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110 lines
3.7 KiB
110 lines
3.7 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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from re import I
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import sys
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import os
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import pathlib
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import pickle
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from argparse import Namespace
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import torch
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import torchvision
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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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class Magic(NNOperator):
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"""
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Magic 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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path = str(pathlib.Path(__file__).parent)
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sys.path.append(path + '/clip')
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sys.path.append(path + '/language_model')
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print(sys.path)
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from clip import CLIP
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from simctg import SimCTG
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sys.path.pop()
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sys.path.pop()
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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# Load Language Model
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cfg = self._configs()[model_name]
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language_model_name = cfg['language_model'] # or r'/path/to/downloaded/cambridgeltl/magic_mscoco'
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sos_token, pad_token = r'<-start_of_text->', r'<-pad->'
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self.generation_model = SimCTG(language_model_name, sos_token, pad_token).to(self.device)
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self.generation_model.eval()
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model_name = cfg['clip_model'] # or r"/path/to/downloaded/openai/clip-vit-base-patch32"
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self.clip = CLIP(model_name).to(self.device)
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self.clip.to(self.device)
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self.clip.eval()
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sos_token = r'<-start_of_text->'
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start_token = self.generation_model.tokenizer.tokenize(sos_token)
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start_token_id = self.generation_model.tokenizer.convert_tokens_to_ids(start_token)
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self.input_ids = torch.LongTensor(start_token_id).view(1,-1).to(self.device)
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def _preprocess(self, img):
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img = to_pil(img)
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processed_img = self.transf_1(img)
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processed_img = self.transf_2(processed_img)
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processed_img = processed_img.to(self.device)
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return processed_img
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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 __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).unsqueeze(0)
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k, alpha, beta, decoding_len = 45, 0.1, 2.0, 16
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eos_token = '<|endoftext|>'
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with torch.no_grad():
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print(type(img))
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output = self.generation_model.magic_search(self.input_ids, k,
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alpha, decoding_len, beta, img, self.clip, 60)
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return output
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def _configs(self):
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config = {}
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config['magic_mscoco'] = {}
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config['magic_mscoco']['language_model'] = 'cambridgeltl/magic_mscoco'
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config['magic_mscoco']['clip_model'] = 'openai/clip-vit-base-patch32'
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return config
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