Pass the use of cuda to the VignetteSet constructor.

[pysvrt.git] / README.md

diff --git a/README.md b/README.md
index 95e6206..a774d62 100644 (file)
--- a/README.md
+++ b/README.md
@@ -20,6 +20,28 @@ The returned ByteTensor has three dimensions:
  * Pixel row
  * Pixel col
 
  * Pixel row
  * Pixel col
 

+Two functions additional functions
+
+```
+torch.ByteStorage svrt.compress(torch.ByteStorage x)
+```
+
+and
+
+```
+torch.ByteStorage svrt.uncompress(torch.ByteStorage x)
+```
+
+provides 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
 # 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
 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.
 slow as 40 on a 4GHz i7-6700K.