可以根据你想让该类元组表示怎样的含义来定义。 比如你想创建一个坐标点元组子类,可以通过语句pt = collections.namedtuple('point', ['x', 'y'])来创建该坐标点元组类。如果你想要获取一个该元组类的对象,点A,可以通过语句A = pt(2,3)来获取。这样就可以通过p.x等方式获取该点的信息。 又比如你想创建...
作为CVPR2016的best paper,何凯明的文章【1】针对深层网络梯度弥散导致的SGD优化难题,提出了residual(残差)结构,很好的解决了模型退化问题,在50层、101层...) 图2. 常规56层网络无论训练还是测试,精度都比20层网络差针对该问题,方法提出一个Residual(残差)结构,对于1000多层的网络也能保持很好的 ...
remove("mnist_cifar_10_resnet.pt"); printf("Finish training!\n"); torch::serialize::OutputArchive archive; net1.save(archive); //将训练模型写到文件中 archive.save_to("mnist_cifar_10_resnet.pt"); printf("Save the training result to mnist_cifar_10_resnet.pt.\n"); } 1. 2. 3. 4...
(112+2-3)/2+1=56 kernel_size=3, #池化核是3x3 stride=2, #步长2 padding=1) # ResNet的第二到第五个模块c2、c3、c4、c5 self.bottleneck_block_list = [] num_channels = 64 for block in range(len(depth)): shortcut = False for i in range(depth[block]): #i在[3,4,6,3]中 ...
ts.DEFAULT` to get the most up-to-date weights. warnings.warn(msg) /home/gabeferns/pt-envs/fuzzer/torch/_inductor/compile_fx.py:194: UserWarning: TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider set ...
[64,7] logpt = -self.ce_fn(preds, labels) #这块报错 二者维度不同 pt = paddle.exp(logpt) loss = -((1 - pt) ** self.gamma) * self.alpha * logpt #print("loss") #print(loss) return loss class crossentropyloss(nn.Layer): def __init__(self,weight=None,): super().__init...
这里我们使用torch.onnx.export保存模型为onnx模型(也可以导出pt模型等): resnet18.py(截取部分,参考配套例程) 1 2 3 # export onnx (rknn-toolkit2 only support to opset_version=12) x = torch.randn((1, 3, 32, 32)) torch.onnx.export(model, x, './resnet18_pytorch_100.onnx', opset_...
NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94 NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,...
这里我们使用torch.onnx.export保存模型为onnx模型(也可以导出pt模型等): resnet18.py(截取部分,参考配套例程) 1 2 3 # export onnx (rknn-toolkit2 only support to opset_version=12) x = torch.randn((1, 3, 32, 32)) torch.onnx.export(model, x, './resnet18_pytorch_100.onnx', opset_...
In this study, to reduce the number of network parameters, improve the detection accuracy and solve the problem of weak robustness of the detection algorithm, we propose a new lightweight network model SE-ResNet56 to detect fake face images generated by GAN. The proposed algorithm has high ...