X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=README.md;h=735bee39a9514619a4f4e158ad4fa0927149d17c;hb=05414734a8c423314aaa9447db04ed348066c2f9;hp=26b40f26b4ee35b451ba8ab691fbc668388dcec5;hpb=53d2096f8797d219a265a38375fbe1e238a13c57;p=pysvrt.git

diff --git a/README.md b/README.md
index 26b40f2..735bee3 100644
--- 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
@@ -20,6 +20,28 @@ The returned ByteTensor has three dimensions:
  * Pixel row
  * Pixel col
 
+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).
+
+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.
+
+See vignette_set.py for a class CompressedVignetteSet using it.
+
 # Installation and test #
 
 Executing
@@ -32,5 +54,5 @@ make -j -k
 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 3,000 vignettes per second and as
+multi-core, and can be as fast as 10,000 vignettes per second and as
 slow as 40 on a 4GHz i7-6700K.