| """Official ReLA Swin-Tiny architecture and trained checkpoint adapter.""" |
| from pathlib import Path |
| import sys,json,os |
| import numpy as np |
| from PIL import Image |
| ROOT=Path(__file__).resolve().parents[1] |
| class ReLA: |
| threshold=.5 |
| def __init__(self,device='cuda'): |
| import torch |
| self.torch=torch;self.device=device |
| repo=ROOT/'third_party/ReLA-3ca955198da8ace68f8980b6fcbada0791cc51c2';sys.path.insert(0,str(repo)) |
| if not hasattr(Image, "LINEAR"):Image.LINEAR=Image.Resampling.BILINEAR |
| from detectron2.config import get_cfg |
| from detectron2.modeling import build_model |
| from detectron2.data import transforms as T |
| from gres_model import add_maskformer2_config,add_refcoco_config |
| from transformers import BertTokenizer |
| from detectron2.projects.deeplab import add_deeplab_config |
| cfg=get_cfg();add_deeplab_config(cfg);add_maskformer2_config(cfg);add_refcoco_config(cfg) |
| cfg.merge_from_file(str(repo/'configs/referring_swin_tiny.yaml')) |
| cfg.MODEL.DEVICE=device;cfg.REFERRING.BERT_TYPE=str(ROOT/'models/bert-base-uncased') |
| self.model=build_model(cfg).eval() |
| checkpoint=torch.load(ROOT/'models/gres_swin_tiny.pth',map_location='cpu',weights_only=False) |
| state=checkpoint['model'] if 'model' in checkpoint else checkpoint |
| key='text_encoder.embeddings.position_ids' |
| if key in state: |
| assert torch.equal(state[key],self.model.text_encoder.embeddings.position_ids.cpu()) |
| state.pop(key) |
| incompat=self.model.load_state_dict(state,strict=True) |
| self.model.to(device);self.tokenizer=BertTokenizer.from_pretrained(cfg.REFERRING.BERT_TYPE,local_files_only=True) |
| self.max_tokens=cfg.REFERRING.MAX_TOKENS;self.size=cfg.INPUT.IMAGE_SIZE |
| self.resize=T.Resize((self.size,self.size)) |
| print('RELA_STRICT_LOAD_OK',len(state),self.size,self.max_tokens,str(incompat),flush=True) |
| def predict(self,image,texts): |
| t=self.torch;arr=np.array(image);resized=self.resize.get_transform(arr).apply_image(arr) |
| rgb=t.as_tensor(np.ascontiguousarray(resized.transpose(2,0,1))) |
| out={};nt={};official={} |
| with t.inference_mode(): |
| for start in range(0,len(texts),2): |
| batch=texts[start:start+2];inputs=[] |
| for q in batch: |
| ids=self.tokenizer.encode(text=q,add_special_tokens=True)[:self.max_tokens] |
| padding=[0]*(self.max_tokens-len(ids)) |
| inputs.append(dict(image=rgb,lang_tokens=t.tensor(ids+padding).unsqueeze(0),lang_mask=t.tensor([1]*len(ids)+padding).unsqueeze(0))) |
| outputs=self.model(inputs) |
| for q,p in zip(batch,outputs): |
| official[q]=p['ref_seg'].argmax(dim=0).bool().cpu().numpy() |
| nt[q]=bool(p['nt_label'].argmax(dim=0).item()) |
| |
| |
| margin=(p['ref_seg'][1]-p['ref_seg'][0]+1)/2 |
| out[q]=t.nn.functional.interpolate(margin[None,None],size=(image.height,image.width),mode='bilinear',align_corners=False)[0,0].float().cpu().numpy() |
| return out,dict(language_queries=len(texts),nt=nt,official_masks=official,sam_boxes=0,truncated_boxes=0) |
|
|
| if __name__=='__main__': |
| import torch |
| torch.set_num_threads(4) |
| engine=ReLA(device=os.getenv('R2_DEVICE','cuda')) |
| old=ROOT.parent/'refseg_scale_20260906' |
| row=next(json.loads(s) for s in (old/'data/manifest.jsonl').read_text().splitlines() if json.loads(s)['domain']=='natural' and json.loads(s)['split']=='fit') |
| image=Image.open(old/row['image_path']).convert('RGB') |
| maps,stats=engine.predict(image,[row['query']]) |
| print('RELA_PILOT_OK',image.size,row['query'],{q:dict(shape=v.shape,min=float(v.min()),max=float(v.max())) for q,v in maps.items()},stats['nt'],flush=True) |
|
|
