaler
670 )
File /kaggle/working/flow-forecast/flood_forecast/temporal_decoding.py:63, in decoding_function(model, src, trg, forecast_length, src_temp, tar_temp, unknown_cols_st, decoder_seq_len, max_len, device)
61 out = model(src, src_temp, filled_target, tar_temp[:, i:i + residual, :])
62 residual1 = forecast_length if i + forecast_length <= max_len else max_len % forecast_length
---> 63 out1[:, i: i + residual1, :n_target] = out[:, -residual1:, :]
64 # Need better variable names
65 filled_target1 = torch.zeros_like(filled_target[:, 0:forecast_length * 2, :])
RuntimeError: The expanded size of the tensor (1) must match the existing size (4) at non-singleton dimension 2. Target sizes: [1, 96, 1]. Tensor sizes: [96, 4]```
When using the Inverted Transformer takes multiple feature columns but has only one target it results in an error in the decoding process.
ERROR Run oy5tkp61 errored: RuntimeError('The expanded size of the tensor (1) must match the existing size (4) at non-singleton dimension 2. Target sizes: [1, 6, 1]. Tensor sizes [6, 4]).