def __init__(self, block, blocks_num, num_classes=1000, include_top=True, norm_layer=None): super(ResNet, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.include_top = include_top self.in_channel = 64#通过Maxpooling之后...
def resnet34(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnet34-333f7ec4.pth return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) def resnet50(num_classes=1000, include_top=True): # https://download.pytorch....
class ResNet(nn.Module): #初始化网络结构和参数 def __init__(self,block,layers,num_classes=1000): #self.inplane为当前的fm的通道数 self.inplane=64 super(ResNet,self).__init__() #参数 self.block=block self.layers=layers #stem的网络层 self.conv1=nn.Conv2d(3,self.inplane,kernel_size...
model_ft = pretrainedmodels.__dict__['se_resnet50'](num_classes=1000, pretrained='imagenet') model_ft.fc = classifier = nn.Sequential( nn.Linear(2048, 512), nn.LeakyReLU(True), nn.Dropout(0.5), nn.Linear(512, 12), ) model_ft.to(DEVICE) # 选择简单暴力的Adam优化器,学习率调低 o...
importmindspore.nnasnnfrommindsporeimportTensorfrommindsporeimportdtypeasmstypefrommindspore.nnimportLossBaseimportmindspore.opsasops# define cross entropy lossclassCrossEntropySmooth(LossBase):"""CrossEntropy"""def__init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):super...
class ResNet50(nn.Module):def __init__(self, num_classes=1000):# ... 前几层代码 ...# 4个残差块的block1self.layer1 = self._make_layer(ResidualBlock, 64, 3, stride=1)# 4个残差块的block2self.layer2 = self._make_layer(ResidualBlock, 128, 4, stride=2)# 4个残差块的block3self...
size of training dataworkers =2# Number of parallel workersnum_classes =1000# Number of classes...
class ResNet(nn.Module): #初始化网络结构和参数 def __init__(self,block,layers,num_classes=1000): #self.inplane为当前的fm的通道数,初始化值为64 self.inplane=64 super(ResNet,self).__init__() #调用了父类 nn.Module 的构造方法 #参数 self.block=block self.layers=layers #stem的网络层 ...
classResNet(nn.Module):def__init__(self,block,layers,num_classes=1000):self.inplanes=64super(ResNet,self).__init__()self.conv1=nn.Conv2d(3,64,kernel_size=7,stride=2,padding=3,bias=False)self.bn1=nn.BatchNorm2d(64)self.relu=nn.ReLU(inplace=True)self.maxpool=nn.MaxPool2d(kernel...
There are no plans to remove support for theresnet50function. However, theimagePretrainedNetworkfunction has additional functionality that helps with transfer learning workflows. For example, you can specify the number of classes in your data using thenumClassesoption, and the function returns a netw...