mpvit
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82 lines
2.8 KiB
82 lines
2.8 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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import torch
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from typing import List, Union
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from torch import nn
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from towhee.types.arg import arg, to_image_color
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from timm.data import resolve_data_config, create_transform
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from towhee.models import mpvit
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from towhee.operator.base import NNOperator
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from towhee import register
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import towhee
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from PIL import Image as PILImage
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@register(output_schema=['vec'])
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class MPViT(NNOperator):
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"""
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MPViT embedding operator
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"""
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def __init__(self, model_name,
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num_classes: int = 1000,
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weights_path: str = None,
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device: str = None,
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skip_preprocess: bool = False):
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super().__init__()
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if device is None:
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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self.model = mpvit.create_model(model_name=model_name,
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num_classes=num_classes,
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pretrained=True,
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weights_path=weights_path,
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device=device)
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self.model.eval()
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self.config = resolve_data_config({}, model=self.model)
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self.tfms = create_transform(**self.config)
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self.skip_tfms = skip_preprocess
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self.device = device
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def __call__(self, data: Union[List['towhee.types.Image'], 'towhee.types.Image']):
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if not isinstance(data, list):
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imgs = [data]
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else:
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imgs = data
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img_list = []
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for img in imgs:
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img = self.convert_img(img)
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img = img if self.skip_tfms else self.tfms(img)
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img_list.append(img)
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inputs = torch.stack(img_list)
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inputs = inputs.to(self.device)
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features = self.model.forward_features(inputs)
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global_pool = nn.AdaptiveAvgPool2d(1)
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features = global_pool(features)
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features = features.to('cpu').flatten(1)
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if isinstance(data, list):
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vecs = list(features.detach().numpy())
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else:
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vecs = features.squeeze(0).detach().numpy()
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return vecs
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@arg(1, to_image_color('RGB'))
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def convert_img(self, img: 'towhee.types.Image'):
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img = PILImage.fromarray(img.astype('uint8'), 'RGB')
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return img
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