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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)
# Currently ", " is better for detecting single tags
# while ". " is a little worse in some case
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
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