440 lines
19 KiB
Python
440 lines
19 KiB
Python
import os
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import sys
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import time
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import json
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import math
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import logging
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from dataclasses import dataclass
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from datetime import datetime
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from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torch.utils.data import DataLoader
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from torchvision import datasets, transforms
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from tqdm import tqdm
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import matplotlib.pyplot as plt
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# 将项目根目录加入路径，便于导入
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from net.resnet_embedding import create_resnet50_embedding
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from settings import settings
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# 日志
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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print(f'使用设备: {device}')
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@dataclass
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class TaskConfig:
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name: str
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train_dir: str
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val_dir: str
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test_dir: str # 测试集目录（用于网格搜索评估）
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embedding_dim: int
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batch_size: int
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lr: float
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aug_strength: str # "strong" | "medium" | "shape"
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cosface_s: float = 64.0 # CosFace scale factor
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cosface_m: float = 0.35 # CosFace margin
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TASKS = {
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'dish': TaskConfig(
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name='DishClassification',
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train_dir=os.path.join(settings.BASE_DIR, 'dataset', 'DishClassification', 'train'),
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val_dir=os.path.join(settings.BASE_DIR, 'dataset', 'DishClassification', 'val'),
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test_dir=os.path.join(settings.BASE_DIR, 'dataset', 'DishClassification', 'test'),
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embedding_dim=512,
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batch_size=32,
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lr=5e-4,
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aug_strength='medium',
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# cosface_s=60.0, # 菜品分类：类内差异大，使用中等scale
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cosface_s=64.0, # 菜品分类：类内差异大，使用中等scale
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# cosface_m=0.32, # 较小margin，适应类内多样性（不同做法、角度）
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cosface_m=0.40, # 较小margin，适应类内多样性（不同做法、角度）
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),
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'whole_ingredient': TaskConfig(
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name='WholeIngredientRecognition',
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train_dir=os.path.join(settings.BASE_DIR, 'dataset', 'WholeIngredientRecognition', 'train'),
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val_dir=os.path.join(settings.BASE_DIR, 'dataset', 'WholeIngredientRecognition', 'val'),
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test_dir=os.path.join(settings.BASE_DIR, 'dataset', 'WholeIngredientRecognition', 'test'),
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embedding_dim=512,
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batch_size=64,
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lr=8e-4,
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aug_strength='medium',
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cosface_s=64.0, # 完整食材：类间区分度高，使用标准scale
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cosface_m=0.38, # 较大margin，强化类间分离（番茄vs土豆差异明显）
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),
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'processed_ingredient': TaskConfig(
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name='ProcessedIngredientRecognition',
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train_dir=os.path.join(settings.BASE_DIR, 'dataset', 'ProcessedIngredientRecognition', 'train'),
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val_dir=os.path.join(settings.BASE_DIR, 'dataset', 'ProcessedIngredientRecognition', 'val'),
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test_dir=os.path.join(settings.BASE_DIR, 'dataset', 'ProcessedIngredientRecognition', 'test'),
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embedding_dim=512,
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batch_size=32,
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lr=5e-4,
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aug_strength='medium',
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cosface_s=60.0, # 加工食材：中等难度任务
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cosface_m=0.33, # 中等margin，平衡类内多样性和类间区分
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),
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}
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def build_transforms(aug_strength: str):
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if aug_strength == 'strong':
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return transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.RandomHorizontalFlip(p=0.5),
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transforms.RandomRotation(20),
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transforms.ColorJitter(0.3, 0.3, 0.3, 0.1),
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transforms.RandomAffine(degrees=0, translate=(0.12, 0.12)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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]), transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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if aug_strength == 'medium':
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return transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.RandomHorizontalFlip(p=0.5),
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transforms.RandomRotation(15),
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transforms.ColorJitter(0.2, 0.2, 0.2, 0.1),
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transforms.RandomAffine(degrees=0, translate=(0.1, 0.1)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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]), transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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# shape: 强调几何与尺度，弱化强色抖动
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return transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.RandomHorizontalFlip(p=0.5),
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transforms.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1)),
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transforms.RandomPerspective(distortion_scale=0.3, p=0.3),
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transforms.GaussianBlur(kernel_size=3, sigma=(0.1, 1.0)),
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transforms.ColorJitter(0.1, 0.1, 0.1, 0.03),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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]), transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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class CosFaceHead(nn.Module):
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def __init__(self, in_features: int, num_classes: int, s: float = 64.0, m: float = 0.35):
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super().__init__()
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self.s = s
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self.m = m
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self.weight = nn.Parameter(torch.randn(num_classes, in_features))
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nn.init.xavier_normal_(self.weight)
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def forward(self, features: torch.Tensor, labels: Optional[torch.Tensor] = None):
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# 假设输入features已L2归一化（backbone已做），此处仍做一次以保证数值稳健
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x = F.normalize(features, dim=1)
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W = F.normalize(self.weight, dim=1)
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cos_theta = torch.mm(x, W.t()) # [B, C]
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if labels is None:
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return self.s * cos_theta
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one_hot = F.one_hot(labels, num_classes=W.size(0)).float()
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cos_theta_m = cos_theta - one_hot * self.m
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logits = self.s * cos_theta_m
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return logits, cos_theta
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def accuracy_top1(logits: torch.Tensor, labels: torch.Tensor) -> float:
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pred = logits.argmax(dim=1)
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return (pred == labels).float().mean().item() * 100.0
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def evaluate_open_set(cos_scores_known: torch.Tensor, cos_scores_unknown: torch.Tensor, far: float = 0.05) -> Tuple[float, float, float]:
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"""
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基于max_cos分数的简单阈值估计：给定未知集FAR，返回阈值和两侧TPR/FPR。
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返回: (threshold, known_accept_rate, unknown_reject_rate)
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"""
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# 阈值取未知集分布的(1 - FAR)分位数（使未知中约 FAR 被错误接受）
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threshold = torch.quantile(cos_scores_unknown, 1 - far).item() if len(cos_scores_unknown) > 0 else 0.5
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known_accept = (cos_scores_known >= threshold).float().mean().item() if len(cos_scores_known) > 0 else 0.0
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unknown_reject = (cos_scores_unknown < threshold).float().mean().item() if len(cos_scores_unknown) > 0 else 0.0
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return threshold, known_accept, unknown_reject
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def plot_training_curves(train_losses, val_losses, train_accuracies, val_accuracies, save_path: str):
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epochs = range(1, len(train_losses) + 1)
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plt.figure(figsize=(12, 5))
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plt.subplot(1, 2, 1)
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plt.plot(epochs, train_losses, label='Train Loss')
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plt.plot(epochs, val_losses, label='Val Loss')
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plt.xlabel('Epoch'); plt.ylabel('Loss'); plt.title('Loss Curve'); plt.grid(True); plt.legend()
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plt.subplot(1, 2, 2)
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plt.plot(epochs, train_accuracies, label='Train Acc')
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plt.plot(epochs, val_accuracies, label='Val Acc')
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plt.xlabel('Epoch'); plt.ylabel('Accuracy (%)'); plt.title('Accuracy Curve'); plt.grid(True); plt.legend()
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plt.tight_layout(); plt.savefig(save_path, dpi=300, bbox_inches='tight'); plt.close()
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def run_epoch(model, head, loader, criterion, optimizer=None, train: bool = True):
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if train:
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model.train(); head.train()
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else:
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model.eval(); head.eval()
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running_loss = 0.0
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n_batches = 0
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# 改为统计总样本数和正确样本数（全局准确率）
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total_samples = 0
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correct_samples = 0
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with torch.set_grad_enabled(train):
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pbar = tqdm(loader, desc='训练中' if train else '验证中')
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for images, labels in pbar:
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images = images.to(device); labels = labels.to(device)
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feats = model(images) # 已L2归一化
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if train:
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logits, _ = head(feats, labels)
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loss = criterion(logits, labels)
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optimizer.zero_grad(); loss.backward(); optimizer.step()
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else:
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logits = head(feats) # 不加边距
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loss = criterion(logits, labels)
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# 统计样本级别的正确数
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pred = logits.argmax(dim=1)
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correct = (pred == labels).sum().item()
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batch_size = labels.size(0)
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total_samples += batch_size
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correct_samples += correct
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running_loss += loss.item()
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n_batches += 1
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# 计算当前的全局准确率
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current_acc = 100.0 * correct_samples / total_samples
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pbar.set_postfix({
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'Loss': f'{running_loss / n_batches:.4f}',
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'Acc': f'{current_acc:.2f}%'
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})
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avg_loss = running_loss / max(n_batches, 1)
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global_acc = 100.0 * correct_samples / max(total_samples, 1)
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return avg_loss, global_acc
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def collect_max_cos_scores(model, head, loader) -> torch.Tensor:
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model.eval(); head.eval()
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scores = []
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with torch.no_grad():
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for images, _ in loader:
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images = images.to(device)
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feats = model(images)
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_, cos = head(feats, labels=None), None # head(feats)返回logits= s*cos
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# 还需要原始cos分数：logits/s
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logits = head(feats) # s*cos
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cos_scores = logits / getattr(head, 's', 64.0)
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max_cos = cos_scores.max(dim=1).values
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scores.append(max_cos.cpu())
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return torch.cat(scores, dim=0) if scores else torch.tensor([])
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def main(task_key: str = 'dish', s: Optional[float] = None, m: Optional[float] = None, num_epochs: int = 60,
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unknown_dir: Optional[str] = None, far: float = 0.05,
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patience: int = 10, min_delta: float = 0.001, min_epochs: int = 25):
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"""
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参数:
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patience: 早停容忍轮数（验证损失不改善的最大轮数）
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min_delta: 最小改善阈值（验证损失改善小于此值不算改善）
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min_epochs: 最小训练轮数（早停不会在此之前触发）
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"""
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cfg = TASKS[task_key]
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# 优先级：命令行参数 > 配置文件默认值
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s = s if s is not None else cfg.cosface_s
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m = m if m is not None else cfg.cosface_m
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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save_dir = os.path.join(settings.BASE_DIR, 'model', cfg.name, f'cosface_{timestamp}')
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os.makedirs(save_dir, exist_ok=True)
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logger.info(f'[{cfg.name}] 模型保存目录: {save_dir}')
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logger.info(f'[{cfg.name}] CosFace超参数: s={s}, m={m}')
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logger.info(f'[{cfg.name}] 早停参数: patience={patience}, min_delta={min_delta}, min_epochs={min_epochs}')
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transform_train, transform_val = build_transforms(cfg.aug_strength)
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# 数据集
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train_ds = datasets.ImageFolder(cfg.train_dir, transform=transform_train)
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val_ds = datasets.ImageFolder(cfg.val_dir, transform=transform_val)
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train_loader = DataLoader(train_ds, batch_size=cfg.batch_size, shuffle=True, num_workers=0, drop_last=True)
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val_loader = DataLoader(val_ds, batch_size=cfg.batch_size, shuffle=False, num_workers=0)
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class_names = train_ds.classes
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num_classes = len(class_names)
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logger.info(f'类别数: {num_classes}; 类别: {class_names}')
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# 模型与头
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model = create_resnet50_embedding(embedding_dim=cfg.embedding_dim, pretrained=True, use_internal_preprocess=False).to(device)
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head = CosFaceHead(in_features=cfg.embedding_dim, num_classes=num_classes, s=s, m=m).to(device)
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# 损失与优化器
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.AdamW(list(model.parameters()) + list(head.parameters()), lr=cfg.lr, weight_decay=1e-4)
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scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs)
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# 早停相关变量
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best_val_acc = 0.0
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best_val_loss = float('inf')
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early_stop_counter = 0
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best_model_path = os.path.join(save_dir, 'best_cosface_model.pth')
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train_losses, val_losses, train_accs, val_accs = [], [], [], []
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start = time.time()
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logger.info('开始训练...')
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for epoch in range(num_epochs):
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logger.info(f'Epoch {epoch+1}/{num_epochs}')
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tr_loss, tr_acc = run_epoch(model, head, train_loader, criterion, optimizer, train=True)
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va_loss, va_acc = run_epoch(model, head, val_loader, criterion, optimizer=None, train=False)
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scheduler.step()
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train_losses.append(tr_loss); train_accs.append(tr_acc)
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val_losses.append(va_loss); val_accs.append(va_acc)
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logger.info(f' Train Loss: {tr_loss:.4f}, Acc: {tr_acc:.2f}%')
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logger.info(f' Val Loss: {va_loss:.4f}, Acc: {va_acc:.2f}%')
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logger.info(f' LR: {optimizer.param_groups[0]["lr"]:.6f}')
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# 保存最佳准确率模型
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if va_acc > best_val_acc:
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best_val_acc = va_acc
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torch.save({
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'epoch': epoch,
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'backbone_state_dict': model.state_dict(),
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'head_state_dict': head.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'val_acc': va_acc,
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'val_loss': va_loss,
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'class_names': class_names,
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'embedding_dim': cfg.embedding_dim,
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's': s,
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'm': m,
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}, best_model_path)
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logger.info(f'✓ 保存最佳模型: {best_model_path} (Val Acc: {va_acc:.2f}%)')
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# 早停逻辑：基于验证损失
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if va_loss < best_val_loss - min_delta:
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best_val_loss = va_loss
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early_stop_counter = 0
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logger.info(f'✓ 验证损失改善: {va_loss:.4f} (改善 {best_val_loss - va_loss:.4f})')
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else:
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early_stop_counter += 1
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logger.info(f'⚠ 验证损失未改善，早停计数: {early_stop_counter}/{patience}')
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# 早停判断（需满足最小epoch要求）
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if epoch >= min_epochs and early_stop_counter >= patience:
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logger.info('=' * 60)
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logger.info(f'🛑 早停触发：验证损失连续 {patience} 轮未改善超过 {min_delta}')
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logger.info(f' 触发轮次: Epoch {epoch+1}/{num_epochs}')
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logger.info(f' 最佳验证损失: {best_val_loss:.4f}')
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logger.info(f' 最佳验证准确率: {best_val_acc:.2f}%')
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logger.info('=' * 60)
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break
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else:
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logger.info(f'✓ 完成全部 {num_epochs} 轮训练（未触发早停）')
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elapsed = time.time() - start
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logger.info(f'训练完成，总耗时: {elapsed/3600:.2f} 小时')
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# 曲线
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curves_path = os.path.join(save_dir, 'training_curves.png')
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plot_training_curves(train_losses, val_losses, train_accs, val_accs, curves_path)
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# 开放集评估（可选）
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threshold = None
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known_accept = None
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unknown_reject = None
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if unknown_dir is not None and os.path.isdir(unknown_dir):
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unknown_ds = datasets.ImageFolder(unknown_dir, transform=transform_val) if os.path.isdir(os.path.join(unknown_dir, os.listdir(unknown_dir)[0])) else datasets.ImageFolder(unknown_dir, transform=transform_val)
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unknown_loader = DataLoader(unknown_ds, batch_size=cfg.batch_size, shuffle=False, num_workers=0)
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# 已知集用验证集
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known_scores = collect_max_cos_scores(model, head, val_loader)
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unknown_scores = collect_max_cos_scores(model, head, unknown_loader)
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if len(unknown_scores) > 0 and len(known_scores) > 0:
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threshold, known_accept, unknown_reject = evaluate_open_set(known_scores, unknown_scores, far=far)
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logger.info(f'开放集阈值@FAR={far*100:.1f}%: T={threshold:.4f}, 已知接受率={known_accept*100:.2f}%, 未知拒识率={unknown_reject*100:.2f}%')
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else:
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logger.warning('开放集评估数据不足，跳过阈值估计。')
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# 结果保存
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actual_epochs = len(train_losses)
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early_stopped = actual_epochs < num_epochs
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results = {
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'training_time': f'{elapsed/3600:.2f} 小时',
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'total_epochs': actual_epochs,
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'early_stopped': early_stopped,
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'early_stop_epoch': actual_epochs if early_stopped else None,
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'best_val_accuracy': best_val_acc,
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'best_val_loss': best_val_loss,
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'final_train_loss': train_losses[-1] if train_losses else None,
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'final_val_loss': val_losses[-1] if val_losses else None,
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'final_train_accuracy': train_accs[-1] if train_accs else None,
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'final_val_accuracy': val_accs[-1] if val_accs else None,
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'model_parameters': sum(p.numel() for p in list(model.parameters()) + list(head.parameters()) if p.requires_grad),
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'embedding_dim': cfg.embedding_dim,
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'learning_rate': cfg.lr,
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'batch_size': cfg.batch_size,
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's': s,
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|
'm': m,
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|
'patience': patience,
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|
'min_delta': min_delta,
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|
'min_epochs': min_epochs,
|
|
'device': str(device),
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|
'threshold_max_cos': threshold,
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|
'known_accept_rate_at_threshold': known_accept,
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|
'unknown_reject_rate_at_threshold': unknown_reject,
|
|
}
|
|
|
|
with open(os.path.join(save_dir, 'training_results.txt'), 'w', encoding='utf-8') as f:
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f.write('=== ResNet50 + CosFace 训练结果 ===\n\n')
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|
for k, v in results.items():
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|
f.write(f'{k}: {v}\n')
|
|
|
|
# 保存类别信息
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|
with open(os.path.join(save_dir, 'class_info.json'), 'w', encoding='utf-8') as f:
|
|
json.dump({'class_names': class_names, 'num_classes': num_classes, 'embedding_dim': cfg.embedding_dim}, f, ensure_ascii=False, indent=2)
|
|
|
|
logger.info(f'训练结果已保存到: {save_dir}')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
import argparse
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('task', choices=list(TASKS.keys()), nargs='?', default='dish')
|
|
# parser.add_argument('task', choices=list(TASKS.keys()), nargs='?', default='whole_ingredient')
|
|
parser.add_argument('--s', type=float, default=None, help='CosFace scale factor (默认使用任务配置值)')
|
|
parser.add_argument('--m', type=float, default=None, help='CosFace margin (默认使用任务配置值)')
|
|
# parser.add_argument('--epochs', type=int, default=60)
|
|
parser.add_argument('--epochs', type=int, default=100)
|
|
parser.add_argument('--patience', type=int, default=10, help='早停容忍轮数')
|
|
parser.add_argument('--min_delta', type=float, default=0.001, help='早停最小改善阈值')
|
|
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')
|
|
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)
|