Feat/multi batch processing #127
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| @@ -37,6 +37,8 @@ class MapProcessingParameters: | |||
| resolution_cm_per_px: float # image resolution to used during processing | |||
| processed_area_type: ProcessedAreaType # whether to perform operation on the entire field or part | |||
| tile_size_px: int # Tile size for processing (model input size) | |||
| batch_size: int # Batch size for processing | |||
| local_cache: bool # Whether to use local cache for tiles | |||
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How about adding in comment it is caching on disk?
przemyslaw-aszkowski
approved these changes
Jan 3, 2024
| params=params, | ||
| ) | ||
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| map_processor.run() |
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How about adding an assert for the result (also in the original non-batched test)
| map_processor.run() | ||
| result_img = map_processor.get_result_img() | ||
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| assert result_img.shape == (2351, 2068) |
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how about asserting some pixels also, to check the result?
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Check the last commit, please.
przemyslaw-aszkowski
approved these changes
Jan 7, 2024
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Tests show poor acceleration with multi-batch processing. For example, large area map processing using RTX4090 results 5m11s (batch=1) vs 4m42s (batch=32). Profiling results: |
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