./test-svrt.py
```
-should generate an image example.png in the current directory.
+should generate an image
+[`example.png`](https://fleuret.org/git-extract/pysvrt/example.png) in
+the current directory.
Note that the image generation does not take advantage of GPUs or
multi-core, and can be as fast as 10,000 vignettes per second and as
## Vignette sets ##
-The svrtset.py implements the classes `VignetteSet` and
-`CompressedVignetteSet` with the following constructor
+The file [`svrtset.py`](https://fleuret.org/git-extract/pysvrt/svrtset.py) implements the classes `VignetteSet` and
+`CompressedVignetteSet` both with a constructor
```
-def __init__(self, problem_number, nb_samples, batch_size, cuda = False, logger = None):
+__init__(problem_number, nb_samples, batch_size, cuda = False, logger = None)
```
-and the following method to return one batch
+and a method
```
-def get_batch(self, b):
+(torch.FloatTensor, torch.LongTensor) get_batch(b)
```
+which returns a pair composed of a 4d 'input' Tensor (i.e. single
+channel 128x128 images), and a 1d 'target' Tensor (i.e. Boolean
+labels).
+
## Low-level functions ##
The main function for genering vignettes is
# Testing convolution networks #
The file
-
-```
-cnn-svrt.py
-```
-
-provides the implementation of two deep networks, and use the
-compressed vignette code to allow the training with several millions
-vignettes on a PC with 16Gb and a GPU with 8Gb.
-
-The networks were designed by Afroze Baqapuri during an internship at
-Idiap.
+[`cnn-svrt.py`](https://fleuret.org/git-extract/pysvrt/cnn-svrt.py)
+provides the implementation of two deep networks designed by Afroze
+Baqapuri during an internship at Idiap, and allows to train them with
+several millions vignettes on a PC with 16Gb and a GPU with 8Gb.