Merge pull request #109 from initze/108-cant-find-inferecepy

108 cant find inferecepy

README.md

@@ -5,8 +5,7 @@
 * Latest development version: `pip install git+https://github.com/initze/thaw-slump-segmentation`
 * Latest release: `pip install https://github.com/initze/thaw-slump-segmentation/releases/download/untagged-f6739f56e0ee4c2c64fe/thaw_slump_segmentation-0.10.0-py3-none-any.whl`
 
-This will pull the CUDA 12 version of pytorch. If you are running CUDA 11, you need to manually switch to the corresponding Pytorch package afterwards by running `pip3 install torch==2.1.2+cu118 torchvision==0.16.2+cu118 --index-url https://download.pytorch.org/whl/cu118`
-
+This will pull the CUDA 12 version of pytorch. If you are running CUDA 11, you need to manually switch to the corresponding Pytorch package afterwards by running `pip3 install torch==2.2.0+cu118 torchvision==0.17.0+cu118 --index-url https://download.pytorch.org/whl/cu118`
 
 gdal incl. gdal-utilities (preferably version >=3.6) need to be installed in your environment, e.g. with conda
 

src/thaw_slump_segmentation/postprocessing.py

@@ -24,7 +24,7 @@ def run_inference(df, model, processing_dir, inference_dir, model_dir=Path('/isi
         print('Empty dataframe')
     else:
         tiles = ' '.join(df.name.values)
-        run_string = f"CUDA_VISIBLE_DEVICES='{gpu}' python inference.py -n {model} --data_dir {processing_dir} --inference_dir {inference_dir}  --patch_size {patch_size} --margin_size {margin_size} {model_dir/model} {tiles}"
+        run_string = f"CUDA_VISIBLE_DEVICES='{gpu}' inference -n {model} --data_dir {processing_dir} --inference_dir {inference_dir}  --patch_size {patch_size} --margin_size {margin_size} {model_dir/model} {tiles}"
         print(run_string)
         if run:
             os.system(run_string)

src/thaw_slump_segmentation/scripts/inference.py

@@ -100,7 +100,7 @@ def flush_rio(filepath):
         pass
 
 
-def do_inference(tilename, args=None, log_path=None):
+def do_inference(tilename, sources, model, dev, logger, args=None, log_path=None):
     tile_logger = get_logger(f'inference.{tilename}')
     # ===== PREPARE THE DATA =====
     DATA_ROOT = args.data_dir
@@ -307,7 +307,7 @@ def main():
     torch.set_grad_enabled(False)
 
     for tilename in tqdm(args.tile_to_predict):
-        do_inference(tilename, args, log_path)
+        do_inference(tilename, sources, model, dev, logger, args, log_path)
 
 if __name__ == "__main__":
   main()

src/thaw_slump_segmentation/scripts/process_02_inference.py

@@ -37,6 +37,8 @@
                     help="Maximum number of images to process (optional)")
 parser.add_argument("--skip_vrt", action="store_false", 
                     help="set to skip DEM vrt creation")
+parser.add_argument("--skip_vector_save", action="store_true", 
+                    help="set to skip output vector creation")
 
 # TODO, make flag to skip vrt
 args = parser.parse_args()
@@ -94,7 +96,7 @@ def main():
     N_JOBS=40
     print(f'Preprocessing {len(df_preprocess)} images') #fix this
     if len(df_preprocess) > 0:
-        pp_string = f'python setup_raw_data.py --data_dir {args.processing_dir} --n_jobs {N_JOBS} --nolabel'
+        pp_string = f'setup_raw_data --data_dir {args.processing_dir} --n_jobs {N_JOBS} --nolabel'
         os.system(pp_string)
 
     # ## Processing/Inference
@@ -126,30 +128,33 @@ def main():
     _ = Parallel(n_jobs=n_splits)(delayed(run_inference)(df_split[split], model=args.model, processing_dir=args.processing_dir, inference_dir=args.inference_dir, model_dir=args.model_dir, gpu=gpu_split[split], run=True) for split in range(n_splits))
     # #### Merge output files
 
+    if not args.skip_vector_save:
     # read all files which following the above defined threshold
-    flist = list((args.inference_dir / args.model).glob(f'*/*pred_binarized.shp'))
-    len(flist)
-    # TODO:uncomment here
-    if len(df_process_final) > 0:
-        # load them in parallel
-        out = Parallel(n_jobs=6)(delayed(load_and_parse_vector)(f) for f in tqdm(flist[:]))
-
-        # merge them and save to geopackage file
-        merged_gdf = gpd.pd.concat(out)
-        save_file = args.inference_dir / args.model / f'{args.model}_merged.gpkg'
-
-        # check if file already exists, create backup file if exists
-        if save_file.exists():
-            # Get the current timestamp
-            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-            # Create the backup file name
-            save_file_bk = args.inference_dir / args.model / f"{args.model}_merged_bk_{timestamp}.gpkg"
-            print (f'Creating backup of file {save_file} to {save_file_bk}')
-            shutil.move(save_file, save_file_bk)
-
-        # save to files
-        print(f'Saving vectors to {save_file}')
-        merged_gdf.to_file(save_file)
+        flist = list((args.inference_dir / args.model).glob(f'*/*pred_binarized.shp'))
+        len(flist)
+        # TODO:uncomment here
+        if len(df_process_final) > 0:
+            # load them in parallel
+            out = Parallel(n_jobs=6)(delayed(load_and_parse_vector)(f) for f in tqdm(flist[:]))
+
+            # merge them and save to geopackage file
+            merged_gdf = gpd.pd.concat(out)
+            save_file = args.inference_dir / args.model / f'{args.model}_merged.gpkg'
+
+            # check if file already exists, create backup file if exists
+            if save_file.exists():
+                # Get the current timestamp
+                timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+                # Create the backup file name
+                save_file_bk = args.inference_dir / args.model / f"{args.model}_merged_bk_{timestamp}.gpkg"
+                print (f'Creating backup of file {save_file} to {save_file_bk}')
+                shutil.move(save_file, save_file_bk)
+
+            # save to files
+            print(f'Saving vectors to {save_file}')
+            merged_gdf.to_file(save_file)
+    else:
+        print('Skipping output vector creation!')
 
 if __name__ == "__main__":
     main()