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fast_neural_style.lua

The script fast_neural_style.lua runs a trained model on new images. It has the following flags:

Model options:

  • -model: Path to a .t7 model file from train.lua.
  • -image_size: Before being input to the network, images are resized so their longer side is this many pixels long. If 0 then use the original size of the image.
  • -median_filter: If nonzero, use a median filter of this kernel size as a post-processing step. Default is 3.

Input / Output:

  • -input_image: Path to a single image on which to run the model.
  • -input_dir: Path to a directory of image files; the model will be run on all images in the directory.
  • -output_image: When using -input_image to specify input, the path to which the stylized image will be written.
  • -output_dir: When using -input_dir to specify input, this gives a path to a directory where stylized images will be written. Each output image will have the same filename as its corresponding input image.

Backend options:

  • -gpu: Which GPU to use for processing (zero-indexed); use -1 to process on CPU.
  • -backend: Which GPU backend to use; either cuda or opencl. Default is cuda; this is ignored in CPU mode.
  • -use_cudnn: Whether to use cuDNN with the CUDA backend; 1 for yes and 0 for no. Ignored in CPU mode or when using the OpenCL backend. Default is 1.
  • -cudnn_benchmark: Whether to use the cuDNN autotuner when running with cuDNN; 1 for yes or 0 for no. Default is 0. If you want to run the model on many images of the same size, then setting this to 1 may give a speed boost.

webcam_demo.lua

The script webcam_demo.lua runs models off the video stream from a webcam. It has the following flags:

Model options:

  • -models: A comma-separated list of models to use.

Webcam options:

  • -webcam_idx: Which webcam to use; default is 0.
  • -webcam_fps: Frames per second to request from the webcam; default is 60.
  • -height, -width: Image resolution to request from the webcam.

Backend options:

  • -gpu: Which GPU to use (zero-indexed); use -1 for CPU. You will likely need a GPU to get good results.
  • -backend: GPU backend to use, either cuda or opencl.
  • -use_cudnn: Whether to use cuDNN when using CUDA; 1 for yes, 0 for no.

train.lua

The script train.lua trains new feedforward style transfer models. It has the following flags:

Model options:

  • -arch: String specifying the architecture to use. Architectures are specified as comma-separated strings. The architecture used in the paper is c9s1-32,d64,d128,R128,R128,R128,R128,R128,u64,u32,c9s1-3. All internal convolutional layers are followed by a ReLU and either batch normalization or instance normalization.
    • cXsY-Z: A convolutional layer with a kernel size of X, a stride of Y, and Z filters.
    • dX: A downsampling convolutional layer with X filters, 3x3 kernels, and stride 2.
    • RX: A residual block with two convolutional layers and X filters per layer.
    • uX: An upsampling convolutional layer with X filters, 3x3 kernels, and stride 1/2.
  • -use_instance_norm: 1 to use instance normalization or 0 to use batch normalization. Default is 1.
  • -padding_type: What type of padding to use for convolutions in residual blocks. The following choices are available:
    • zero: Normal zero padding everywhere.
    • none: No padding for convolutions in residual blocks.
    • reflect: Spatial reflection padding for all convolutions in residual blocks.
    • replicate: Spatial replication padding for all convolutions in residual blocks.
    • reflect-start (default): Spatial reflection padding at the beginning of the model and no padding for convolutions in residual blocks.
  • -tanh_constant: There is a tanh nonlinearity after the final convolutional layer; this puts outputs in the range [-1, 1]. Outputs are then multiplied by the -tanh_constant so the outputs are in a more standard image range.
  • -preprocessing: What type of preprocessing and deprocessing to use; either vgg or resnet. Default is vgg. If you want to use a ResNet as loss network you should set this to resnet.
  • -resume_from_checkpoint: Path to a .t7 checkpoint created by train.lua to initialize the model from. If you use this option then all other model architecture options will be ignored.

Loss options:

  • -loss_network: Path to a .t7 file containing a pretrained CNN to be used as a loss network. The default is VGG-16, but the code should support many models such as VGG-19 and ResNets.
  • -content_layers: Which layers of the loss network to use for the content reconstruction loss. This will usually be a comma-separated list of integers, but for complicated loss networks like ResNets it can be a list of of layer strings.
  • -content_weights: Weights to use for each content reconstruction loss. This can either be a single number, in which case the same weight is used for all content reconstruction terms, or it can be a comma-separated list of real values of the same length as -content_layers.
  • -style_image: Path to the style image to use.
  • -style_image_size: Before computing the style loss targets, the style image will be resized so its smaller side is this many pixels long. This can have a big effect on the types of features transferred from the style image.
  • -style_layers: Which layers of the loss network to use for the style reconstruction loss. This is a comma-separated list of the same format as -content_layers.
  • -style_weights: Weights to use for style reconstruction terms. Either a single number, in which case the same weight is used for all style reconstruction terms, or a comma-separated list of weights of the same length as -style_layers.
  • -style_target_type: What type of style targets to use; either gram or mean. Default is gram, in which case style targets are Gram matrices as described by Gatys et al. If this is mean then the spatial average will be used as a style target instead of a Gram matrix.
  • -tv_strength: Strength for total variation regularization on the output of the transformation network. Default is 1e-6; higher values encourage the network to produce outputs that are spatially smooth.

Training options:

  • -h5_file: HDF5 dataset created with scripts/make_style_dataset.py.
  • -num_iterations: The number of gradient descent iterations to run.
  • -max_train: The maximum number of training images to use; default is -1 which uses the entire training set from the HDF5 dataset.
  • -batch_size: The number of content images per minibatch. Default is 4.
  • -learning_rate: Learning rate to use for Adam. Default is 1e-3.
  • -lr_decay_every, -lr_decay_after: Learning rate decay. After every -lr_decay_every iterations the learning rate is multiplied by -lr_decay_factor. Setting -lr_decay_every to -1 disables learning rate decay.
  • -weight_decay: L2 regularization strength on the weights of the transformation network. Default is 0 (no L2 regularization).

Checkpointing:

  • -checkpoint_every: Every -checkpoint_every iterations, check performance on the validation set and save both a .t7 model checkpoint and a .json checkpoint with loss history.
  • -checkpoint_name: Path where checkpoints are saved. Default is checkpoint, meaining that every -checkpoint_every iterations we will write files checkpoint.t7 and checkpoint.json.

Backend:

  • -gpu: Which GPU to use; default is 0. Set this to -1 to train in CPU mode.
  • -backend: Which backend to use for GPU, either cuda or opencl.
  • -use_cudnn: Whether to use cuDNN when using CUDA; 0 for no and 1 for yes.

slow_neural_style.lua

The script slow_neural_style.lua uses the optimization-based style transfer method similar to the original neural-style.

It has the following flags:

Basic Options

  • -content_image: Path to the content image to use.
  • -style_image: Path to the style image to use.
  • -image_size: Size of the generated image; its longest side is this many pixels long.

Output Options

  • -output_image: Path where the output image will be written.
  • -print_every: Losses will be printed after every -print_every iterations.
  • -save_every: Images will be written every -save_ever iterations.

Loss options All of these flags are the same as those in train.lua:

  • -loss_network: Path to a .t7 file containing a pretrained CNN to be used as a loss network. The default is VGG-16, but the code should support many models such as VGG-19 and ResNets.
  • -content_layers: Which layers of the loss network to use for the content reconstruction loss. This will usually be a comma-separated list of integers, but for complicated loss networks like ResNets it can be a list of of layer strings.
  • -content_weights: Weights to use for each content reconstruction loss. This can either be a single number, in which case the same weight is used for all content reconstruction terms, or it can be a comma-separated list of real values of the same length as -content_layers.
  • -style_image_size: Before computing the style loss targets, the style image will be resized so its smaller side is this many pixels long. This can have a big effect on the types of features transferred from the style image.
  • -style_layers: Which layers of the loss network to use for the style reconstruction loss. This is a comma-separated list of the same format as -content_layers.
  • -style_weights: Weights to use for style reconstruction terms. Either a single number, in which case the same weight is used for all style reconstruction terms, or a comma-separated list of weights of the same length as -style_layers.
  • -style_target_type: What type of style targets to use; either gram or mean. Default is gram, in which case style targets are Gram matrices as described by Gatys et al. If this is mean then the spatial average will be used as a style target instead of a Gram matrix.
  • -tv_strength: Strength for total variation regularization on the output of the transformation network. Default is 1e-6; higher values encourage the network to produce outputs that are spatially smooth.

Optimization Options

  • -learning_rate: Learning rate to use for optimization
  • -optimizer: Either lbfgs, adam, or any other method from torch/optim.
  • -num_iterations: Number of iterations to run for

Backend Options:

  • -gpu: Which GPU to use; default is 0. Set this to -1 to train in CPU mode.
  • -backend: Which backend to use for GPU, either cuda or opencl.
  • -use_cudnn: Whether to use cuDNN when using CUDA; 0 for no and 1 for yes.

Other Options

  • -preprocessing: What type of preprocessing and deprocessing to use; either vgg or resnet. Default is vgg. If you want to use a ResNet as loss network you should set this to resnet.