Neural Style学习3—

Basic usage:

th neural_style.lua -style_image <image.jpg> -content_image <image.jpg>

OpenCL usage with NIN Model (This requires you download the NIN Imagenet model files as described above):

th neural_style.lua -style_image examples/inputs/picasso_selfport1907.jpg -content_image examples/inputs/brad_pitt.jpg -output_image profile.png -model_file models/nin_imagenet_conv.caffemodel -proto_file models/train_val.prototxt -gpu 0 -backend clnn -num_iterations 1000 -seed 123 -content_layers relu0,relu3,relu7,relu12 -style_layers relu0,relu3,relu7,relu12 -content_weight 10 -style_weight 1000 -image_size 512 -optimizer adam

To use multiple style images, pass a comma-separated list like this:

-style_image starry_night.jpg,the_scream.jpg.

Note that paths to images should not contain the ~ character to represent your home directory; you should instead use a relative

path or a full absolute path.

Options:

-image_size: Maximum side length (in pixels) of of the generated image. Default is 512.
-style_blend_weights: The weight for blending the style of multiple style images, as a

comma-separated list, such as -style_blend_weights 3,7. By default all style images

are equally weighted.
-gpu: Zero-indexed ID of the GPU to use; for CPU mode set -gpu to -1.

Optimization options:

-content_weight: How much to weight the content reconstruction term. Default is 5e0.
-style_weight: How much to weight the style reconstruction term. Default is 1e2.
-tv_weight: Weight of total-variation (TV) regularization; this helps to smooth the image.

Default is 1e-3. Set to 0 to disable TV regularization.
-num_iterations: Default is 1000.
-init: Method for generating the generated image; one of random or image.

Default is random which uses a noise initialization as in the paper; image

initializes with the content image.
-optimizer: The optimization algorithm to use; either lbfgs or adam; default is lbfgs.

L-BFGS tends to give better results, but uses more memory. Switching to ADAM will reduce memory usage;

when using ADAM you will probably need to play with other parameters to get good results, especially

the style weight, content weight, and learning rate; you may also want to normalize gradients when

using ADAM.
-learning_rate: Learning rate to use with the ADAM optimizer. Default is 1e1.
-normalize_gradients: If this flag is present, style and content gradients from each layer will be

L1 normalized. Idea from andersbll/neural_artistic_style.

Output options:

-output_image: Name of the output image. Default is out.png.
-print_iter: Print progress every print_iter iterations. Set to 0 to disable printing.
-save_iter: Save the image every save_iter iterations. Set to 0 to disable saving intermediate results.

Layer options:

-content_layers: Comma-separated list of layer names to use for content reconstruction.

Default is relu4_2.
-style_layers: Comma-separated list of layer names to use for style reconstruction.

Default is relu1_1,relu2_1,relu3_1,relu4_1,relu5_1.

Other options:

-style_scale: Scale at which to extract features from the style image. Default is 1.0.
-original_colors: If you set this to 1, then the output image will keep the colors of the content image.
-proto_file: Path to the deploy.txt file for the VGG Caffe model.
-model_file: Path to the .caffemodel file for the VGG Caffe model.

Default is the original VGG-19 model; you can also try the normalized VGG-19 model used in the paper.
-pooling: The type of pooling layers to use; one of max or avg. Default is max.

The VGG-19 models uses max pooling layers, but the paper mentions that replacing these layers with average

pooling layers can improve the results. I haven't been able to get good results using average pooling, but

the option is here.
-backend: nn, cudnn, or clnn. Default is nn. cudnn requires

cudnn.torch and may reduce memory usage.

clnn requires cltorch and clnn
-cudnn_autotune: When using the cuDNN backend, pass this flag to use the built-in cuDNN autotuner to select

the best convolution algorithms for your architecture. This will make the first iteration a bit slower and can

take a bit more memory, but may significantly speed up the cuDNN backend.

Frequently Asked Questions

Problem: Generated image has saturation artifacts:

Solution: Update the image packge to the latest version: luarocks install image

Problem: Running without a GPU gives an error message complaining about cutorch not found

Solution:

Pass the flag -gpu -1 when running in CPU-only mode

Problem: The program runs out of memory and dies

Solution: Try reducing the image size: -image_size 256 (or lower). Note that different image sizes will likely

require non-default values for -style_weight and -content_weight for optimal results.

If you are running on a GPU, you can also try running with -backend cudnn to reduce memory usage.

Problem: Get the following error message:

models/VGG_ILSVRC_19_layers_deploy.prototxt.cpu.lua:7: attempt to call method 'ceil' (a nil value)

Solution: Update nn package to the latest version: luarocks install nn

Problem: Get an error message complaining about paths.extname

Solution: Update torch.paths package to the latest version: luarocks install paths

Problem: NIN Imagenet model is not giving good results.

Solution: Make sure the correct -proto_file is selected. Also make sure the correct parameters for -content_layers and -style_layers are set. (See OpenCL usage example above.)

Problem: -backend cudnn is slower than default NN backend

Solution: Add the flag -cudnn_autotune; this will use the built-in cuDNN autotuner to select the best convolution algorithms.

Memory Usage

By default, neural-style uses the nn backend for convolutions and L-BFGS for optimization.

These give good results, but can both use a lot of memory. You can reduce memory usage with the following:

Use cuDNN: Add the flag -backend cudnn to use the cuDNN backend. This will only work in GPU mode.
Use ADAM: Add the flag -optimizer adam to use ADAM instead of L-BFGS. This should significantly

reduce memory usage, but may require tuning of other parameters for good results; in particular you should

play with the learning rate, content weight, style weight, and also consider using gradient normalization.

This should work in both CPU and GPU modes.
Reduce image size: If the above tricks are not enough, you can reduce the size of the generated image;

pass the flag -image_size 256 to generate an image at half the default size.

With the default settings, neural-style uses about 3.5GB of GPU memory on my system;

switching to ADAM and cuDNN reduces the GPU memory footprint to about 1GB.

Speed

Speed can vary a lot depending on the backend and the optimizer.

Here are some times for running 500 iterations with -image_size=512 on a Maxwell Titan X with different settings:

-backend nn -optimizer lbfgs: 62 seconds
-backend nn -optimizer adam: 49 seconds
-backend cudnn -optimizer lbfgs: 79 seconds
-backend cudnn -cudnn_autotune -optimizer lbfgs: 58 seconds
-backend cudnn -cudnn_autotune -optimizer adam: 44 seconds
-backend clnn -optimizer lbfgs: 169 seconds
-backend clnn -optimizer adam: 106 seconds

Here are the same benchmarks on a Pascal Titan X with cuDNN 5.0 on CUDA 8.0 RC:

-backend nn -optimizer lbfgs: 43 seconds
-backend nn -optimizer adam: 36 seconds
-backend cudnn -optimizer lbfgs: 45 seconds
-backend cudnn -cudnn_autotune -optimizer lbfgs: 30 seconds
-backend cudnn -cudnn_autotune -optimizer adam: 22 seconds

Multi-GPU scaling

You can use multiple GPUs to process images at higher resolutions; different layers of the network will be

computed on different GPUs. You can control which GPUs are used with the -gpu flag, and you can control

how to split layers across GPUs using the -multigpu_strategy flag.

For example in a server with four GPUs, you can give the flag -gpu 0,1,2,3 to process on GPUs 0, 1, 2, and

3 in that order; by also giving the flag -multigpu_strategy 3,6,12 you indicate that the first two layers

should be computed on GPU 0, layers 3 to 5 should be computed on GPU 1, layers 6 to 11 should be computed on

GPU 2, and the remaining layers should be computed on GPU 3. You will need to tune the -multigpu_strategy

for your setup in order to achieve maximal resolution.

We can achieve very high quality results at high resolution by combining multi-GPU processing with multiscale

generation as described in the paper

Controlling Perceptual Factors in Neural Style Transfer by Leon A. Gatys,

Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann and Eli Shechtman.

Here is a 3620 x 1905 image generated on a server with four Pascal Titan X GPUs:

The script used to generate this image can be found here.