| |
| |
|
|
| import cv2 |
| import torch |
| import numpy as np |
| from torchvision.transforms import Normalize, Compose, Resize, ToTensor |
| from .utils import convert_to_pil |
|
|
| class RAMAnnotator: |
| def __init__(self, cfg, device=None): |
| try: |
| from ram.models import ram_plus, ram, tag2text |
| from ram import inference_ram |
| except: |
| import warnings |
| warnings.warn("please pip install ram package, or you can refer to models/VACE-Annotators/ram/ram-0.0.1-py3-none-any.whl") |
|
|
| delete_tag_index = [] |
| image_size = cfg.get('IMAGE_SIZE', 384) |
| ram_tokenizer_path = cfg['TOKENIZER_PATH'] |
| ram_checkpoint_path = cfg['PRETRAINED_MODEL'] |
| ram_type = cfg.get('RAM_TYPE', 'swin_l') |
| self.return_lang = cfg.get('RETURN_LANG', ['en']) |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device |
| self.model = ram_plus(pretrained=ram_checkpoint_path, image_size=image_size, vit=ram_type, |
| text_encoder_type=ram_tokenizer_path, delete_tag_index=delete_tag_index).eval().to(self.device) |
| self.ram_transform = Compose([ |
| Resize((image_size, image_size)), |
| ToTensor(), |
| Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
| ]) |
| self.inference_ram = inference_ram |
|
|
| def forward(self, image): |
| image = convert_to_pil(image) |
| image_ann_trans = self.ram_transform(image).unsqueeze(0).to(self.device) |
| tags_e, tags_c = self.inference_ram(image_ann_trans, self.model) |
| tags_e_list = [tag.strip() for tag in tags_e.strip().split("|")] |
| tags_c_list = [tag.strip() for tag in tags_c.strip().split("|")] |
| if len(self.return_lang) == 1 and 'en' in self.return_lang: |
| return tags_e_list |
| elif len(self.return_lang) == 1 and 'zh' in self.return_lang: |
| return tags_c_list |
| else: |
| return { |
| "tags_e": tags_e_list, |
| "tags_c": tags_c_list |
| } |
|
|
