X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=README.md;h=d0ec215a1767bd07bfc53b0e0758ce1891c39bd1;hb=77153ad6f6acb94a5132e9930722500cd93a6960;hp=4efe67fbc4538875228fdacddbb3ab38c3c9e113;hpb=f2da660a51ed51cab4d81df29109e26e0da347bc;p=pysvrt.git

diff --git a/README.md b/README.md
index 4efe67f..d0ec215 100644
--- a/README.md
+++ b/README.md
@@ -4,23 +4,6 @@ This is a port of the Synthetic Visual Reasoning Test problems to the
 pytorch framework, with an implementation of two convolutional
 networks to solve them.
 
-The main function is
-
-```
-torch.ByteTensor svrt.generate_vignettes(int problem_number, torch.LongTensor labels)
-```
-
-where
-
- * `problem_number` indicates which of the 23 problem to use
- * `labels` indicates the boolean labels of the vignettes to generate
-
-The returned ByteTensor has three dimensions:
-
- * Vignette index
- * Pixel row
- * Pixel col
-
 # Installation and test #
 
 Executing
@@ -36,7 +19,41 @@ 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 compression #
+# Vignette generation and compression #
+
+## Vignette sets ##
+
+The svrtset.py implements the classes `VignetteSet` and
+`CompressedVignetteSet` with the following constructor
+
+```
+__init__(problem_number, nb_samples, batch_size, cuda = False, logger = None)
+```
+
+and the following method to return one batch
+
+```
+(torch.FloatTensor, torch.LongTensor) get_batch(b)
+```
+
+## Low-level functions ##
+
+The main function for genering vignettes is
+
+```
+torch.ByteTensor svrt.generate_vignettes(int problem_number, torch.LongTensor labels)
+```
+
+where
+
+ * `problem_number` indicates which of the 23 problem to use
+ * `labels` indicates the boolean labels of the vignettes to generate
+
+The returned ByteTensor has three dimensions:
+
+ * Vignette index
+ * Pixel row
+ * Pixel col
 
 The two additional functions