inceptionresnetv1
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63 lines
2.1 KiB
63 lines
2.1 KiB
# Copyright 2021 Zilliz. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import NamedTuple
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import numpy as np
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import torch
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import cv2
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from facenet_pytorch import InceptionResnetV1
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from towhee import register
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from towhee.operator import NNOperator
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from towhee.types.image_utils import to_pil
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from towhee._types import Image
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from towhee.types import arg, to_image_color
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@register(output_schema=['vec'])
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class Inceptionresnetv1(NNOperator):
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"""
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comment placeholder
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"""
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def __init__(self, image_size = 160):
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self.image_size = image_size
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self._model = InceptionResnetV1(pretrained='vggface2')
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self._model.eval()
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@arg(1, to_image_color('RGB') )
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def __call__(self, img: Image) -> np.ndarray:
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img = self.preprocess(img)
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embs = self._model(torch.FloatTensor(img).permute(0,3,1,2)).detach().numpy()
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return embs
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def preprocess(self, img: Image):
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#img shape expected to be [n, h, w, c=3] or [h, w, c=3].
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if len(img.shape) == 3:
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img = cv2.resize(img, (self.image_size, self.image_size))
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img = np.expand_dims(img, 0)
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elif len(img.shape) == 4:
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pass
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else:
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raise ValueError('unknown tensor shape, need to be [n, h, w, c=3] or [h, w, c=3].')
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img = self._fixed_image_standardization(img)
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return img
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def _fixed_image_standardization(self, image_tensor):
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processed_tensor = (image_tensor - 127.5) / 128.0
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return processed_tensor
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def train(self):
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"""
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For training model
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"""
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pass
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