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utils/helper.py

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+
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+import jittor as jt
+from jittor import transform
+from jittor.optim import Adam, AdamW, SGD, Adan
+from PIL import Image
+from datetime import datetime
+from natsort import natsorted
+
+class EarlyStop(object):
+    """早停
+    1. 当模型的损失长时间不下降时，停止训练
+    2. 当模型的损失长时间增大时，也提前停止训练
+    """
+    def __init__(self, patience=7, delta=0.0001, patience_up=20):
+        self.patience = patience
+        self.delta = delta
+        self.counter = 0
+        self.counter_up = 0
+        self.last_loss = None
+        self.early_stop = False
+        self.patience_up = patience_up
+        
+    def __call__(self, loss):
+        """当输入的loss多次不下降或者上升的时候，返回True，正常时返回False
+
+        Args:
+            loss (float): 当前的损失值
+
+        Returns:
+            bool: 是否早停
+        """
+        if self.last_loss is None:
+            self.last_loss = loss
+            return False
+
+        # loss下降明显低于delta，当前清零
+        if loss < self.last_loss - self.delta:
+            self.counter = 0
+            self.counter_up = 0
+            self.last_loss = loss
+        
+        # loss上升明显高于delta，counter_up开始计数
+        elif loss > self.last_loss + self.delta:
+            self.counter_up += 1
+            if self.counter_up >= self.patience_up:
+                self.early_stop = True
+                return True
+        
+        # loss上升和下降均小于delta，在区间震荡，counter开始计数
+        else:
+            self.counter += 1
+            if self.counter >= self.patience:
+                self.early_stop = True
+                return True
+
+        return False
+    
+    
+def accuracy(model, dataloader, zeroshot_weights):
+    """计算模型的准确率"""
+    model.eval()
+    corrct = 0
+    total_count = 0
+    with jt.no_grad():
+        for i, batch in enumerate(dataloader):
+            images, targets, texts  = batch
+            total_count += len(images)
+            image_features = model.encode_image(images)
+            image_features = image_features / image_features.norm(dim=1, keepdim=True)
+            logits = (100 * image_features @ zeroshot_weights).softmax(dim=-1)
+            preds = jt.argmax(logits, dim=1)[0]
+            corrct += jt.equal(preds, targets).sum().item()
+    return corrct / total_count
+
+def get_current_date(end_time='day'):
+    # 获取当前日期时间对象
+    current_date = datetime.now()
+    # 格式化日期为月日时分格式
+    if end_time == 'day':
+        formatted_date = current_date.strftime("%m-%d")
+    elif end_time == 'minute':
+        formatted_date = current_date.strftime("%m-%d_%H:%M")
+    return formatted_date
+
+def get_save_path(given_path, optimizer):
+    """获取tensorboard日志/模型保存路径"""
+    # 文件保存路径如下：
+    # given_path/date/optimizer/version_x
+    
+    path = os.path.join(given_path, get_current_date(end_time='day'))
+    os.makedirs(path, exist_ok=True)
+    
+    try:
+        last_version = int(natsorted(os.listdir(path))[-1].split('_')[-1])
+        current_path = os.path.join(path, f'version_{last_version + 1}')
+        os.makedirs(current_path, exist_ok=True)
+    except IndexError:
+        current_path = os.path.join(path, 'version_0')
+        os.makedirs(current_path, exist_ok=True)
+    return current_path
+
+
+
+        
+
+def get_optimizer(args, model):
+    """根据输入参数获取优化器"""
+    if args.optimizer == 'Adam':
+        optimizer = Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay,
+                         betas=args.betas, eps=args.eps)
+        
+    elif args.optimizer == 'AdamW':
+        optimizer = AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay,
+                          betas=args.betas, eps=args.eps)
+
+    elif args.optimizer == 'Adan':
+        if len(args.betas) == 2:
+            raise ValueError('Adan optimizer requires betas has the shape like (0.9,0.98, 0.99)')
+        optimizer = Adan(model.parameters(), lr=args.lr, weight_decay=args.weight_decay,
+                         betas=args.betas, eps=args.eps)
+        
+    elif args.optimizer == 'SGD':
+        optimizer = SGD(model.parameters(), lr=args.lr, weight_decay=args.weight_decay,
+                        momentum=0.9)
+        
+    else:
+        raise ValueError('Unsupported optimizer, please check the optimizer name.')
+    
+    return optimizer
+
+def get_scheduler(optimizer, args):
+    """根据输入参数获取学习率调度器"""
+    pass
+
+def get_transform(args):
+    """根据输入参数获取数据预处理"""
+    if args.data_preprocess == 1:
+        transforms = transform.Compose([
+            transform.Resize(224, mode=Image.BICUBIC),
+            transform.CenterCrop(224), lambda image: image.convert("RGB"),
+            transform.ImageNormalize(mean=(0.48145466, 0.4578275, 0.40821073), 
+                                    std=(0.26862954, 0.26130258, 0.27577711))
+        ])
+        return transforms
+    elif args.data_preprocess == 2:
+        transforms = transform.Compose([
+            transform.Resize(224, mode=Image.BICUBIC),
+            transform.CenterCrop(224), lambda image: image.convert("RGB"),
+            transform.ColorJitter(brightness=0.2, contrast=0.3, saturation=0.4, hue=0.1),
+            transform.RandomRotation(10),
+            transform.RandomHorizontalFlip(),
+            transform.ImageNormalize(mean=(0.48145466, 0.4578275, 0.40821073), 
+                                    std=(0.26862954, 0.26130258, 0.27577711))])
+            
+
+def compute_loss(logits_image, logits_text):
+    """计算损失函数，用来建立文本与图像的语义关系，实现语义对其"""
+    ground_truth = jt.arange(len(logits_image), dtype=jt.int32)
+    loss = (jt.nn.cross_entropy_loss(logits_image, ground_truth) +\
+        jt.nn.cross_entropy_loss(logits_text, ground_truth)) / 2
+    return loss