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。