updates

models.py

@@ -45,8 +45,7 @@ def create_modules(module_defs):
             modules.add_module('maxpool_%d' % i, maxpool)
 
         elif module_def['type'] == 'upsample':
-            # upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest')  # WARNING: deprecated
-            upsample = Upsample(scale_factor=int(module_def['stride']))
+            upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest')
             modules.add_module('upsample_%d' % i, upsample)
 
         elif module_def['type'] == 'route':
@@ -87,18 +86,6 @@ class EmptyLayer(nn.Module):
         return x
 
 
-class Upsample(nn.Module):
-    # Custom Upsample layer (nn.Upsample gives deprecated warning message)
-
-    def __init__(self, scale_factor=1, mode='nearest'):
-        super(Upsample, self).__init__()
-        self.scale_factor = scale_factor
-        self.mode = mode
-
-    def forward(self, x):
-        return F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
-
-
 class YOLOLayer(nn.Module):
     def __init__(self, anchors, nc, img_size, yolo_index):
         super(YOLOLayer, self).__init__()

train.py

@@ -12,6 +12,7 @@ from utils.datasets import *
 from utils.utils import *
 
 #      0.149      0.241      0.126      0.156       6.85      1.008      1.421    0.07989      16.94      6.215      10.61      4.272      0.251      0.001         -4        0.9     0.0005   320 64-1 giou
+#      0.111       0.27      0.132      0.131       3.96      1.276     0.3156     0.1425      21.21      6.224      11.59       8.83      0.376      0.001         -4        0.9     0.0005
 hyp = {'giou': 1.008,  # giou loss gain
        'xy': 1.421,  # xy loss gain
        'wh': 0.07989,  # wh loss gain