Module super().__init__() (#4065)

* Module `super().__init__()`

* remove NMS

models/common.py

@@ -36,7 +36,7 @@ def DWConv(c1, c2, k=1, s=1, act=True):
 class Conv(nn.Module):
     # Standard convolution
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Conv, self).__init__()
+        super().__init__()
         self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
         self.bn = nn.BatchNorm2d(c2)
         self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
@@ -87,7 +87,7 @@ class TransformerBlock(nn.Module):
 class Bottleneck(nn.Module):
     # Standard bottleneck
     def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super(Bottleneck, self).__init__()
+        super().__init__()
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, c_, 1, 1)
         self.cv2 = Conv(c_, c2, 3, 1, g=g)
@@ -100,7 +100,7 @@ class Bottleneck(nn.Module):
 class BottleneckCSP(nn.Module):
     # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(BottleneckCSP, self).__init__()
+        super().__init__()
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, c_, 1, 1)
         self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
@@ -119,7 +119,7 @@ class BottleneckCSP(nn.Module):
 class C3(nn.Module):
     # CSP Bottleneck with 3 convolutions
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(C3, self).__init__()
+        super().__init__()
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, c_, 1, 1)
         self.cv2 = Conv(c1, c_, 1, 1)
@@ -139,10 +139,18 @@ class C3TR(C3):
         self.m = TransformerBlock(c_, c_, 4, n)
 
 
+class C3SPP(C3):
+    # C3 module with SPP()
+    def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        c_ = int(c2 * e)
+        self.m = SPP(c_, c_, k)
+
+
 class SPP(nn.Module):
     # Spatial pyramid pooling layer used in YOLOv3-SPP
     def __init__(self, c1, c2, k=(5, 9, 13)):
-        super(SPP, self).__init__()
+        super().__init__()
         c_ = c1 // 2  # hidden channels
         self.cv1 = Conv(c1, c_, 1, 1)
         self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
@@ -156,7 +164,7 @@ class SPP(nn.Module):
 class Focus(nn.Module):
     # Focus wh information into c-space
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Focus, self).__init__()
+        super().__init__()
         self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
         # self.contract = Contract(gain=2)
 
@@ -196,27 +204,13 @@ class Expand(nn.Module):
 class Concat(nn.Module):
     # Concatenate a list of tensors along dimension
     def __init__(self, dimension=1):
-        super(Concat, self).__init__()
+        super().__init__()
         self.d = dimension
 
     def forward(self, x):
         return torch.cat(x, self.d)
 
 
-class NMS(nn.Module):
-    # Non-Maximum Suppression (NMS) module
-    conf = 0.25  # confidence threshold
-    iou = 0.45  # IoU threshold
-    classes = None  # (optional list) filter by class
-    max_det = 1000  # maximum number of detections per image
-
-    def __init__(self):
-        super(NMS, self).__init__()
-
-    def forward(self, x):
-        return non_max_suppression(x[0], self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det)
-
-
 class AutoShape(nn.Module):
     # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
     conf = 0.25  # NMS confidence threshold
@@ -225,7 +219,7 @@ class AutoShape(nn.Module):
     max_det = 1000  # maximum number of detections per image
 
     def __init__(self, model):
-        super(AutoShape, self).__init__()
+        super().__init__()
         self.model = model.eval()
 
     def autoshape(self):
@@ -292,7 +286,7 @@ class AutoShape(nn.Module):
 class Detections:
     # YOLOv5 detections class for inference results
     def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
-        super(Detections, self).__init__()
+        super().__init__()
         d = pred[0].device  # device
         gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]  # normalizations
         self.imgs = imgs  # list of images as numpy arrays
@@ -383,7 +377,7 @@ class Detections:
 class Classify(nn.Module):
     # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Classify, self).__init__()
+        super().__init__()
         self.aap = nn.AdaptiveAvgPool2d(1)  # to x(b,c1,1,1)
         self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g)  # to x(b,c2,1,1)
         self.flat = nn.Flatten()