add NMS to pretrained pytorch hub models

hubconf.py

@@ -10,6 +10,7 @@
 
 import torch
 
+from models.common import NMS
 from models.yolo import Model
 from utils.google_utils import attempt_download
 
@@ -35,6 +36,12 @@ def create(name, pretrained, channels, classes):
             state_dict = torch.load(ckpt, map_location=torch.device('cpu'))['model'].float().state_dict()  # to FP32
             state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape}  # filter
             model.load_state_dict(state_dict, strict=False)  # load
+
+            m = NMS()
+            m.f = -1  # from
+            m.i = model.model[-1].i + 1  # index
+            model.model.add_module(name='%s' % m.i, module=m)  # add NMS
+            model.eval()
         return model
 
     except Exception as e:

models/common.py

@@ -3,6 +3,7 @@
 
 import torch
 import torch.nn as nn
+from utils.general import non_max_suppression
 
 
 def autopad(k, p=None):  # kernel, padding
@@ -98,6 +99,19 @@ def forward(self, x):
         return torch.cat(x, self.d)
 
 
+class NMS(nn.Module):
+    # Non-Maximum Suppression (NMS) module
+    conf = 0.3  # confidence threshold
+    iou = 0.6  # IoU threshold
+    classes = None  # (optional list) filter by class
+
+    def __init__(self, dimension=1):
+        super(NMS, self).__init__()
+
+    def forward(self, x):
+        return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
+
+
 class Flatten(nn.Module):
     # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
     @staticmethod