Skip to content
Permalink
Browse files

add config.toml doc generation

  • Loading branch information...
ocourtin committed Mar 15, 2019
1 parent 1e485c3 commit 6f5af671f84deda06a3284d05eba9e51eb5b6769
Showing with 83 additions and 2 deletions.
  1. +6 −0 Makefile
  2. +75 −0 docs/config.md
  3. +2 −2 docs/tools.md
@@ -27,6 +27,12 @@ doc:
rsp $$tool -h >> docs/tools.md; \
echo '```' >> docs/tools.md; \
done
@echo "Doc generation: config.toml"
@echo "## config.toml" > docs/config.md; \
echo '```' >> docs/config.md; \
cat config.toml >> docs/config.md; \
echo '```' >> docs/config.md;


it: it_preparation it_train it_post

@@ -0,0 +1,75 @@
## config.toml
```
# RoboSat.pink Configuration
[dataset]
# The datasets base directory.
path = "~/rsp_dataset"
# Optional PostgreSQL Database connection, using psycopg2 syntax (could be use by rasterize tool).
pg_dsn = "host=127.0.0.1 dbname=rsp user=postgres"
# Input channels configuration.
# You can, add several channels blocks to compose your input Tensor. Order is meaningful.
#
# sub: dataset subdirectory name
# bands: bands to keep from sub source. Order is meaningful
# mean: bands mean value
# std: bands std value
# Nota: (default mean and std are based on ImageNet DataSet, cf pretrained model)
[[channels]]
sub = "images"
bands = [1, 2, 3]
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
# Output Classes configuration.
# Nota: available colors are either CSS3 colors names or #RRGGBB hexadecimal representation.
# Nota: only support binary classification for now.
[[classes]]
title = "background"
color = "white"
[[classes]]
title = "building"
color = "deeppink"
[model]
# Model name.
name = "albunet"
# Encoder model name.
encoder = "resnet50"
# Use, or not, ImageNet weights pretraining.
pretrained = true
# Loss function name.
loss = "lovasz"
# Batch size for training.
# Nota: can be increase upon your available GPU RAM.
batch_size = 2
# tile side size in pixels.
tile_size = 512
# Total number of epochs to train for.
epochs = 10
# Learning rate for the optimizer.
# NOTA: should be increase to ~0.0001 if you're not using pretrained models.
lr = 0.000025
# Data augmentation, Flip or Rotate probability.
data_augmentation = 0.75
# Weight decay l2 penalty for the optimizer.
decay = 0.0
```
@@ -158,15 +158,15 @@ Web UI:
```
## rsp subset
```
usage: rsp subset [-h] [--mode {delete,move,copy}] --dir DIR --cover COVER
usage: rsp subset [-h] [--mode {copy,delete,move}] --dir DIR --cover COVER
[--out OUT] [--web_ui] [--web_ui_base_url WEB_UI_BASE_URL]
[--web_ui_template WEB_UI_TEMPLATE]
optional arguments:
-h, --help show this help message and exit
Inputs:
--mode {delete,move,copy} subset mode [default: copy]
--mode {copy,delete,move} subset mode [default: copy]
--dir DIR path to inputs XYZ tiles dir [mandatory]
--cover COVER path to csv cover file to subset tiles by [mandatory]

0 comments on commit 6f5af67

Please sign in to comment.
You can’t perform that action at this time.