PyTorch Deep Learning Toolbox

PyDLT is a set of tools aimed to make experimenting with PyTorch easier (than it already is).

Documentation is available here.

Features

• Trainers (currently Vanilla, VanillaGAN, WGAN-GP, BEGAN, FisherGAN)

```python trainer = dlt.train.VanillaGANTrainer(generator, discriminator, g_optim, d_optim) for batch, (prediction, losses) in trainer(data_loader):

# Training happens in the iterator and relevant results are returned for each step

``` - Built in configurable parser with arguments.

`python opt = dlt.config.parse() # Has built in options (can add extra) print('Some Settings: ', opt.experiment_name, opt.batch_size, opt.lr) `

• Configuration files support and parser compatible functions.

`bash $ python main.py @settings.cfg Some Settings: config_test 32 0.0001 `

• HDR imaging support (.hdr, .exr, and .pfm formats)

`python img = dlt.hdr.imread('test.pfm') dlt.hdr.imwrite('test.exr', img) `

• Checkpointing of (torch serializable) objects; Network state dicts supported.

`python data_chkp = Checkpointer('data') data_chkp.save(np.array([1,2,3])) a = data_chkp.load() `

• Image operations and easy conversions between multiple library views (torch, cv, plt)

`python img = cv2.imread('image.jpg') # Height x Width x Channels - BGR dlt.viz.imshow(img, view='cv') # Height x Width x Channels - RGB tensor_with_torch_view = cv2torch(img) # Channels x Height x Width - RGB `

• Easy visualization (and make_grid supporting Arrays, Tensors, Variables and lists)

```python for batch, (prediction, loss) in trainer(loader):

grid = dlt.util.make_grid([ batch[0], batch[1], prediction], size(3, opt.batch_size)) dlt.viz.imshow(grid, pause=0.01, title='Training Progress')

```

• Model parameter and layer input/outputs/gradients visualization.

`python net = nn.Sequential(nn.Linear(10, 10)) dlt.viz.modules.forward_hook(net, [nn.Linear], tag='layer_outputs', histogram=False) net(Variable(torch.Tensor(3,10))) `

• CSV Logger.

`python log = dlt.util.Logger('losses', ['train_loss', 'val_loss']) log({'train_loss': 10, 'val_loss':20}) `

• Command line tool for easy plotting of CSV files (with live updating).

`bash $ dlt-plot --file losses.csv train_loss val_loss --refresh 5 --loglog True --tail 100 `

• A minimal Progress bar (with global on/off switch).

```python from dlt.util import barit barit.silent = False # Default is False for batch in barit(loader, start='Loading'):

pass

```

Installation

Make sure you have PyTorch installed. OpenCV is also required:

`bash conda install -c menpo opencv `

conda install (recommended):

`bash conda install -c demetris pydlt `

From source:

`bash git clone https://github.com/dmarnerides/pydlt.git cd pydlt python setup.py install `

About

I created this toolbox while learning Python and PyTorch, after working with (Lua) Torch, to help speed up experiment prototyping.

If you notice something is wrong or missing please do a pull request or open up an issue.

Contact

Demetris Marnerides: [email protected]