1classNeuralNetwork(nn.Module):2def__init__(self, layer_num):3super(NeuralNetwork, self).__init__()4self.layers = [nn.Linear(28*28,28*28)for_inrange(layer_num)]5fori,layerinenumerate(self.layers):6self.add_module('layer_{}'.format(i),layer)7self.linear_relu_stack =nn.Sequenti...
1.3Module类的named_children()方法 通过Module类定义的模型,还可以从其实例化对象中通过named_children()方法取得模型中各层的名字及结构信息。 for name, nodule in model.named_children(): # 调用模型的children()方法取得模型中各层的名字及结构信息 print(name,"is:",module) 1. 2. 输出: Linear1 is:L...
这次调试,也对pytorch中Dataparallel()和add_module()有了更深的了解,在init()初始化一个模型的时候,model是生成在cpu上了,因此定义好model之后,再Dataparallel(model),模型就到了gpu上面,而add_module()必须要在cpu上面才行,总的来说觉得还是蛮坑的,所以以后还是尽量使用nn.ModuleList()吧编辑...
(1)通过self.module=xxx_module的方式(如下面第3行代码),添加网络模块; (2)通过add_module函数对网络中添加模块。 (3)通过用nn.Sequential对模块进行封装等等。 1 class NeuralNetwork(nn.Module): 2 def __init__(self): 3 super(NeuralNetwork, self).__init__() 4 self.layers = nn.Linear(28*28...
pytorch在注册模块的时候,会查看成员的类型,如果成员变量类型是Module的子类,那么pytorch就会注册这个模块,否则就不会。 这里的self.layers是python中的List类型,所以不会自动注册,那么就需要我们再定义后,…
[pytree] Add public pytree module torch.utils.pytree #295421 Sign in to view logs Summary Jobs before-test get-label-type linux-jammy-py3.9-gcc11 linux-jammy-py3.9-gcc11-no-ops linux-jammy-py3.9-gcc11-pch linux-jammy-py3.10-clang15-asan linux-focal-py3.9-clang10-onnx...
[pytree] Add public pytree module torch.utils.pytree #10215 Sign in to view logs Summary Jobs get-label-type docker-build (linux.12xlarge, pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9) docker-build (linux.12xlarge, pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-bench...
>>> import torch Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.8/dist-packages/torch/__init__.py", line 235, in <module> from torch._C import * # noqa: F403 ImportError: /usr/local/lib/python3.8/dist-pack...
classtorch.nn.DataParallel(module,device_ids=None, output_device=None, dim=0) DataParallel 自动分割您的数据,并将作业订单发送到多个 GPU 上的多个模型。每个模型完成工作后,DataParallel 会收集并合并结果,然后再将结果返回给您。DataParallel 将相同的模型复制...
class QuickNat(nn.Module):"""A PyTorch implementation of QuickNAT """ def __init__(self, params):""":param params: {'num_channels':1,'num_filters':64,'kernel_h':5,'kernel_w':5,'stride_conv':1,'pool':2...