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Add documentation
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‎.gitignore

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*.egg-info/
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.installed.cfg
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*.egg
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# Sphix
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_build/

‎README.md

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<p align="center">
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<img src="images/tensornet.png" alt="tensornet" />
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<img src="images/tensornet_logo.png" alt="tensornet" />
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<br />
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<br />
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<img src="https://img.shields.io/badge/version-0.9.1-blue.svg" alt="Version">
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<a href='https://tensornet.readthedocs.io/en/latest/?badge=latest'><img src='https://readthedocs.org/projects/tensornet/badge/?version=latest' alt='Documentation Status' /></a>
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<a href="https://github.com/shan18/TensorNet/blob/master/LICENSE"><img src="https://img.shields.io/apm/l/atomic-design-ui.svg?" alt="MIT License"></a>
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<br />
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</p>
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TensorNet is a high-level deep learning library built on top of PyTorch.
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NOTE: This documentation applies to the MASTER version of TensorNet only.
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## Installation
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You can use pip to install tensornet
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pip install git+https://github.com/shan18/TensorNet.git#egg=torch-tensornet
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```
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## Features
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TensorNet currently supports the following features
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## Documentation
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- Model architectures
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- ResNet18
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- Model utilities
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- Loss functions
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- Cross Entropy Loss
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- Binary Cross Entropy Loss
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- Mean Square Error Loss
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- SSIM and MS-SSIM Loss
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- Dice Loss
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- Evaluation Metrics
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- Accuracy
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- RMSE
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- MAE
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- ABS_REL
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- Optimizers
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- Stochastic Gradient Descent
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- Regularizers
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- L1 regularization
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- L2 regularization
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- LR Schedulers
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- Step LR
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- Reduce LR on Plateau
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- One Cycle Policy
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- LR Range Test
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- Model Checkpointing
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- Tensorboard
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- Model training and validation
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- Datasets (data is is returned via data loaders)
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- MNIST
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- CIFAR10
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- TinyImageNet
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- MODEST Museum Dataset
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- Data Augmentation
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- Resize
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- Padding
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- Random Crop
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- Horizontal Flip
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- Vertical Flip
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- Gaussian Blur
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- Random Rotation
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- CutOut
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- GradCAM and GradCAM++ (Gradient-weighted Class Activation Map)
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- Result Analysis Tools
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- Plotting changes in validation accuracy and loss during model training
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- Displaying correct and incorrect predictions of a trained model
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Documentation is available at: https://tensornet.readthedocs.io/en/latest/
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## How to Use
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- albumentations
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- opencv-python
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## Documentation
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Documentation making for the library is currently in progress. So until a documentation is available please refer to the following table for various functionalities and their corresponding module names.
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| Functionality | Module Name |
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| ------------------------------------- | ----------- |
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| Learner, Callbacks and Tensorboard | engine |
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| Dataset downloading and preprocessing | data |
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| GradCAM and GradCAM++ | gradcam |
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| Models, loss functions and optimizers | models |
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| CUDA setup and result analysis | utils |
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For a demo on how to use these modules, refer to the notebooks present in the [examples](https://github.com/shan18/TensorNet/tree/master/examples) directory.
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## Contact/Getting Help
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If you need any help or want to report a bug, raise an issue in the repo.

‎docs/Makefile

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# Minimal makefile for Sphinx documentation
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#
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# You can set these variables from the command line, and also
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# from the environment for the first two.
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SPHINXOPTS ?=
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SPHINXBUILD ?= sphinx-build
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SOURCEDIR = .
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BUILDDIR = _build
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# Put it first so that "make" without argument is like "make help".
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help:
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@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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.PHONY: help Makefile
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# Catch-all target: route all unknown targets to Sphinx using the new
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# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
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%: Makefile
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -b coverage

‎docs/_static/favicon.png

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‎docs/conf.py

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# Configuration file for TensorNet documentation builder.
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import os
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import re
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import sys
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sys.path.insert(0, os.path.abspath('.'))
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sys.path.insert(0, os.path.abspath('../'))
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def get_version():
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with open(
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os.path.join(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
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'tensornet',
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'__init__.py'
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)
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) as f:
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return re.search(r'^__version__ = [\'"]([^\'"]*)[\'"]', f.read(), re.M).group(1)
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def skip(app, what, name, obj, would_skip, options):
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if name == "__call__":
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return False
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return would_skip
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def setup(app):
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app.connect("autodoc-skip-member", skip)
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# -- Project information -----------------------------------------------------
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project = 'torch-tensornet'
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copyright = '2020, Shantanu Acharya'
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author = 'Shantanu Acharya'
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version = get_version()
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release = version
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# -- General configuration ---------------------------------------------------
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# Custom configurations
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# add_module_names = False
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autodoc_member_order = 'bysource'
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extensions = [
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'sphinx.ext.napoleon',
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'sphinx.ext.autodoc',
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'sphinx.ext.viewcode',
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'sphinx.ext.coverage',
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]
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# Add any paths that contain templates here, relative to this directory.
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templates_path = ['_templates']
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# The suffix(es) of source filenames.
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source_suffix = '.rst'
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# The master toctree document.
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master_doc = 'index'
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# List of patterns, relative to source directory, that match files and
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# directories to ignore when looking for source files.
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# This pattern also affects html_static_path and html_extra_path.
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exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
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# The name of the Pygments (syntax highlighting) style to use.
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pygments_style = 'sphinx'
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# -- Options for HTML output -------------------------------------------------
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# The theme to use for HTML and HTML Help pages. See the documentation for
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# a list of builtin themes.
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#
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html_theme = 'sphinx_rtd_theme'
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# The name of an image file (relative to this directory) to place at the top
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# of the sidebar.
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# html_logo = "_static/tensornet_logo.png"
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# The name of an image file (within the static path) to use as favicon of the
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# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
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# pixels large.
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html_favicon = "_static/favicon.png"
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# Add any paths that contain custom static files (such as style sheets) here,
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# relative to this directory. They are copied after the builtin static files,
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# so a file named "default.css" will overwrite the builtin "default.css".
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html_static_path = ['_static']

‎docs/index.rst

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:github_url: https://github.com/shan18/TensorNet
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TensorNet Documentation
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=======================
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TensorNet is a high-level deep learning library built on top of PyTorch.
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To install and use TensorNet all you have to do is:
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.. code-block:: bash
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pip install torch-tensornet
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If you want to get the latest version of the code before it is released on PyPI you can install the library from GitHub
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.. code-block:: bash
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pip install git+https://github.com/shan18/TensorNet.git#egg=torch-tensornet
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.. toctree::
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:maxdepth: 2
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:caption: Contents
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source/data
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source/models
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source/models.loss
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source/models.optimizer
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source/engine
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source/engine.ops
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source/engine.ops.regularizer
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source/gradcam
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source/utils

‎docs/make.bat

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@ECHO OFF
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pushd %~dp0
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REM Command file for Sphinx documentation
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if "%SPHINXBUILD%" == "" (
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set SPHINXBUILD=sphinx-build
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)
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set SOURCEDIR=.
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set BUILDDIR=_build
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if "%1" == "" goto help
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%SPHINXBUILD% >NUL 2>NUL
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if errorlevel 9009 (
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echo.
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echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
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echo.installed, then set the SPHINXBUILD environment variable to point
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echo.to the full path of the 'sphinx-build' executable. Alternatively you
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echo.may add the Sphinx directory to PATH.
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echo.
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echo.If you don't have Sphinx installed, grab it from
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echo.http://sphinx-doc.org/
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exit /b 1
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)
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%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
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goto end
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:help
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%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
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:end
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popd

‎docs/requirements.txt

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numpy==1.18.5
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torch==1.5.1
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torchvision==0.6.1
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Pillow==7.0.0
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matplotlib==3.2.2
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albumentations==0.4.5
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opencv-python==4.1.2.30
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Sphinx==3.2.1
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sphinx-rtd-theme

‎docs/source/data.rst

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Data
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====
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Classes and methods which can be used to create and modify datasets.
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Datasets
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--------
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.. automodule:: tensornet.data
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:members:
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:undoc-members:
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Processing
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----------
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.. automodule:: tensornet.data.processing
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:members:
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:undoc-members:

‎docs/source/engine.ops.regularizer.rst

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Regularizers
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============
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.. automodule:: tensornet.engine.ops.regularizer
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:members:
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:undoc-members:

‎docs/source/engine.ops.rst

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Callbacks
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=========
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Model Checkpoint
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----------------
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.. autoclass:: tensornet.engine.ops.ModelCheckpoint
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:members:
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:undoc-members:
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TensorBoard
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-----------
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.. autoclass:: tensornet.engine.ops.TensorBoard
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:members:
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:undoc-members:
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LR Schedulers
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-------------
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.. automodule:: tensornet.engine.ops.lr_scheduler
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:members:
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:undoc-members:

‎docs/source/engine.rst

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Engine
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======
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Classes and methods used to train and test models.
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.. automodule:: tensornet.engine
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:members:
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:undoc-members:

‎docs/source/gradcam.rst

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GradCAM
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=======
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.. automodule:: tensornet.gradcam
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:members:
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:undoc-members:

‎docs/source/models.loss.rst

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Criterion
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=========
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This module contains loss functions.
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.. automodule:: tensornet.models.loss
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:members:
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:undoc-members:

‎docs/source/models.optimizer.rst

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Optimizers
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==========
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This module contains optimizers.
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.. automodule:: tensornet.models.optimizer
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:members:
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:undoc-members:

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