capdec
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110 lines
4.1 KiB
110 lines
4.1 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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from towhee.command.s3 import S3Bucket
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from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup
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class Capdec(NNOperator):
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"""
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CapDec 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 modules import ClipCaptionModel, generate_beam, generate2
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path = str(Path(__file__).parent)
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config = self._configs()[model_name]
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s3_bucket = S3Bucket()
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s3_bucket.download_file(config['weights'], path + '/weights/')
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model_path = path + '/weights/' + os.path.basename(config['weights'])
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.clip_caption_model = ClipCaptionModel()
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self.clip_caption_model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
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self.clip_caption_model.to(self.device)
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self.clip_caption_model.eval()
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self.clip_model = clip.create_model(model_name='clip_resnet_r50x4', pretrained=True, jit=True)
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self.clip_model.to(self.device)
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self.clip_tfms = clip.get_transforms(model_name='clip_resnet_r50x4')
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self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2").to(self.device)
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self.generate_beam = generate_beam
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self.generate2 = generate2
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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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use_beam_search = True
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with torch.no_grad():
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prefix = self.clip_model.encode_image(img)[0].to(self.device, dtype=torch.float32).unsqueeze(0)
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prefix_embed = self.clip_caption_model.clip_project(prefix).reshape(1, 40, -1)
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if use_beam_search:
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generated_text_prefix = self.generate_beam(self.clip_caption_model, self.tokenizer, embed=prefix_embed)[0]
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else:
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generated_text_prefix = self.generate2(self.clip_caption_model, self.tokenizer, embed=prefix_embed)
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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['capdec_noise_0'] = {}
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config['capdec_noise_0']['weights'] = 'image-captioning/capdec/0.pt'
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config['capdec_noise_01'] = {}
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config['capdec_noise_01']['weights'] = 'image-captioning/capdec/01.pt'
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config['capdec_noise_001'] = {}
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config['capdec_noise_001']['weights'] = 'image-captioning/capdec/001.pt'
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config['capdec_noise_0001'] = {}
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config['capdec_noise_0001']['weights'] = 'image-captioning/capdec/0001.pt'
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return config
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