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""" |
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Created on Wed Mar 13 14:03:54 2024 |
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@author: rezer |
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""" |
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from sdeval.fidelity import CCIPMetrics |
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from sdeval.controllability import BikiniPlusMetrics |
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from sdeval.corrupt import AICorruptMetrics |
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import os |
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ccip = CCIPMetrics(images=r'jerry_test\train\1_1girl') |
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bp = BikiniPlusMetrics( |
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tag_blacklist=[ |
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'bangs', 'long_hair', 'blue_eyes', 'animal_ears', 'sleeveless', |
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'breasts', 'grey_hair', 'medium_breasts' |
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] |
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) |
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metrics = AICorruptMetrics() |
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lora_base_name_list=["surtr_arknights-000010", |
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"surtr_arknights-000012", |
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"surtr_arknights-000014", |
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"surtr_arknights-000016", |
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"surtr_arknights-000018", |
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"surtr_arknights-000020", |
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"surtr_arknights-000022", |
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"surtr_arknights",] |
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base_path=r'jerry_test' |
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import pandas as pd |
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l=[] |
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for lora_base_name in lora_base_name_list: |
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test_image_dir=os.path.join(base_path,lora_base_name) |
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ccip_score=ccip.score( test_image_dir) |
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metrics_score=metrics.score(test_image_dir) |
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bp_score=bp.score(test_image_dir) |
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score=[lora_base_name,ccip_score,metrics_score,bp_score] |
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print(f"lora_name:{lora_base_name},ccip:{ccip_score},bp:{bp_score},AI-C:{metrics_score}") |
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l.append(score) |
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pd.DataFrame(l).to_excel("report.xlsx") |
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