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        本文约2000字，建议阅读10分钟
        
         本文将使用PyTorch中从头开始实现CLIP模型，以便对CLIP有一个更好的理解。

在2021年1月，OpenAI宣布了两个新模型：DALL-E和CLIP，它们都是以某种方式连接文本和图像的多模态模型。CLIP全称是Contrastive Language–Image Pre-training，一种基于对比文本-图像对的预训练方法。为什么要介绍CLIP呢？因为现在大火得Stable Diffusion 并不是单一模型，而是多个模型组成。其中会用到一个 Text encoder 将用户的文本输入进行编码，这个 text encoder 就是 CLIP 模型中 text encoder。

CLIP模型在训练时，可以给它一个输入句子，并提取最相关的图像来配合它。CLIP学习了一个完整的句子和它所描述的图像之间的关系。也就是说它是在完整的句子上训练的，而不是像“汽车”、“狗”等离散的分类，这一点对于应用至关重要。当训练完整的短语时，模型可以学习更多的东西，并识别照片和文本之间的模式。他们还证明，当在相当大的照片和与之相对应的句子数据集上进行训练时，该模型是可以作为分类器的。CLIP在发布的时候能在无任何微调的情况下（zero-shot ），在 ImageNet 数据集上的分类表现超 ResNets-50 微调后的效果，也就是说他是非常有用的。

所以在本文中，我们将使用PyTorch中从头开始实现CLIP模型，以便我们对CLIP有一个更好的理解

这里就需要用到2个库：timm和transformers，我们先导入代码

 import os import cv2 import gc import numpy as np import pandas as pd import itertools from tqdm.autonotebook import tqdm import albumentations as A import matplotlib.pyplot as plt  import torch from torch import nn import torch.nn.functional as F import timm from transformers import DistilBertModel, DistilBertConfig, DistilBertTokenizer

下一步就是预处理数据和通用配置config。config是一个普通的python文件，我们将所有的超参数放在里面，如果使用Jupyter Notebook的情况下，它是一个在Notebook开头定义的类。

class CFG:    debug = False    image_path = "../input/flickr-image-dataset/flickr30k_images/flickr30k_images"    captions_path = "."    batch_size = 32    num_workers = 4    head_lr = 1e-3    image_encoder_lr = 1e-4    text_encoder_lr = 1e-5    weight_decay = 1e-3    patience = 1    factor = 0.8    epochs = 2    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")     model_name = 'resnet50'    image_embedding = 2048    text_encoder_model = "distilbert-base-uncased"    text_embedding = 768    text_tokenizer = "distilbert-base-uncased"    max_length = 200     pretrained = True # for both image encoder and text encoder    trainable = True # for both image encoder and text encoder    temperature = 1.0     # image size    size = 224     # for projection head; used for both image and text encoders    num_projection_layers = 1    projection_dim = 256    dropout = 0.1

还有一些我们自定义指标的辅助类：

class AvgMeter:    def __init__(self, name="Metric"):        self.name = name        self.reset()     def reset(self):        self.avg, self.sum, self.count = [0] * 3     def update(self, val, count=1):        self.count += count        self.sum += val * count        self.avg = self.sum / self.count     def __repr__(self):        text = f"{self.name}: {self.avg:.4f}"        return text  def get_lr(optimizer):    for param_group in optimizer.param_groups:        return param_group["lr"]

我们的目标是描述图像和句子。所以数据集必须同时返回句子和图像。所以需要使用DistilBERT标记器对句子(标题)进行标记，然后将标记id (input_ids)和注意掩码提供给DistilBERT。DistilBERT比BERT 模型要小，但是模型的结果都差不多，所以我们选择使用它。

下一步就是使用HuggingFace tokenizer进行标记化。在__init__中获得的tokenizer对象，将在模型运行时加载。标题被填充并截断到预定的最大长度。在加载相关图像之前，我们将在__getitem__中加载一个编码的标题，这是一个带有键input_ids和attention_mask的字典，并对其进行转换和扩充(如果有的话)。然后把它变成一个张量，并以“image”作为键存储在字典中。最后我们将标题的原始文本与关键字“标题”一起输入字典。

class CLIPDataset(torch.utils.data.Dataset):    def __init__(self, image_filenames, captions, tokenizer, transforms):        """        image_filenames and cpations must have the same length; so, if there are        multiple captions for each image, the image_filenames must have repetitive        file names        """         self.image_filenames = image_filenames        self.captions = list(captions)        self.encoded_captions = tokenizer(            list(captions), padding=True, truncation=True, max_length=CFG.max_length        )        self.transforms = transforms     def __getitem__(self, idx):        item = {            key: torch.tensor(values[idx])            for key, values in self.encoded_captions.items()        }         image = cv2.imread(f"{CFG.image_path}/{self.image_filenames[idx]}")        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)        image = self.transforms(image=image)['image']        item['image'] = torch.tensor(image).permute(2, 0, 1).float()        item['caption'] = self.captions[idx]         return item      def __len__(self):        return len(self.captions)    def get_transforms(mode="train"):    if mode == "train":        return A.Compose(            [                A.Resize(CFG.size, CFG.size, always_apply=True),                A.Normalize(max_pixel_value=255.0, always_apply=True),            ]        )    else:        return A.Compose(            [                A.Resize(CFG.size, CFG.size, always_apply=True),                A.Normalize(max_pixel_value=255.0, always_apply=True),            ]        )

图像和文本编码器：我们将使用ResNet50作为图像编码器。

class ImageEncoder(nn.Module):    """    Encode images to a fixed size vector    """     def __init__(        self, model_name=CFG.model_name, pretrained=CFG.pretrained, trainable=CFG.trainable    ):        super().__init__()        self.model = timm.create_model(            model_name, pretrained, num_classes=0, global_pool="avg"        )        for p in self.model.parameters():            p.requires_grad = trainable     def forward(self, x):        return self.model(x)

使用DistilBERT作为文本编码器。使用CLS令牌的最终表示来获得句子的整个表示。

class TextEncoder(nn.Module):    def __init__(self, model_name=CFG.text_encoder_model, pretrained=CFG.pretrained, trainable=CFG.trainable):        super().__init__()        if pretrained:            self.model = DistilBertModel.from_pretrained(model_name)        else:            self.model = DistilBertModel(config=DistilBertConfig())                     for p in self.model.parameters():            p.requires_grad = trainable         # we are using the CLS token hidden representation as the sentence's embedding        self.target_token_idx = 0     def forward(self, input_ids, attention_mask):        output = self.model(input_ids=input_ids, attention_mask=attention_mask)        last_hidden_state = output.last_hidden_state        return last_hidden_state[:, self.target_token_idx, :]

上面的代码已经将图像和文本编码为固定大小的向量(图像2048，文本768)，我们需要图像和文本具有相似的尺寸，以便能够比较它们，所以我们把2048维和768维向量投影到256维(projection_dim)，只有维度相同我们才能比较它们。

class ProjectionHead(nn.Module):    def __init__(        self,        embedding_dim,        projection_dim=CFG.projection_dim,        dropout=CFG.dropout    ):        super().__init__()        self.projection = nn.Linear(embedding_dim, projection_dim)        self.gelu = nn.GELU()        self.fc = nn.Linear(projection_dim, projection_dim)        self.dropout = nn.Dropout(dropout)        self.layer_norm = nn.LayerNorm(projection_dim)         def forward(self, x):        projected = self.projection(x)        x = self.gelu(projected)        x = self.fc(x)        x = self.dropout(x)        x = x + projected        x = self.layer_norm(x)        return x

所以最后我们的CLIP模型就是这样：

 class CLIPModel(nn.Module):    def __init__(        self,        temperature=CFG.temperature,        image_embedding=CFG.image_embedding,        text_embedding=CFG.text_embedding,    ):        super().__init__()        self.image_encoder = ImageEncoder()        self.text_encoder = TextEncoder()        self.image_projection = ProjectionHead(embedding_dim=image_embedding)        self.text_projection = ProjectionHead(embedding_dim=text_embedding)        self.temperature = temperature     def forward(self, batch):        # Getting Image and Text Features        image_features = self.image_encoder(batch["image"])        text_features = self.text_encoder(            input_ids=batch["input_ids"], attention_mask=batch["attention_mask"]        )        # Getting Image and Text Embeddings (with same dimension)        image_embeddings = self.image_projection(image_features)        text_embeddings = self.text_projection(text_features)         # Calculating the Loss        logits = (text_embeddings @ image_embeddings.T) / self.temperature        images_similarity = image_embeddings @ image_embeddings.T        texts_similarity = text_embeddings @ text_embeddings.T        targets = F.softmax(            (images_similarity + texts_similarity) / 2 * self.temperature, dim=-1        )        texts_loss = cross_entropy(logits, targets, reduction='none')        images_loss = cross_entropy(logits.T, targets.T, reduction='none')        loss = (images_loss + texts_loss) / 2.0 # shape: (batch_size)        return loss.mean()  #这里还加了一个交叉熵函数 def cross_entropy(preds, targets, reduction='none'):    log_softmax = nn.LogSoftmax(dim=-1)    loss = (-targets * log_softmax(preds)).sum(1)    if reduction == "none":        return loss    elif reduction == "mean":        return loss.mean()

这里需要说明下，CLIP使用 symmetric cross entropy 作为损失函数，可以降低噪音影响，提高模型鲁棒性，我们这里为了简单只是用cross entropy。

我们可以进行测试：

 # A simple Example  batch_size = 4 dim = 256 embeddings = torch.randn(batch_size, dim) out = embeddings @ embeddings.T print(F.softmax(out, dim=-1))

‍下一步就是训练了，有一些函数可以帮助我们加载训练和验证的dataloader：

def make_train_valid_dfs():    dataframe = pd.read_csv(f"{CFG.captions_path}/captions.csv")    max_id = dataframe["id"].max() + 1 if not CFG.debug else 100    image_ids = np.arange(0, max_id)    np.random.seed(42)    valid_ids = np.random.choice(        image_ids, size=int(0.2 * len(image_ids)), replace=False    )    train_ids = [id_ for id_ in image_ids if id_ not in valid_ids]    train_dataframe = dataframe[dataframe["id"].isin(train_ids)].reset_index(drop=True)    valid_dataframe = dataframe[dataframe["id"].isin(valid_ids)].reset_index(drop=True)    return train_dataframe, valid_dataframe   def build_loaders(dataframe, tokenizer, mode):    transforms = get_transforms(mode=mode)    dataset = CLIPDataset(        dataframe["image"].values,        dataframe["caption"].values,        tokenizer=tokenizer,        transforms=transforms,    )    dataloader = torch.utils.data.DataLoader(        dataset,        batch_size=CFG.batch_size,        num_workers=CFG.num_workers,        shuffle=True if mode == "train" else False,    )    return dataloader

然后就是训练和评估：

def train_epoch(model, train_loader, optimizer, lr_scheduler, step):    loss_meter = AvgMeter()    tqdm_object = tqdm(train_loader, total=len(train_loader))    for batch in tqdm_object:        batch = {k: v.to(CFG.device) for k, v in batch.items() if k != "caption"}        loss = model(batch)        optimizer.zero_grad()        loss.backward()        optimizer.step()        if step == "batch":            lr_scheduler.step()         count = batch["image"].size(0)        loss_meter.update(loss.item(), count)         tqdm_object.set_postfix(train_loss=loss_meter.avg, lr=get_lr(optimizer))    return loss_meter   def valid_epoch(model, valid_loader):    loss_meter = AvgMeter()     tqdm_object = tqdm(valid_loader, total=len(valid_loader))    for batch in tqdm_object:        batch = {k: v.to(CFG.device) for k, v in batch.items() if k != "caption"}        loss = model(batch)         count = batch["image"].size(0)        loss_meter.update(loss.item(), count)         tqdm_object.set_postfix(valid_loss=loss_meter.avg)    return loss_meter

最后整合起来就是全部流程：

def main():    train_df, valid_df = make_train_valid_dfs()    tokenizer = DistilBertTokenizer.from_pretrained(CFG.text_tokenizer)    train_loader = build_loaders(train_df, tokenizer, mode="train")    valid_loader = build_loaders(valid_df, tokenizer, mode="valid")      model = CLIPModel().to(CFG.device)    params = [        {"params": model.image_encoder.parameters(), "lr": CFG.image_encoder_lr},        {"params": model.text_encoder.parameters(), "lr": CFG.text_encoder_lr},        {"params": itertools.chain(            model.image_projection.parameters(), model.text_projection.parameters()        ), "lr": CFG.head_lr, "weight_decay": CFG.weight_decay}    ]    optimizer = torch.optim.AdamW(params, weight_decay=0.)    lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(        optimizer, mode="min", patience=CFG.patience, factor=CFG.factor    )    step = "epoch"     best_loss = float('inf')    for epoch in range(CFG.epochs):        print(f"Epoch: {epoch + 1}")        model.train()        train_loss = train_epoch(model, train_loader, optimizer, lr_scheduler, step)        model.eval()        with torch.no_grad():            valid_loss = valid_epoch(model, valid_loader)                 if valid_loss.avg < best_loss:            best_loss = valid_loss.avg            torch.save(model.state_dict(), "best.pt")            print("Saved Best Model!")                 lr_scheduler.step(valid_loss.avg)

应用：获取图像嵌入并找到匹配。

我们训练完成后如何实际应用呢？我们需要编写一个函数加载训练后的模型，为其提供验证集中的图像，并返回形状(valid_set_size, 256)和模型本身的image_embeddings。

def get_image_embeddings(valid_df, model_path):    tokenizer = DistilBertTokenizer.from_pretrained(CFG.text_tokenizer)    valid_loader = build_loaders(valid_df, tokenizer, mode="valid")         model = CLIPModel().to(CFG.device)    model.load_state_dict(torch.load(model_path, map_location=CFG.device))    model.eval()         valid_image_embeddings = []    with torch.no_grad():        for batch in tqdm(valid_loader):            image_features = model.image_encoder(batch["image"].to(CFG.device))            image_embeddings = model.image_projection(image_features)            valid_image_embeddings.append(image_embeddings)    return model, torch.cat(valid_image_embeddings) _, valid_df = make_train_valid_dfs() model, image_embeddings = get_image_embeddings(valid_df, "best.pt")  def find_matches(model, image_embeddings, query, image_filenames, n=9):    tokenizer = DistilBertTokenizer.from_pretrained(CFG.text_tokenizer)    encoded_query = tokenizer([query])    batch = {        key: torch.tensor(values).to(CFG.device)        for key, values in encoded_query.items()    }    with torch.no_grad():        text_features = model.text_encoder(            input_ids=batch["input_ids"], attention_mask=batch["attention_mask"]        )        text_embeddings = model.text_projection(text_features)         image_embeddings_n = F.normalize(image_embeddings, p=2, dim=-1)    text_embeddings_n = F.normalize(text_embeddings, p=2, dim=-1)    dot_similarity = text_embeddings_n @ image_embeddings_n.T         values, indices = torch.topk(dot_similarity.squeeze(0), n * 5)    matches = [image_filenames[idx] for idx in indices[::5]]         _, axes = plt.subplots(3, 3, figsize=(10, 10))    for match, ax in zip(matches, axes.flatten()):        image = cv2.imread(f"{CFG.image_path}/{match}")        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)        ax.imshow(image)        ax.axis("off")         plt.show()

调用方法如下：

 find_matches(model,              image_embeddings,              query="one dog sitting on the grass",              image_filenames=valid_df['image'].values,              n=9)

可以看到我们自定义效果还是不错的（但是图里面有个猫，哈）。也就是说CLIP这种方法在小数据集上自定义也是可行的。

以下是本文的代码和数据集：

https://www.kaggle.com/code/jyotidabas/simple-openai-clip-implementation

作者：Jyoti Dabass, Ph.D

编辑：黄继彦

在自定义数据集上实现OpenAI CLIP