Update clip_model.py

clip_model.py

@@ -1,330 +1,331 @@
-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
-
-
-
-class CFG:
-    debug = False
-    image_path = "/content/flickr30k_images/flickr30k_images"
-    captions_path = "/content"
-    batch_size = 32
-    num_workers = 2
-    head_lr = 1e-3
-    image_encoder_lr = 1e-4
-    text_encoder_lr = 1e-5
-    weight_decay = 1e-3
-    patience = 1
-    factor = 0.8
-    epochs = 4
-    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
-
-
-
-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"]
-
-
-
-
-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),
-            ]
-        )
-  
-
-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
-
-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()
-
-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
-
-
-
-
-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)
-
-
-
-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))
-
-    results = []
-    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")
-        results.append(image)
-    return results
-
-
-
-
-
-
-
-
-
-
-
-
+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
+
+
+
+class CFG:
+    debug = False
+    image_path = "/content/flickr30k_images/flickr30k_images"
+    captions_path = "/content"
+    batch_size = 32
+    num_workers = 2
+    head_lr = 1e-3
+    image_encoder_lr = 1e-4
+    text_encoder_lr = 1e-5
+    weight_decay = 1e-3
+    patience = 1
+    factor = 0.8
+    epochs = 4
+    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
+
+
+
+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"]
+
+
+
+
+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),
+            ]
+        )
+  
+
+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
+
+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()
+
+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
+
+
+
+
+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)
+
+
+
+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))
+
+    results = []
+    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")
+        results.append(image)
+    return results
+
+
+def clip_image_search(query_text,model,image_embeddings,
+             query=query_text,
+             image_filenames=valid_df['image'].values,
+             n=9 ):
+  # _, valid_df = make_train_valid_dfs()
+  # model, image_embeddings = get_image_embeddings(valid_df, "best.pt")
+  return find_matches(model,
+             image_embeddings,
+             query=query_text,
+             image_filenames=valid_df['image'].values,
+             n=9)