scripts and template

extract_acc.py

@@ -0,0 +1,119 @@
+import os
+import re
+from glob import glob
+from collections import defaultdict
+
+def extract_accuracy(log_path):
+    """从日志文件中提取accuracy"""
+    try:
+        with open(log_path, 'r') as f:
+            content = f.read()
+        match = re.search(r'\* accuracy: (\d+\.\d+)%', content)
+        if match:
+            return float(match.group(1))
+    except:
+        pass
+    return None
+
+def collect_model_results(root_dir, target_model):
+    """收集指定模型在所有数据集上的结果，z按seed分组"""
+    results = {
+        'base': defaultdict(list),  # 使用列表存储多个seed的结果
+        'new': defaultdict(list),
+        'datasets': set()
+    }
+    # 查找所有base训练的log文件
+    base_logs = glob(os.path.join(root_dir, '**/train_base/**/log.txt'), recursive=True)
+    for log_path in base_logs:
+        parts = log_path.split(os.sep)
+        dataset = parts[-6]
+        model = parts[-4]
+        
+        if model != target_model:
+            continue
+            
+        accuracy = extract_accuracy(log_path)
+        if accuracy is not None:
+            results['base'][dataset].append(accuracy)
+            results['datasets'].add(dataset)
+    
+    # 查找所有new测试的log文件
+    new_logs = glob(os.path.join(root_dir, '**/test_new/**/log.txt'), recursive=True)
+    for log_path in new_logs:
+        parts = log_path.split(os.sep)
+        dataset = parts[-6]
+        model = parts[-4]
+        
+        if model != target_model:
+            continue
+            
+        accuracy = extract_accuracy(log_path)
+        if accuracy is not None:
+            results['new'][dataset].append(accuracy)
+            results['datasets'].add(dataset)
+    
+    return results
+
+def calculate_harmonic_mean(base, new):
+    """计算调和平均数"""
+    if base == 0 or new == 0:
+        return 0
+    return 2 * base * new / (base + new)
+
+def calculate_average(values):
+    """计算平均值"""
+    if not values:
+        return None
+    return sum(values) / len(values)
+
+def print_model_results(results, model_name):
+    """打印指定模型在所有数据集上的结果（平均所有seed）"""
+    datasets = sorted(results['datasets'])
+    
+    # 准备数据用于计算总体平均值
+    base_sum = 0
+    new_sum = 0
+    valid_datasets = 0
+    
+    print(f"\nResults for model: {model_name}")
+    print(f"{'Dataset':<15} {'Base':<10} {'New':<10} {'H':<10} {'Seeds':<10}")
+    print("-" * 60)
+    
+    for dataset in datasets:
+        base_accs = results['base'].get(dataset, [])
+        new_accs = results['new'].get(dataset, [0.0, 0.0, 0.0])
+        
+        if base_accs and new_accs:
+            avg_base = calculate_average(base_accs)
+            avg_new = calculate_average(new_accs)
+            h = calculate_harmonic_mean(avg_base, avg_new)
+            
+            # 获取seed数量（取base和new中较小的seed数）
+            num_seeds = min(len(base_accs), len(new_accs))
+            
+            print(f"{dataset:<15} {avg_base:.2f}{'':<6} {avg_new:.2f}{'':<6} {h:.2f}{'':<6} {num_seeds}")
+            
+            base_sum += avg_base
+            new_sum += avg_new
+            valid_datasets += 1
+    
+    # 计算并打印总体平均值
+    if valid_datasets > 0:
+        avg_base = base_sum / valid_datasets
+        avg_new = new_sum / valid_datasets
+        avg_h = calculate_harmonic_mean(avg_base, avg_new)
+        print("-" * 60)
+        print(f"{'Average':<15} {avg_base:.2f}{'':<6} {avg_new:.2f}{'':<6} {avg_h:.2f}")
+    else:
+        print("No complete dataset results found for this model.")
+
+def main():
+    root_dir = 'output'  # 修改为你的output目录路径
+    target_model = 'PromptSRC'  # 指定要分析的模型
+    
+    results = collect_model_results(root_dir, target_model)
+    print_model_results(results, target_model)
+
+if __name__ == '__main__':
+    main()
+

scripts/promptsrc/base2new_all.sh

@@ -1,16 +1,16 @@
 seeds=(1 2 3)
 datasets=(
-    "ucf101"
-    "eurosat"
-    "oxford_pets"
-    "food101"
-    "oxford_flowers"
-    "dtd"
-    "caltech101"
-    "fgvc_aircraft"
-    "stanford_cars"
+    # "ucf101"
+    # "eurosat"
+    # "oxford_pets"
+    # "food101"
+    # "oxford_flowers"
+    # "dtd"
+    # "caltech101"
+    # "fgvc_aircraft"
+    # "stanford_cars"
     # "sun397"
-    # "imagenet"
+    "imagenet"
 )
 
 for dataset in "${datasets[@]}"; do

trainers/promptsrc.py

@@ -18,6 +18,24 @@ _tokenizer = _Tokenizer()
 DESC_LLM = "gpt-4.1"
 DESC_TOPK = 4
 
+CUSTOM_TEMPLATES = {
+    "OxfordPets": "a photo of a {}, a type of pet.",
+    "OxfordFlowers": "a photo of a {}, a type of flower.",
+    "FGVCAircraft": "a photo of a {}, a type of aircraft.",
+    "DescribableTextures": "a photo of a {}, a type of texture.",
+    "EuroSAT": "a centered satellite photo of {}.",
+    "StanfordCars": "a photo of a {}.",
+    "Food101": "a photo of {}, a type of food.",
+    "SUN397": "a photo of a {}.",
+    "Caltech101": "a photo of a {}.",
+    "UCF101": "a photo of a person doing {}.",
+    "ImageNet": "a photo of a {}.",
+    "ImageNetSketch": "a photo of a {}.",
+    "ImageNetV2": "a photo of a {}.",
+    "ImageNetA": "a photo of a {}.",
+    "ImageNetR": "a photo of a {}.",
+}
+
 
 def load_clip_to_cpu(cfg, zero_shot_model=False):
     backbone_name = cfg.MODEL.BACKBONE.NAME
@@ -125,8 +143,9 @@ class VLPromptLearner(nn.Module):
             with open(desc_file, "r") as f:
                 all_desc = json.load(f)
 
+            template = CUSTOM_TEMPLATES[cfg.DATASET.NAME]
             for cls in classnames:
-                cls_descs = [f"a photo of {cls}, {desc}" for desc in all_desc[cls]]
+                cls_descs = [template.format(cls)[:-1] + f", {desc}" for desc in all_desc[cls]]
                 cls_token = torch.cat([clip.tokenize(cls_desc) for cls_desc in cls_descs]).cuda()
                 with torch.no_grad():
                     cls_feature = clip_model_temp.encode_text(cls_token)