mobilefacenet
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76 lines
2.5 KiB
76 lines
2.5 KiB
3 years ago
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# 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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# adapted from https://github.com/cunjian/pytorch_face_landmark
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import os
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import sys
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from typing import NamedTuple
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from pathlib import Path
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import numpy as np
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import torch
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from torchvision import transforms
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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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#import mobilefacenet
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@register(output_schema=['landmark'])
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class Mobilefacenet(NNOperator):
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"""
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Mobilefacenet
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"""
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def __init__(self, framework: str = 'pytorch', pretrained = True):
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super().__init__(framework=framework)
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sys.path.append(str(Path(__file__).parent))
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from mobilefacenet_impl import MobileFaceNet
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self.model = MobileFaceNet([112, 112], 136)
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if pretrained == True:
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map_location = 'cpu'
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checkpoint = torch.load(
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os.path.dirname(__file__) +'/mobilefacenet_model_best.pth', map_location=map_location)
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self.model.load_state_dict(checkpoint['state_dict'])
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normalize = transforms.Normalize(
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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self.tfms = transforms.Compose([transforms.Scale(112),
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transforms.ToTensor(),
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normalize])
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@arg(1, to_image_color('RGB') )
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def __call__(self, image: Image):
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image = to_pil(image)
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h, w = image.size
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tensor = self._preprocess(image)
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if len(tensor.shape) == 3:
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tensor = torch.unsqueeze(tensor, 0)
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self.model.eval()
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landmark = self.model(tensor)[0][0]
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landmark = landmark.reshape(-1, 2)
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landmark[:, 0] = landmark[:, 0] * w
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landmark[:, 1] = landmark[:, 1] * h
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return np.asarray(landmark.cpu().detach(), dtype=np.int32)
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def _preprocess(self, image):
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return self.tfms(image)
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def _postprocess(self, landmark):
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pass
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def train(self):
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pass
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