evazion
36 lines
1.5 KiB
Python
36 lines
1.5 KiB
Python
from fastbook import *
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from pandas import read_csv
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import timm
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class Autotagger:
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def __init__(self, model_path="models/model.pth", data_path="test/tags.csv.gz", tags_path="data/tags.json"):
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self.model_path = model_path
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self.learn = self.init_model(data_path=data_path, tags_path=tags_path, model_path=model_path)
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def init_model(self, model_path="model/model.pth", data_path="test/tags.csv.gz", tags_path="data/tags.json"):
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df = read_csv(data_path)
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vocab = json.load(open(tags_path))
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dblock = DataBlock(
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blocks=(ImageBlock, MultiCategoryBlock(vocab=vocab)),
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get_x = lambda df: Path("test") / df["filename"],
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get_y = lambda df: df["tags"].split(" "),
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item_tfms = Resize(224, method = ResizeMethod.Squish),
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batch_tfms = [RandomErasing()]
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)
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dls = dblock.dataloaders(df)
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learn = vision_learner(dls, "resnet152", pretrained=False)
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model_file = open(model_path, "rb")
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learn.load(model_file, with_opt=False)
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return learn
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def predict(self, path, threshold=0.01, limit=50):
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with self.learn.no_bar(), self.learn.no_logging():
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pred = self.learn.predict(path)
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scores = [score.item() for score in pred[2]]
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results = { tag : score for (tag, score) in zip(self.learn.dls.vocab, scores) if score >= threshold }
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results = sorted(results.items(), key = lambda x: -x[1])
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return dict(results[:limit])
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