| import os |
| import sys |
| sys.path.append("/mnt/prev_nas/qhy_1/GenSpace/osdsynth/external/Grounded-Segment-Anything/recognize-anything") |
| from typing import List |
|
|
| import torchvision.transforms as TS |
| from ram import inference_ram |
| from ram.models import ram |
|
|
|
|
| def run_tagging_model(cfg, raw_image, tagging_model): |
| res = inference_ram(raw_image, tagging_model) |
| caption = "NA" |
| tags = res[0].strip(" ").replace(" ", " ").replace(" |", ",") |
| print("Tags: ", tags) |
|
|
| |
| |
| text_prompt = res[0].replace(" |", ",") |
|
|
| if cfg.rm_bg_classes: |
| cfg.remove_classes += cfg.bg_classes |
|
|
| classes = process_tag_classes( |
| text_prompt, |
| add_classes=cfg.add_classes, |
| remove_classes=cfg.remove_classes, |
| ) |
| print("Tags (Final): ", classes) |
| return classes |
|
|
|
|
| def process_tag_classes(text_prompt: str, add_classes: List[str] = [], remove_classes: List[str] = []) -> list[str]: |
| """Convert a text prompt from Tag2Text to a list of classes.""" |
| classes = text_prompt.split(",") |
| classes = [obj_class.strip() for obj_class in classes] |
| classes = [obj_class for obj_class in classes if obj_class != ""] |
|
|
| for c in add_classes: |
| if c not in classes: |
| classes.append(c) |
|
|
| for c in remove_classes: |
| classes = [obj_class for obj_class in classes if c not in obj_class.lower()] |
|
|
| return classes |
|
|
|
|
| def get_tagging_model(cfg, device): |
| RAM_CHECKPOINT_PATH = os.path.abspath( |
| "osdsynth/external/Grounded-Segment-Anything/recognize-anything/ram_swin_large_14m.pth" |
| ) |
| tagging_model = ram(pretrained=RAM_CHECKPOINT_PATH, image_size=384, vit="swin_l") |
|
|
| tagging_model = tagging_model.eval().to(device) |
| tagging_transform = TS.Compose( |
| [ |
| TS.Resize((384, 384)), |
| TS.ToTensor(), |
| TS.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
| ] |
| ) |
|
|
| return tagging_transform, tagging_model |
|
|
