我们通常会根据实际模型来使用torch.nn.init进行初始化,通常使用isinstance()来进行判断模块(回顾3.4模型构建)属于什么类型。 12345678910 import torchimport torch.nn as nnconv = nn.Conv2d(1,3,3)linear = nn.Linear(10,1)isinstance(conv,nn.Conv2d) # 判断conv是否是nn.Conv2d类型isinstance(linear,nn.Con...
model=nn.Sequential()#创建一个model,按顺序放入layer model=model.to(device)gram=loss.Gram().to(device)'''把vgg19中的layer、content_loss以及style_loss按顺序加入到model中:'''i=1forlayerincnn:ifisinstance(layer,nn.Conv2d):name='conv_'+str(i)model.add_module(name,layer)ifnameincontent_laye...
if isinstance(parameters, torch.Tensor): parameters = [parameters] # 梯度提取, 提取所有具有非空梯度的参数 grads = [p.grad for p in parameters if p.grad is not None] max_norm = float(max_norm) norm_type = float(norm_type) if len(grads) == 0: return torch.tensor(0.0) first_device...
AI代码解释 # torch/nn/modules/module.py(version1.0.1)def__call__(self,*input,**kwargs):forhookinself._forward_pre_hooks.values():hook(self,input)iftorch._C._get_tracing_state():result=self._slow_forward(*input,**kwargs)else:result=self.forward(*input,**kwargs)forhookinself._forwa...
assert isinstance(output_size, (int, tuple)) self.output_size = output_size def __call__(self, sample): image, lable = sample['image'], sample['lable'] h, w = image.shape[:2] if isinstance(self.output_size, int): if h > w: ...
if isinstance(layer, nn.Conv2d): 对layer进行处理 3. model.children() 如果把这个网络模型Net按层次从外到内进行划分的话,features和classifier是Net的子层,而conv2d, ReLU, BatchNorm, Maxpool2d这些有时features的子层, Linear, Dropout, ReLU等是classifier的子层,上面的model.modules()不但会遍历模型的子...
if isinstance(m, (nn.Conv2d, nn.Linear)): nn.init.orthogonal(m.weight) 1. 2. 3. Batchnorm Initialization 在非线性激活函数之前,我们想让输出值有比较好的分布(例如高斯分布),以便于计算梯度和更新参数。Batch Normalization将输出值强行做一次Gaussian Normalization和线性变换: ...
(layer.bias, val=0.0)elif isinstance(layer, torch.nn.BatchNorm2d):torch.nn.init.constant_(layer.weight, val=1.0)torch.nn.init.constant_(layer.bias, val=0.0)elif isinstance(layer, torch.nn.Linear):torch.nn.init.xavier_normal_(layer.weight)if layer.bias is...
elifisinstance(elem,float): returntorch.tensor(batch, dtype=torch.float64) elifisinstance(elem, int_classes): returntorch.tensor(batch) elifisinstance(elem, string_classes): returnbatch elifisinstance(elem, container_abcs.Mapping): return{key: default_collate([d[key]fordinbatch])forkeyinelem} ...
# 初始化网络结构def initialize_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):# 采用了何凯明的初始化方法nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')elif isinstance(m, nn.BatchNorm2d):# 常数初始化: 使值为常数nn.init.constant_(m.weight, ...