import os import gc 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 torchvision.transforms.v2 as T import warnings warnings.filterwarnings("ignore") # 🔥 强行指定国内镜像 os.environ["HF_ENDPOINT"] = "https://hf-mirror.com" os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" # ================= 配置 ================= # 🔥🔥🔥 修正路径：指向 cooldown 文件夹 🔥🔥🔥 CHECKPOINT_PATH = "/root/autodl-tmp/checkpoints_cooldown/cooldown_best.pth" BERT_ID = "bert-base-uncased" MODEL_ID = "OpenGVLab/VideoMAEv2-giant" NUM_FRAMES = 16 IMG_SIZE = 224 BATCH_SIZE = 8 CACHE_DIR = "/root/autodl-tmp/hf_cache" print(f"🚀 开始终极评估 | 权重: {CHECKPOINT_PATH}") # ================= 1. 语义字典 ================= 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') } ALL_CLASSES = list(SEMANTIC_DICT.keys()) # ================= 2. 离线计算 Prototypes ================= def compute_text_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 ALL_CLASSES] 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() gc.collect() return protos TEXT_PROTOTYPES = compute_text_prototypes() # ================= 3. 验证集处理 ================= class VideoValTransform: def __init__(self): self.transform = T.Compose([ T.ConvertImageDtype(torch.float32), T.Resize((IMG_SIZE, IMG_SIZE), antialias=True), ]) self.mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1, 1) self.std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1, 1) def __call__(self, video_tensor): video_tensor = self.transform(video_tensor) video_tensor = (video_tensor - self.mean) / self.std return video_tensor # ================= 4. 数据集 ================= class HRI30_Eval(Dataset): def __init__(self, root="/root/hri30/train"): self.data = [] self.aug = VideoValTransform() target_root = root if os.path.exists(root) and os.listdir(root) else "/root/hri30/train_set" print(f"Scanning data from: {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)) print(f"Found {len(self.data)} videos for evaluation.") 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) # [T, H, W, C] -> [C, T, H, W] buffer = torch.from_numpy(batch.asnumpy()).permute(3, 0, 1, 2) buffer = self.aug(buffer) return buffer, torch.tensor(label) # ================= 5. Dual-Head 模型 ================= class DualHeadMAE(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 else: self.v_dim = 1408 self.fc_cls = nn.Linear(self.v_dim, 30) 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 is [B, C, T, H, W] outputs = self.visual(x) if hasattr(outputs, 'last_hidden_state'): feat = outputs.last_hidden_state.mean(dim=1) elif isinstance(outputs, tuple): feat = outputs[0].mean(dim=1) if outputs[0].dim()==3 else outputs[0] else: feat = outputs.mean(dim=1) if outputs.dim()==3 else outputs # Head 1 (Class) logits_cls = self.fc_cls(feat) # Head 2 (Semantic) v_emb = F.normalize(self.video_proj(feat), dim=-1) text_protos = self.text_prototypes.to(feat.device).to(feat.dtype) logits_sem = torch.matmul(v_emb, text_protos.t()) * self.logit_scale.exp() # 融合策略 final_logits = 0.8 * logits_cls + 0.2 * logits_sem return final_logits # ================= 6. 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 = DualHeadMAE(MODEL_ID, TEXT_PROTOTYPES).cuda().to(torch.bfloat16) print(f"Loading Weights: {CHECKPOINT_PATH}") # 注意：Cool-down 保存的是整个 state_dict，包括 backbone 和 heads state_dict = torch.load(CHECKPOINT_PATH) model.load_state_dict(state_dict) 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"🏆 Final Cool-down Result:") print(f"✅ Top-1 Accuracy: {acc*100:.2f}%") print("="*40)