ScoNet_V1

opengait/evaluation/evaluator.py

@@ -5,7 +5,7 @@ from utils import get_msg_mgr, mkdir
 
 from .metric import mean_iou, cuda_dist, compute_ACC_mAP, evaluate_rank, evaluate_many
 from .re_rank import re_ranking
-
+from sklearn.metrics import confusion_matrix, accuracy_score
 
 def de_diag(acc, each_angle=False):
     # Exclude identical-view cases
@@ -415,3 +415,45 @@ def evaluate_CCPG(data, dataset, metric='euc'):
         msg_mgr.log_info('DN: {}'.format(de_diag(acc[2, :, :, i], True)))
         msg_mgr.log_info('BG: {}'.format(de_diag(acc[3, :, :, i], True)))
     return result_dict
+
+def evaluate_scoliosis(data, dataset, metric='euc'):
+    msg_mgr = get_msg_mgr()
+
+    feature, label, class_id, view = data['embeddings'], data['labels'], data['types'], data['views']
+
+    label = np.array(label)
+    class_id = np.array(class_id)
+
+    # Update class_id with integer labels based on status
+    class_id_int = np.array([1 if status == 'positive' else 2 if status == 'critical' else 0 for status in class_id])
+    print('class_id=', class_id_int)
+
+    features = np.array(feature)
+    c_id_int = np.argmax(features.mean(-1), axis=-1)
+    print('predicted_labels', c_id_int)
+
+    # Calculate sensitivity and specificity
+    cm = confusion_matrix(class_id_int, c_id_int, labels=[0, 1, 2])
+    FP = cm.sum(axis=0) - np.diag(cm)
+    FN = cm.sum(axis=1) - np.diag(cm)
+    TP = np.diag(cm)
+    TN = cm.sum() - (FP + FN + TP)
+
+    # Sensitivity, hit rate, recall, or true positive rate
+    TPR = TP / (TP + FN)
+    # Specificity or true negative rate
+    TNR = TN / (TN + FP)
+    accuracy = accuracy_score(class_id_int, c_id_int)
+
+    result_dict = {}
+    result_dict["scalar/test_accuracy/"] = accuracy
+    result_dict["scalar/test_sensitivity/"] = TPR
+    result_dict["scalar/test_specificity/"] = TNR
+
+    # Printing the sensitivity and specificity
+    for i, cls in enumerate(['Positive']):
+        print(f"{cls} Sensitivity (Recall): {TPR[i] * 100:.2f}%")
+        print(f"{cls} Specificity: {TNR[i] * 100:.2f}%")
+    print(f"Accuracy: {accuracy * 100:.2f}%")
+
+    return result_dict

opengait/modeling/models/sconet.py

@@ -0,0 +1,53 @@
+import torch
+
+from ..base_model import BaseModel
+from ..modules import SetBlockWrapper, HorizontalPoolingPyramid, PackSequenceWrapper, SeparateFCs, SeparateBNNecks
+
+from einops import rearrange
+import numpy as np
+class ScoNet(BaseModel):
+
+    def build_network(self, model_cfg):
+        self.Backbone = self.get_backbone(model_cfg['backbone_cfg'])
+        self.Backbone = SetBlockWrapper(self.Backbone)
+        self.FCs = SeparateFCs(**model_cfg['SeparateFCs'])
+        self.BNNecks = SeparateBNNecks(**model_cfg['SeparateBNNecks'])
+        self.TP = PackSequenceWrapper(torch.max)
+        self.HPP = HorizontalPoolingPyramid(bin_num=model_cfg['bin_num'])
+
+    def forward(self, inputs):
+        ipts, labs, class_id, _, seqL = inputs
+
+        class_id_int = np.array([1 if status == 'positive' else 2 if status == 'critical' else 0 for status in class_id])
+        class_id = torch.tensor(class_id_int).cuda()
+
+        sils = ipts[0]
+        if len(sils.size()) == 4:
+            sils = sils.unsqueeze(1)
+        else:
+            sils = rearrange(sils, 'n s c h w -> n c s h w')
+
+        del ipts
+        outs = self.Backbone(sils)  # [n, c, s, h, w]
+
+        # Temporal Pooling, TP
+        outs = self.TP(outs, seqL, options={"dim": 2})[0]  # [n, c, h, w]
+        # Horizontal Pooling Matching, HPM
+        feat = self.HPP(outs)  # [n, c, p]
+
+        embed_1 = self.FCs(feat)  # [n, c, p]
+        embed_2, logits = self.BNNecks(embed_1)  # [n, c, p]
+        embed = embed_1
+        retval = {
+            'training_feat': {
+                'triplet': {'embeddings': embed, 'labels': labs},
+                'softmax': {'logits': logits, 'labels': class_id},
+            },
+            'visual_summary': {
+                'image/sils': rearrange(sils,'n c s h w -> (n s) c h w')
+            },
+            'inference_feat': {
+                'embeddings': logits
+            }
+        }
+        return retval