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[SaliencyEvaluationMetrics][Added] Normalization option

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Salvador E. Tropea
2025-11-12 11:22:15 -03:00
parent 5c2230b078
commit d4ba71ae5a
1 changed files with 10 additions and 7 deletions
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src/nodes/nodes_img.py
+10 -7
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@@ -733,6 +733,7 @@ class SaliencyEvaluationMetrics:
},
"optional": {
"img_name": ("STRING", {"forceInput": True, "tooltip": "Name used as base to save the parameters"}),
"normalize": ("BOOLEAN", {"default": False, "tooltip": "Normalize input masks to be in the [0, 1] range"}),
"result_save": ("BOOLEAN", {"default": False, "tooltip": "Save computed values to IMG_NAME.csv"}),
"mae_enable": ("BOOLEAN", {"default": True, "tooltip": "Compute the MAE"}),
"mae_save": ("BOOLEAN", {"default": False, "tooltip": "Save the MAE using IMG_NAME_MAE.csv"}),
@@ -759,11 +760,12 @@ class SaliencyEvaluationMetrics:
DISPLAY_NAME = "Saliency Evaluation Metrics"
def evaluate(self, prediction: torch.Tensor, ground_truth: torch.Tensor, unique_id,
img_name, result_save: bool = False, mae_enable: bool = True, mae_save: bool = False,
img_name, normalize, result_save: bool = False, mae_enable: bool = True, mae_save: bool = False,
max_f_mes_enable: bool = True, max_f_mes_save: bool = False, s_mes_enable: bool = True,
s_mes_save: bool = False, e_mes_enable: bool = True, e_mes_save: bool = False,
wf_mes_enable: bool = True, wf_mes_save: bool = True):
# Flatten arguments that aren't really expected to be lists
normalize = normalize[0]
mae_enable = mae_enable[0]
mae_save = mae_save[0]
max_f_mes_enable = max_f_mes_enable[0]
@@ -805,12 +807,13 @@ class SaliencyEvaluationMetrics:
pred = prediction[index_img].to(device)
# Ensure masks are normalized to [0, 1] range
gt, skipped = batched_min_max_norm(gt, in_place=inputs_are_copies, ret_status=True)
if skipped:
logger.debug("GT mask already [0, 1]")
pred, skipped = batched_min_max_norm(pred, in_place=inputs_are_copies, ret_status=True)
if skipped:
logger.debug("Prediction mask already [0, 1]")
if normalize:
gt, skipped = batched_min_max_norm(gt, in_place=inputs_are_copies, ret_status=True)
if skipped:
logger.debug("GT mask already [0, 1]")
pred, skipped = batched_min_max_norm(pred, in_place=inputs_are_copies, ret_status=True)
if skipped:
logger.debug("Prediction mask already [0, 1]")
for i in range(gt.shape[0]):
if img_name[index_name] is None: