clipcap
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103 lines
3.6 KiB
103 lines
3.6 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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import os
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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 transformers import GPT2Tokenizer
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from towhee.types.arg import arg, to_image_color
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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 import register
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from towhee.models import clip
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class ClipCap(NNOperator):
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"""
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ClipCap image captioning operator
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"""
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def __init__(self, model_name: str):
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super().__init__()
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sys.path.append(str(Path(__file__).parent))
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from clipcap_model.clipcap import ClipCaptionModel, generate_beam
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.generate_beam = generate_beam
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self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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config = self._configs()[model_name]
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self.prefix_length = 10
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self.clip_tfms = self.tfms = transforms.Compose([
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transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize(
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(0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
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])
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clip_model_type = 'clip_vit_b32'
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self.clip_model = clip.create_model(model_name=clip_model_type, pretrained=True, jit=True)
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self.model = ClipCaptionModel(self.prefix_length)
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model_path = os.path.dirname(__file__) + '/weights/' + config['weights']
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self.model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
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self.model = self.model.eval()
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@arg(1, to_image_color('RGB'))
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def inference_single_data(self, data):
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text = self._inference_from_image(data)
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return text
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def _preprocess(self, img):
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img = to_pil(img)
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processed_img = self.clip_tfms(img).unsqueeze(0).to(self.device)
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return processed_img
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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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@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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clip_feat = self.clip_model.encode_image(img)
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self.prefix_length = 10
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prefix_embed = self.model.clip_project(clip_feat).reshape(1, self.prefix_length, -1)
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generated_text_prefix = self.generate_beam(self.model, self.tokenizer, embed=prefix_embed)[0]
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return generated_text_prefix
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def _configs(self):
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config = {}
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config['clipcap_coco'] = {}
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config['clipcap_coco']['weights'] = 'coco_weights.pt'
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config['clipcap_conceptual'] = {}
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config['clipcap_conceptual']['weights'] = 'conceptual_weights.pt'
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return config
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