{
    "info": {
        "author": "Nikolay Novik",
        "author_email": "nickolainovik@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "torch-inspect\n=============\n.. image:: https://travis-ci.com/jettify/pytorch-inspect.svg?branch=master\n    :target: https://travis-ci.com/jettify/pytorch-inspect\n.. image:: https://codecov.io/gh/jettify/pytorch-inspect/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/jettify/pytorch-inspect\n.. image:: https://img.shields.io/pypi/pyversions/torch-inspect.svg\n    :target: https://pypi.org/project/torch-inspect\n.. image:: https://img.shields.io/pypi/v/torch-inspect.svg\n    :target: https://pypi.python.org/pypi/torch-inspect\n\n**torch-inspect** -- collection of utility functions to inspect low level information of neural network for PyTorch_\n\nFeatures\n========\n* Provides helper function ``summary`` that prints Keras style model summary.\n* Provides helper function ``inspect`` that returns object with network summary information for programmatic access.\n* RNN/LSTM support.\n* Library has tests and reasonable code coverage.\n\n\nSimple example\n--------------\n\n.. code:: python\n\n    import torch.nn as nn\n    import torch.nn.functional as F\n    import torch_inspect as ti\n\n    class SimpleNet(nn.Module):\n        def __init__(self):\n            super(SimpleNet, self).__init__()\n            self.conv1 = nn.Conv2d(1, 6, 3)\n            self.conv2 = nn.Conv2d(6, 16, 3)\n            self.fc1 = nn.Linear(16 * 6 * 6, 120)\n            self.fc2 = nn.Linear(120, 84)\n            self.fc3 = nn.Linear(84, 10)\n\n        def forward(self, x):\n            x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n            x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n            x = x.view(-1, self.num_flat_features(x))\n            x = F.relu(self.fc1(x))\n            x = F.relu(self.fc2(x))\n            x = self.fc3(x)\n            return x\n\n        def num_flat_features(self, x):\n            size = x.size()[1:]\n            num_features = 1\n            for s in size:\n                num_features *= s\n            return num_features\n\n\n      net = SimpleNet()\n      ti.summary(net, (1, 32, 32))\n\n\nWill produce following output:\n\n.. code::\n\n   ----------------------------------------------------------------\n           Layer (type)               Output Shape         Param #\n   ================================================================\n               Conv2d-1           [100, 6, 30, 30]              60\n               Conv2d-2          [100, 16, 13, 13]             880\n               Linear-3                 [100, 120]          69,240\n               Linear-4                  [100, 84]          10,164\n               Linear-5                  [100, 10]             850\n   ================================================================\n   Total params: 81,194\n   Trainable params: 81,194\n   Non-trainable params: 0\n   ----------------------------------------------------------------\n   Input size (MB): 0.39\n   Forward/backward pass size (MB): 6.35\n   Params size (MB): 0.31\n   Estimated Total Size (MB): 7.05\n   ----------------------------------------------------------------\n\nFor programmatic access to network information there is ``inspect`` function:\n\n.. code:: python\n\n      info = ti.inspect(net, (1, 32, 32))\n      print(info)\n\n\n.. code::\n\n     [LayerInfo(name='Conv2d-1', input_shape=[100, 1, 32, 32], output_shape=[100, 6, 30, 30], trainable_params=60, non_trainable_params=0),\n      LayerInfo(name='Conv2d-2', input_shape=[100, 6, 15, 15], output_shape=[100, 16, 13, 13], trainable_params=880, non_trainable_params=0),\n      LayerInfo(name='Linear-3', input_shape=[100, 576], output_shape=[100, 120], trainable_params=69240, non_trainable_params=0),\n      LayerInfo(name='Linear-4', input_shape=[100, 120], output_shape=[100, 84], trainable_params=10164, non_trainable_params=0),\n      LayerInfo(name='Linear-5', input_shape=[100, 84], output_shape=[100, 10], trainable_params=850, non_trainable_params=0)]\n\n\nInstallation\n------------\nInstallation process is simple, just::\n\n    $ pip install torch-inspect\n\n\nRequirements\n------------\n\n* Python_ 3.6+\n* PyTorch_ 1.0+\n\n\nReferences and Thanks\n---------------------\nThis package is based on pytorch-summary_ and  PyTorch issue_ . Compared to\npytorch-summary_, *pytorch-inspect* has support of RNN/LSTMs, also provides programmatic\naccess to the network summary information. With a bit more modular structure and presence of tests\nit is easier to extend and support more features.\n\n\n.. _Python: https://www.python.org\n.. _PyTorch: https://github.com/pytorch/pytorch\n.. _pytorch-summary:  https://github.com/sksq96/pytorch-summary\n.. _issue:  https://github.com/pytorch/pytorch/issues/2001\n\nChanges\n-------\n\n0.0.3 (2019-09-22)\n------------------\n* Added LSTM support\n* Fixed multi input/output support\n* Added more network test cases\n* Batch size no longer -1 by default\n\n\n0.0.2 (2019-09-22)\n------------------\n* Added batch norm support\n* Removed device parameter\n\n\n0.0.1 (2019-09-1)\n------------------\n* Initial release.",
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