gen masks and celeba_example

Author: Aleksei Silvestrov

README.md

@@ -199,9 +199,19 @@ On the host machine:
     # unzip & split into train/test/visualization & create config for it
     bash fetch_data/celebahq_dataset_prepare.sh
 
-    
-    TODO: trian
-    TODO: eval
+    # generate masks for test and viz at the end of epoch
+    bash fetch_data/celebahq_gen_masks.sh
+
+    # Run training
+    # You can change bs with data.batch_size=10
+    python bin/train.py -cn lama-fourier-celeba location=celeba
+
+    # Infer model on thick/thin/medium masks in 256 and run evaluation 
+    # like this:
+    python3 bin/predict.py \
+    model.path=$(pwd)/experiments/<user>_<date:time>_lama-fourier-celeba_/ \
+    indir=$(pwd)/celeba-hq-dataset/visual_test_256/random_thick_256/ \
+    outdir=$(pwd)/inference/celeba_random_thick_256 model.checkpoint=last.ckpt
     
     
 Docker: TODO

configs/training/location/celeba_example.yaml

@@ -0,0 +1,5 @@
+# @package _group_
+data_root_dir: /home/user/lama/celeba-hq-dataset/
+out_root_dir: /home/user/lama/experiments/
+tb_dir: /home/user/lama/tb_logs/
+pretrained_models: /home/user/lama/

fetch_data/celebahq_dataset_prepare.sh

@@ -22,8 +22,8 @@ for mode in train \
     val \
     visual_test
 do
-    mkdir celeba-hq-dataset/$mode"_256"
-    cat celeba-hq-dataset/$mode"_shuffled.flist" | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/$mode"_256/"
+    mkdir celeba-hq-dataset/$mode"_source_256/"
+    cat celeba-hq-dataset/$mode"_shuffled.flist" | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/$mode"_source_256/"
 done
 
 

fetch_data/celebahq_gen_masks.sh

@@ -0,0 +1,29 @@
+python3 bin/gen_mask_dataset.py \
+$(pwd)/configs/data_gen/random_thick_256.yaml \
+celeba-hq-dataset/val_source_256/ \
+celeba-hq-dataset/val_256/random_thick_256/
+
+python3 bin/gen_mask_dataset.py \
+$(pwd)/configs/data_gen/random_thin_256.yaml \
+celeba-hq-dataset/val_source_256/ \
+celeba-hq-dataset/val_256/random_thin_256/
+
+python3 bin/gen_mask_dataset.py \
+$(pwd)/configs/data_gen/random_medium_256.yaml \
+celeba-hq-dataset/val_source_256/ \
+celeba-hq-dataset/val_256/random_medium_256/
+
+python3 bin/gen_mask_dataset.py \
+$(pwd)/configs/data_gen/random_thick_256.yaml \
+celeba-hq-dataset/visual_test_source_256/ \
+celeba-hq-dataset/visual_test_256/random_thick_256/
+
+python3 bin/gen_mask_dataset.py \
+$(pwd)/configs/data_gen/random_thin_256.yaml \
+celeba-hq-dataset/visual_test_source_256/ \
+celeba-hq-dataset/visual_test_256/random_thin_256/
+
+python3 bin/gen_mask_dataset.py \
+$(pwd)/configs/data_gen/random_medium_256.yaml \
+celeba-hq-dataset/visual_test_source_256/ \
+celeba-hq-dataset/visual_test_256/random_medium_256/