pytorch网络模型可视化方法：tensorboardX

步骤：

1. pip 安装tensorboardX
2. from tensorboardX import SummaryWriter
3. 关键代码：

with SummaryWriter(comment='tcn') as w:
    w.add_graph(model, input_to_model=dummy_input)

4. 在runs所在文件夹下命令行输入：tensorboard --logdir runs
5. 打开 http://localhost:6006/

可参考：Pytorch的网络结构可视化（tensorboardX）（详细）

示例：

import torch.nn.functional as F
from torch import nn
import torch
from ttt import TemporalConvNet
from tensorboardX import SummaryWriter
from torch.autograd import Variable

class TCN(nn.Module):
    def __init__(self, input_size, output_size, num_channels, kernel_size, dropout):
        super(TCN, self).__init__()
        self.tcn = TemporalConvNet(input_size, num_channels, kernel_size=kernel_size, dropout=dropout)
        self.linear = nn.Linear(num_channels[-1], output_size)

    def forward(self, inputs):
        """Inputs have to have dimension (N, C_in, L_in)"""
        y1 = self.tcn(inputs)  # input should have dimension (N, C, L)
        o = self.linear(y1[:, :, -1])
        return F.log_softmax(o, dim=1)

model = TCN(1, 3, [25,25,25,25], 7 ,0.2)

dummy_input = Variable(torch.rand(13, 1, 18176))  # 假设输入13张1*28*28的图片

with SummaryWriter(comment='tcn') as w:
    w.add_graph(model, input_to_model=dummy_input)