torchview
- TorchView 0.2.6
Torchview以视觉图的形式提供了Pytorch模型的可视化。可视化包括张量，模块，火炬函数和信息，例如输入/输出形状。
plot_model of keras的Pytorch版本（以及更多）
支持Pytorch版本
首先，您需要安装GraphViz，
pip install graphviz为了使Graphiz的Python接口工作，您需要在系统中使用点布局命令。如果尚未安装，我建议您在操作系统上运行以下内容
总部位于Debian的Linux发行版（例如Ubuntu）：
apt-get install graphviz视窗：
choco install graphvizmacos
brew install graphviz在此处查看更多详细信息
然后，继续使用PIP安装Torchview
pip install torchview或者如果您想通过Conda
conda install -c conda-forge torchview或者，如果您想要最新的版本，请直接从repo安装
pip install git+https://github.com/mert-kurttutan/torchview.git from torchview import draw_graph
model = MLP ()
batch_size = 2
# device='meta' -> no memory is consumed for visualization
model_graph = draw_graph ( model , input_size = ( batch_size , 128 ), device = 'meta' )
model_graph . visual_graph 
有关更多示例，请参见下面的COLAB笔记本，
简介笔记本：
计算机视觉模型：
NLP模型：
注意：输出GraphViz视觉效果返回带有所需尺寸的图像。但是有时，在vscode上，由于尺寸较大和vScode呈现SVG呈现，有些形状会被裁剪。要解决这个问题，我建议您运行以下内容
import graphviz
graphviz . set_jupyter_format ( 'png' )此问题不会在其他平台上发生，例如Jupyterlab或Google Colab。
def draw_graph (
model : nn . Module ,
input_data : INPUT_DATA_TYPE | None = None ,
input_size : INPUT_SIZE_TYPE | None = None ,
graph_name : str = 'model' ,
depth : int | float = 3 ,
device : torch . device | str | None = None ,
dtypes : list [ torch . dtype ] | None = None ,
mode : str | None = None ,
strict : bool = True ,
expand_nested : bool = False ,
graph_dir : str | None = None ,
hide_module_functions : bool = True ,
hide_inner_tensors : bool = True ,
roll : bool = False ,
show_shapes : bool = True ,
save_graph : bool = False ,
filename : str | None = None ,
directory : str = '.' ,
** kwargs : Any ,
) -> ComputationGraph :
'''Returns visual representation of the input Pytorch Module with
ComputationGraph object. ComputationGraph object contains:
1) Root nodes (usually tensor node for input tensors) which connect to all
the other nodes of computation graph of pytorch module recorded during forward
propagation.
2) graphviz.Digraph object that contains visual representation of computation
graph of pytorch module. This graph visual shows modules/ module hierarchy,
torch_functions, shapes and tensors recorded during forward prop, for examples
see documentation, and colab notebooks.
Args:
model (nn.Module):
Pytorch model to represent visually.
input_data (data structure containing torch.Tensor):
input for forward method of model. Wrap it in a list for
multiple args or in a dict or kwargs
input_size (Sequence of Sizes):
Shape of input data as a List/Tuple/torch.Size
(dtypes must match model input, default is FloatTensors).
Default: None
graph_name (str):
Name for graphviz.Digraph object. Also default name graphviz file
of Graph Visualization
Default: 'model'
depth (int):
Upper limit for depth of nodes to be shown in visualization.
Depth is measured how far is module/tensor inside the module hierarchy.
For instance, main module has depth=0, whereas submodule of main module
has depth=1, and so on.
Default: 3
device (str or torch.device):
Device to place and input tensors. Defaults to
gpu if cuda is seen by pytorch, otherwise to cpu.
Default: None
dtypes (list of torch.dtype):
Uses dtypes to set the types of input tensor if
input size is given.
mode (str):
Mode of model to use for forward prop. Defaults
to Eval mode if not given
Default: None
strict (bool):
if true, graphviz visual does not allow multiple edges
between nodes. Mutiple edge occurs e.g. when there are tensors
from module node to module node and hiding those tensors
Default: True
expand_nested(bool):
if true shows nested modules with dashed borders
graph_dir (str):
Sets the direction of visual graph
'TB' -> Top to Bottom
'LR' -> Left to Right
'BT' -> Bottom to Top
'RL' -> Right to Left
Default: None -> TB
hide_module_function (bool):
Determines whether to hide module torch_functions. Some
modules consist only of torch_functions (no submodule),
e.g. nn.Conv2d.
True => Dont include module functions in graphviz
False => Include modules function in graphviz
Default: True
hide_inner_tensors (bool):
Inner tensor is all the tensors of computation graph
but input and output tensors
True => Does not show inner tensors in graphviz
False => Shows inner tensors in graphviz
Default: True
roll (bool):
If true, rolls recursive modules.
Default: False
show_shapes (bool):
True => Show shape of tensor, input, and output
False => Dont show
Default: True
save_graph (bool):
True => Saves output file of graphviz graph
False => Does not save
Default: False
filename (str):
name of the file to store dot syntax representation and
image file of graphviz graph. Defaults to graph_name
directory (str):
directory in which to store graphviz output files.
Default: .
Returns:
ComputationGraph object that contains visualization of the input
pytorch model in the form of graphviz Digraph object
''' from torchview import draw_graph
model_graph = draw_graph (
SimpleRNN (), input_size = ( 2 , 3 ),
graph_name = 'RecursiveNet' ,
roll = True
)
model_graph . visual_graph 
# Show inner tensors and Functionals
model_graph = draw_graph (
MLP (), input_size = ( 2 , 128 ),
graph_name = 'MLP' ,
hide_inner_tensors = False ,
hide_module_functions = False ,
)
model_graph . visual_graph 
import torchvision
model_graph = draw_graph ( resnet18 (), input_size = ( 1 , 3 , 32 , 32 ), expand_nested = True )
model_graph . visual_graph 
所有问题和拉的请求都非常感谢！如果您想知道如何构建项目：
pip install -r requirements-dev.txt 。我们使用所有开发软件包的最新版本。pytest 。pytest --overwrite 。pytest --no-output __torch_function__ and subslassing torch.Tensor 。非常感谢所有开发此API的人！！ 在这里，基于火炬的功能是指任何仅使用火炬功能和模块的功能。这比模块更通用。 ↩
