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93 lines
3.3 KiB
93 lines
3.3 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 sys
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from pathlib import Path
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import torch
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from torchvision import transforms
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from towhee import register
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from towhee.operator.base import NNOperator, OperatorFlag
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from towhee.types.arg import arg, to_image_color
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from towhee.types.image_utils import from_pil, to_pil
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from tokenizer import SimpleTokenizer
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def get_model(model):
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if isinstance(model, torch.nn.DataParallel) \
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or isinstance(model, torch.nn.parallel.DistributedDataParallel):
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return model.module
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else:
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return model
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@register(output_schema=['vec'])
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class Slip(NNOperator)
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"""
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SLIP multi-modal embedding operator
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"""
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def __init__(self, model_name: str, modality: str):
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super().__init__()
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sys.path.append(str(Path(__file__).parent))
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self.tokenizer = SimpleTokenizer()
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.model.to(self.device)
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self.model.eval()
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self.tfms = transforms.Compose([
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transforms.Resize(224),
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transforms.CenterCrop(224),
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lambda x: x.convert('RGB'),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225])
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])
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def __call__(self, data):
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if self._modality == 'image':
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vec = self._inference_from_image(data)
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elif self._modality == 'text':
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vec = self._inference_from_text(data)
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else:
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raise ValueError("modality[{}] not implemented.".format(self._modality))
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return vec.detach().cpu().numpy().flatten()
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def _inference_from_text(self, text):
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texts = tokenizer(texts).cuda(non_blocking=True)
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texts = texts.view(-1, 77).contiguous()
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embedding = get_model(self.model).encode_text(texts)
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embedding = embedding / embedding.norm(dim=-1, keepdim=True)
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@arg(1, to_image_color('RGB'))
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def _inference_from_image(self, img):
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img = self._preprocess(img)
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img = img.to(self.device)
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embedding = get_model(self.model).encode_image(img)
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return embedding
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def _preprocess(self, img):
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img = to_pil(img)
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processed_img = self.tfms(img).unsqueeze(0).to(self.device)
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return processed_img
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def _configs(self):
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config = {}
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config['slip_vit_small'] = {}
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config['slip_vit_small']['weights'] = 'https://dl.fbaipublicfiles.com/slip/slip_small_100ep.pt'
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config['slip_vit_base'] = {}
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config['slip_vit_base']['weights'] = 'https://dl.fbaipublicfiles.com/slip/slip_base_100ep.pt'
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config['slip_vit_large'] = {}
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config['slip_vit_large']['weights'] = 'https://dl.fbaipublicfiles.com/slip/slip_large_100ep.pt'
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return config
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