torchview
- TorchView 0.2.6
Torchview以視覺圖的形式提供了Pytorch模型的可視化。可視化包括張量，模塊，火炬函數和信息，例如輸入/輸出形狀。
plot_model of keras的Pytorch版本（以及更多）
支持Pytorch版本
首先，您需要安裝GraphViz，
pip install graphviz為了使Graphiz的Python接口工作，您需要在系統中使用點佈局命令。如果尚未安裝，我建議您在操作系統上運行以下內容
總部位於Debian的Linux發行版（例如Ubuntu）：
apt-get install graphviz視窗：
choco install graphvizmacos
brew install graphviz在此處查看更多詳細信息
然後，繼續使用PIP安裝Torchview
pip install torchview或者如果您想通過Conda
conda install -c conda-forge torchview或者，如果您想要最新的版本，請直接從repo安裝
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 
有關更多示例，請參見下面的COLAB筆記本，
簡介筆記本：
計算機視覺模型：
NLP模型：
注意：輸出GraphViz視覺效果返回帶有所需尺寸的圖像。但是有時，在vscode上，由於尺寸較大和vScode呈現SVG呈現，有些形狀會被裁剪。要解決這個問題，我建議您運行以下內容
import graphviz
graphviz . set_jupyter_format ( 'png' )此問題不會在其他平台上發生，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 
所有問題和拉的請求都非常感謝！如果您想知道如何構建項目：
pip install -r requirements-dev.txt 。我們使用所有開發軟件包的最新版本。pytest 。pytest --overwrite 。pytest --no-output __torch_function__ and subslassing torch.Tensor 。非常感謝所有開發此API的人！ ！ 在這裡，基於火炬的功能是指任何僅使用火炬功能和模塊的功能。這比模塊更通用。 ↩
