DOC: add help to select the trainig parameters values

Author: Remi Cresson

doc/HOW_TO.md

@@ -36,7 +36,6 @@ Karnewar et Al. (multi scale gradient)
 }
 ```
 
-
 ## How to install
 
 Use the [OTBTF Docker image](https://github.com/remicres/otbtf#how-to-install) with version >= 2.0.
@@ -73,6 +72,8 @@ The LR and HR images have the same number of bands, ordered in the same followin
 
 ### Step 2: train your network
 
+![](train.png)
+
 Use `train.py` to train a super-resolution network and export the SavedModel.
 **A (known bug)[https://github.com/remicres/sr4rs/issues/36] affects the training when `--streaming` is not used, for OTBTF versions < 2.5. Use `--streaming` in this case!**
 Well, here you might want to play with the parameters. The result will depend likely on your images!
@@ -114,13 +115,24 @@ python train.py \
 --lr_scale 0.0001 --hr_scale 0.0001 \
 --savedmodel /path/to/savedmodel
 ```
-This gave me some cool results, but you might find some settings better suited to your images.
+These settings helped to generate good looking images from our Sentinel-2 products. 
+Since this is quite subjective, other settings might be better suited, depending on your images.
 The important thing is, after the training is complete, the SavedModel will be generated in `/path/to/savedmodel`.
 Take care not to use an existing folder for `--savedmodel`, else TensorFlow will cancel the save. You must provide a non-existing folder for this one.
 Note that you can provide multiple HR and LR patches: just be sure that there is the same number of patches images, in the same order. Use the `--streaming` option if this takes too much RAM.
 
+#### How to chose the training parameters?
+
+Here are a few rules of thumb to follow:
+- use `lr_scale` and `hr_scale` so that the images pixels values range is generally in the [0, 1] interval,
+- use small `depth` and `nresblocks` to train a small model: this would be useful if you have small dataset, or low GPU memory budget.
+- Play with `batchsize` and `adam_lr` to train your model. The outcome mostly depends on the distribution of your data. Generally, a small batch size helps (4, 8, 16) and a learning rate in the 10e-2 and 10e-5 range.
+- `l1weight`, `l2weight`, `vggweight`: tune these parameters so that their contribution is in the same magnitude as the GAN loss.
+
 ### Step 3: generate your fake HR images
 
+![](inference.png)
+
 Now we use OTBTF `TensorFlowModelServe` application through `sr.py`.
 We just apply our SavedModel to a LR image, and transform it in a fake HR image.
 ```
@@ -131,6 +143,8 @@ python sr.py \
 ```
 
 #### Useful parameters
+
 - Depending on your hardware, you can set the memory footprint using the parameter `--ts` (as **t**ile **s**ize). Use large tile size to speed-up the process. If the tile size is too large, TensorFlow will likely throw an OOM (out of memory) exception. **Note the the default value (512) is quite small, and you will like to manually change that value to gain a great speed-up** for instance on an old GTX1080Ti (12Gb RAM) you can use 1024.
 - Change the output image encoding using the `--encoding` parameter. e.g for Sentinel-2 images you can use `--encoding int16`.
 - The image generation process avoids blocking artifacts in the generated output image. Use the `--pad` parameter to change the margin used to keep the pseudo-valid region, in spatial dimensions, of the output tensor.
+