X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=pysvrt.git;a=blobdiff_plain;f=README.md;h=a4ea4fd956284572b20fdc650a9023021687a13b;hp=735bee39a9514619a4f4e158ad4fa0927149d17c;hb=HEAD;hpb=05414734a8c423314aaa9447db04ed348066c2f9

diff --git a/README.md b/README.md
index 735bee3..a4ea4fd 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,50 @@
 # Introduction #
 
-This is the port of the Synthetic Visual Reasoning Test to the pytorch
-framework.
+This is a wrapper for [`PyTorch`](http://pytorch.org) for the
+[`Synthetic Visual Reasoning Test,`](https://fleuret.org/git/svrt)
+with an implementation of two convolutional networks to solve them.
 
-The main function is
+# Installation and test #
+
+Executing
+
+```
+make -j -k
+./test-svrt.py
+```
+
+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
+slow as 40 on a 4GHz i7-6700K.
+
+# Vignette generation and compression #
+
+## Vignette sets ##
+
+The file [`svrtset.py`](https://fleuret.org/git-extract/pysvrt/svrtset.py) implements the classes `VignetteSet` and
+`CompressedVignetteSet` both with a constructor
+
+```
+__init__(problem_number, nb_samples, batch_size, cuda = False, logger = None)
+```
+
+and a method
+
+```
+(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 generating vignettes is
 
 ```
 torch.ByteTensor svrt.generate_vignettes(int problem_number, torch.LongTensor labels)
@@ -36,23 +77,17 @@ provide a lossless compression scheme adapted to the ByteStorage of
 the vignette ByteTensor (i.e. expecting a lot of 255s, a few 0s, and
 no other value).
 
-They allow to reduce the memory footprint by a factor ~50, and may be
-usefull to deal with very large data-sets and avoid re-generating
-images at every batch.
+This compression reduces the memory footprint by a factor ~50, and may
+be usefull to deal with very large data-sets and avoid re-generating
+images at every batch. It induces a little overhead for decompression,
+and moving from CPU to GPU memory.
 
 See vignette_set.py for a class CompressedVignetteSet using it.
 
-# Installation and test #
-
-Executing
+# Testing convolution networks #
 
-```
-make -j -k
-./test-svrt.py
-```
-
-should generate an image 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
-slow as 40 on a 4GHz i7-6700K.
+The file
+[`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.