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