【精选】如何使用tensorboard及打开tensorboard生成文件 |
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一、使用tensorboard
tensorboard中常用函数
1、writer.add_scalar() def add_scalar( self, tag, scalar_value, global_step=None, walltime=None, new_style=False, double_precision=False, ): Args: tag (string): Data identifier scalar_value (float or string/blobname): Value to save global_step (int): Global step value to record walltime (float): Optional override default walltime (time.time())with seconds after epoch of event new_style (boolean): Whether to use new style (tensor field) or old style (simple_value field). New style could lead to faster data loading.tag:所画图标的title,str类型,注意引号 scalar_value:需要保存的数值,对应y轴的y值 global_step:当前的全局步数,对应x轴 from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter() for i in range(100): writer.add_scalar('y=2x', i * 2, i) writer.close()2、writer.add_image() 作用:Add image data to summary. def add_image( self, tag, img_tensor, global_step=None, walltime=None, dataformats="CHW" ): Args: tag (string): Data identifier img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data#注意此处要求数据类型 global_step (int): Global step value to record walltime (float): Optional override default walltime (time.time()) seconds after epoch of event dataformats (string): Image data format specification of the form CHW, HWC, HW, WH, etc. #其中dataformats默认为(3, H, W),C指channel,H指height,W指wide。3、writer.add_images() 作用:Add batched image data to summary. def add_images( self, tag, img_tensor, global_step=None, walltime=None, dataformats="NCHW" ): Args: tag (string): Data identifier img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data global_step (int): Global step value to record walltime (float): Optional override default walltime (time.time()) seconds after epoch of event dataformats (string): Image data format specification of the form CHW, NHWC, CHW, HWC, HW, WH, etc. #其中dataformats默认为(N, 3, H, W),N指batch_size. C指channel,H指height,W指wide。若img_tensor不是3 channels,一定要先用torch.reshape()方法将其修改为3 channels,否则会报错 output = torch.reshape(output,([-1, 3, H, W]))4、writer.add_graph() add_graph(model, input_to_model=None, verbose=False, use_strict_trace=True) Parameters model (torch.nn.Module) – Model to draw. input_to_model (torch.Tensor or list of torch.Tensor) – A variable or a tuple of variables to be fed. verbose (bool) – Whether to print graph structure in console. use_strict_trace (bool) – Whether to pass keyword argument strict to torch.jit.trace. Pass False when you want the tracer to record your mutable container types (list, dict) 二、打开tensorboard生成的文件1、安装tensorboard,我使用的是自己创建的命名为pytorch的conda环境 在cmd中输入tensorboard -h,出现下面的结果说明已经添加了 2、训练模型。训练后得到一个log文件,它就是我们要可视化的数据,复制该文件的路径。 若需要同时打开多个,可以输入 tensorboard --logdir={log文件所在路径} –port=6007 创建不同端口号的网址。 如果代码和项目都在云服务器中,这时如果需要在本地浏览器中查看Tensorboard的运行效果,就需要按照如下的步骤进行操作: 将服务器中Tensorboard的端口6006映射到本地端口16006,在终端中输入: ssh -L 16006:127.0.0.1:6006 用户名@服务器ip -p 22 22为服务器的端口号 输入服务器登录密码 激活服务器端python环境,在终端中运行:tensorboard --logdir={tensorboard文件位置} 在本地服务器输入:http://localhost:16006 更简单的查看服务器中Tensorboard内容的方法下载vnc viewer,连上后打开火狐浏览器直接输入http://localhost:6006/即可访问到了。 |
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