Glenn Jocher
GitHub
parent
f7d8562060
commit
b1be685005
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@ -36,7 +36,7 @@ def DWConv(c1, c2, k=1, s=1, act=True):
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class Conv(nn.Module):
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# Standard convolution
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
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super(Conv, self).__init__()
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super().__init__()
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self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
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self.bn = nn.BatchNorm2d(c2)
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self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
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@ -87,7 +87,7 @@ class TransformerBlock(nn.Module):
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class Bottleneck(nn.Module):
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# Standard bottleneck
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def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
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super(Bottleneck, self).__init__()
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super().__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_, c2, 3, 1, g=g)
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@ -100,7 +100,7 @@ class Bottleneck(nn.Module):
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class BottleneckCSP(nn.Module):
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# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super(BottleneckCSP, self).__init__()
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super().__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
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@ -119,7 +119,7 @@ class BottleneckCSP(nn.Module):
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class C3(nn.Module):
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# CSP Bottleneck with 3 convolutions
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super(C3, self).__init__()
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super().__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c1, c_, 1, 1)
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@ -139,10 +139,18 @@ class C3TR(C3):
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self.m = TransformerBlock(c_, c_, 4, n)
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class C3SPP(C3):
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# C3 module with SPP()
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def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
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super().__init__(c1, c2, n, shortcut, g, e)
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c_ = int(c2 * e)
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self.m = SPP(c_, c_, k)
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class SPP(nn.Module):
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# Spatial pyramid pooling layer used in YOLOv3-SPP
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def __init__(self, c1, c2, k=(5, 9, 13)):
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super(SPP, self).__init__()
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super().__init__()
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c_ = c1 // 2 # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
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@ -156,7 +164,7 @@ class SPP(nn.Module):
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class Focus(nn.Module):
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# Focus wh information into c-space
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
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super(Focus, self).__init__()
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super().__init__()
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self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
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# self.contract = Contract(gain=2)
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@ -196,27 +204,13 @@ class Expand(nn.Module):
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class Concat(nn.Module):
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# Concatenate a list of tensors along dimension
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def __init__(self, dimension=1):
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super(Concat, self).__init__()
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super().__init__()
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self.d = dimension
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def forward(self, x):
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return torch.cat(x, self.d)
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class NMS(nn.Module):
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# Non-Maximum Suppression (NMS) module
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conf = 0.25 # confidence threshold
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iou = 0.45 # IoU threshold
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classes = None # (optional list) filter by class
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max_det = 1000 # maximum number of detections per image
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def __init__(self):
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super(NMS, self).__init__()
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def forward(self, x):
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return non_max_suppression(x[0], self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det)
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class AutoShape(nn.Module):
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# YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
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conf = 0.25 # NMS confidence threshold
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@ -225,7 +219,7 @@ class AutoShape(nn.Module):
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max_det = 1000 # maximum number of detections per image
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def __init__(self, model):
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super(AutoShape, self).__init__()
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super().__init__()
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self.model = model.eval()
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def autoshape(self):
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@ -292,7 +286,7 @@ class AutoShape(nn.Module):
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class Detections:
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# YOLOv5 detections class for inference results
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def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
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super(Detections, self).__init__()
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super().__init__()
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d = pred[0].device # device
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gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations
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self.imgs = imgs # list of images as numpy arrays
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@ -383,7 +377,7 @@ class Detections:
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class Classify(nn.Module):
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# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
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super(Classify, self).__init__()
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super().__init__()
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self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
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self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
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self.flat = nn.Flatten()
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@ -12,7 +12,7 @@ class CrossConv(nn.Module):
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# Cross Convolution Downsample
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def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
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# ch_in, ch_out, kernel, stride, groups, expansion, shortcut
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super(CrossConv, self).__init__()
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super().__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, (1, k), (1, s))
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self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
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@ -25,7 +25,7 @@ class CrossConv(nn.Module):
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class Sum(nn.Module):
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# Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
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def __init__(self, n, weight=False): # n: number of inputs
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super(Sum, self).__init__()
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super().__init__()
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self.weight = weight # apply weights boolean
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self.iter = range(n - 1) # iter object
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if weight:
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@ -46,7 +46,7 @@ class Sum(nn.Module):
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class GhostConv(nn.Module):
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# Ghost Convolution https://github.com/huawei-noah/ghostnet
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def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
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super(GhostConv, self).__init__()
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super().__init__()
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c_ = c2 // 2 # hidden channels
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self.cv1 = Conv(c1, c_, k, s, None, g, act)
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self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
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@ -59,7 +59,7 @@ class GhostConv(nn.Module):
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class GhostBottleneck(nn.Module):
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# Ghost Bottleneck https://github.com/huawei-noah/ghostnet
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def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
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super(GhostBottleneck, self).__init__()
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super().__init__()
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c_ = c2 // 2
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self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
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DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
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@ -74,7 +74,7 @@ class GhostBottleneck(nn.Module):
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class MixConv2d(nn.Module):
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# Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
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def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
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super(MixConv2d, self).__init__()
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super().__init__()
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groups = len(k)
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if equal_ch: # equal c_ per group
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i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices
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@ -98,7 +98,7 @@ class MixConv2d(nn.Module):
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class Ensemble(nn.ModuleList):
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# Ensemble of models
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def __init__(self):
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super(Ensemble, self).__init__()
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super().__init__()
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def forward(self, x, augment=False, profile=False, visualize=False):
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y = []
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@ -33,7 +33,7 @@ class Detect(nn.Module):
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onnx_dynamic = False # ONNX export parameter
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def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer
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super(Detect, self).__init__()
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super().__init__()
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self.nc = nc # number of classes
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self.no = nc + 5 # number of outputs per anchor
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self.nl = len(anchors) # number of detection layers
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@ -77,7 +77,7 @@ class Detect(nn.Module):
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class Model(nn.Module):
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def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
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super(Model, self).__init__()
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super().__init__()
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if isinstance(cfg, dict):
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self.yaml = cfg # model dict
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else: # is *.yaml
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@ -209,20 +209,6 @@ class Model(nn.Module):
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self.info()
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return self
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def nms(self, mode=True): # add or remove NMS module
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present = type(self.model[-1]) is NMS # last layer is NMS
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if mode and not present:
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LOGGER.info('Adding NMS... ')
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m = NMS() # module
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m.f = -1 # from
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m.i = self.model[-1].i + 1 # index
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self.model.add_module(name='%s' % m.i, module=m) # add
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self.eval()
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elif not mode and present:
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LOGGER.info('Removing NMS... ')
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self.model = self.model[:-1] # remove
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return self
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def autoshape(self): # add AutoShape module
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LOGGER.info('Adding AutoShape... ')
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m = AutoShape(self) # wrap model
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@ -250,7 +236,7 @@ def parse_model(d, ch): # model_dict, input_channels(3)
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n = max(round(n * gd), 1) if n > 1 else n # depth gain
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if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP,
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C3, C3TR]:
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C3, C3TR, C3SPP]:
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c1, c2 = ch[f], args[0]
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if c2 != no: # if not output
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c2 = make_divisible(c2 * gw, 8)
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