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synced 2025-10-29 21:16:06 +02:00
fixing smell code smells
Этот коммит содержится в:
родитель
d1f65d016d
Коммит
ea297ea23d
@ -350,7 +350,7 @@ class MultimodalSearch(AnalysisMethod):
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def itm_text_precessing(self, search_query):
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def itm_text_precessing(self, search_query):
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for query in search_query:
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for query in search_query:
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if not (len(query) == 1) and (query in ("image", "text_input")):
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if (len(query) != 1) and (query in ("image", "text_input")):
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raise SyntaxError(
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raise SyntaxError(
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'Each querry must contain either an "image" or a "text_input"'
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'Each querry must contain either an "image" or a "text_input"'
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)
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)
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@ -58,9 +58,9 @@ class SummaryDetector(AnalysisMethod):
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def analyse_questions(self, list_of_questions):
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def analyse_questions(self, list_of_questions):
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(
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(
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summary_VQA_model,
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summary_vqa_model,
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summary_VQA_vis_processors,
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summary_vqa_vis_processors,
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summary_VQA_txt_processors,
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summary_vqa_txt_processors,
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) = load_model_and_preprocess(
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) = load_model_and_preprocess(
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name="blip_vqa",
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name="blip_vqa",
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model_type="vqav2",
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model_type="vqav2",
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@ -71,18 +71,18 @@ class SummaryDetector(AnalysisMethod):
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path = self.subdict["filename"]
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path = self.subdict["filename"]
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raw_image = Image.open(path).convert("RGB")
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raw_image = Image.open(path).convert("RGB")
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image = (
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image = (
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summary_VQA_vis_processors["eval"](raw_image)
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summary_vqa_vis_processors["eval"](raw_image)
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.unsqueeze(0)
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.unsqueeze(0)
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.to(self.summary_device)
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.to(self.summary_device)
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)
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)
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question_batch = []
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question_batch = []
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for quest in list_of_questions:
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for quest in list_of_questions:
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question_batch.append(summary_VQA_txt_processors["eval"](quest))
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question_batch.append(summary_vqa_txt_processors["eval"](quest))
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batch_size = len(list_of_questions)
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batch_size = len(list_of_questions)
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image_batch = image.repeat(batch_size, 1, 1, 1)
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image_batch = image.repeat(batch_size, 1, 1, 1)
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with no_grad():
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with no_grad():
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answers_batch = summary_VQA_model.predict_answers(
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answers_batch = summary_vqa_model.predict_answers(
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samples={"image": image_batch, "text_input": question_batch},
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samples={"image": image_batch, "text_input": question_batch},
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inference_method="generate",
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inference_method="generate",
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)
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)
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