Adds multilabel efficientnet

wbia/algo/detect/efficientnet.py

@@ -30,7 +30,8 @@
     'deer_effnet_v0': 'https://wildbookiarepository.azureedge.net/models/labeler_deer_effnet.v0.zip',
     'leopard_shark_effnet_v0': 'https://wildbookiarepository.azureedge.net/models/labeler_leopard_shark_effnet.v0.zip',
     'trout_effnet_v0': 'https://wildbookiarepository.azureedge.net/models/labeler_trout_effnet.v0.zip',
-    'shark_effnet_v0': 'https://wildbookiarepository.azureedge.net/models/labeler_shark_effnet.v0.zip'
+    'shark_effnet_v0': 'https://wildbookiarepository.azureedge.net/models/labeler_shark_effnet.v0.zip',
+    'msv2_multilabel_effnet_v0': 'https://cthulhu.dyn.wildme.io/public/models/labeler_msv2_multilabel_effnet.v2.zip',
 }
 
 
@@ -126,13 +127,49 @@ def _init_transforms(**kwargs):
 
 
 class EfficientnetModel(nn.Module):
-    def __init__(self, n_class, model_arch='tf_efficientnet_b4_ns', pretrained=False):
+    def __init__(self, n_class, model_arch='tf_efficientnet_b4_ns', pretrained=False, multilabel=False):
         super().__init__()
         self.model = timm.create_model(model_arch, pretrained=pretrained)
+        self.multilabel = multilabel
+
+        self.labels = np.array(['back', 'down', 'front', 'left', 'right', 'up'])
+        self.sort_weights = {
+            'up': 1,
+            'down': 1,
+            'front': 2,
+            'back': 2,
+            '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,
+        }
+
+        if multilabel:
+            n_class = len(self.sort_weights)
 
         if n_class is not None:
             n_features = self.model.classifier.in_features
             self.model.classifier = nn.Linear(n_features, n_class)
+
         else:
             self.model.classifier = nn.Identity(n_features, n_class)
         '''
@@ -142,8 +179,44 @@ def __init__(self, n_class, model_arch='tf_efficientnet_b4_ns', pretrained=False
             nn.Linear(n_features, n_class, bias=True)
         )
         '''
+
+    def process_row(self, row_labels, preds, sort_weights, labels):
+        multi_labels = labels[row_labels == True]
+        preds = preds[row_labels == True]
+        # 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
+        label_pred_weight.sort(key=lambda x: (x[2], -x[1]))
+        # Create a dictionary to keep the highest value string for each weight
+        best_labels = {}
+        for label, pred, weight in label_pred_weight:
+            if weight not in best_labels or pred > best_labels[weight][1]:
+                best_labels[weight] = (label, pred)
+        # 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)]
+        fused_labels = [''.join(x) for x in sorted_labels]
+
+        num_labels = len(reverse_label_map)
+        one_hot_matrix = np.zeros((len(fused_labels), num_labels))
+
+        for i, label in enumerate(fused_labels):
+            if label in reverse_label_map:
+                one_hot_matrix[i, reverse_label_map[label] - 1] = 1
+
+        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:
+            x = self.process_multilabel_preds(x, self.sort_weights, self.labels, self.reverse_label_map)
+
         return x
 
 
@@ -553,7 +626,8 @@ def test_single(filepath_list, weights_path, batch_size=1792, multi=PARALLEL, **
     num_classes = len(classes)
 
     # Initialize the model for this run
-    model = EfficientnetModel(n_class=num_classes)
+    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)