[feature] change val loss arg name

wang-tf · May 22, 2023 · 9671ed8 · 9671ed8
1 parent 344ac21
commit 9671ed8
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Showing 3 changed files with 9 additions and 14 deletions.
diff --git a/configs/resnet/resnet50_4xb128_bp_ppg.py b/configs/resnet/resnet50_4xb128_bp_ppg.py
@@ -38,7 +38,7 @@
 )
 val_evaluator = [
     dict(type='MAE', gt_key='gt_label',
-         pred_key='pred_label', loss_key='pred_loss'),
+         pred_key='pred_label', compute_loss=True),
     # dict(type='BlandAltmanPlot')
 ]
 test_dataloader = dict(
@@ -51,7 +51,10 @@
                  pipeline=test_pipeline),
     sampler=dict(type='DefaultSampler', shuffle=False),
 )
-test_evaluator = val_evaluator
+test_evaluator = [
+    dict(type='MAE', gt_key='gt_label',
+         pred_key='pred_label')
+]
 
 # optimizer
 optim_wrapper = dict(optimizer=dict(type='Adam',

diff --git a/deep_vital/evaluation/metrics/mae.py b/deep_vital/evaluation/metrics/mae.py
@@ -2,11 +2,8 @@
 import torch
 import torch.nn.functional as F
 from mmengine.evaluator import BaseMetric
-from mmengine import MessageHub
 from deep_vital.registry import METRICS
 
-message_hub = MessageHub.get_current_instance()
-
 
 @METRICS.register_module()
 class MAE(BaseMetric):
@@ -16,32 +13,27 @@ class MAE(BaseMetric):
     def __init__(self,
                  gt_key='gt_label',
                  pred_key='pred_label',
-                 loss_key=None,
+                 compute_loss=False,
                  collect_device: str = 'cpu',
                  prefix: Optional[str] = None):
         super().__init__(collect_device=collect_device, prefix=prefix)
         self.gt_key = gt_key
         self.pred_key = pred_key
-        self.loss_key = loss_key
+        self.compute_loss = compute_loss
 
     def process(self, data_batch, data_samples):
         for data_sample in data_samples:
             result = {
                 'pred_label': data_sample[self.pred_key],
                 'gt_label': data_sample[self.gt_key]
             }
-            # if self.loss_key:
-            #     result['pred_loss'] = data_sample[self.loss_key]
             self.results.append(result)
 
     def compute_metrics(self, results):
         metrics = {}
         target = torch.stack([res['gt_label'] for res in results])
         pred = torch.stack([res['pred_label'] for res in results])
-        if self.loss_key:
-            # loss = torch.stack([res['pred_loss'] for res in results])
-            # val_loss = loss.mean()
-            # metrics['pred_loss'] = val_loss
+        if self.compute_loss:
             sbp_pred = pred[:, 0]
             dbp_pred = pred[:, 1]
             sbp_target = target[:, 0]

diff --git a/deep_vital/version.py b/deep_vital/version.py
@@ -1,4 +1,4 @@
-__version__ = '0.1.0'
+__version__ = '1.0.0'
 short_version = __version__