remove inference speed from hash

Author: RJT1990

torchbench/image_classification/utils.py

@@ -42,8 +42,7 @@ def evaluate_classification(
 
             if i == 5:  # for sotabench.com caching of evaluation
                 memory_allocated = torch.cuda.memory_allocated(device=device)
-                tasks_per_second = test_loader.batch_size*inference_time.avg
-                run_hash = calculate_run_hash([prec1, prec5, np.round(tasks_per_second, 1)], output)
+                run_hash = calculate_run_hash([prec1, prec5], output)
                 # if we are in check model we don't need to go beyond the first
                 # batch
                 if in_check_mode():

torchbench/object_detection/utils.py

@@ -221,8 +221,7 @@ def evaluate_detection_coco(
 
             if i == 5:  # for sotabench.com caching of evaluation
                 memory_allocated = torch.cuda.memory_allocated(device=device)
-                tasks_per_second = test_loader.batch_size*inference_time.avg
-                run_hash = calculate_run_hash([np.round(tasks_per_second, 1)], original_output)
+                run_hash = calculate_run_hash([], original_output)
                 # if we are in check model we don't need to go beyond the first
                 # batch
                 if in_check_mode():

torchbench/semantic_segmentation/utils.py

@@ -160,8 +160,7 @@ def evaluate_segmentation(
 
             if i == 5:  # for sotabench.com caching of evaluation
                 memory_allocated = torch.cuda.memory_allocated(device=device)
-                tasks_per_second = test_loader.batch_size*inference_time.avg
-                run_hash = calculate_run_hash([np.round(tasks_per_second, 1)], output)
+                run_hash = calculate_run_hash([], output)
                 # if we are in check model we don't need to go beyond the first
                 # batch
                 if in_check_mode():