torchview
- TorchView 0.2.6
Torchview menyediakan visualisasi model Pytorch dalam bentuk grafik visual. Visualisasi meliputi tensor, modul, fungsi dan info seperti bentuk input/output.
Versi PyTorch dari plot_model of keras (dan banyak lagi)
Mendukung versi Pytorch
Pertama, Anda perlu menginstal GraphViz,
pip install graphvizAgar antarmuka graphiz python berfungsi, Anda harus memiliki perintah tata letak dot bekerja di sistem Anda. Jika belum diinstal, saya sarankan Anda menjalankan depeding berikut pada OS Anda,
Linux Distro yang berbasis di Debian (misalnya Ubuntu):
apt-get install graphvizWindows:
choco install graphvizMacOS
brew install graphvizLihat detail lebih lanjut di sini
Kemudian, lanjutkan dengan menginstal Torchview menggunakan PIP
pip install torchviewatau jika Anda mau melalui conda
conda install -c conda-forge torchviewAtau jika Anda menginginkan versi terbaru, instal langsung dari repo
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 
Untuk contoh lebih lanjut, lihat Colab Notebooks di bawah ini,
Pendahuluan Notebook:
Model Visi Komputer:
Model NLP:
Catatan: Visual GraphViz Output Mengembalikan gambar dengan ukuran yang diinginkan. Tetapi kadang -kadang, pada vscode, beberapa bentuk sedang dipotong karena ukuran besar dan rendering SVG oleh vScode. Untuk menyelesaikan ini, saya sarankan Anda menjalankan berikut ini
import graphviz
graphviz . set_jupyter_format ( 'png' )Masalah ini tidak terjadi pada platform lain misalnya Jupyterlab atau 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 
Semua masalah dan permintaan menarik sangat dihargai! Jika Anda bertanya -tanya bagaimana cara membangun proyek:
pip install -r requirements-dev.txt . Kami menggunakan versi terbaru dari semua paket dev.pytest .pytest --overwrite .pytest --no-output __torch_function__ dan subklassing torch.Tensor . Terima kasih banyak untuk semua yang mengembangkan API ini !!. Di sini, fungsi berbasis obor mengacu pada fungsi apa pun yang hanya menggunakan fungsi dan modul obor. Ini lebih umum daripada modul. ↩
