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TorchSketch is an open source software library for free-hand sketch oriented deep learning research, which is built on the top of PyTorch.

Project description



TorchSketch is an open source software library for free-hand sketch oriented deep learning research, which is built on the top of PyTorch.

The project is under continuous update!

1. Installation

TorchSketch is developed based on Python 3.7.

To avoid any conflicts with your existing Python setup, it's better to install TorchSketch into a standalone environment, e.g., an Anaconda virtual environment.

Assume that you have installed Anaconda. Please create a virtual environment before installation of TorchSketch, as follows.

# Create a virtual environment in Anaconda.
conda create --name ${CUSTOMIZED_ENVIRONMENT_NAME} python=3.7

# Activate it.

1.1 Using pip

Please use the following command to install TorchSketch.

pip install torchsketch

Then, TorchSketch can be imported into your Python console as follows.

import torchsketch

1.2 From Source

In addition, TorchSketch also can be installed from source.

# Choose your workspace and download this repository.
git clone

# Enter the folder of TorchSketch.
cd torchsketch

# Install.
python install

2. Major Modules and Features of TorchSketch

2.1 Major Modules

TorchSketch has three main modules, including data, networks, utils, as shown in follows. The documents are provided in docs.

  • torchsketch
    • data
      • dataloaders: provides the dataloader class files for the frequently-used sketch datasets, e.g., TU-Berlin, Sketchy, QuickDraw.
      • datasets: provides the specific API for each dataset, which integrates a series of functions including downloading, extraction, cleaning, MD5 checksum, and other preprocessings.
    • networks
      • cnn: provides all the SOTA CNNs.
      • gnn: provides the sketch-applicable implementations of GNNs, including GCN, GAT, graph transformer, etc.
      • rnn: provides the sketch-applicable implementations of RNNs.
      • tcn: provides the sketch-applicable implementations of TCNs.
    • utils
      • data_augmentation_utils
      • general_utils
      • metric_utils
      • self_supervised_utils
      • svg_specific_utils
    • docs
      • api_reference
      • examples

These modules and sub-modules can be imported as follows.

import as dataloaders
import as datasets

import torchsketch.networks.cnn as cnns
import torchsketch.networks.gnn as gnns
import torchsketch.networks.rnn as rnns
import torchsketch.networks.tcn as tcns

import torchsketch.utils.data_augmentation_utils as data_augmentation_utils
import torchsketch.utils.general_utils as general_utils
import torchsketch.utils.metric_utils as metric_utils
import torchsketch.utils.self_supervised_utils as self_supervised_utils
import torchsketch.utils.svg_specific_utils as svg_specific_utils

2.2 Major Features

  • TorchSketch supports both GPU based and Python built-in multi-processing acceleration.
  • TorchSketch is modular, flexible, and extensible, without overly complex design patterns and excessive encapsulation.
  • TorchSketch provides four kinds of network architectures that are applicable to sketch, i.e., CNN, RNN, GNN, TCN.
  • TorchSketch is compatible to not only numerous datasets but also various formats of free-hand sketch, e.g., SVG, NumPy, PNG, JPEG, by providing numerous format-convert APIs, format-specific APIs, etc.
  • TorchSketch supports self-supervised learning study for sketch.
  • TorchSketch, beyond free-hand sketch research, also has some universal components that are applicable to the studies for other deep learning topics.

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