import os os.environ["HF_ENDPOINT"] = "https://hf-mirror.com" os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" import torch import torch.nn as nn import torch.nn.functional as F import decord import numpy as np from torch.utils.data import Dataset, DataLoader from transformers import AutoModel, AutoConfig, AutoTokenizer from tqdm import tqdm from sklearn.metrics import accuracy_score import gc # ================= 配置 ================= MODEL_ID = "OpenGVLab/VideoMAEv2-giant" BERT_ID = "bert-base-uncased" CHECKPOINT_PATH = "/root/autodl-tmp/checkpoints_decomae/decomae_best.pth" CACHE_DIR = "/root/autodl-tmp/hf_cache" NUM_FRAMES = 16 IMG_SIZE = 224 BATCH_SIZE = 8 # 语义字典 (必须与训练一致) SEMANTIC_DICT = { 'DeliverObject': ('Deliver', 'Forward', 'Object'), 'MoveBackwardsWhileDrilling': ('Move', 'Backwards', 'Drill'), 'MoveBackwardsWhilePolishing': ('Move', 'Backwards', 'Polisher'), 'MoveDiagonallyBackwardLeftWithDrill': ('Move', 'Diagonally Backward Left', 'Drill'), 'MoveDiagonallyBackwardLeftWithPolisher': ('Move', 'Diagonally Backward Left', 'Polisher'), 'MoveDiagonallyBackwardRightWithDrill': ('Move', 'Diagonally Backward Right', 'Drill'), 'MoveDiagonallyBackwardRightWithPolisher': ('Move', 'Diagonally Backward Right', 'Polisher'), 'MoveDiagonallyForwardLeftWithDrill': ('Move', 'Diagonally Forward Left', 'Drill'), 'MoveDiagonallyForwardLeftWithPolisher': ('Move', 'Diagonally Forward Left', 'Polisher'), 'MoveDiagonallyForwardRightWithDrill': ('Move', 'Diagonally Forward Right', 'Drill'), 'MoveDiagonallyForwardRightWithPolisher': ('Move', 'Diagonally Forward Right', 'Polisher'), 'MoveForwardWhileDrilling': ('Move', 'Forward', 'Drill'), 'MoveForwardWhilePolishing': ('Move', 'Forward', 'Polisher'), 'MoveLeftWhileDrilling': ('Move', 'Left', 'Drill'), 'MoveLeftWhilePolishing': ('Move', 'Left', 'Polisher'), 'MoveRightWhileDrilling': ('Move', 'Right', 'Drill'), 'MoveRightWhilePolishing': ('Move', 'Right', 'Polisher'), 'NoCollaborativeWithDrilll': ('Stand', 'No Action', 'Drill'), 'NoCollaborativeWithPolisher': ('Stand', 'No Action', 'Polisher'), 'PickUpDrill': ('Pick Up', 'Upward', 'Drill'), 'PickUpPolisher': ('Pick Up', 'Upward', 'Polisher'), 'PickUpTheObject': ('Pick Up', 'Upward', 'Object'), 'PutDownDrill': ('Put Down', 'Downward', 'Drill'), 'PutDownPolisher': ('Put Down', 'Downward', 'Polisher'), 'UsingTheDrill': ('Operate', 'Stationary', 'Drill'), 'UsingThePolisher': ('Operate', 'Stationary', 'Polisher'), 'Walking': ('Walk', 'Forward', 'Nothing'), 'WalkingWithDrill': ('Walk', 'Forward', 'Drill'), 'WalkingWithObject': ('Walk', 'Forward', 'Object'), 'WalkingWithPolisher': ('Walk', 'Forward', 'Polisher') } ID2LABEL = list(SEMANTIC_DICT.keys()) # ================= 1. 离线计算语义原型 ================= def compute_prototypes(): print("🚀 Pre-computing Semantic Prototypes...") tokenizer = AutoTokenizer.from_pretrained(BERT_ID, cache_dir=CACHE_DIR) bert = AutoModel.from_pretrained(BERT_ID, cache_dir=CACHE_DIR).cuda() bert.eval() prompts = [f"A worker {SEMANTIC_DICT[c][0]} {SEMANTIC_DICT[c][1]} using {SEMANTIC_DICT[c][2]}" for c in ID2LABEL] with torch.no_grad(): inputs = tokenizer(prompts, padding=True, truncation=True, return_tensors="pt").to('cuda') outputs = bert(**inputs) embeddings = outputs.last_hidden_state[:, 0, :] embeddings = F.normalize(embeddings, dim=-1) protos = embeddings.cpu() del bert, tokenizer, inputs, outputs torch.cuda.empty_cache() return protos TEXT_PROTOTYPES = compute_prototypes() # ================= 2. DeCo-MAE 模型 ================= class DeCoMAE_Eval(nn.Module): def __init__(self, video_model_id, prototypes): super().__init__() print("Loading Video Backbone...") v_config = AutoConfig.from_pretrained(video_model_id, trust_remote_code=True, cache_dir=CACHE_DIR) v_config.use_cache = False self.visual = AutoModel.from_pretrained(video_model_id, trust_remote_code=True, config=v_config, cache_dir=CACHE_DIR, torch_dtype=torch.bfloat16) if hasattr(v_config, "hidden_size"): self.v_dim = v_config.hidden_size elif hasattr(v_config, "embed_dim"): self.v_dim = v_config.embed_dim else: self.v_dim = 1408 self.register_buffer("text_prototypes", prototypes) self.video_proj = nn.Linear(self.v_dim, 768) self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) def forward(self, x): x = x.permute(0, 2, 1, 3, 4) v_out = self.visual(x) if hasattr(v_out, 'last_hidden_state'): v_feat = v_out.last_hidden_state.mean(dim=1) elif isinstance(v_out, tuple): v_feat = v_out[0].mean(dim=1) if v_out[0].dim()==3 else v_out[0] else: v_feat = v_out.mean(dim=1) if v_out.dim()==3 else v_out v_emb = self.video_proj(v_feat) v_emb = F.normalize(v_emb, dim=-1) text_protos = self.text_prototypes.to(v_emb.device).to(v_emb.dtype) logits = torch.matmul(v_emb, text_protos.t()) * self.logit_scale.exp() return logits # ================= 3. 数据集 ================= class HRI30_Eval(Dataset): def __init__(self, root="/root/hri30/train"): self.data = [] target_root = root if os.path.exists(root) and os.listdir(root) else "/root/hri30/train_set" print(f"Scanning {target_root}...") for i in range(1, 31): p = f"{target_root}/{i}" if not os.path.exists(p): continue for f in os.listdir(p): if f.endswith('.avi'): self.data.append((os.path.join(p, f), i-1)) def __len__(self): return len(self.data) def __getitem__(self, i): path, label = self.data[i] vr = decord.VideoReader(path) idx = torch.linspace(0, len(vr)-1, NUM_FRAMES).long() batch = vr.get_batch(idx) buffer = batch.asnumpy().transpose(0, 3, 1, 2) buffer = torch.from_numpy(buffer).float() / 255.0 buffer = torch.nn.functional.interpolate(buffer, (IMG_SIZE, IMG_SIZE)) return buffer, torch.tensor(label) # ================= 4. 执行评估 ================= if __name__ == "__main__": ds = HRI30_Eval() dl = DataLoader(ds, batch_size=BATCH_SIZE, shuffle=False, num_workers=4) print("Initializing Model...") model = DeCoMAE_Eval(MODEL_ID, TEXT_PROTOTYPES).cuda().to(torch.bfloat16) print(f"Loading Weights: {CHECKPOINT_PATH}") state_dict = torch.load(CHECKPOINT_PATH) model.load_state_dict(state_dict) # 这里必须严格匹配 model.eval() preds, targets = [], [] print("🔥 Running Inference...") with torch.no_grad(): for x, y in tqdm(dl): x = x.cuda().to(torch.bfloat16) logits = model(x) preds.extend(torch.argmax(logits, dim=1).cpu().numpy()) targets.extend(y.numpy()) acc = accuracy_score(targets, preds) print("\n" + "="*40) print(f"🏆 DeCo-MAE (Semantic Decomposition) Result:") print(f"✅ Top-1 Accuracy: {acc*100:.2f}%") print("="*40)