Apply deprecated evaluation_strategy

examples/onnxruntime/training/image-classification/README.md

@@ -39,7 +39,7 @@ torchrun --nproc_per_node=NUM_GPUS_YOU_HAVE run_image_classification.py \
     --per_device_eval_batch_size 32 \
     --logging_strategy steps \
     --logging_steps 10 \
-    --evaluation_strategy epoch \
+    --eval_strategy epoch \
     --seed 1337
 ```
 

optimum/onnxruntime/training_args.py

@@ -117,32 +117,32 @@ def __post_init__(self):
         if self.disable_tqdm is None:
             self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN
 
-        if isinstance(self.evaluation_strategy, EvaluationStrategy):
+        if isinstance(self.eval_strategy, EvaluationStrategy):
             warnings.warn(
-                "using `EvaluationStrategy` for `evaluation_strategy` is deprecated and will be removed in version 5"
+                "using `EvaluationStrategy` for `eval_strategy` is deprecated and will be removed in version 5"
                 " of 🤗 Transformers. Use `IntervalStrategy` instead",
                 FutureWarning,
             )
             # Go back to the underlying string or we won't be able to instantiate `IntervalStrategy` on it.
-            self.evaluation_strategy = self.evaluation_strategy.value
+            self.eval_strategy = self.eval_strategy.value
 
-        self.evaluation_strategy = IntervalStrategy(self.evaluation_strategy)
+        self.eval_strategy = IntervalStrategy(self.eval_strategy)
         self.logging_strategy = IntervalStrategy(self.logging_strategy)
         self.save_strategy = IntervalStrategy(self.save_strategy)
         self.hub_strategy = HubStrategy(self.hub_strategy)
 
         self.lr_scheduler_type = SchedulerType(self.lr_scheduler_type)
-        if self.do_eval is False and self.evaluation_strategy != IntervalStrategy.NO:
+        if self.do_eval is False and self.eval_strategy != IntervalStrategy.NO:
             self.do_eval = True
 
         # eval_steps has to be defined and non-zero, fallbacks to logging_steps if the latter is non-zero
-        if self.evaluation_strategy == IntervalStrategy.STEPS and (self.eval_steps is None or self.eval_steps == 0):
+        if self.eval_strategy == IntervalStrategy.STEPS and (self.eval_steps is None or self.eval_steps == 0):
             if self.logging_steps > 0:
                 logger.info(f"using `logging_steps` to initialize `eval_steps` to {self.logging_steps}")
                 self.eval_steps = self.logging_steps
             else:
                 raise ValueError(
-                    f"evaluation strategy {self.evaluation_strategy} requires either non-zero --eval_steps or"
+                    f"evaluation strategy {self.eval_strategy} requires either non-zero --eval_steps or"
                     " --logging_steps"
                 )
 
@@ -154,7 +154,7 @@ def __post_init__(self):
             if self.logging_steps != int(self.logging_steps):
                 raise ValueError(f"--logging_steps must be an integer if bigger than 1: {self.logging_steps}")
             self.logging_steps = int(self.logging_steps)
-        if self.evaluation_strategy == IntervalStrategy.STEPS and self.eval_steps > 1:
+        if self.eval_strategy == IntervalStrategy.STEPS and self.eval_steps > 1:
             if self.eval_steps != int(self.eval_steps):
                 raise ValueError(f"--eval_steps must be an integer if bigger than 1: {self.eval_steps}")
             self.eval_steps = int(self.eval_steps)
@@ -165,13 +165,13 @@ def __post_init__(self):
 
         # Sanity checks for load_best_model_at_end: we require save and eval strategies to be compatible.
         if self.load_best_model_at_end:
-            if self.evaluation_strategy != self.save_strategy:
+            if self.eval_strategy != self.save_strategy:
                 raise ValueError(
                     "--load_best_model_at_end requires the saving steps to be a multiple of the evaluation "
                     "steps, which cannot get guaranteed when mixing ratio and absolute steps for save_steps "
                     f"{self.save_steps} and eval_steps {self.eval_steps}."
                 )
-            if self.evaluation_strategy == IntervalStrategy.STEPS and self.save_steps % self.eval_steps != 0:
+            if self.eval_strategy == IntervalStrategy.STEPS and self.save_steps % self.eval_steps != 0:
                 if self.eval_steps < 1 or self.save_steps < 1:
                     if not (self.eval_steps < 1 and self.save_steps < 1):
                         raise ValueError(
@@ -244,7 +244,7 @@ def __post_init__(self):
                 )
 
         if self.lr_scheduler_type == SchedulerType.REDUCE_ON_PLATEAU:
-            if self.evaluation_strategy == IntervalStrategy.NO:
+            if self.eval_strategy == IntervalStrategy.NO:
                 raise ValueError("lr_scheduler_type reduce_lr_on_plateau requires an eval strategy")
             if not is_torch_available():
                 raise ValueError("lr_scheduler_type reduce_lr_on_plateau requires torch>=0.2.0")