775 lines
32 KiB
Python
775 lines
32 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import numbers
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import re
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import time
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from torchvision.transforms.functional import InterpolationMode
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import torchvision.transforms as T
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import torch
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from PIL import Image
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from transformers import AutoModel, AutoTokenizer
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from scepter.modules.utils.distribute import we
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from scepter.modules.utils.file_system import FS
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import numpy as np
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from scepter.studio.preprocess.processors.base_processor import \
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BaseCaptionProcessor
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import io
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from decord import cpu, VideoReader, bridge
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__all__ = ['BlipImageBase', 'QWVL', 'QWVLQuantize', 'InternVL15', 'CogVLM2Llama3Caption',
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'OpusMtZhEn', 'OpusMtEnZh']
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def get_region(image, mask, mask_id):
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locs = np.where(np.array(mask) == mask_id)
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if len(locs) < 1 or locs[0].shape[0] < 1 or locs[1].shape[0] < 1:
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return None
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left, right = np.min(locs[1]), np.max(locs[1])
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top, bottom = np.min(locs[0]), np.max(locs[0])
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box = [left, top, right, bottom]
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region_image = image.crop(box)
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region_image.save("1.jpg")
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return region_image
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class BlipImageBase(BaseCaptionProcessor):
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def __init__(self, cfg, language='en'):
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super().__init__(cfg, language=language)
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self.model_path = cfg.MODEL_PATH
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self.model_info = {
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'device': 'offline',
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'model': None,
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'tokenizer': None
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}
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def load_model(self):
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is_flg, msg = super().load_model()
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if not is_flg:
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return is_flg, msg
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if self.model_info['device'] == 'offline':
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model = None
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processor = None
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try:
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from transformers import BlipProcessor, BlipForConditionalGeneration
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local_model_dir = FS.get_dir_to_local_dir(self.model_path)
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processor = BlipProcessor.from_pretrained(local_model_dir)
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model = BlipForConditionalGeneration.from_pretrained(
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local_model_dir).to(we.device_id)
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except Exception as e:
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if model is not None:
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del model
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if processor is not None:
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del model
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return False, f"Load model error '{e}'"
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self.model_info['device'] = model.device
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self.model_info['model'] = model
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self.model_info['processor'] = processor
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elif self.model_info['device'] == 'cpu':
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try:
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self.model_info['model'].to(we.device_id)
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self.model_info['device'] = we.device_id
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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except Exception as e:
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del self.model_info['model']
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self.model_info['model'] = None
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self.model_info['device'] = 'offline'
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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return False, f"Load model error '{e}'"
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return True, ''
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def unload_model(self):
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super().unload_model()
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if self.delete_instance:
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self.model_info['device'] = 'offline'
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if self.model_info['model'] is not None:
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self.model_info['model'] = self.model_info['model'].to('cpu')
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del self.model_info['model']
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self.model_info['model'] = None
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elif (isinstance(self.model_info['device'], numbers.Number)
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or str(self.model_info['device']).startswith('cuda')):
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self.model_info['device'] = 'cpu'
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self.model_info['model'] = self.model_info['model'].to('cpu')
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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return True, ''
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def get_caption(self, image, use_local, mask):
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image = image.convert('RGB')
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if use_local:
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image = get_region(image,
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mask.convert('L'),
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255)
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if image is None:
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return ""
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inputs = self.model_info['processor'](image, return_tensors='pt').to(we.device_id)
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out = self.model_info['model'].generate(**inputs)
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caption = self.model_info['processor'].decode(out[0], skip_special_tokens=True)
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return caption
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def __call__(self, **kwargs):
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target_image = kwargs.get('target_image', None)
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src_mask = kwargs.get('src_mask', None)
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src_image = kwargs.get('src_image', None)
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use_preview = kwargs.get('use_preview', True)
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caption = kwargs.get('caption', None)
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use_local = kwargs.get('use_local', False)
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cache = kwargs.get('cache', None)
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preview_target_image = kwargs.get('preview_target_image', None) if use_preview else target_image
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preview_src_mask = kwargs.get('preview_src_mask', None) if use_preview else src_mask
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preview_src_image = kwargs.get('preview_src_image', None) if use_preview else src_image
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preview_caption = kwargs.get('preview_caption', None) if use_preview else caption
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response = ""
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if preview_src_image is not None:
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response += ("src_caption" + ": " +
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self.get_caption(preview_src_image, use_local, preview_src_mask) + "\n")
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if preview_target_image is not None:
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response += ("target_caption" + ": " +
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self.get_caption(preview_target_image, use_local, preview_src_mask) + "\n")
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return response
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class QWVL(BaseCaptionProcessor):
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def __init__(self, cfg, language='en'):
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super().__init__(cfg, language=language)
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self.model_path = cfg.MODEL_PATH
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self.model_info = {
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'device': 'offline',
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'model': None,
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'tokenizer': None
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}
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def load_model(self):
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is_flg, msg = super().load_model()
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if not is_flg:
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return is_flg, msg
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if self.model_info['device'] == 'offline':
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model = None
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try:
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from modelscope import (AutoModelForCausalLM, AutoTokenizer,
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GenerationConfig)
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local_model_dir = FS.get_dir_to_local_dir(self.model_path)
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# without quantization using 19.52G memory
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# with quantization using 7.7G memory
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tokenizer = AutoTokenizer.from_pretrained(
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local_model_dir, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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local_model_dir,
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device_map='auto',
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trust_remote_code=True,
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fp16=True).eval()
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model.generation_config = GenerationConfig.from_pretrained(
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local_model_dir, trust_remote_code=True)
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except Exception as e:
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if model is not None:
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del model
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return False, f"Load model error '{e}'"
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self.model_info['device'] = model.device
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self.model_info['model'] = model
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self.model_info['tokenizer'] = tokenizer
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elif self.model_info['device'] == 'cpu':
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try:
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self.model_info['model'].to(we.device_id)
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self.model_info['device'] = we.device_id
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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except Exception as e:
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del self.model_info['model']
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self.model_info['model'] = None
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self.model_info['device'] = 'offline'
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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return False, f"Load model error '{e}'"
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return True, ''
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def unload_model(self):
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super().unload_model()
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if self.delete_instance:
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if self.model_info['model'] is not None:
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self.model_info['model'] = self.model_info['model'].to('cpu')
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del self.model_info['model']
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self.model_info['model'] = None
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self.model_info['device'] = 'offline'
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elif (isinstance(self.model_info['device'], numbers.Number)
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or str(self.model_info['device']).startswith('cuda')):
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self.model_info['device'] = 'cpu'
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self.model_info['model'] = self.model_info['model'].to('cpu')
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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return True, ''
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def get_caption(self, image, prompt, kwargs, use_local, mask):
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image = image.convert('RGB')
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if use_local:
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image = get_region(image,
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mask.convert('L'),
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255)
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if image is None:
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return ""
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torch.manual_seed(int(time.time()) % 100000)
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query = self.model_info['tokenizer'].from_list_format([
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{
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'image': image
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},
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{
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'text': prompt
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},
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])
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inputs = self.model_info['tokenizer'](query, return_tensors='pt')
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inputs = inputs.to(self.model_info['device'])
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pred = self.model_info['model'].generate(**inputs, **kwargs)
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response = self.model_info['tokenizer'].decode(
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pred.cpu()[0], skip_special_tokens=True)
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ret_caption = response.split(prompt)[-1]
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if ret_caption.startswith(','):
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ret_caption = ret_caption[1:]
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regex = re.compile(r'[' + '#®•©™&@·º½¾¿¡§~' + ')' + '(' + ']' + '[' +
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'}' + '{' + '|' + '\\' + '/' + '*' +
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r']{1,}') # noqa: E501
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ret_caption = re.sub(regex, r' ', ret_caption)
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regex = re.compile(r'^[\-\_]+')
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ret_caption = re.sub(regex, r'', ret_caption)
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return ret_caption
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def __call__(self, **kwargs):
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target_image = kwargs.pop('target_image', None)
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src_mask = kwargs.pop('src_mask', None)
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src_image = kwargs.pop('src_image', None)
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use_preview = kwargs.pop('use_preview', True)
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sys_prompt = kwargs.pop('sys_prompt', 'Generate the caption in English')
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caption = kwargs.pop('caption', None)
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use_local = kwargs.pop('use_local', False)
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cache = kwargs.pop('cache', None)
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preview_target_image = kwargs.pop('preview_target_image', None) if use_preview else target_image
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preview_src_mask = kwargs.pop('preview_src_mask', None) if use_preview else src_mask
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preview_src_image = kwargs.pop('preview_src_image', None) if use_preview else src_image
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preview_caption = kwargs.pop('preview_caption', None) if use_preview else caption
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kwargs_keys = ["max_new_tokens", "min_new_tokens", "num_beams", "repetition_penalty", "temperature"]
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process_kwargs = {}
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for key in kwargs_keys:
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if key in kwargs:
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process_kwargs[key] = kwargs[key]
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response = ""
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if preview_src_image is not None:
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response += ("src_caption" + ": " +
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self.get_caption(preview_src_image, sys_prompt, process_kwargs, use_local, preview_src_mask) + "\n")
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if preview_target_image is not None:
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response += ("target_caption" + ": " +
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self.get_caption(preview_target_image, sys_prompt, process_kwargs, use_local, preview_src_mask) + "\n")
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return response
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class QWVLQuantize(QWVL):
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def load_model(self):
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is_flg, msg = super(QWVL, self).load_model()
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if not is_flg:
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return is_flg, msg
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self.model = None
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if self.model_info['device'] == 'offline':
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try:
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from transformers import BitsAndBytesConfig
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torch.manual_seed(int(time.time()))
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from modelscope import (AutoModelForCausalLM, AutoTokenizer,
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GenerationConfig)
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local_model_dir = FS.get_dir_to_local_dir(self.model_path)
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# without quantization using 19.52G memory
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# with quantization using 7.7G memory
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_quant_type='nf4',
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bnb_4bit_use_double_quant=True,
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llm_int8_skip_modules=['lm_head', 'attn_pool.attn'])
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tokenizer = AutoTokenizer.from_pretrained(
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local_model_dir, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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local_model_dir,
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device_map='auto',
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trust_remote_code=True,
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fp16=True,
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quantization_config=quantization_config).eval()
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model.generation_config = GenerationConfig.from_pretrained(
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local_model_dir, trust_remote_code=True)
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# model.to(we.device_id)
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except Exception as e:
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if self.model is not None:
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del self.model
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return False, f"Load model error '{e}'"
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self.model_info['device'] = model.device
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self.model_info['model'] = model
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self.model_info['tokenizer'] = tokenizer
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elif self.model_info['device'] == 'cpu':
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try:
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self.model_info['model'].to(we.device_id)
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self.model_info['device'] = we.device_id
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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except Exception as e:
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del self.model_info['model']
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self.model_info['model'] = None
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self.model_info['device'] = 'offline'
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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return False, f"Load model error '{e}'"
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return True, ''
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def unload_model(self):
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print(self.model_info['device'])
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if (isinstance(self.model_info['device'], numbers.Number)
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or str(self.model_info['device']).startswith('cuda')):
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del self.model_info['model']
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self.model_info['model'] = None
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self.model_info['device'] = 'offline'
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torch.cuda.empty_cache()
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return True, ''
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class InternVL15(QWVL):
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# AI-ModelScope/InternVL-Chat-V1-5
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def load_model(self):
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is_flg, msg = super(QWVL, self).load_model()
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if not is_flg:
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return is_flg, msg
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self.model = None
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if self.model_info['device'] == 'offline':
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try:
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local_path = FS.get_dir_to_local_dir(self.model_path)
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# If you have an 80G A100 GPU, you can put the entire model on a single GPU.
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model = AutoModel.from_pretrained(
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local_path,
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True).eval().to(we.device_id)
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tokenizer = AutoTokenizer.from_pretrained(local_path, trust_remote_code=True)
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# model.to(we.device_id)
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except Exception as e:
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if self.model is not None:
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del self.model
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return False, f"Load model error '{e}'"
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self.model_info['device'] = model.device
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self.model_info['model'] = model
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self.model_info['tokenizer'] = tokenizer
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elif self.model_info['device'] == 'cpu':
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try:
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self.model_info['model'].to(we.device_id)
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self.model_info['device'] = we.device_id
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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except Exception as e:
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del self.model_info['model']
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self.model_info['model'] = None
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self.model_info['device'] = 'offline'
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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return False, f"Load model error '{e}'"
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return True, ''
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def unload_model(self):
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print(self.model_info['device'])
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if (isinstance(self.model_info['device'], numbers.Number)
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or str(self.model_info['device']).startswith('cuda')):
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del self.model_info['model']
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self.model_info['model'] = None
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self.model_info['device'] = 'offline'
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torch.cuda.empty_cache()
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return True, ''
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def build_transform(self, input_size):
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IMAGENET_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_STD = (0.229, 0.224, 0.225)
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MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
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transform = T.Compose([
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T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
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T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
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T.ToTensor(),
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T.Normalize(mean=MEAN, std=STD)
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])
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return transform
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def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height, image_size):
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best_ratio_diff = float('inf')
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best_ratio = (1, 1)
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area = width * height
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for ratio in target_ratios:
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target_aspect_ratio = ratio[0] / ratio[1]
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ratio_diff = abs(aspect_ratio - target_aspect_ratio)
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if ratio_diff < best_ratio_diff:
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best_ratio_diff = ratio_diff
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best_ratio = ratio
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elif ratio_diff == best_ratio_diff:
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if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
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best_ratio = ratio
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return best_ratio
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def dynamic_preprocess(self, image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
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orig_width, orig_height = image.size
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aspect_ratio = orig_width / orig_height
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# calculate the existing image aspect ratio
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target_ratios = set(
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(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
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i * j <= max_num and i * j >= min_num)
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
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# find the closest aspect ratio to the target
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target_aspect_ratio = self.find_closest_aspect_ratio(
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aspect_ratio, target_ratios, orig_width, orig_height, image_size)
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# calculate the target width and height
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target_width = image_size * target_aspect_ratio[0]
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target_height = image_size * target_aspect_ratio[1]
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blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
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# resize the image
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resized_img = image.resize((target_width, target_height))
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processed_images = []
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for i in range(blocks):
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box = (
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(i % (target_width // image_size)) * image_size,
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(i // (target_width // image_size)) * image_size,
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((i % (target_width // image_size)) + 1) * image_size,
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((i // (target_width // image_size)) + 1) * image_size
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)
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# split the image
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split_img = resized_img.crop(box)
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processed_images.append(split_img)
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assert len(processed_images) == blocks
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if use_thumbnail and len(processed_images) != 1:
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thumbnail_img = image.resize((image_size, image_size))
|
|
processed_images.append(thumbnail_img)
|
|
return processed_images
|
|
|
|
def load_image(self, image_file, input_size=448, max_num=6):
|
|
if isinstance(image_file, str):
|
|
image = Image.open(image_file).convert('RGB')
|
|
else:
|
|
image = image_file
|
|
transform = self.build_transform(input_size=input_size)
|
|
images = self.dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
|
|
pixel_values = [transform(image) for image in images]
|
|
pixel_values = torch.stack(pixel_values)
|
|
return pixel_values
|
|
|
|
def get_caption(self, image, prompt, kwargs, use_local, mask):
|
|
if use_local:
|
|
image = get_region(image,
|
|
mask.convert('L'),
|
|
255)
|
|
if image is None:
|
|
return ""
|
|
torch.manual_seed(int(time.time()) % 100000)
|
|
generation_config = dict(
|
|
num_beams=1,
|
|
max_new_tokens=4096,
|
|
do_sample=True
|
|
)
|
|
image = self.load_image(image, max_num=6).to(torch.bfloat16).cuda(we.device_id)
|
|
response = self.model_info['model'].chat(self.model_info['tokenizer'], image, prompt, generation_config=generation_config)
|
|
response = response.replace("\n", "").strip()
|
|
return response
|
|
|
|
|
|
class CogVLM2Llama3Caption(BaseCaptionProcessor):
|
|
def __init__(self, cfg, language='en'):
|
|
super().__init__(cfg, language=language)
|
|
self.model_path = cfg.MODEL_PATH
|
|
self.model_info = {
|
|
'device': 'offline',
|
|
'model': None,
|
|
'tokenizer': None
|
|
}
|
|
self.prompt = cfg.PROMPT
|
|
self.temperature = cfg.TEMPERATURE
|
|
self.max_new_tokens = cfg.MAX_NEW_TOKENS
|
|
self.pad_token_id = cfg.PAD_TOKEN_ID
|
|
self.top_k = cfg.TOP_K
|
|
self.top_p = cfg.TOP_P
|
|
self.TORCH_TYPE = torch.bfloat16 if (torch.cuda.is_available() and
|
|
torch.cuda.get_device_capability()
|
|
[0] >= 8) else torch.float16
|
|
|
|
def load_model(self):
|
|
is_flg, msg = super().load_model()
|
|
if not is_flg:
|
|
return is_flg, msg
|
|
if self.model_info['device'] == 'offline':
|
|
model = None
|
|
try:
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
local_model_dir = FS.get_dir_to_local_dir(self.model_path)
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
local_model_dir,
|
|
trust_remote_code=True,
|
|
)
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
local_model_dir,
|
|
device_map='auto',
|
|
torch_dtype=self.TORCH_TYPE,
|
|
trust_remote_code=True
|
|
).eval().to(we.device_id)
|
|
except Exception as e:
|
|
if model is not None:
|
|
del model
|
|
return False, f"Load model error '{e}'"
|
|
self.model_info['device'] = model.device
|
|
self.model_info['model'] = model
|
|
self.model_info['tokenizer'] = tokenizer
|
|
elif self.model_info['device'] == 'cpu':
|
|
try:
|
|
self.model_info['model'].to(we.device_id)
|
|
self.model_info['device'] = we.device_id
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
except Exception as e:
|
|
del self.model_info['model']
|
|
self.model_info['model'] = None
|
|
self.model_info['device'] = 'offline'
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
return False, f"Load model error '{e}'"
|
|
return True, ''
|
|
|
|
def unload_model(self):
|
|
super().unload_model()
|
|
if self.delete_instance:
|
|
self.model_info['device'] = 'offline'
|
|
if self.model_info['model'] is not None:
|
|
self.model_info['model'] = self.model_info['model'].to('cpu')
|
|
del self.model_info['model']
|
|
self.model_info['model'] = None
|
|
elif (isinstance(self.model_info['device'], numbers.Number)
|
|
or str(self.model_info['device']).startswith('cuda')):
|
|
self.model_info['device'] = 'cpu'
|
|
self.model_info['model'] = self.model_info['model'].to('cpu')
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
return True, ''
|
|
|
|
def load_video(self, video_data, strategy='chat'):
|
|
bridge.set_bridge('torch')
|
|
mp4_stream = video_data
|
|
num_frames = 24
|
|
decord_vr = VideoReader(io.BytesIO(mp4_stream), ctx=cpu(0))
|
|
|
|
frame_id_list = None
|
|
total_frames = len(decord_vr)
|
|
if strategy == 'base':
|
|
clip_end_sec = 60
|
|
clip_start_sec = 0
|
|
start_frame = int(clip_start_sec * decord_vr.get_avg_fps())
|
|
end_frame = min(total_frames,
|
|
int(clip_end_sec * decord_vr.get_avg_fps())) if clip_end_sec is not None else total_frames
|
|
frame_id_list = np.linspace(start_frame, end_frame - 1, num_frames, dtype=int)
|
|
elif strategy == 'chat':
|
|
timestamps = decord_vr.get_frame_timestamp(np.arange(total_frames))
|
|
timestamps = [i[0] for i in timestamps]
|
|
max_second = round(max(timestamps)) + 1
|
|
frame_id_list = []
|
|
for second in range(max_second):
|
|
closest_num = min(timestamps, key=lambda x: abs(x - second))
|
|
index = timestamps.index(closest_num)
|
|
frame_id_list.append(index)
|
|
if len(frame_id_list) >= num_frames:
|
|
break
|
|
video_data = decord_vr.get_batch(frame_id_list)
|
|
video_data = video_data.permute(3, 0, 1, 2)
|
|
return video_data
|
|
|
|
def get_caption(self, prompt, video_data, temperature):
|
|
strategy = 'chat'
|
|
video = self.load_video(video_data, strategy=strategy)
|
|
|
|
history = []
|
|
query = prompt
|
|
model = self.model_info['model']
|
|
tokenizer = self.model_info['tokenizer']
|
|
inputs = model.build_conversation_input_ids(
|
|
tokenizer=tokenizer,
|
|
query=query,
|
|
images=[video],
|
|
history=history,
|
|
template_version=strategy
|
|
)
|
|
inputs = {
|
|
'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'),
|
|
'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to(we.device_id),
|
|
'attention_mask': inputs['attention_mask'].unsqueeze(0).to(we.device_id),
|
|
'images': [[inputs['images'][0].to(we.device_id).to(self.TORCH_TYPE)]],
|
|
}
|
|
gen_kwargs = {
|
|
"max_new_tokens": self.max_new_tokens,
|
|
"pad_token_id": self.pad_token_id,
|
|
"top_k": self.top_k,
|
|
"do_sample": True,
|
|
"top_p": self.top_p,
|
|
"temperature": temperature,
|
|
}
|
|
with torch.no_grad():
|
|
outputs = model.generate(**inputs, **gen_kwargs)
|
|
outputs = outputs[:, inputs['input_ids'].shape[1]:]
|
|
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
|
return response
|
|
|
|
def __call__(self, *args, **kwargs):
|
|
video_path = kwargs.pop('video_path', None)
|
|
with open(video_path, 'rb') as f:
|
|
video_data = f.read()
|
|
response = (self.get_caption(self.prompt, video_data, self.temperature))
|
|
return response
|
|
|
|
|
|
class OpusMtZhEn(BaseCaptionProcessor):
|
|
def __init__(self, cfg, language='en'):
|
|
super().__init__(cfg, language=language)
|
|
self.model_path = cfg.MODEL_PATH
|
|
self.model_info = {
|
|
'device': 'offline',
|
|
'model': None,
|
|
'tokenizer': None
|
|
}
|
|
|
|
def load_model(self):
|
|
is_flg, msg = super().load_model()
|
|
if not is_flg:
|
|
return is_flg, msg
|
|
if self.model_info['device'] == 'offline':
|
|
model = None
|
|
try:
|
|
from transformers import MarianMTModel, AutoTokenizer
|
|
local_model_dir = FS.get_dir_to_local_dir(self.model_path)
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
local_model_dir
|
|
)
|
|
model = MarianMTModel.from_pretrained(
|
|
local_model_dir
|
|
).to(we.device_id)
|
|
except Exception as e:
|
|
if model is not None:
|
|
del model
|
|
return False, f"Load model error '{e}'"
|
|
self.model_info['device'] = model.device
|
|
self.model_info['model'] = model
|
|
self.model_info['tokenizer'] = tokenizer
|
|
elif self.model_info['device'] == 'cpu':
|
|
try:
|
|
self.model_info['model'].to(we.device_id)
|
|
self.model_info['device'] = we.device_id
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
except Exception as e:
|
|
del self.model_info['model']
|
|
self.model_info['model'] = None
|
|
self.model_info['device'] = 'offline'
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
return False, f"Load model error '{e}'"
|
|
return True, ''
|
|
|
|
def unload_model(self):
|
|
super().unload_model()
|
|
if self.delete_instance:
|
|
self.model_info['device'] = 'offline'
|
|
if self.model_info['model'] is not None:
|
|
self.model_info['model'] = self.model_info['model'].to('cpu')
|
|
del self.model_info['model']
|
|
self.model_info['model'] = None
|
|
elif (isinstance(self.model_info['device'], numbers.Number)
|
|
or str(self.model_info['device']).startswith('cuda')):
|
|
self.model_info['device'] = 'cpu'
|
|
self.model_info['model'] = self.model_info['model'].to('cpu')
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
return True, ''
|
|
|
|
def get_caption(self, data):
|
|
model = self.model_info['model']
|
|
tokenizer = self.model_info['tokenizer']
|
|
batch = tokenizer(data, return_tensors="pt").to(we.device_id)
|
|
generated_ids = model.generate(**batch)
|
|
translated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
|
return translated_text
|
|
|
|
def __call__(self, *args, **kwargs):
|
|
caption = kwargs.pop('caption', None)
|
|
response = (self.get_caption(caption))
|
|
return response
|
|
|
|
|
|
class OpusMtEnZh(BaseCaptionProcessor):
|
|
def __init__(self, cfg, language='en'):
|
|
super().__init__(cfg, language=language)
|
|
self.model_path = cfg.MODEL_PATH
|
|
self.model_info = {
|
|
'device': 'offline',
|
|
'model': None,
|
|
'tokenizer': None
|
|
}
|
|
|
|
def load_model(self):
|
|
is_flg, msg = super().load_model()
|
|
if not is_flg:
|
|
return is_flg, msg
|
|
if self.model_info['device'] == 'offline':
|
|
model = None
|
|
try:
|
|
from transformers import MarianMTModel, AutoTokenizer
|
|
local_model_dir = FS.get_dir_to_local_dir(self.model_path)
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
local_model_dir
|
|
)
|
|
model = MarianMTModel.from_pretrained(
|
|
local_model_dir
|
|
).to(we.device_id)
|
|
except Exception as e:
|
|
if model is not None:
|
|
del model
|
|
return False, f"Load model error '{e}'"
|
|
self.model_info['device'] = model.device
|
|
self.model_info['model'] = model
|
|
self.model_info['tokenizer'] = tokenizer
|
|
elif self.model_info['device'] == 'cpu':
|
|
try:
|
|
self.model_info['model'].to(we.device_id)
|
|
self.model_info['device'] = we.device_id
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
except Exception as e:
|
|
del self.model_info['model']
|
|
self.model_info['model'] = None
|
|
self.model_info['device'] = 'offline'
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
return False, f"Load model error '{e}'"
|
|
return True, ''
|
|
|
|
def unload_model(self):
|
|
super().unload_model()
|
|
if self.delete_instance:
|
|
self.model_info['device'] = 'offline'
|
|
if self.model_info['model'] is not None:
|
|
self.model_info['model'] = self.model_info['model'].to('cpu')
|
|
del self.model_info['model']
|
|
self.model_info['model'] = None
|
|
elif (isinstance(self.model_info['device'], numbers.Number)
|
|
or str(self.model_info['device']).startswith('cuda')):
|
|
self.model_info['device'] = 'cpu'
|
|
self.model_info['model'] = self.model_info['model'].to('cpu')
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
return True, ''
|
|
|
|
def get_caption(self, data):
|
|
model = self.model_info['model']
|
|
tokenizer = self.model_info['tokenizer']
|
|
batch = tokenizer(data, return_tensors="pt").to(we.device_id)
|
|
generated_ids = model.generate(**batch)
|
|
translated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
|
return translated_text
|
|
|
|
def __call__(self, *args, **kwargs):
|
|
caption = kwargs.pop('caption', None)
|
|
response = (self.get_caption(caption))
|
|
return response |