revert changes

Author: yawenzzzz

olmoearth_pretrain/internal/all_evals.py

@@ -400,39 +400,39 @@ def load_user_module(path: str) -> Any:
 }
 
 FT_EVAL_TASKS = {
-    # "m_eurosat": DownstreamTaskConfig(
-    #     dataset="m-eurosat",
-    #     ft_batch_size=64,
-    #     num_workers=0,
-    #     pooling_type=PoolingType.MEAN,
-    #     norm_stats_from_pretrained=True,
-    #     epochs=50,
-    # ),
-    # "m_bigearthnet": DownstreamTaskConfig(
-    #     dataset="m-bigearthnet",
-    #     ft_batch_size=16,
-    #     num_workers=4,
-    #     pooling_type=PoolingType.MEAN,
-    #     norm_stats_from_pretrained=True,
-    #     epochs=50,
-    # ),
-    # "m_so2sat": DownstreamTaskConfig(
-    #     dataset="m-so2sat",
-    #     ft_batch_size=32,
-    #     num_workers=4,
-    #     pooling_type=PoolingType.MEAN,
-    #     norm_stats_from_pretrained=True,
-    #     epochs=50,
-    # ),
-    # "m_sa_crop_type": DownstreamTaskConfig(
-    #     dataset="m-sa-crop-type",
-    #     ft_batch_size=32,
-    #     num_workers=2,
-    #     pooling_type=PoolingType.MEAN,
-    #     norm_stats_from_pretrained=False,
-    #     norm_method=NormMethod.NORM_NO_CLIP_2_STD,
-    #     epochs=50,
-    # ),
+    "m_eurosat": DownstreamTaskConfig(
+        dataset="m-eurosat",
+        ft_batch_size=64,
+        num_workers=0,
+        pooling_type=PoolingType.MEAN,
+        norm_stats_from_pretrained=True,
+        epochs=50,
+    ),
+    "m_bigearthnet": DownstreamTaskConfig(
+        dataset="m-bigearthnet",
+        ft_batch_size=16,
+        num_workers=4,
+        pooling_type=PoolingType.MEAN,
+        norm_stats_from_pretrained=True,
+        epochs=50,
+    ),
+    "m_so2sat": DownstreamTaskConfig(
+        dataset="m-so2sat",
+        ft_batch_size=32,
+        num_workers=4,
+        pooling_type=PoolingType.MEAN,
+        norm_stats_from_pretrained=True,
+        epochs=50,
+    ),
+    "m_sa_crop_type": DownstreamTaskConfig(
+        dataset="m-sa-crop-type",
+        ft_batch_size=32,
+        num_workers=2,
+        pooling_type=PoolingType.MEAN,
+        norm_stats_from_pretrained=False,
+        norm_method=NormMethod.NORM_NO_CLIP_2_STD,
+        epochs=50,
+    ),
     "mados": DownstreamTaskConfig(
         dataset="mados",
         ft_batch_size=16,

olmoearth_pretrain/internal/full_eval_sweep_finetune.py

@@ -99,12 +99,12 @@ class ModelPreset:
     ),
     "anysat": ModelPreset(
         per_task_overrides={"norm_method": "NormMethod.STANDARDIZE"},
-        # task_specific_overrides={
-        #     "m_sa_crop_type": {"ft_batch_size": 8, "patch_size": 8},
-        #     "pastis_sentinel2": {"ft_batch_size": 4},
-        #     "m_cashew_plant": {"ft_batch_size": 4, "patch_size": 8},
-        #     "m_forestnet": {"ft_batch_size": 2, "patch_size": 16},
-        # },
+        task_specific_overrides={
+            "m_sa_crop_type": {"ft_batch_size": 8, "patch_size": 8},
+            "pastis_sentinel2": {"ft_batch_size": 4},
+            "m_cashew_plant": {"ft_batch_size": 4, "patch_size": 8},
+            "m_forestnet": {"ft_batch_size": 2, "patch_size": 16},
+        },
     ),
     # Models with pretrained normalizer
     "terramind": ModelPreset(