class ResNet(nn.Module): # 网络框架 # 参数:block 如果定义的是18层或34层的框架 就是BasicBlock, 如果定义的是50,101,152层的框架,就是Bottleneck # blocks_num 残差层的个数,对应34层的残差网络就是 [3,4,6,3] # include_top 方便以后在resnet的基础上搭建更复杂的网络 def __init__(self, blo...
✌ResNet # 总的残差网络模型class ResNet(nn.Module):def __init__(self,block,block_num,num_classes=1000,include_top=True):super(ResNet,self).__init__()self.include_top=include_topself.in_channel=64# 第一个卷积层,使用7*7的卷积核,步长为2,使数据维度减半self.conv1=nn.Conv2d(in_chan...
# 可参考Resnet v1.5 https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch """ expansion = 4 # 残差结构中第三层卷积核个数是第1/2层卷积核个数的4倍 def __init__(self, in_channel, out_channel, stride=1, downsample=None, groups=1, width_per_group=64): ...
(self, block, blocks_num, num_classes=1000, include_top=True): super(ResNet, self).__init__() self.include_top = include_top self.in_channel = 64 self.conv1 = nn.Conv2d( 3, self.in_channel, kernel_size=7, stride=2, padding=3, bias=False ) self.bn1 = nn.BatchNorm2d(self...
if self.include_top: x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x)return xdefresnet34(num_classes=1000, include_top=True):return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)defresnet101(num_classes=1000, include_...
(out) return out class ResNet(nn.Module): def __init__(self, block, blocks_num, num_classes=1000, include_top=True):#block残差结构 include_top为了之后搭建更加复杂的网络 super(ResNet, self).__init__() self.include_top = include_top self.in_channel = 64 self.conv1 = nn.Conv2d(...
1.1ResNet特点 深层网络结构 残差模块 Batch Normalization加速训练 使一批feature map满足均值为0,方差为1的分布。 ResNet解决了网络层数增加带来的梯度消失,梯度爆炸和梯度退化问题。 1.2网络结构 residua block的虚线代表主分支和shortcut的shape不同,所以要在shortcut中加入kernel,使得输出的维度和主分支相同,才能进行...
models.ResNet50V2(include_top=True, classes=10, input_shape=(3, 224, 224), backend='pytorch') # Optimizer, loss function, etc. for epoch in epochs: for batch in train_loader: optimizer.zero_grad() inputs, labels = batch outputs = model(inputs) loss = criterion(outputs, labels) ...
导出的推理模型使用的是Minist中训练预测率为99%的ResNet模型,从上面两张图来看,大部分数字识别是没问题的,但是两张图中数字7都识别为数字1了。这个暂时不是本篇要解决的问题,我们先看看怎么实现的导出模型和推理。 微卡智享 导出模型 由于不想再重新写一篇网络模型了,所以将原来train.py中的加载训练集和测试集,...
distill import DistillableViT, DistillWrapper teacher = resnet50(pretrained = True) v = DistillableViT( image_size = 256, patch_size = 32, num_classes = 1000, dim = 1024, depth = 6, heads = 8, mlp_dim = 2048, dropout = 0.1, emb_dropout = 0.1 ) distiller = DistillWrapper( ...