Fixed issue with confidence for single class detectors when exporting (…

…WongKinYiu#607)

* Fixed issue with confidence for single class detectors when exporting

* Typo

export.py

@@ -146,7 +146,7 @@
         if opt.grid:
             if opt.end2end:
                 print('\nStarting export end2end onnx model for %s...' % 'TensorRT' if opt.max_wh is None else 'onnxruntime')
-                model = End2End(model,opt.topk_all,opt.iou_thres,opt.conf_thres,opt.max_wh,device)
+                model = End2End(model,opt.topk_all,opt.iou_thres,opt.conf_thres,opt.max_wh,device,len(labels))
                 if opt.end2end and opt.max_wh is None:
                     output_names = ['num_dets', 'det_boxes', 'det_scores', 'det_classes']
                     shapes = [opt.batch_size, 1, opt.batch_size, opt.topk_all, 4,

models/experimental.py

@@ -158,7 +158,7 @@ def symbolic(g,
 
 class ONNX_ORT(nn.Module):
     '''onnx module with ONNX-Runtime NMS operation.'''
-    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=640, device=None):
+    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=640, device=None, n_classes=80):
         super().__init__()
         self.device = device if device else torch.device("cpu")
         self.max_obj = torch.tensor([max_obj]).to(device)
@@ -168,12 +168,17 @@ def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=640, de
         self.convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]],
                                            dtype=torch.float32,
                                            device=self.device)
+        self.n_classes=n_classes
 
     def forward(self, x):
         boxes = x[:, :, :4]
         conf = x[:, :, 4:5]
         scores = x[:, :, 5:]
-        scores *= conf
+        if self.n_classes == 1:
+            scores = conf # for models with one class, cls_loss is 0 and cls_conf is always 0.5,
+                                 # so there is no need to multiplicate.
+        else:
+            scores *= conf  # conf = obj_conf * cls_conf
         boxes @= self.convert_matrix
         max_score, category_id = scores.max(2, keepdim=True)
         dis = category_id.float() * self.max_wh
@@ -189,7 +194,7 @@ def forward(self, x):
 
 class ONNX_TRT(nn.Module):
     '''onnx module with TensorRT NMS operation.'''
-    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None ,device=None):
+    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None ,device=None, n_classes=80):
         super().__init__()
         assert max_wh is None
         self.device = device if device else torch.device('cpu')
@@ -200,12 +205,17 @@ def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None ,d
         self.plugin_version = '1'
         self.score_activation = 0
         self.score_threshold = score_thres
+        self.n_classes=n_classes
 
     def forward(self, x):
         boxes = x[:, :, :4]
         conf = x[:, :, 4:5]
         scores = x[:, :, 5:]
-        scores *= conf
+        if self.n_classes == 1:
+            scores = conf # for models with one class, cls_loss is 0 and cls_conf is always 0.5,
+                                 # so there is no need to multiplicate.
+        else:
+            scores *= conf  # conf = obj_conf * cls_conf
         num_det, det_boxes, det_scores, det_classes = TRT_NMS.apply(boxes, scores, self.background_class, self.box_coding,
                                                                     self.iou_threshold, self.max_obj,
                                                                     self.plugin_version, self.score_activation,
@@ -215,14 +225,14 @@ def forward(self, x):
 
 class End2End(nn.Module):
     '''export onnx or tensorrt model with NMS operation.'''
-    def __init__(self, model, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None, device=None):
+    def __init__(self, model, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None, device=None, n_classes=80):
         super().__init__()
         device = device if device else torch.device('cpu')
         assert isinstance(max_wh,(int)) or max_wh is None
         self.model = model.to(device)
         self.model.model[-1].end2end = True
         self.patch_model = ONNX_TRT if max_wh is None else ONNX_ORT
-        self.end2end = self.patch_model(max_obj, iou_thres, score_thres, max_wh, device)
+        self.end2end = self.patch_model(max_obj, iou_thres, score_thres, max_wh, device, n_classes)
         self.end2end.eval()
 
     def forward(self, x):