Copyright 2021 Zilliz. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,

WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and

limitations under the License.

@register(output_schema=['vec']) class Clip(NNOperator): """ CLIP multi-modal embedding operator """ def init(self, model_name: str, modality: str): self.modality = modality self.device = "cuda" if torch.cuda.is_available() else "cpu" self.model = clip.create_model(model_name=model_name, pretrained=True, jit=True) self.tokenize = clip.tokenize self.tfms = transforms.Compose([ transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize( (0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) ])

def inference_single_data(self, data):
    if self.modality == 'image':
        vec = self._inference_from_image(data)
    elif self.modality == 'text': 
        vec = self._inference_from_text(data)
    else:
        raise ValueError("modality[{}] not implemented.".format(self._modality))
    return vec.detach().cpu().numpy().flatten()

def __call__(self, data):
    if not isinstance(data, list):
        data = [data]
    else:
        data = data
    results = []
    for single_data in data:
        result = self.inference_single_data(single_data)
        results.append(result)
    if len(data) == 1:        
        return results[0]
    else:
        return results

def _inference_from_text(self, text):
    text = self.tokenize(text).to(self.device)
    text_features = self.model.encode_text(text)
    return text_features

@arg(1, to_image_color('RGB'))
def _inference_from_image(self, img):
    img = to_pil(img)
    image = self.tfms(img).unsqueeze(0).to(self.device)
    image_features = self.model.encode_image(image)
    return image_features