08 multisource run

lib/postprocessing.py

@@ -65,6 +65,7 @@ def get_date_from_PSfilename(name):
     return date
 
 
+# TODO: create empty dataframe if no files found
 def get_datasets(path, depth=0, preprocessed=False):
     dirs = listdirs2(path, depth=depth)
     df = pd.DataFrame(data=dirs, columns=['path'])
@@ -141,6 +142,7 @@ def get_processing_status(raw_data_dir, processing_dir, inference_dir, model):
     except:
         df_raw = get_datasets(raw_data_dir, depth=0)
     # get processed
+    # TODO: check here for both options - make 2 runs 
     df_processed = get_datasets(processing_dir / 'tiles', depth=0, preprocessed=True)
     # calculate prperties
     diff = df_raw[~df_raw['name'].isin(df_processed['name'])]

process_02_inference.py

@@ -0,0 +1,126 @@
+
+from pathlib import Path
+import torch
+import pandas as pd
+import os
+import numpy as np
+import tqdm
+from joblib import delayed, Parallel
+import shutil
+from tqdm.notebook import tqdm
+import swifter
+
+from lib.postprocessing import *
+
+# ### Settings 
+
+# Local code dir
+CODE_DIR = Path('/isipd/projects/p_aicore_pf/initze/code/aicore_inference')
+# Location of raw data
+RAW_DATA_DIR = Path('/isipd/projects/p_aicore_pf/initze/data/planet/planet_data_inference_grid/tiles')
+# Location data processing
+PROCESSING_DIR = Path('/isipd/projects/p_aicore_pf/initze/processing')
+# Target directory for
+INFERENCE_DIR = Path('/isipd/projects/p_aicore_pf/initze/processed/inference')
+
+# Target to models - RTS
+MODEL_DIR = Path('/isipd/projects/p_aicore_pf/initze/models/thaw_slumps')
+
+MODEL='RTS_v6_notcvis'
+models = ['RTS_v6_notcvis', 'RTS_v6_tcvis']
+
+#USE_GPU = [1,2,3,4]
+USE_GPU = [1,2]
+RUNS_PER_GPU = 5
+MAX_IMAGES = None
+
+# ### List all files with properties
+df_processing_status = get_processing_status(RAW_DATA_DIR, PROCESSING_DIR, INFERENCE_DIR, MODEL)
+
+df_final = df_processing_status
+
+total_images = len(df_final)
+preprocessed_images = df_final.preprocessed.sum()
+finished_images = df_final.inference_finished.sum()
+print(f'Number of images: {total_images}')
+print(f'Number of preprocessed images: {preprocessed_images}')
+print(f'Number of finished images: {finished_images}')
+print(f'Number of image to process: {preprocessed_images - finished_images}')
+
+# ## Preprocessing
+
+# #### Update Arctic DEM data 
+print('Updating Elevation VRTs!')
+dem_data_dir = Path('/isipd/projects/p_aicore_pf/initze/data/ArcticDEM')
+vrt_target_dir = Path('/isipd/projects/p_aicore_pf/initze/processing/auxiliary/ArcticDEM')
+#update_DEM(vrt_target_dir)
+update_DEM2(dem_data_dir=dem_data_dir, vrt_target_dir=vrt_target_dir)
+
+# #### Copy data for Preprocessing 
+# make better documentation
+
+df_preprocess = df_final[~df_final.preprocessed]
+print(f'Number of images to preprocess: {len(df_preprocess)}')
+
+# Cleanup processing directories to avoid incomplete processing
+input_dir_dslist = list((PROCESSING_DIR / 'input').glob('*'))
+if len(input_dir_dslist) > 0:
+    print(input_dir_dslist)
+    for d in input_dir_dslist:
+        print('Delete', d)
+        shutil.rmtree(d)
+else:
+    print('Processing directory is ready, nothing to do!')
+
+# Copy Data
+_ = df_preprocess.swifter.apply(lambda x: copy_unprocessed_files(x, PROCESSING_DIR), axis=1)
+
+# #### Run Preprocessing 
+import warnings
+warnings.filterwarnings('ignore')
+
+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 {PROCESSING_DIR} --n_jobs {N_JOBS} --nolabel'
+    os.system(pp_string)
+
+# ## Processing/Inference
+# rerun processing status
+df_processing_status2 = get_processing_status(RAW_DATA_DIR, PROCESSING_DIR, INFERENCE_DIR, MODEL)
+
+# Filter to images that are not preprocessed yet
+df_process = df_final[~df_final.inference_finished]
+# update overview and filter accordingly - really necessary?
+df_process_final = df_process.set_index('name').join(df_processing_status2[df_processing_status2['preprocessed']][['name']].set_index('name'), how='inner').reset_index(drop=False).iloc[:MAX_IMAGES]
+# validate if images are correctly preprocessed
+df_process_final['preprocessing_valid'] = (df_process_final.apply(lambda x: len(list(x['path'].glob('*'))), axis=1) >= 5)
+# final filtering process to remove incorrectly preprocessed data
+df_process_final = df_process_final[df_process_final['preprocessing_valid']]
+
+print(f'Number of images:', len(df_process_final))
+
+# #### Parallel runs 
+# Make splits to distribute the processing
+n_splits = len(USE_GPU) * RUNS_PER_GPU
+df_split = np.array_split(df_process_final, n_splits)
+gpu_split = USE_GPU * RUNS_PER_GPU
+
+#for split in df_split:
+#    print(f'Number of images: {len(split)}')
+
+print('Run inference!')
+# ### Parallel Inference execution
+_ = Parallel(n_jobs=n_splits)(delayed(run_inference)(df_split[split], model=MODEL, processing_dir=PROCESSING_DIR, inference_dir=INFERENCE_DIR, model_dir=MODEL_DIR, gpu=gpu_split[split], run=True) for split in range(n_splits))
+# #### Merge output files
+
+# read all files which followiw the above defined threshold
+flist = list((INFERENCE_DIR / MODEL).glob(f'*/*pred_binarized.shp'))
+len(flist)
+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)
+    print(INFERENCE_DIR / MODEL / f'{MODEL}_merged.gpkg')
+    merged_gdf.to_file(INFERENCE_DIR / MODEL / f'{MODEL}_merged.gpkg')

process_03_ensemble.py

@@ -0,0 +1,100 @@
+# # Create ensemble results from several model outputs
+# ### Imports 
+
+from pathlib import Path
+import pandas as pd
+from joblib import delayed, Parallel
+#from tqdm.notebook import tqdm
+from tqdm import tqdm
+from lib.postprocessing import *
+import geopandas as gpd
+
+# ### Settings 
+# Local code dir
+CODE_DIR = Path('.')
+BASE_DIR = Path('../..')
+# Location of raw data
+# TODO: make support for multiple sources
+RAW_DATA_DIR = BASE_DIR / Path('data/planet/planet_data_inference_grid/tiles')
+# Location data processing
+PROCESSING_DIR = BASE_DIR / 'processing'
+# Target directory for
+INFERENCE_DIR = BASE_DIR / Path('processed/inference')
+# Target to models - RTS
+MODEL_DIR = BASE_DIR / Path('models/thaw_slumps')
+# Ensemble Target
+ENSEMBLE_NAME = 'RTS_v6_ensemble_v2'
+MODEL_NAMES = ['RTS_v6_notcvis', 'RTS_v6_tcvis']
+N_IMAGES = None # automatically run full set
+N_JOBS = 15 # number of cpu jobs for ensembling
+N_VECTOR_LOADERS = 6 # number of parallel vector loaders for final merge
+
+# ### Select Data 
+# * create list of available files
+# * filter whats available
+
+# check if cucim is available
+try:
+    import cucim
+    try_gpu = True
+    print ('Running ensembling with GPU!')
+except:
+    try_gpu = False
+    print ('Cucim import failed')
+
+# setup all params
+kwargs_ensemble = {
+    'ensemblename': ENSEMBLE_NAME,
+    'inference_dir': INFERENCE_DIR,
+    'modelnames': MODEL_NAMES,
+    'binary_threshold': [0.4, 0.45, 0.5],
+    'border_size': 10,
+    'minimum_mapping_unit': 32,
+    'delete_binary': True,
+    'try_gpu': False, # currently default to CPU only
+    'gpu' : 0,
+}
+
+
+# Check for finalized products
+df_processing_status = get_processing_status(RAW_DATA_DIR, PROCESSING_DIR, INFERENCE_DIR, model=kwargs_ensemble['ensemblename'])
+df_ensemble_status = get_processing_status_ensemble(INFERENCE_DIR, model_input_names=kwargs_ensemble['modelnames'], model_ensemble_name=kwargs_ensemble['ensemblename'])
+# Check which need to be process - check for already processed and invalid files
+process = df_ensemble_status[df_ensemble_status['process']]
+
+# #### Filter by tile_ids
+
+#process = process[process.apply(lambda x: x['name'].split('_')[1].startswith('42'), axis=1)]
+df_processing_status.groupby('inference_finished').count()
+
+# #### Documentation 
+
+print('Number of files to process')
+process.groupby('process').count().iloc[0,0]
+
+# #### Run Ensemble Merging
+
+print(f'Start running ensemble with {N_JOBS} jobs!')
+print(f'Target ensemble name:', kwargs_ensemble['ensemblename'])
+print(f'Source model output', kwargs_ensemble['modelnames'])
+_ = Parallel(n_jobs=N_JOBS)(delayed(create_ensemble_v2)(image_id=process.iloc[row]['name'], **kwargs_ensemble) for row in tqdm(range(len(process.iloc[:N_IMAGES]))))
+
+# #### run parallelized batch 
+
+# ### Merge vectors to complete dataset 
+
+
+ensemblename = ENSEMBLE_NAME
+# set probability levels: 'class_05' means 50%, 'class_045' means 45%. This is the regex to search for vector naming
+proba_strings = ['class_05', 'class_045','class_04']
+
+for proba_string in proba_strings:
+    # read all files which followiw the above defined threshold
+    flist = list((INFERENCE_DIR / ensemblename).glob(f'*/*_{proba_string}.gpkg'))
+    len(flist)
+    # load them in parallel
+    out = Parallel(n_jobs=6)(delayed(load_and_parse_vector)(f) for f in tqdm(flist[:N_IMAGES]))
+    # merge them and save to geopackage file
+    print ('Merging results')
+    merged_gdf = gpd.pd.concat(out)
+    merged_gdf.to_file(INFERENCE_DIR / ensemblename / f'merged_{proba_string}.gpkg')