ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels # ch into module c = Classify(ch, nc) # Classify() c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type model.model[-1] = c # replace self.model = model.model self.s...
c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e) # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False...
5.4 Classify'''===4.Classify:二级分类模块===''' 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().__init__() self.aap...
() self.d = dimension def forward(self, x): return torch.cat(x, self.d) class Flatten(nn.Module): # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions @staticmethod def forward(x): return x.view(x.size(0), -1) class Classify(nn.Module): # Classification head, i.e...
然后运行classify/predict.py进行识别,识别完成的结果如下: 这里每一张图片会对应一个label,label中包含了最大5个类别的概率和名称。 格式转换与可视化 下面就需要把识别出来的结果转回到YOLO检测格式。 编写脚本cls2corp.py 代码语言:javascript 复制 #--coding:utf-8--""" ...
model.half()# to FP16# Second-stage classifierclassify =Falseifclassify: modelc = load_classifier(name='resnet101', n=2)# initializemodelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()# Set Dataloadervid_path, vid_writer =None,...
if classify: # second-stage classifier modelc = load_classifier(name='resnet50', n=2) # initialize modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval() elif onnx: if dnn: # check_requirements(('opencv-python>=4.5.4',)) ...
half() # to FP16 # Second-stage classifier classify = False if classify: modelc = load_classifier(name='resnet101', n=2) # initialize modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights modelc.to(device).eval() # Set ...
c = Classify(ch, nc) # 在backbone后面接分类器,Classify(),分类器 c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type model.model[-1] = c # 将最后一层换成分类器 self.model = model.model self.stride = model.stride ...
然后运行classify/predict.py进行识别,识别完成的结果如下: 这里每一张图片会对应一个label,label中包含了最大5个类别的概率和名称。 格式转换与可视化 下面就需要把识别出来的结果转回到YOLO检测格式。 编写脚本cls2corp.py ...