Update app.py

app.py

@@ -1,57 +1,335 @@
-import streamlit as st
-import pickle
-import pandas as pd
+from torch import nn
+from tqdm.autonotebook import tqdm
+from transformers import AutoTokenizer, AutoModel
+from transformers import DistilBertModel, DistilBertConfig, DistilBertTokenizer
+import albumentations as A
+import cv2
+import timm
 import torch
-from PIL import Image
-import numpy as np
-from main import predict_caption, CLIPModel , get_text_embeddings
+import torch.nn.functional as F
+
+device = torch.device("cpu")
+
+class CFG:
+    debug = False
+    image_path = '/content/content/new_images_v5'
+    captions_path = '/content/content/all_data/new_caption.csv'
+    batch_size = 12
+    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 = 2
+    saved_model_clinical = '/content/content/new_weights.pt'
+    trained_model = 'clinical_bert_weights.pt'
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    model_name = 'resnet50'
+    image_embedding = 2048
+    text_encoder_model = "distilbert-base-uncased"
+    clinical_encoder_model = "emilyalsentzer/Bio_ClinicalBERT"
+    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
+
+
+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"]
 
 
-st.markdown(
+# Custom dataset object. Will tokenize text and apply transforms to images before yielding them.
+
+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.skippedImgCount = 0
+        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]}")
+        if image is None:
+            # Skip the current example and move to the next one
+            self.skippedImgCount += 1
+            return self.__getitem__((idx + 1) % len(self))
+
+        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
     """
-<style>
-    body {
-        background-color: transparent;
-    }
-</style>
-""",
-    unsafe_allow_html=True,
-)
 
+    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
 
-device = torch.device("cpu")
+    def forward(self, x):
+        return self.model(x)
 
-testing_df = pd.read_csv("testing_df.csv")
-model = CLIPModel().to(device)
-model.load_state_dict(torch.load("weights.pt", map_location=torch.device('cpu')))
-text_embeddings = torch.load('saved_text_embeddings.pt', map_location=device)
+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)
 
+            # Use Bio-ClinicalBERT
+            self.model = AutoModel.from_pretrained(CFG.clinical_encoder_model)
 
-def show_predicted_caption(image, index=0):
-    matches = predict_caption(
-        image, model, text_embeddings, testing_df["caption"]
-    )[index]
-    return matches
+        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
 
-st.title("Medical Image Captioning")
-st.write("Upload an image to get a caption:")
+    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, :]
+
+
+# Get both image and text encodings into a same size matrix
+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()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# INFERENCE CODE
+def get_image_embeddings(image):
+    # preprocess the image
+    if image is None:
+        print("Image not found!")
+        return None
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image = get_transforms("valid")(image=image)['image']
+    image = image.reshape(3, 224, 224)
+    model = CLIPModel().to(device)
+    model.load_state_dict(torch.load('weights.pt', map_location=device))
+    model.eval()
+
+    with torch.no_grad():
+        image_tensor = torch.from_numpy(image)
+        image_features = model.image_encoder(image_tensor.unsqueeze(0).to(device))
+        image_embeddings = model.image_projection(image_features)
+        image_embeddings = F.normalize(image_embeddings, p=2, dim=-1)
+
+    return image_embeddings
+
+
+def predict_caption(image, model, text_embeddings, captions, n=2):
+    # get the image embeddings
+    image_embeddings = get_image_embeddings(image)
+    if image_embeddings is None:
+        return None
+
+    # normalize the embeddings
+    image_embeddings_n = F.normalize(image_embeddings, p=2, dim=-1)
+    text_embeddings_n = F.normalize(text_embeddings, p=2, dim=-1)
+    # calculate the dot product of image and text embeddings
+    dot_similarity = image_embeddings_n @ text_embeddings_n.T
+
+    # get the top n matches
+    values, indices = torch.topk(dot_similarity.squeeze(0), n)
+    indices = indices.cpu().numpy().tolist()
+    matches = [captions[idx] for idx in indices]
+
+    return matches
 
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image", use_column_width=True)
-    st.write("")
+def get_text_embeddings(valid_df):
+    tokenizer = AutoTokenizer.from_pretrained(CFG.clinical_encoder_model)
+    valid_loader = build_loaders(valid_df, tokenizer, mode="valid")
 
-    if st.button("Generate Caption"):
-        with st.spinner("Generating caption..."):
-            image_np = np.array(image)
-            caption = show_predicted_caption(image_np)
-            st.success(f"Caption: {caption}")
+    model = CLIPModel().to(device)
+    model.load_state_dict(torch.load("weights.pt", map_location=device))
+    model.eval()
 
-    if st.button("Regenerate Caption"):
-        with st.spinner("Regenerating caption..."):
-            image_np = np.array(image)
-            caption = show_predicted_caption(image_np, index=1)
-            st.success(f"Caption: {caption}")
+    valid_text_embeddings = []
+    with torch.no_grad():
+        for batch in tqdm(valid_loader):
+            text_features = model.text_encoder(
+                input_ids=batch["input_ids"].to(device), attention_mask=batch["attention_mask"].to(device)
+            )
+            text_embeddings = model.text_projection(text_features)
+            valid_text_embeddings.append(text_embeddings)
 
-            
+    return model, torch.cat(valid_text_embeddings)