Pytorch Helpers

This repo can be cloned as a submodule into any project in order to provide:

- helper functions
- grapher module
- metrics
- layers
- fid

Helper functions

These functions include things like:

- ones_like / zeros_like functions
- directory creation / csv appender
- expand / squeeze dims
- zero padding
- one_hot generataion
- normalization
- int_type / long_type / float_type for cuda vs. regular tensors

Metrics

Include functions like:

- compute softmax / bce accuracies
- frechet distance calculations
- compute EWC
- compute FID

Layers

Include layers such as :

- dense with many sequential layers & bn
- conv stack
- dense / conv encoder + decoder stack
- bw2rgb module
- Identity, View layers, and more!

FID:

To compute FID use train_fid_model from fid.py. You can use a simple conv model or inceptionv3. The FID batch size can smaller than or equal to the model you train. For inceptionv3 you need small batch sizes unless you have a badass P100 or something.

After this you can use calculate_fid from metrics.

Grapher

The grapher can plot to visdom or tensorboardX Currently there exists only a grapher, the visdom grapher. This helper utilizes a matplotlib style API for sending data to visdom.

from helpers.grapher import Grapher

# for visdom:
grapher = Grapher('visdom', env='my_experiment',
                  server='http://localhost',
                  port=8097)

# for tensorboardX
grapher = Grapher('tensorboard', 'my_experiment')

# to add a scalar
grapher.add_scalar('my_scalar', value, epoch)

jramapuram / helpers