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

* Module `super().__init__()`

* remove NMS

models/common.py

 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())
 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)
 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)
 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)
         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)
 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)
 
 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
     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):
 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
 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()

models/experimental.py

     # Cross Convolution Downsample
     def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
         # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
-        super(CrossConv, self).__init__()
+        super().__init__()
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, c_, (1, k), (1, s))
         self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
 class Sum(nn.Module):
     # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
     def __init__(self, n, weight=False):  # n: number of inputs
-        super(Sum, self).__init__()
+        super().__init__()
         self.weight = weight  # apply weights boolean
         self.iter = range(n - 1)  # iter object
         if weight:
 class GhostConv(nn.Module):
     # Ghost Convolution https://github.com/huawei-noah/ghostnet
     def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
-        super(GhostConv, self).__init__()
+        super().__init__()
         c_ = c2 // 2  # hidden channels
         self.cv1 = Conv(c1, c_, k, s, None, g, act)
         self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
 class GhostBottleneck(nn.Module):
     # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
     def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
-        super(GhostBottleneck, self).__init__()
+        super().__init__()
         c_ = c2 // 2
         self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1),  # pw
                                   DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
 class MixConv2d(nn.Module):
     # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
     def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
-        super(MixConv2d, self).__init__()
+        super().__init__()
         groups = len(k)
         if equal_ch:  # equal c_ per group
             i = torch.linspace(0, groups - 1E-6, c2).floor()  # c2 indices
 class Ensemble(nn.ModuleList):
     # Ensemble of models
     def __init__(self):
-        super(Ensemble, self).__init__()
+        super().__init__()
 
     def forward(self, x, augment=False, profile=False, visualize=False):
         y = []

models/yolo.py

     onnx_dynamic = False  # ONNX export parameter
 
     def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer
-        super(Detect, self).__init__()
+        super().__init__()
         self.nc = nc  # number of classes
         self.no = nc + 5  # number of outputs per anchor
         self.nl = len(anchors)  # number of detection layers
 
 class Model(nn.Module):
     def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes
-        super(Model, self).__init__()
+        super().__init__()
         if isinstance(cfg, dict):
             self.yaml = cfg  # model dict
         else:  # is *.yaml
         self.info()
         return self
 
-    def nms(self, mode=True):  # add or remove NMS module
-        present = type(self.model[-1]) is NMS  # last layer is NMS
-        if mode and not present:
-            LOGGER.info('Adding NMS... ')
-            m = NMS()  # module
-            m.f = -1  # from
-            m.i = self.model[-1].i + 1  # index
-            self.model.add_module(name='%s' % m.i, module=m)  # add
-            self.eval()
-        elif not mode and present:
-            LOGGER.info('Removing NMS... ')
-            self.model = self.model[:-1]  # remove
-        return self
-
     def autoshape(self):  # add AutoShape module
         LOGGER.info('Adding AutoShape... ')
         m = AutoShape(self)  # wrap model
 
         n = max(round(n * gd), 1) if n > 1 else n  # depth gain
         if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP,
-                 C3, C3TR]:
+                 C3, C3TR, C3SPP]:
             c1, c2 = ch[f], args[0]
             if c2 != no:  # if not output
                 c2 = make_divisible(c2 * gw, 8)