towhee
/
efficientnet-image-embedding
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49 lines
1.8 KiB
49 lines
1.8 KiB
4 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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from typing import NamedTuple
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from PIL import Image
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import torch
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from torchvision import transforms
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import sys
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from pathlib import Path
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from towhee.operator import Operator
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class EfficientnetEmbeddingOperator(Operator):
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"""
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Embedding extractor using efficientnet.
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Args:
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model_name (`string`):
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Model name.
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weights_path (`string`):
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Path to local weights.
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"""
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def __init__(self, model_name: str = 'efficientnet-b7', framework: str = 'pytorch', weights_path: str = None) -> None:
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super().__init__()
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sys.path.append(str(Path(__file__).parent))
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if framework == 'pytorch':
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from pytorch.model import Model
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self.model = Model(model_name, weights_path)
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self.tfms = transforms.Compose([transforms.Resize(224), transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ])
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def __call__(self, img_path: str) -> NamedTuple('Outputs', [('embedding', torch.Tensor)]):
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Outputs = NamedTuple('Outputs', [('embedding', torch.Tensor)])
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img = self.tfms(Image.open(img_path)).unsqueeze(0)
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features = self.model._model.extract_features(img)
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return Outputs(features.flatten().detach().numpy())
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