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修改参数

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zhangpu
2025-11-21 10:19:04 +08:00
parent 99d9eea757
commit 95d3099cee
3 changed files with 85 additions and 9 deletions
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train/grid_search_cosface.py
+10 -4
View File
@@ -41,11 +41,17 @@ logging.basicConfig(
logger = logging.getLogger(__name__)
# ==================== 网格搜索空间定义 ====================
GRID_PARAMS = {
'dish': {
"""
'dish': {
's': [56.0, 60.0, 64.0, 68.0], # scale参数
'm': [0.32, 0.35, 0.38, 0.40], # margin参数
},
"""
GRID_PARAMS = {
'dish': {
's': [56.0, 60.0], # scale参数
'm': [0.32, 0.40], # margin参数
},
'whole_ingredient': {
's': [56.0, 60.0, 64.0, 68.0],
'm': [0.32, 0.35, 0.38, 0.40],
@@ -354,8 +360,8 @@ def grid_search_main(
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='CosFace超参数网格搜索')
parser.add_argument('--task', choices=list(TASKS.keys()), default='dish', help='任务名称')
# parser.add_argument('--task', choices=list(TASKS.keys()), default='whole_ingredient', help='任务名称')
# parser.add_argument('--task', choices=list(TASKS.keys()), default='dish', help='任务名称')
parser.add_argument('--task', choices=list(TASKS.keys()), default='whole_ingredient', help='任务名称')
parser.add_argument('--max_configs', type=int, default=None, help='最大配置数（用于测试）')
parser.add_argument('--epochs', type=int, default=100, help='每个配置的最大训练轮数')
parser.add_argument('--patience', type=int, default=10, help='早停容忍轮数')
train/train_cosface_embedding.py
+74 -4
View File
@@ -42,6 +42,9 @@ class TaskConfig:
aug_strength: str # "strong" | "medium" | "shape"
cosface_s: float = 64.0 # CosFace scale factor
cosface_m: float = 0.35 # CosFace margin
# use_class_weights: bool = False # 是否使用类别权重（处理不平衡数据）
use_class_weights: bool = True # 是否使用类别权重（处理不平衡数据）
weight_strategy: str = 'sqrt' # 权重策略: 'inverse'|'sqrt'|'focal'
TASKS = {
@@ -170,6 +173,47 @@ def evaluate_open_set(cos_scores_known: torch.Tensor, cos_scores_unknown: torch.
return threshold, known_accept, unknown_reject
def compute_class_weights(dataset, strategy: str = 'sqrt') -> torch.Tensor:
"""
计算类别权重以处理不平衡问题
Args:
dataset: ImageFolder数据集
strategy: 权重策略
- 'inverse': 逆频率权重 (激进)
- 'sqrt': 平方根逆频率 (温和，推荐)
- 'focal': Focal Loss风格权重
Returns:
class_weights: [num_classes] 权重张量
"""
num_classes = len(dataset.classes)
class_counts = torch.zeros(num_classes)
for _, label in dataset.samples:
class_counts[label] += 1
logger.info(f'类别样本数统计: min={class_counts.min().item():.0f}, '
f'max={class_counts.max().item():.0f}, '
f'mean={class_counts.mean().item():.0f}')
if strategy == 'inverse':
weights = 1.0 / class_counts
elif strategy == 'sqrt':
weights = torch.sqrt(1.0 / class_counts)
elif strategy == 'focal':
# 类似Focal Loss的平滑权重
weights = torch.pow(1.0 / class_counts, 0.25)
else:
raise ValueError(f"Unknown strategy: {strategy}")
# 归一化权重，使均值为1（保持loss scale不变）
weights = weights / weights.mean()
logger.info(f'类别权重 ({strategy}): min={weights.min().item():.3f}, '
f'max={weights.max().item():.3f}, mean={weights.mean().item():.3f}')
return weights
def plot_training_curves(train_losses, val_losses, train_accuracies, val_accuracies, save_path: str):
epochs = range(1, len(train_losses) + 1)
plt.figure(figsize=(12, 5))
@@ -251,7 +295,9 @@ def collect_max_cos_scores(model, head, loader) -> torch.Tensor:
def main(task_key: str = 'dish', s: Optional[float] = None, m: Optional[float] = None, num_epochs: int = 60,
unknown_dir: Optional[str] = None, far: float = 0.05,
patience: int = 10, min_delta: float = 0.001, min_epochs: int = 25):
patience: int = 10, min_delta: float = 0.001, min_epochs: int = 25,
use_class_weights: Optional[bool] = None, # 是否启用类别加权
weight_strategy: str = 'sqrt'): # 权重计算策略
"""
参数:
patience: 早停容忍轮数（验证损失不改善的最大轮数）
@@ -284,12 +330,27 @@ def main(task_key: str = 'dish', s: Optional[float] = None, m: Optional[float] =
num_classes = len(class_names)
logger.info(f'类别数: {num_classes}; 类别: {class_names}')
# 计算类别权重（如果启用）
use_weights = use_class_weights if use_class_weights is not None else cfg.use_class_weights
class_weights = None
if use_weights:
logger.info(f'启用类别加权，策略: {weight_strategy}')
class_weights = compute_class_weights(train_ds, strategy=weight_strategy)
# 记录每个类别的权重（仅在类别数较少时详细显示）
if num_classes <= 20:
weight_dict = {name: f'{weight:.3f}' for name, weight in zip(class_names, class_weights.tolist())}
logger.info(f'各类别权重: {weight_dict}')
criterion = nn.CrossEntropyLoss(weight=class_weights.to(device))
else:
logger.info('未启用类别加权')
criterion = nn.CrossEntropyLoss()
# 模型与头
model = create_resnet50_embedding(embedding_dim=cfg.embedding_dim, pretrained=True, use_internal_preprocess=False).to(device)
head = CosFaceHead(in_features=cfg.embedding_dim, num_classes=num_classes, s=s, m=m).to(device)
# 损失与优化器
criterion = nn.CrossEntropyLoss()
# 优化器
optimizer = optim.AdamW(list(model.parameters()) + list(head.parameters()), lr=cfg.lr, weight_decay=1e-4)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs)
@@ -398,6 +459,9 @@ def main(task_key: str = 'dish', s: Optional[float] = None, m: Optional[float] =
'batch_size': cfg.batch_size,
's': s,
'm': m,
'use_class_weights': use_weights,
'weight_strategy': weight_strategy if use_weights else None,
'class_weights': class_weights.tolist() if use_weights else None,
'patience': patience,
'min_delta': min_delta,
'min_epochs': min_epochs,
@@ -433,7 +497,13 @@ if __name__ == '__main__':
parser.add_argument('--min_epochs', type=int, default=25, help='最小训练轮数（早停保护）')
parser.add_argument('--unknown_dir', type=str, default=None, help='开放集评估用未知类目录（可选）')
parser.add_argument('--far', type=float, default=0.05, help='未知集允许的FAR，用于阈值估计')
parser.add_argument('--use_class_weights', action='store_true',
help='启用类别加权（处理不平衡数据）')
parser.add_argument('--weight_strategy', choices=['inverse', 'sqrt', 'focal'],
default='sqrt', help='权重计算策略: inverse(激进)|sqrt(温和,推荐)|focal(平滑)')
args = parser.parse_args()
main(task_key=args.task, s=args.s, m=args.m, num_epochs=args.epochs,
unknown_dir=args.unknown_dir, far=args.far,
patience=args.patience, min_delta=args.min_delta, min_epochs=args.min_epochs)
patience=args.patience, min_delta=args.min_delta, min_epochs=args.min_epochs,
use_class_weights=args.use_class_weights,
weight_strategy=args.weight_strategy)