SummaryWriter详解之add_graph示例分析

上一篇分析了SummaryWriter主要函数，本篇借助TensorboardX官方demo，解释add_graph用法

add_graph参数

add_graph(self, model, input_to_model=None, verbose=False）model： model (torch.nn.Module): model to draw 对于model = Linear（），参数形式直接用model，不加括号 否则需要用Linear（），否则需要加括号input_to_model：input_to_model (torch.Tensor or list of torch.Tensor): a variable or a tuple of variables to be fed.模型输入，可以有一个或者多个。搞不清楚shape时，建议用debug模式观察一下。模型输入必须存在。verbose：字面意思是冗长的、啰嗦的。bool变量，默认为False启动tensorboard时，–logdir指向的文件夹下，将会递归所有所有文件（存储在子文件夹中不影响）tensorboard --logdir runs有时不工作，可以使用tensorboard --logdir runs --host localhost采用with SummaryWriter() as writer:写法，可以省掉writer.close()

demo示例

本文选用demo中代表性的条目，完整demo参见：https://github.com/lanpa/tensorboardX/blob/master/examples/demo_graph.py

import torch import torch.nn as nn import torch.nn.functional as F import torchvision from torch.autograd import Variable from tensorboardX import SummaryWriter dummy_input = torch.ones(1,3) class LinearInLinear(nn.Module): def __init__(self): super(LinearInLinear,self).__init__() self.l = nn.Linear(3,5) def forward(self,x): return self.l(x) with SummaryWriter(comment='LinearInLinear2') as w: w.add_graph(LinearInLinear(),dummy_input,False) class MutipleInput(nn.Module): def __init__(self): super(MutipleInput,self).__init__() self.Linear_1 = nn.Linear(3,5) def forward(self, x,y): return self.Linear_1(x+y) model_m = MutipleInput() with SummaryWriter(comment='MutipleInput') as w: w.add_graph(model_m,(torch.ones(1,3),torch.zeros(1,3)),True) def conv3x3(in_channels,out_channels,stride=1): return nn.Conv2d(in_channels,out_channels,kernel_size=3,stride=stride,padding=1,bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self,inplanes,planes,stride=1,downsample=None): super(BasicBlock,self).__init__() self.conv1 = conv3x3(inplanes,planes,stride) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = conv3x3(planes,planes) self.bn2 = nn.BatchNorm2d(planes) self.stride = stride def forward(self,x): residual = x out = self.conv1(x) out = self.bn1(out) out = F.relu(out) out = self.conv2(out) out = self.bn2(out) out += residual out = F.relu(out) return out dummy_input = torch.rand(1, 3, 224, 224) with SummaryWriter(comment='basicblock') as w: model = BasicBlock(3, 3) w.add_graph(model, (dummy_input, ), verbose=True)

截取一个输出图（basicblock）如下：

不解：

官方demo中如下的代码片段：

dummy_input = torch.Tensor(1,3,224,224) with SummaryWriter(comment='vgg19') as w: model = torchvision.models.vgg19() w.add_graph(model, (dummy_input,)) with SummaryWriter(comment='resnet18') as w: model = torchvision.models.resnet18() w.add_graph(model, (dummy_input,))

无法运行，报错信息如下：

assert output_size == [1, 1], "Only output_size=[1, 1] is supported" AssertionError: Only output_size=[1, 1] is supported

仅仅是增加一个graph，和output有什么关系？

也许是版本不一致。

仍将持续更新，感谢关注!