Paramenters和Modules一起使用的时候会有一些特殊的属性,即:当Paramenters赋值给Module的属性的时候,他会自动的被加到Module的参数列表中,也就是会出现在parameters()迭代器中。常被用于模块参数module parameter。 将Varibale赋值给Module属性则不会有这样的影响。 这样做的原因是:我们有时候会需要缓存一些临时的状态st...
], requires_grad=True)) ('linear.weight', Parameter containing: tensor([[0.9703]], requires_grad=True)) ('linear.bias', Parameter containing: tensor([0.3660], requires_grad=True)) 5、添加新的操作add_module() def add_module(self, name, module): r"""Adds a child module to the ...
1.1 Module类的add_module()方法1.1.1 概述add_module():将XX层插入到模型结构中1.1.2 add_module()---LogicNet_fun.py(第1部分)import torch.nn as nn import torch import numpy as np import matplotlib.pyplot as plt class LogicNet(nn.Module): def __init__(self,inputdim,hiddendim,outputdim):...
classModule(object):def__init__(self):defforward(self,*input):defadd_module(self,name,module):defcuda(self,device=None):defcpu(self):def__call__(self,*input,**kwargs):defparameters(self,recurse=True):defnamed_parameters(self,prefix='',recurse=True):defchildren(self):defnamed_children(s...
assert input_size%16==0,"input_size has to be a multiple of 16"models=nn.Sequential() models.add_module('Conv2_{0}_{1}'.format(input_channels, base_channnes), nn.Conv2d(input_channels, base_channnes,4,2,1, bias=False))
memo.add(v) name = module_prefix + ('.' if module_prefix else '') + k yield name, v def named_parameters(self, prefix='', recurse=True): r"""Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself. ...
self.parameters_to_ignore = []# 检查 parameters# Check that a module does not have Uninitialized parametersforparaminmodule.parameters():ifisinstance(param, torch.nn.parameter.UninitializedParameter):raiseRuntimeError("Modules with uninitialized parameters can't be used with `DistributedDataParallel`. ...
🚀 Feature Add name to Class Parameter() [link] (https://github.com/pytorch/pytorch/blob/master/torch/nn/parameter.py). Motivation Currently, parameter names are available via nn.Module.name_parameter(), it is good enough for a model that...
class BN(torch.nn.Module)def __init__(self):...self.register_buffer('running_mean', torch.zeros(num_features)) def forward(self, X):...self.running_mean += momentum * (current - self.running_mean) 计算模型整体参数量 num_parameters = sum(torch.numel(...
(can be a file or file-like object)export_params=True, # store the trained parameter weights inside the model fileopset_version=10, # the ONNX version to export the model todo_constant_folding=True, # whether to execute constant folding for optimizationinput_names = ['input'], # the ...