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pvnet/models/base_model.py

@@ -10,7 +10,6 @@
 import pandas as pd
 import torch
 import torch.nn.functional as F
-import wandb
 import yaml
 from huggingface_hub import ModelCard, ModelCardData, PyTorchModelHubMixin
 from huggingface_hub.constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME
@@ -27,7 +26,7 @@
     WeightedLosses,
 )
 from pvnet.optimizers import AbstractOptimizer
-from pvnet.utils import construct_ocf_ml_metrics_batch_df, plot_batch_forecasts
+from pvnet.utils import construct_ocf_ml_metrics_batch_df
 
 DATA_CONFIG_NAME = "data_config.yaml"
 
@@ -485,24 +484,24 @@ def validation_step(self, batch: dict, batch_idx):
             self._val_y_hats.append(y_hat)
             self._val_batches.append(batch)
             # if batch had accumulated
-            #if (batch_idx + 1) % self.trainer.accumulate_grad_batches == 0:
-                #y_hat = self._val_y_hats.flush()
-                #batch = self._val_batches.flush()
-
-                #fig = plot_batch_forecasts(
-                #    batch,
-                #    y_hat,
-                #    quantiles=self.output_quantiles,
-                #    key_to_plot="gsp" if self._target_key == BatchKey.gsp else "sensor",
-                #)
-
-                #self.logger.experiment.log(
-                #    {
-                #        f"val_forecast_samples/batch_idx_{accum_batch_num}": wandb.Image(fig),
-                #    }
-                #)
-                #del self._val_y_hats
-                #del self._val_batches
+            # if (batch_idx + 1) % self.trainer.accumulate_grad_batches == 0:
+            # y_hat = self._val_y_hats.flush()
+            # batch = self._val_batches.flush()
+
+            # fig = plot_batch_forecasts(
+            #    batch,
+            #    y_hat,
+            #    quantiles=self.output_quantiles,
+            #    key_to_plot="gsp" if self._target_key == BatchKey.gsp else "sensor",
+            # )
+
+            # self.logger.experiment.log(
+            #    {
+            #        f"val_forecast_samples/batch_idx_{accum_batch_num}": wandb.Image(fig),
+            #    }
+            # )
+            # del self._val_y_hats
+            # del self._val_batches
 
         return logged_losses