Oups.

[pysvrt.git] / README.md

diff --git a/README.md b/README.md
index 4efe67f..2234832 100644 (file)
--- a/README.md
+++ b/README.md
@@ -4,39 +4,62 @@ 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
+# Installation and test #
+
+Executing

 
 ```
 
 ```

-torch.ByteTensor svrt.generate_vignettes(int problem_number, torch.LongTensor labels)
+make -j -k
+./test-svrt.py

 ```
 
 ```
 

-where
+should generate an image
+[`example.png`](https://fleuret.org/git-extract/pysvrt/example.png) in
+the current directory.

 
 

- * `problem_number` indicates which of the 23 problem to use
- * `labels` indicates the boolean labels of the vignettes to generate
+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.

 
 

-The returned ByteTensor has three dimensions:
+# Vignette generation and compression #

 
 

- * Vignette index
- * Pixel row
- * Pixel col
+## Vignette sets ##

 
 

-# Installation and test #
+The file [`svrtset.py`](https://fleuret.org/git-extract/pysvrt/svrtset.py) implements the classes `VignetteSet` and
+`CompressedVignetteSet` both with a constructor

 
 

-Executing
+```
+__init__(problem_number, nb_samples, batch_size, cuda = False, logger = None)
+```
+
+and a method

 
 ```
 
 ```

-make -j -k
-./test-svrt.py
+(torch.FloatTensor, torch.LongTensor) get_batch(b)

 ```
 
 ```
 

-should generate an image example.png in the current directory.
+which returns a pair composed of a 4d 'input' Tensor (i.e. single
+channel 128x128 images), and a 1d 'target' Tensor (i.e. Boolean
+labels).

 
 

-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.
+## Low-level functions ##
+
+The main function for generating vignettes is
+
+```
+torch.ByteTensor svrt.generate_vignettes(int problem_number, torch.LongTensor labels)
+```
+
+where

 
 

-# Vignette compression #
+ * `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
 


@@ -64,14 +87,7 @@ See vignette_set.py for a class CompressedVignetteSet using it.
 # Testing convolution networks #
 
 The file
 # 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.
+[`cnn-svrt.py`](https://fleuret.org/git-extract/pysvrt/cnn-svrt.py)
+provides the implementation of two deep networks designed by Afroze
+Baqapuri during an internship at Idiap, and allows to train them with
+several millions vignettes on a PC with 16Gb and a GPU with 8Gb.