torch-inspect

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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