Own data train

README.md

@@ -240,14 +240,121 @@ On the host machine:
 Docker: TODO
 
 ## Create your data
+
+Please check bash scripts for data preparation and mask generation from CelebaHQ section,
+if you stuck at one of the following steps.
+
+
 On the host machine:
 
-Explain explain explain
+    # Make shure you are in lama folder
+    cd lama
+    export TORCH_HOME=$(pwd) && export PYTHONPATH=.
+
+    # You need to prepare following image folders:
+    $ ls my_dataset
+    train
+    val_source # 2000 or more images
+    visual_test_source # 100 or more images
+    eval_source # 2000 or more images
+
+    # LaMa generates random masks for the train data on the flight,
+    # but needs fixed masks for test and visual_test for consistency of evaluation.
+
+    # Suppose, we want to evaluate and pick best models 
+    # on 512x512 val dataset  with thick/thin/medium masks 
+    # And your images have .jpg extention:
+
+    python3 bin/gen_mask_dataset.py \
+    $(pwd)/configs/data_gen/random_<size>_512.yaml \ # thick, thin, medium
+    my_dataset/val_source/ \
+    my_dataset/val/random_<size>_512.yaml # thick, thin, medium
+    --ext jpg
+
+    # So the mask generator will: 
+    # 1. resize and crop val images and save them as .png
+    # 2. generate masks
+
+    ls my_dataset/val/random_medium_512/
+    image1_crop000_mask000.png
+    image1_crop000.png
+    image2_crop000_mask000.png
+    image2_crop000.png
+    ...
+
+    # Generate thick, thin, medium masks for visual_test folder:
+
+    python3 bin/gen_mask_dataset.py \
+    $(pwd)/configs/data_gen/random_<size>_512.yaml \  #thick, thin, medium
+    my_dataset/visual_test_source/ \
+    my_dataset/visual_test/random_<size>_512/  #thick, thin, medium
+    --ext jpg
+
+
+    ls my_dataset/visual_test/random_thick_512/
+    image1_crop000_mask000.png
+    image1_crop000.png
+    image2_crop000_mask000.png
+    image2_crop000.png
+    ...
+
+    # Same process for eval_source image folder:
+
+    python3 bin/gen_mask_dataset.py \
+    $(pwd)/configs/data_gen/random_<size>_512.yaml \  #thick, thin, medium
+    my_dataset/eval_source/ \
+    my_dataset/eval/random_<size>_512/  #thick, thin, medium
+    --ext jpg
+
+
+
+    # Generate location config file which locate these folders:
+
+    touch my_dataset.yaml
+    echo "data_root_dir: $(pwd)/my_dataset/" >> my_dataset.yaml
+    echo "out_root_dir: $(pwd)/experiments/" my_dataset.yaml
+    echo "tb_dir: $(pwd)/tb_logs/" my_dataset.yaml
+    mv my_dataset.yaml ${PWD}/configs/training/location/
+
+
+    # Check data config for consistency with my_dataset folder structure:
+    $ cat ${PWD}/configs/training/data/abl-04-256-mh-dist
+    ...
+    train:
+      indir: ${location.data_root_dir}/train
+      ...
+    val:
+      indir: ${location.data_root_dir}/val
+      img_suffix: .png
+    visual_test:
+      indir: ${location.data_root_dir}/visual_test
+      img_suffix: .png
+
+
+    # Run training
+    python bin/train.py -cn lama-fourier location=my_dataset data.batch_size=10
+
+    # Evaluation: LaMa training procedure picks best few models according to 
+    # scores on my_dataset/val/ 
+
+    # To evaluate one of your best models (i.e. at epoch=32) 
+    # on previously unseen my_dataset/eval do the following 
+    # for thin, thick and medium:
+
+    # infer:
+    python3 bin/predict.py \
+    model.path=$(pwd)/experiments/<user>_<date:time>_lama-fourier_/ \
+    indir=$(pwd)/my_dataset/eval/random_<size>_512/ \
+    outdir=$(pwd)/inference/my_dataset/random_<size>_512 \
+    model.checkpoint=epoch32.ckpt
+
+    # metrics calculation:
+    python3 bin/evaluate_predicts.py \
+    $(pwd)/configs/eval_2gpu.yaml \
+    $(pwd)/my_dataset/eval/random_<size>_512/ \
+    $(pwd)/inference/my_dataset/random_<size>_512 \
+    $(pwd)/inference/my_dataset/random_<size>_512_metrics.csv
 
-    TODO: format
-    TODO: configs 
-    TODO: run training
-    TODO: run eval
 
 **OR** in the docker: