Merge branch 'main' into removed-pretrained-fallback

terratorch/models/backbones/prithvi_vit.py

@@ -51,7 +51,8 @@ def _cfg(**kwargs):
         **kwargs
     }
 
-prithvi_default_cfgs = {
+
+prithvi_cfgs = {
     "prithvi_eo_tiny": _cfg(num_frames=1, embed_dim=256, depth=4, num_heads=4,
                             decoder_embed_dim=128, decoder_depth=4, decoder_num_heads=4),
     "prithvi_eo_v1_100": _cfg(num_frames=3, mean=PRITHVI_V1_MEAN, std=PRITHVI_V1_STD),
@@ -64,8 +65,8 @@ def _cfg(**kwargs):
                                  coords_encoding=["time", "location"], coords_scale_learn=True),
 }
 
-
-pretrained_cfgs = generate_default_cfgs(
+# Timm pretrained configs
+default_cfgs = generate_default_cfgs(
     {
         "prithvi_eo_v1_100": {
             "hf_hub_id": "ibm-nasa-geospatial/Prithvi-EO-1.0-100M",
@@ -203,19 +204,20 @@ def checkpoint_filter_wrapper_fn(state_dict, model):
             return checkpoint_filter_fn_mae(state_dict, model, pretrained_bands, model_bands)
 
     if pretrained:
-        assert variant in pretrained_cfgs, (f"No pre-trained model found for variant {variant} "
-                                            f"(pretrained models: {pretrained_cfgs.keys()})")
+        assert variant in default_cfgs, (f"No pre-trained model found for variant {variant} "
+                                            f"(pretrained models: {default_cfgs.keys()})")
         # Load pre-trained config from hf
         try:
-            model_args, _ = load_model_config_from_hf(pretrained_cfgs[variant].default.hf_hub_id)
+            model_args, _ = load_model_config_from_hf(default_cfgs[variant].default.hf_hub_id)
             model_args.update(kwargs)
         except:
-            logger.warning(f"No pretrained configuration was found on HuggingFace for the model {variant}.")
-            model_args = prithvi_default_cfgs[variant].copy()
+            logger.warning(f"No pretrained configuration was found on HuggingFace for the model {variant}."
+                           f"Using random initialization.")
+            model_args = prithvi_cfgs[variant].copy()
             model_args.update(kwargs)
     else:
         # Load default config
-        model_args = prithvi_default_cfgs[variant].copy()
+        model_args = prithvi_cfgs[variant].copy()
         model_args.update(kwargs)
 
     try: