albef
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113 lines
4.3 KiB
113 lines
4.3 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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from PIL import Image
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import torch
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import yaml
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from torchvision import transforms
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from towhee.types.image_utils import to_pil
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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 import register
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@register(output_schema=['vec'])
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class Albef(NNOperator):
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"""
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ALBEF multi-modal embedding operator
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"""
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def prepare_model(checkpoint_path, model):
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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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state_dict = checkpoint['model']
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pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
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state_dict['visual_encoder.pos_embed'] = pos_embed_reshaped
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m_pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],model.visual_encoder_m)
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state_dict['visual_encoder_m.pos_embed'] = m_pos_embed_reshaped
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for key in list(state_dict.keys()):
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if 'bert' in key:
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encoder_key = key.replace('bert.','')
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state_dict[encoder_key] = state_dict[key]
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del state_dict[key]
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msg = model.load_state_dict(state_dict,strict=False)
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print('load checkpoint from ' + checkpoint_path)
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return model
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def __init__(self, model_name: str, modality: str):
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self.modality = modality
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config = self._configs()[model_name]
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
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tokenizer = BertTokenizer.from_pretrained(config)
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model = ALBEF(config=config, text_encoder=config['text_encoder'], tokenizer=tokenizer)
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cfg = yaml.load(open(config['cfg'], 'r'), Loader=yaml.Loader)
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checkpoint_path = cfg['ckpt_path']
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self.model = self.prepare_model(checkpoint_path, model)
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self.test_transform = transforms.Compose([
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transforms.Resize((cfg['image_res'],cfg['image_res']),interpolation=Image.BICUBIC),
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transforms.ToTensor(),
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normalize,
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])
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def inference_single_data(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 __call__(self, data):
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if not isinstance(data, list):
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data = [data]
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else:
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data = data
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results = []
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for single_data in data:
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result = self.inference_single_data(single_data)
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results.append(result)
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if len(data) == 1:
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return results[0]
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else:
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return results
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def _inference_from_text(self, text):
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tokens = self.text_tokenizer(text, return_tensors='pt', padding=True)['input_ids'].to(self.device)
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text_features = self.text_encoder(tokens).logits
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return text_features
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@arg(1, to_image_color('RGB'))
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def _inference_from_image(self, img):
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image = to_pil(img)
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image = self.processor(images=image, return_tensors="pt").to(self.device)
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image_features = self.clip_model.get_image_features(**image)
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return image_features
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def _configs(self):
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config = {}
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config['albef_4m'] = {}
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config['albef_4m']['tokenizer'] = 'bert-base-uncased'
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config['albef_4m']['text_encoder'] = 'bert-base-uncased'
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config['albef_4m']['cfg_path'] = './configs/Retrieval_flickr.yaml'
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config['albef_4m']['ckpt_path'] = ''
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