https://github.com/jettify/pytorch-inspect
torch-inspect -- collection of utility functions to inspect low level information of neural network for PyTorch
https://github.com/jettify/pytorch-inspect
pytorch
Last synced: about 1 month ago
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torch-inspect -- collection of utility functions to inspect low level information of neural network for PyTorch
- Host: GitHub
- URL: https://github.com/jettify/pytorch-inspect
- Owner: jettify
- License: apache-2.0
- Created: 2019-08-29T04:58:59.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-03-06T11:00:30.000Z (about 2 years ago)
- Last Synced: 2025-03-01T15:01:38.830Z (about 2 months ago)
- Topics: pytorch
- Language: Python
- Homepage:
- Size: 105 KB
- Stars: 17
- Watchers: 2
- Forks: 0
- Open Issues: 21
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGES.rst
- License: LICENSE
Awesome Lists containing this project
README
torch-inspect
=============
.. image:: https://travis-ci.com/jettify/pytorch-inspect.svg?branch=master
:target: https://travis-ci.com/jettify/pytorch-inspect
.. image:: https://codecov.io/gh/jettify/pytorch-inspect/branch/master/graph/badge.svg
:target: https://codecov.io/gh/jettify/pytorch-inspect
.. image:: https://img.shields.io/pypi/pyversions/torch-inspect.svg
:target: https://pypi.org/project/torch-inspect
.. image:: https://img.shields.io/pypi/v/torch-inspect.svg
:target: https://pypi.python.org/pypi/torch-inspect
**torch-inspect** -- collection of utility functions to inspect low level information of neural network for PyTorch_
Features
========
* Provides helper function ``summary`` that prints Keras style model summary.
* Provides helper function ``inspect`` that returns object with network summary information for programmatic access.
* RNN/LSTM support.
* Library has tests and reasonable code coverage.
Simple example
--------------
.. code:: python
import torch.nn as nn
import torch.nn.functional as F
import torch_inspect as ti
class SimpleNet(nn.Module):
def __init__(self):
super(SimpleNet, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 3)
self.conv2 = nn.Conv2d(6, 16, 3)
self.fc1 = nn.Linear(16 * 6 * 6, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:]
num_features = 1
for s in size:
num_features *= s
return num_features
net = SimpleNet()
ti.summary(net, (1, 32, 32))
Will produce following output:
.. code::
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [100, 6, 30, 30] 60
Conv2d-2 [100, 16, 13, 13] 880
Linear-3 [100, 120] 69,240
Linear-4 [100, 84] 10,164
Linear-5 [100, 10] 850
================================================================
Total params: 81,194
Trainable params: 81,194
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.39
Forward/backward pass size (MB): 6.35
Params size (MB): 0.31
Estimated Total Size (MB): 7.05
----------------------------------------------------------------
For programmatic access to network information there is ``inspect`` function:
.. code:: python
info = ti.inspect(net, (1, 32, 32))
print(info)
.. code::
[LayerInfo(name='Conv2d-1', input_shape=[100, 1, 32, 32], output_shape=[100, 6, 30, 30], trainable_params=60, non_trainable_params=0),
LayerInfo(name='Conv2d-2', input_shape=[100, 6, 15, 15], output_shape=[100, 16, 13, 13], trainable_params=880, non_trainable_params=0),
LayerInfo(name='Linear-3', input_shape=[100, 576], output_shape=[100, 120], trainable_params=69240, non_trainable_params=0),
LayerInfo(name='Linear-4', input_shape=[100, 120], output_shape=[100, 84], trainable_params=10164, non_trainable_params=0),
LayerInfo(name='Linear-5', input_shape=[100, 84], output_shape=[100, 10], trainable_params=850, non_trainable_params=0)]
Installation
------------
Installation process is simple, just::
$ pip install torch-inspect
Requirements
------------
* Python_ 3.6+
* PyTorch_ 1.0+
References and Thanks
---------------------
This package is based on pytorch-summary_ and PyTorch issue_ . Compared to
pytorch-summary_, *pytorch-inspect* has support of RNN/LSTMs, also provides programmatic
access to the network summary information. With a bit more modular structure and presence of tests
it is easier to extend and support more features.
.. _Python: https://www.python.org
.. _PyTorch: https://github.com/pytorch/pytorch
.. _pytorch-summary: https://github.com/sksq96/pytorch-summary
.. _issue: https://github.com/pytorch/pytorch/issues/2001