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Run benchmark after training. Other improvements (project conversion,…
… progress bar, search for bg class) (#61)
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Original file line number | Diff line number | Diff line change |
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PYTHONUNBUFFERED=1 | ||
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DEBUG_APP_DIR="/tmp/mmsegmentation" | ||
DEBUG_CACHE_DIR="/tmp/mmsegmentation/cache" | ||
DEBUG_APP_DIR="tmp/mmsegmentation" | ||
DEBUG_CACHE_DIR="tmp/mmsegmentation/cache" | ||
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LOG_LEVEL="debug" |
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PYTHONUNBUFFERED=1 | ||
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modal.state.slyProjectId=43218 | ||
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context.teamId=447 | ||
context.workspaceId=680 | ||
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AGENT_ID=341 | ||
TASK_ID=68365 | ||
DEBUG_APP_DIR="tmp/mmsegmentation" | ||
DEBUG_CACHE_DIR="tmp/mmsegmentation/cache" | ||
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LOG_LEVEL="debug" |
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import supervisely as sly | ||
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def get_bg_class_name(class_names): | ||
possible_bg_names = ["background", "bg", "unlabeled", "neutral", "__bg__"] | ||
for name in class_names: | ||
if name.lower() in possible_bg_names: | ||
return name | ||
return None | ||
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def get_eval_results_dir_name(api, task_id, project_info): | ||
task_info = api.task.get_info_by_id(task_id) | ||
task_dir = f"{task_id}_{task_info['meta']['app']['name']}" | ||
eval_res_dir = f"/model-benchmark/{project_info.id}_{project_info.name}/{task_dir}/" | ||
eval_res_dir = api.file.get_free_dir_name(sly.env.team_id(), eval_res_dir) | ||
return eval_res_dir |
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import sys | ||
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sys.path.insert(0, "../") | ||
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from serve.src.mmsegm_model import MMSegmentationModel | ||
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class MMSegmentationModelBench(MMSegmentationModel): | ||
in_train = True |
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