Update with information about the convnets.

[pysvrt.git] / README.md

diff --git a/README.md b/README.md
index 65c70a2..9e350b8 100644 (file)
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@ framework.
 The main function is
 
 ```
-torch.ByteTensor generate_vignettes(int problem_number, torch.LongTensor labels)
+torch.ByteTensor svrt.generate_vignettes(int problem_number, torch.LongTensor labels)
 ```
 
 where
@@ -26,12 +26,51 @@ Executing
 
 ```
 make -j -k
-./build.py
 ./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 slow as 40 vignettes per second s on a 4GHz
-i7-6700K.
+multi-core, and can be as fast as 10,000 vignettes per second and as
+slow as 40 on a 4GHz i7-6700K.
+
+# Vignette compression #
+
+The two additional functions
+
+```
+torch.ByteStorage svrt.compress(torch.ByteStorage x)
+```
+
+and
+
+```
+torch.ByteStorage svrt.uncompress(torch.ByteStorage x)
+```
+
+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).
+
+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.
+
+# 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.