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test_tea

Image Classification fastai

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test_tea / pipeline.py

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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 ImageClassificationPipeline:

	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.labels = config["labels"]

	def __call__(self, inputs: "Image.Image") -> List[Dict[str, Any]]:
	print('call')
	"""
	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()
	return [{
	"label": label,
	"score": preds[idx]
	} for idx, label in enumerate(self.labels)]