fix: BioSR dataloder and pathing of the project.

casus · Oct 28, 2024 · c745ab3 · c745ab3
1 parent 7e52f60
commit c745ab3
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diff --git a/cvdm/utils/metrics_utils.py b/cvdm/utils/metrics_utils.py
diff --git a/scripts/eval.py → eval.py b/scripts/eval.py → eval.py
diff --git a/scripts/train.py → train.py b/scripts/train.py → train.py
@@ -67,16 +67,11 @@ def main() -> None:
     print("Getting data...")
     batch_size = data_config.batch_size
     dataset, x_shape, y_shape = prepare_dataset(task, data_config, training=True)
-    val_dataset, x_shape, y_shape = prepare_dataset(task, data_config, training=False)
-
     dataset = dataset.shuffle(5000, reshuffle_each_iteration=False)
-
     val_len = eval_config.val_len
-    val_dataset = val_dataset.take(val_len)
     dataset = dataset.skip(val_len)
 
     dataset = dataset.batch(batch_size, drop_remainder=True)
-    val_dataset = val_dataset.batch(batch_size, drop_remainder=True)
 
     epochs = training_config.epochs
     generation_timesteps = eval_config.generation_timesteps
@@ -107,7 +102,6 @@ def main() -> None:
     log_freq = eval_config.log_freq
     checkpoint_freq = eval_config.checkpoint_freq
     image_freq = eval_config.image_freq
-    val_freq = eval_config.val_freq
     output_path = eval_config.output_path
     diff_inp = model_config.diff_inp
 
@@ -161,46 +155,6 @@ def main() -> None:
                     prefix="train",
                     cmap=cmap,
                 )
-
-            if step % val_freq == 0:
-                if model_config.zmd:
-                    val_loss = np.zeros(6)
-                else:
-                    val_loss = np.zeros(5)
-                for batch in val_dataset:
-                    batch_x, batch_y = batch
-                    model_input = prepare_model_input(
-                        batch_x, batch_y, diff_inp=diff_inp
-                    )
-                    val_loss += joint_model.evaluate(
-                        model_input, np.zeros_like(batch_y), verbose=0
-                    )
-
-                log_loss(run=run, avg_loss=val_loss, prefix="val")
-                # To speed up, images are only generated and metrics are calculated only for one batch.
-                random_batch = val_dataset.take(1)
-                for batch_x, batch_y in random_batch:
-                    output_montage, metrics = obtain_output_montage_and_metrics(
-                        batch_x,
-                        batch_y.numpy(),
-                        noise_model,
-                        schedule_model,
-                        mu_model,
-                        generation_timesteps,
-                        diff_inp,
-                        task,
-                    )
-                    log_metrics(run, metrics, prefix="val")
-                    save_output_montage(
-                        run=run,
-                        output_montage=output_montage,
-                        step=step,
-                        output_path=output_path,
-                        run_id=run_id,
-                        prefix="val",
-                        cmap=cmap,
-                    )
-
             step += 1
 
     if run is not None: