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from typing import Dict, List, Any
from fastai.learner import load_learner
from PIL import Image
import os
import json
import numpy as np
class PreTrainedPipeline:
def __init__(self, path=""):
# IMPLEMENT_THIS
# Preload all the elements you are going to need at inference.
# For instance your model, processors, tokenizer that might be needed.
# This function is only called once, so do all the heavy processing I/O here"""
self.model = load_learner(os.path.join(path, "model.pkl"))
with open(os.path.join(path, "config.json")) as config:
config = json.load(config)
self.id2label = config["id2label"]
def __call__(self, inputs: "Image.Image") -> List[Dict[str, Any]]:
"""
Args:
inputs (:obj:`PIL.Image`):
The raw image representation as PIL.
No transformation made whatsoever from the input. Make all necessary transformations here.
Return:
A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
It is preferred if the returned list is in decreasing `score` order
"""
# IMPLEMENT_THIS
# FastAI expects a np array, not a PIL Image.
_, _, preds = self.model.predict(np.array(inputs))
preds = preds.tolist()
labels = [
{"label": str(self.id2label["0"]), "score": preds[0]},
{"label": str(self.id2label["1"]), "score": preds[1]},
{"label": str(self.id2label["2"]), "score": preds[2]},
{"label": str(self.id2label["3"]), "score": preds[3]},
{"label": str(self.id2label["4"]), "score": preds[4]},
{"label": str(self.id2label["5"]), "score": preds[5]},
{"label": str(self.id2label["6"]), "score": preds[6]},
{"label": str(self.id2label["7"]), "score": preds[7]},
{"label": str(self.id2label["8"]), "score": preds[8]},
{"label": str(self.id2label["9"]), "score": preds[9]},
{"label": str(self.id2label["10"]), "score": preds[10]},
{"label": str(self.id2label["11"]), "score": preds[11]},
{"label": str(self.id2label["12"]), "score": preds[12]},
{"label": str(self.id2label["13"]), "score": preds[13]},
{"label": str(self.id2label["14"]), "score": preds[14]},
{"label": str(self.id2label["15"]), "score": preds[15]},
{"label": str(self.id2label["16"]), "score": preds[16]},
{"label": str(self.id2label["17"]), "score": preds[17]},
{"label": str(self.id2label["18"]), "score": preds[18]},
{"label": str(self.id2label["19"]), "score": preds[19]},
{"label": str(self.id2label["20"]), "score": preds[20]},
{"label": str(self.id2label["21"]), "score": preds[21]},
{"label": str(self.id2label["22"]), "score": preds[22]},
{"label": str(self.id2label["23"]), "score": preds[23]},
{"label": str(self.id2label["24"]), "score": preds[24]},
{"label": str(self.id2label["25"]), "score": preds[25]},
{"label": str(self.id2label["26"]), "score": preds[26]},
{"label": str(self.id2label["27"]), "score": preds[27]},
{"label": str(self.id2label["28"]), "score": preds[28]},
{"label": str(self.id2label["29"]), "score": preds[29]},
{"label": str(self.id2label["30"]), "score": preds[30]},
{"label": str(self.id2label["31"]), "score": preds[31]},
{"label": str(self.id2label["32"]), "score": preds[32]},
{"label": str(self.id2label["33"]), "score": preds[33]},
{"label": str(self.id2label["34"]), "score": preds[34]},
{"label": str(self.id2label["35"]), "score": preds[35]},
{"label": str(self.id2label["36"]), "score": preds[36]},
]
return labels
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