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geo-inference call returns mask name #20

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merged 3 commits into from
Sep 5, 2024
Merged

geo-inference call returns mask name #20

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return_mask_name
Sep 4, 2024
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return_mask_name
Sep 4, 2024
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return_mask_name
Sep 4, 2024
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16 changes: 12 additions & 4 deletions geo_inference/geo_inference.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import os
import gc
import re
import sys
import time
import torch
import pystac
Expand Down Expand Up @@ -40,6 +41,7 @@
logger = logging.getLogger(__name__)



class GeoInference:

"""
Expand Down Expand Up @@ -114,7 +116,7 @@ async def run_async():
# Start the periodic garbage collection task
self.gc_task = asyncio.create_task(self.constant_gc(5)) # Calls gc.collect() every 5 seconds
# Run the main computation asynchronously
await self.async_run_inference(
self.mask_layer_name = await self.async_run_inference(
inference_input=inference_input,
bands_requested=bands_requested,
patch_size=patch_size,
Expand All @@ -126,9 +128,11 @@ async def run_async():
try:
await self.gc_task
except asyncio.CancelledError:
logger.info("The End of Inference")
pass

asyncio.run(run_async())
return self.mask_layer_name
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The return type of the __call__() function should be str instead of None



async def async_run_inference(self,
inference_input: Union[Path, str],
Expand Down Expand Up @@ -189,6 +193,8 @@ async def async_run_inference(self,
yolo_csv_path = self.work_dir.joinpath(prefix_base_name + "_yolo.csv")
coco_json_path = self.work_dir.joinpath(prefix_base_name + "_coco.json")
stride_patch_size = int(patch_size / 2)


""" Processing starts"""
start_time = time.time()
try:
Expand Down Expand Up @@ -332,6 +338,7 @@ async def async_run_inference(self,
)
)
torch.cuda.empty_cache()
return mask_path.name

except Exception as e:
print(f"Processing on the Dask cluster failed due to: {e}")
Expand All @@ -356,14 +363,15 @@ def main() -> None:
num_classes=arguments["classes"],
prediction_threshold=arguments["prediction_threshold"]
)
geo_inference(
inference_mask_layer_name = geo_inference(
inference_input=arguments["image"],
bands_requested=arguments["bands_requested"],
patch_size=arguments["patch_size"],
workers=arguments["workers"],
bbox=arguments["bbox"],
)



if __name__ == "__main__":
main()
main()
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