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mlcommons · Oct 17, 2024 · 57bb86c · 57bb86c
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Showing 4 changed files with 4 additions and 4 deletions.
diff --git a/algorithmic_efficiency/workloads/fastmri/fastmri_pytorch/workload.py b/algorithmic_efficiency/workloads/fastmri/fastmri_pytorch/workload.py
@@ -253,7 +253,7 @@ def _eval_model_on_split(self,
       batch = next(self._eval_iters[split])
       batch_metrics = self._eval_model(params, batch, model_rng)
       total_metrics = {
-        k: v + batch_metrics[k] for k, v in total_metrics.items()
+          k: v + batch_metrics[k] for k, v in total_metrics.items()
       }
     if USE_PYTORCH_DDP:
       for metric in total_metrics.values():

diff --git a/algorithmic_efficiency/workloads/imagenet_resnet/imagenet_pytorch/workload.py b/algorithmic_efficiency/workloads/imagenet_resnet/imagenet_pytorch/workload.py
@@ -310,7 +310,7 @@ def _eval_model_on_split(self,
       weights = batch.get('weights')
       batch_metrics = self._compute_metrics(logits, batch['targets'], weights)
       total_metrics = {
-        k: v + batch_metrics[k] for k, v in total_metrics.items()
+          k: v + batch_metrics[k] for k, v in total_metrics.items()
       }
     if USE_PYTORCH_DDP:
       for metric in total_metrics.values():

diff --git a/algorithmic_efficiency/workloads/librispeech_conformer/librispeech_pytorch/workload.py b/algorithmic_efficiency/workloads/librispeech_conformer/librispeech_pytorch/workload.py
@@ -331,7 +331,7 @@ def _eval_model_on_split(self,
           'num_words': num_words,
       }
       total_metrics = {
-        k: v + batch_metrics[k] for k, v in total_metrics.items()
+          k: v + batch_metrics[k] for k, v in total_metrics.items()
       }
     if USE_PYTORCH_DDP:
       for metric in total_metrics.values():

diff --git a/algorithmic_efficiency/workloads/mnist/workload.py b/algorithmic_efficiency/workloads/mnist/workload.py
@@ -215,7 +215,7 @@ def _eval_model_on_split(self,
                                        model_state,
                                        per_device_model_rngs)
       total_metrics = {
-        k: v + batch_metrics[k] for k, v in total_metrics.items()
+          k: v + batch_metrics[k] for k, v in total_metrics.items()
       }
 
     return self._normalize_eval_metrics(num_examples, total_metrics)