fixes linting

wbia/algo/detect/efficientnet.py

@@ -141,26 +141,26 @@ def __init__(self, n_class, model_arch='tf_efficientnet_b4_ns', pretrained=False
             'left': 3,
             'right': 3,
         }
-        
+
         self.reverse_label_map = {
-            'up':1,
-            'down':2,
-            'front':3,
-            'back':4,
-            'left':5,
-            'right':6,
-            'upfront':7,
-            'upback':8,
-            'upleft':9,
-            'upright':10,
-            'downfront':11,
-            'downback':12,
-            'downleft':13,
-            'downright':14,
-            'frontleft':15,
-            'frontright':16,
-            'backleft':17,
-            'backright':18,
+            'up': 1,
+            'down': 2,
+            'front': 3,
+            'back': 4,
+            'left': 5,
+            'right': 6,
+            'upfront': 7,
+            'upback': 8,
+            'upleft': 9,
+            'upright': 10,
+            'downfront': 11,
+            'downback': 12,
+            'downleft': 13,
+            'downright': 14,
+            'frontleft': 15,
+            'frontright': 16,
+            'backleft': 17,
+            'backright': 18,
         }
 
         if multilabel:
@@ -181,8 +181,8 @@ def __init__(self, n_class, model_arch='tf_efficientnet_b4_ns', pretrained=False
         '''
 
     def process_row(self, row_labels, preds, sort_weights, labels):
-        multi_labels = labels[row_labels == True]
-        preds = preds[row_labels == True]
+        multi_labels = labels[row_labels.astype(bool)]
+        preds = preds[row_labels.astype(bool)]
         # Combine the multi_labels and preds into a list of tuples
         label_pred_weight = [(label, pred, sort_weights[label]) for label, pred in zip(multi_labels, preds)]
         # Sort by weights first, then by prediction values in descending order
@@ -195,7 +195,7 @@ def process_row(self, row_labels, preds, sort_weights, labels):
         # Extract the labels in the order of weights - top 2 labels
         sorted_labels = [best_labels[weight][0] for weight in sorted(best_labels)[:2]]
         return sorted_labels
-    
+
     def process_multilabel_preds(self, image_preds, sort_weights, labels, reverse_label_map):
         multi_label_matrix = (image_preds > 0.5).cpu().numpy()
         sorted_labels = [self.process_row(row, preds, sort_weights, labels) for row, preds in zip(multi_label_matrix, image_preds)]
@@ -211,7 +211,7 @@ def process_multilabel_preds(self, image_preds, sort_weights, labels, reverse_la
         one_hot_tensor = torch.tensor(one_hot_matrix, dtype=torch.float32)
 
         return one_hot_tensor
-    
+
     def forward(self, x):
         x = self.model(x)
         if self.multilabel:
@@ -626,7 +626,7 @@ def test_single(filepath_list, weights_path, batch_size=1792, multi=PARALLEL, **
     num_classes = len(classes)
 
     # Initialize the model for this run
-    multilabel = 'multilabel' in weights_path 
+    multilabel = 'multilabel' in weights_path
     model = EfficientnetModel(n_class=num_classes, multilabel=multilabel)
     # num_ftrs = model.classifier.in_features
     # model.classifier = nn.Linear(num_ftrs, num_classes)