distill

.gitignore

@@ -0,0 +1,6 @@
+*.pyc
+*.onnx
+*.pth
+*.csv
+*.log
+*.__pycache__

lib/core/api/keypoint.py

@@ -4,7 +4,7 @@
 import numpy as np
 import time
 
-from lib.helper.init import init
+from lib.utils.init import init
 from train_config import config
 
 

lib/core/base_trainer/metric.py

@@ -0,0 +1,71 @@
+import sklearn
+from sklearn import metrics
+from sklearn.metrics import confusion_matrix
+
+import numpy as np
+import torch.nn as nn
+from train_config import config as cfg
+
+import torch
+import torch.nn as nn
+
+from sklearn.metrics import roc_auc_score
+from lib.utils.logger import logger
+
+import warnings
+
+warnings.filterwarnings('ignore')
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+class ROCAUCMeter(object):
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+
+        self.y_true_11 = None
+        self.y_pred_11 = None
+
+    def update(self, y_true, y_pred):
+        y_true = y_true.cpu().numpy()
+
+        y_pred = torch.sigmoid(y_pred).data.cpu().numpy()
+
+        if self.y_true_11 is None:
+            self.y_true_11 = y_true
+            self.y_pred_11 = y_pred
+        else:
+            self.y_true_11 = np.concatenate((self.y_true_11, y_true), axis=0)
+            self.y_pred_11 = np.concatenate((self.y_pred_11, y_pred), axis=0)
+
+    @property
+    def avg(self):
+
+        aucs = []
+        for i in range(11):
+            aucs.append(roc_auc_score(self.y_true_11[:, i], self.y_pred_11[:, i]))
+        print(np.round(aucs, 4))
+
+        return np.mean(aucs)
+
+
+