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- import chainer
- import json
- import numpy as np
- import typing as T
- from munch import munchify
- from pathlib import Path
- from cvmodelz.models import ModelFactory
- from chainercv import transforms as tr
- class Classifier(object):
- def __init__(self, configuration: T.Dict, root: str):
- super().__init__()
- config = munchify(configuration)
- model_type = config.model_type
- n_classes = config.n_classes
- weights = Path(root, config.weights).resolve()
- self.input_size = config.input_size
- self.backbone = ModelFactory.new(model_type)
- self.backbone.load_for_inference(weights,
- n_classes=n_classes,
- path="model/",
- strict=True,
- )
- with open(Path(root, config.mapping)) as f:
- mapping = json.load(f)
- self._cls_id2ref = {int(key): value["kr"] for key, value in mapping.items()}
- def _transform(self, im: np.ndarray):
- _prepare = self.backbone.meta.prepare_func
- size = (self.input_size, self.input_size)
- # print(f"{'Orig:': <14s} {im.shape=}")
- im = _prepare(im, size=size, keep_ratio=True, swap_channels=False)
- # print(f"{'Prepare:': <14s} {im.shape=}")
- im = tr.center_crop(im, size)
- # print(f"{'CenterCrop:': <14s} {im.shape=}")
- return im
- def __call__(self, im: np.ndarray) -> int:
- assert im.ndim in (3, 4), \
- "Classifier accepts only RGB images (3D input) or a batch of images (4D input)!"
- if im.ndim == 3:
- # expand first axis
- # HxWxC -> 1xHxWxC
- im = im[None]
- im = [self._transform(_im) for _im in im]
- x = self.backbone.xp.array(im)
- with chainer.using_config("train", False), chainer.no_backprop_mode():
- pred = self.backbone(x)
- pred.to_cpu()
- cls_id = int(np.argmax(pred.array, axis=1))
- return self._cls_id2ref.get(cls_id, str(cls_id))
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