agressin commited on
Commit
b6fd6ce
·
1 Parent(s): 4f0be60
src/display/utils.py CHANGED
@@ -20,17 +20,17 @@ class ColumnContent:
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  # Init
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  auto_eval_column_dict = [
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  ["eval_name", ColumnContent, ColumnContent("Eval Name", "str", True)],
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- ["result_name", ColumnContent, ColumnContent("Result Name", "str", True)],
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  ["date", ColumnContent, ColumnContent("Submission Date", "str", True)],
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  ["miou", ColumnContent, ColumnContent("mIoU ⬆️", "number", True)],
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  ["accuracy", ColumnContent, ColumnContent("Accuracy ⬆️", "number", False)],
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- ["precision_score", ColumnContent, ColumnContent("Precision ⬆️", "number", False)],
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- ["recall", ColumnContent, ColumnContent("Recall ⬆️", "number", False)],
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- ["f1", ColumnContent, ColumnContent("F1 ⬆️", "number", False)],
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- ["producer_accuracy", ColumnContent, ColumnContent("Producer Accuracy", "list", False)],
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- ["user_accuracy", ColumnContent, ColumnContent("User Accuracy", "list", False)],
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- ["confusion_matrix", ColumnContent, ColumnContent("Confusion Matrix", "matrix", False)],
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- ["num_classes", ColumnContent, ColumnContent("Number of classes", "number", False)],
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  ]
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  # We use make dataclass to dynamically fill the scores from Tasks
 
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  # Init
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  auto_eval_column_dict = [
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  ["eval_name", ColumnContent, ColumnContent("Eval Name", "str", True)],
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+ ["result_name", ColumnContent, ColumnContent("Result Name", "str", False)],
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  ["date", ColumnContent, ColumnContent("Submission Date", "str", True)],
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  ["miou", ColumnContent, ColumnContent("mIoU ⬆️", "number", True)],
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  ["accuracy", ColumnContent, ColumnContent("Accuracy ⬆️", "number", False)],
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+ # ["precision_score", ColumnContent, ColumnContent("Precision ⬆️", "number", False)],
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+ # ["recall", ColumnContent, ColumnContent("Recall ⬆️", "number", False)],
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+ # ["f1", ColumnContent, ColumnContent("F1 ⬆️", "number", False)],
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+ # ["producer_accuracy", ColumnContent, ColumnContent("Producer Accuracy", "list", False)],
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+ # ["user_accuracy", ColumnContent, ColumnContent("User Accuracy", "list", False)],
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+ # ["confusion_matrix", ColumnContent, ColumnContent("Confusion Matrix", "matrix", False)],
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+ # ["num_classes", ColumnContent, ColumnContent("Number of classes", "number", False)],
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  ]
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  # We use make dataclass to dynamically fill the scores from Tasks
src/leaderboard/read_evals.py CHANGED
@@ -10,13 +10,13 @@ class EvalResult:
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  result_name: str
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  date: str
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  accuracy: float
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- precision_score: float
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- recall: float
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- f1: float
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  miou: float
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- producer_accuracy: List[float]
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- user_accuracy: List[float]
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- confusion_matrix: List[List[float]]
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  @classmethod
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  def init_from_json_file(cls, json_filepath):
@@ -39,14 +39,14 @@ class EvalResult:
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  eval_name=result_key,
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  result_name=result_name,
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  date=data.get("submitted_time", ""),
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- accuracy=data.get("accuracy"),
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- precision_score=data.get("precision"),
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- recall=data.get("recall"),
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- f1=data.get("f1"),
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- miou=data.get("miou"),
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- producer_accuracy=data.get("producer_accuracy", []),
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- user_accuracy=data.get("user_accuracy", []),
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- confusion_matrix=data.get("confusion_matrix", []),
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  )
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@@ -58,14 +58,14 @@ class EvalResult:
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  AutoEvalColumn.result_name.name: self.result_name,
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  AutoEvalColumn.date.name: self.date,
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  AutoEvalColumn.accuracy.name: self.accuracy,
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- AutoEvalColumn.precision_score.name: self.precision_score,
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- AutoEvalColumn.recall.name: self.recall,
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- AutoEvalColumn.f1.name: self.f1,
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- AutoEvalColumn.producer_accuracy.name: self.producer_accuracy,
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- AutoEvalColumn.user_accuracy.name: self.user_accuracy,
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- AutoEvalColumn.confusion_matrix.name: self.confusion_matrix,
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  AutoEvalColumn.miou.name: self.miou,
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- AutoEvalColumn.num_classes.name: len(self.producer_accuracy) if self.producer_accuracy else 0,
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  }
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  return data_dict
 
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  result_name: str
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  date: str
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  accuracy: float
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+ # precision_score: float
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+ # recall: float
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+ # f1: float
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  miou: float
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+ # producer_accuracy: List[float]
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+ # user_accuracy: List[float]
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+ # confusion_matrix: List[List[float]]
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  @classmethod
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  def init_from_json_file(cls, json_filepath):
 
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  eval_name=result_key,
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  result_name=result_name,
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  date=data.get("submitted_time", ""),
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+ accuracy=data.get("accuracy", 0)*100,
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+ # precision_score=data.get("precision")*100,
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+ # recall=data.get("recall")*100,
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+ # f1=data.get("f1")*100,
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+ miou=data.get("miou", 0)*100,
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+ # producer_accuracy=data.get("producer_accuracy", []),
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+ # user_accuracy=data.get("user_accuracy", []),
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+ # confusion_matrix=data.get("confusion_matrix", []),
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  )
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  AutoEvalColumn.result_name.name: self.result_name,
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  AutoEvalColumn.date.name: self.date,
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  AutoEvalColumn.accuracy.name: self.accuracy,
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+ # AutoEvalColumn.precision_score.name: self.precision_score,
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+ # AutoEvalColumn.recall.name: self.recall,
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+ # AutoEvalColumn.f1.name: self.f1,
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+ # AutoEvalColumn.producer_accuracy.name: self.producer_accuracy,
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+ # AutoEvalColumn.user_accuracy.name: self.user_accuracy,
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+ # AutoEvalColumn.confusion_matrix.name: self.confusion_matrix,
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  AutoEvalColumn.miou.name: self.miou,
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+ # AutoEvalColumn.num_classes.name: len(self.num_classes) if self.num_classes else 0,
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  }
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  return data_dict