chore: suppress transformers debug message

modelling_magiv2.py

@@ -1,4 +1,4 @@
-from transformers import PreTrainedModel, VisionEncoderDecoderModel, ViTMAEModel, ConditionalDetrModel
+from transformers import PreTrainedModel, VisionEncoderDecoderModel, ViTMAEModel, ConditionalDetrModel, logging
 from transformers.models.conditional_detr.modeling_conditional_detr import (
     ConditionalDetrMLPPredictionHead, 
     ConditionalDetrModelOutput,
@@ -19,13 +19,6 @@ import numpy as np
 import warnings
 from scipy.optimize import linear_sum_assignment
 
-warnings.filterwarnings(
-    "ignore",
-    message=r"for .*: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op.*",
-    category=UserWarning,
-    module=r"torch\.nn\.modules\.(module|batchnorm)",
-)
-
 class Magiv2Model(PreTrainedModel):
     config_class = Magiv2Config
 
@@ -33,6 +26,7 @@ class Magiv2Model(PreTrainedModel):
         super().__init__(config)
         self.config = config
         self.processor = Magiv2Processor(config)
+        self.suppress_debug()
         if not config.disable_ocr:
             self.ocr_model = VisionEncoderDecoderModel(config.ocr_model_config)
         if not config.disable_crop_embeddings:
@@ -72,6 +66,16 @@ class Magiv2Model(PreTrainedModel):
                 giou_cost=config.detection_model_config.giou_cost
             )
 
+    def suppress_debug(self):
+        warnings.filterwarnings(
+            "ignore",
+            message=r"for .*: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op.*",
+            category=UserWarning,
+            module=r"torch\.nn\.modules\.(module|batchnorm)",
+        )
+
+        logging.set_verbosity_error()
+
     def move_to_device(self, input):
         return move_to_device(input, self.device)