Initial commi

.gitignore

+model/
+__pycache__/
+papers/
\ No newline at end of file

clip.py

+from config import CFG
+from encoders import *
+import torch.nn.functional as F
+
+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()
+
+
+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()
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config.py

+import torch
+
+class CFG:
+    debug = False
+    image_path = "/home/saswat/Desktop/flickr30k_images/flickr30k_images/"
+    captions_path = "."
+    batch_size = 8
+    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
+
+    model_path = "./model/best.pt"
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dataset.py

+import torch
+from config import CFG
+import cv2
+import albumentations as A
+
+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),
+            ]
+        )
\ No newline at end of file

encoders.py

+from torch import nn
+from config import CFG
+import timm
+from transformers import DistilBertModel, DistilBertConfig
+
+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)
+
+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, :]
+    
+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

infer.py

+from transformers import DistilBertTokenizer
+import torch
+from utils import *
+from clip import CLIPModel
+from tqdm import tqdm
+from config import CFG
+import matplotlib.pyplot as plt
+import cv2
+import torch.nn.functional as F
+
+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, "./model/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()
+
+find_matches(model, 
+             image_embeddings,
+             query="boy is playing",
+             image_filenames=valid_df['image'].values,
+             n=9)
+

train.py

+import torch
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+from config import CFG
+import itertools
+from clip import CLIPModel
+from utils import AvgMeter, get_lr
+from dataset import CLIPDataset, get_transforms
+from transformers import DistilBertTokenizer
+import torch.nn.functional as F
+from utils import *
+
+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
+
+
+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(), CFG.model_path)
+            print("Saved Best Model!")
+        
+        lr_scheduler.step(valid_loss.avg)
+
+main()

utils.py

+import numpy as np
+import pandas as pd
+from dataset import *
+import torch.nn.functional as F
+
+def generate_context():
+    df = pd.read_csv(CFG.image_path+"/results.csv", delimiter="|")
+    df.columns = ['image', 'caption_number', 'caption']
+    df['caption'] = df['caption'].str.lstrip()
+    df['caption_number'] = df['caption_number'].str.lstrip()
+    df.loc[19999, 'caption_number'] = "4"
+    df.loc[19999, 'caption'] = "A dog runs across the grass ."
+    ids = [id_ for id_ in range(len(df) // 5) for i in range(5)]
+    df['id'] = ids
+    df.to_csv("captions.csv", index=False)
+    df.head()
+
+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
+
+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"]