clip-vision
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71 lines
2.3 KiB
71 lines
2.3 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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import numpy as np
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from torchvision import transforms as T
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from towhee.operator import NNOperator
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from towhee.models import clip
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class Model:
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def __init__(self, model_name, device='cpu'):
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self.model = clip.create_model(model_name=model_name, pretrained=True, device=device).visual
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self.model.eval()
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def __call__(self, data: 'Tensor'):
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return self.model(data)
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class ClipVision(NNOperator):
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def __init__(self, model_name='clip_vit_b32'):
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super().__init__()
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self.tfms = torch.nn.Sequential(
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T.Resize(224, interpolation=T.InterpolationMode.BICUBIC),
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T.CenterCrop(224),
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T.ConvertImageDtype(torch.float),
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T.Normalize(
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(0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
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).to(self.device)
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self.model = Model(model_name, self.device)
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@property
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def device(self):
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if self._device_id < 0:
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return 'cpu'
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else:
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return self._device_id
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def __call__(self, image: 'Image'):
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img = np.transpose(image, [2, 0, 1])
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data = torch.from_numpy(img)
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data = data.to(self.device)
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image_tensor = self.tfms(img)
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features = self.model(image_tensor)
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return features.detach().cpu().numpy().flatten()
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def save_model(self, model_type, output_file, args=None):
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if model_type != 'onnx':
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return False
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x = torch.randn((1, 3, 224, 224))
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torch.onnx.export(self.model, x, output_file, input_names=['INPUT0'],
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output_names=['OUTPUT0'], dynamic_axes={'INPUT0': [0]})
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return True
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@property
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def supported_formats(self):
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return ['onnx']
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