Firewood

Implementation of custom torch layers and models.
All layers are compatible with official pytorch nn.Module and support ddp strategy, onnx exporting.

Installation

Models & Trainers

(nn.Module model & pytorch-lightning trainer)

GAN

• GAN 🌲Model 🔥Trainer
• DCGAN 🌲Model 🔥Trainer
• LSGAN 🌲Model 🔥Trainer
• Pix2Pix 🌲Model 🔥Trainer
• Pix2PixHD 🌲Model 🔥Trainer
• PGGAN 🌲Model
• StyleGAN 🌲Model 🔥Trainer
• StyleGAN2 🌲Model 🔥Trainer

Semantic Segmentation

• BiSeNetV1
• BiSeNetV2

Layers

Separable Convolution

• Depthwise - Pointwise Convolution
  • weight shape: Conv(in, out, K, K) → Conv(in, 1, K, K) X Conv(1, out, 1, 1)
• Spatialwise Convolution
  • weight shape: Conv(in, out, K, K) → Conv(in, smaller, K, 1) X Conv(smaller, out, 1, K)

Denormalizations

• AdaIN
• SPADE

Introduced in PGGAN

• Learning rate Equalizer hooks

Introduced in StyleGAN

• Weight Gradient Fixable Convolution
  • All options are compatible with tensorflow convolution. (e.g. "same" padding)
  • Can implement exactly same with tensorflow.
• Fused Activation (biased activation, cuda extension)
• Up Fir Down filter 1D, 2D, 3D (only 2D support cuda extension)
• Weight Demodulation hooks
  • Support Conv and Linear

PyTorchLightning

Callbacks

• Latent Interpolator
• Latent Sampler
• Condition Interpolator (Multi-Condition)
• Image to Image Translation Sampler
• Save Last K ModelCheckpoint

Metrics

• FID
  • selectable resizing method
    • default: antialiased torchvision
    • original: tf1

Dataset

• NoClassImageFolder for single class model
• PairedImageFolder for I2I model
• ConditionImageFolder for multi-condition(multi-class) model