Update.

diff --git a/README.md b/README.md
index 4efe67f..b6e29c7 100644 (file)
--- 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.
 
 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
 # 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.
 
 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
+
+```
+def __init__(self, problem_number, nb_samples, batch_size, cuda = False, logger = None):
+```
+
+and the following method to return one batch
+
+```
+def get_batch(self, 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
 
 
 The two additional functions