Train on eurocrops_sampling + osm_sampling

olmoearth_pretrain/internal/all_evals.py

@@ -181,7 +181,7 @@ def load_user_module(path: str) -> Any:
         embedding_batch_size=32,
         probe_batch_size=8,
         num_workers=2,
-        pooling_type=PoolingType.MAX,
+        pooling_type=PoolingType.MEAN,
         norm_stats_from_pretrained=True,
         probe_lr=0.1,
         eval_interval=Duration.epochs(50),
@@ -385,20 +385,22 @@ def load_user_module(path: str) -> Any:
         epochs=50,
         eval_mode=EvalMode.LINEAR_PROBE,
     ),
-    "oil_spill_detection": DownstreamTaskConfig(
-        dataset="oil_spill_detection",
-        embedding_batch_size=128,
-        probe_batch_size=8,
-        num_workers=8,
-        pooling_type=PoolingType.MEAN,
-        norm_stats_from_pretrained=True,
-        norm_method=NormMethod.NORM_NO_CLIP_2_STD,
-        probe_lr=0.01,
-        eval_interval=Duration.epochs(10),
-        input_modalities=[Modality.SENTINEL1.name],
-        epochs=50,
-        eval_mode=EvalMode.LINEAR_PROBE,
-    ),
+    # this eval is very large and can lead to
+    # OOM errors. Skipping for now.
+    # "oil_spill_detection": DownstreamTaskConfig(
+    #     dataset="oil_spill_detection",
+    #     embedding_batch_size=128,
+    #     probe_batch_size=8,
+    #     num_workers=8,
+    #     pooling_type=PoolingType.MEAN,
+    #     norm_stats_from_pretrained=True,
+    #     norm_method=NormMethod.NORM_NO_CLIP_2_STD,
+    #     probe_lr=0.01,
+    #     eval_interval=Duration.epochs(10),
+    #     input_modalities=[Modality.SENTINEL1.name],
+    #     epochs=50,
+    #     eval_mode=EvalMode.LINEAR_PROBE,
+    # ),
 }
 
 EMBED_DIAG_TASKS = {