Revized docker file

.ipynb_checkpoints/Dockerfile-checkpoint

@@ -0,0 +1,30 @@
+# Use an official Python runtime as a parent image
+FROM python:3.8-slim-buster
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Install system and Python dependencies
+RUN apt-get update && \
+    apt-get install -y build-essential openslide-tools libgl1-mesa-glx && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+# Add a non-root user with a specified UID
+ARG USER_ID
+RUN adduser --disabled-password --gecos '' --uid $USER_ID myuser
+
+# Copy the entire genomic_plip_model directory contents into the container at /app
+COPY ./ /app/
+
+# Install Python dependencies
+RUN pip install -r requirements.txt
+
+# Set the user to the newly created non-root user
+USER myuser
+
+# Expose a port (if necessary for your application)
+EXPOSE 8888
+
+# Set the entrypoint to a shell command
+ENTRYPOINT ["/bin/bash"]

.ipynb_checkpoints/requirements-checkpoint.txt

@@ -0,0 +1,12 @@
+numpy==1.19.2
+pandas==1.3.4
+matplotlib==3.5.2
+openslide-python==1.1.2
+scikit-image==0.18.1
+scikit-learn==1.2.1
+tqdm==4.62.3
+Pillow==9.4.0 
+transformers==4.33.2
+torch==2.0.1  
+jupyterlab==3.2.1
+tensorflow==2.6.1

.ipynb_checkpoints/train_omics_plip_model-checkpoint.py

@@ -0,0 +1,89 @@
+import torch
+import argparse
+from torch import optim
+from torch.utils.data import DataLoader
+from scripts.genomic_plip_model import GenomicPLIPModel
+from scripts.tile_file_dataloader import FlatTileDataset
+from transformers import CLIPVisionModel
+
+def train_model(data_dir, model_save_path, pretrained_model_path, lr, num_epochs, train_batch_size, validation_batch_size, num_workers):
+
+    # Load datasets   
+    train_dataset = FlatTileDataset(data_dir=f'{data_dir}/train')
+    train_data_loader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True, num_workers=num_workers)
+
+    validation_dataset = FlatTileDataset(data_dir=f'{data_dir}/validate')
+    validation_data_loader = DataLoader(validation_dataset, batch_size=validation_batch_size, shuffle=False, num_workers=num_workers)
+
+    # Initialize the model
+    base_model = CLIPVisionModel.from_pretrained(pretrained_model_path)
+    custom_model = GenomicPLIPModel(base_model)
+
+    criterion = torch.nn.CosineSimilarity(dim=1)
+    optimizer = optim.Adam(custom_model.parameters(), lr=lr)
+
+
+    for epoch in range(num_epochs):
+        # Training loop
+        custom_model.train()
+        train_loss = 0.0
+
+        for batch_images, batch_scores in train_data_loader:
+            optimizer.zero_grad()
+
+            batch_loss = 0
+            for img, score in zip(batch_images, batch_scores):
+                vision_features, score_features = custom_model(img.unsqueeze(0), score.unsqueeze(0))
+                cos_sim = criterion(score_features, vision_features)
+                loss = 1-cos_sim.mean()
+
+                batch_loss += loss.item()
+                loss.backward()
+
+            optimizer.step()
+            train_loss += batch_loss
+            print(f"Batch Cosine Similarity {batch_loss:.4f}")
+
+        avg_train_loss = train_loss / len(train_data_loader)
+        print(f"Epoch [{epoch+1}/{num_epochs}], Training Cosine Similarity: {avg_train_loss:.4f}")
+
+        # Validation loop
+        custom_model.eval()
+        validation_loss = 0.0
+
+        with torch.no_grad():
+            for batch_images, batch_scores in validation_data_loader:
+                batch_loss = 0
+                for img, score in zip(batch_images, batch_scores):
+                    vision_features, score_features = custom_model(img.unsqueeze(0), score.unsqueeze(0))
+                    cos_sim = criterion(score_features, vision_features)
+                    loss = 1-cos_sim.mean()
+
+                    batch_loss += loss.item()
+
+                validation_loss += batch_loss
+                print(f"Validation Batch Cosine Similarity {batch_loss:.4f}")
+
+            avg_validation_loss = validation_loss / len(validation_data_loader)
+            print(f"Epoch [{epoch+1}/{num_epochs}], Validation Cosine Similarity: {avg_validation_loss:.4f}")
+
+    # Save the trained model
+    torch.save(custom_model.state_dict(), model_save_path)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Train the Genomic PLIP Model')
+    parser.add_argument('--data_dir', type=str, default='Datasets/train_03', help='Directory containing the train, validate, and test datasets.')
+    parser.add_argument('--model_save_path', type=str, default='genomic_plip.pth', help='Path to save the trained model.')
+    parser.add_argument('--pretrained_model_path', type=str, default='./plip', help='Path to the pretrained CLIP model.')
+    
+    parser.add_argument('--lr', type=float, default=0.00001, help='Learning rate for the optimizer.')
+    parser.add_argument('--num_epochs', type=int, default=1, help='Number of epochs to train for.')
+    parser.add_argument('--train_batch_size', type=int, default=128, help='Batch size for the training data loader.')
+    parser.add_argument('--validation_batch_size', type=int, default=128, help='Batch size for the validation data loader.')
+    parser.add_argument('--num_workers', type=int, default=32, help='Number of worker threads for data loading.')
+
+
+    args = parser.parse_args()
+
+    train_model(args.data_dir, args.model_save_path, args.pretrained_model_path, args.lr, args.num_epochs, args.train_batch_size, args.validation_batch_size, args.num_workers)
+

Dockerfile

@@ -10,16 +10,20 @@ RUN apt-get update && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
-# Copy the entire genomic_plip_model directory contents into the container at /app
-RUN adduser --disabled-password --gecos '' myuser
-USER myuser
+# Add a non-root user with a specified UID
+ARG USER_ID
+RUN adduser --disabled-password --gecos '' --uid $USER_ID myuser
 
+# Copy the entire genomic_plip_model directory contents into the container at /app
 COPY ./ /app/
+
 # Install Python dependencies
-RUN pip install --no-cache-dir -r requirements.txt
+RUN pip install -r requirements.txt
 
-# Create a non-root user and switch to it for security
+# Set the user to the newly created non-root user
+USER myuser
 
+# Expose a port (if necessary for your application)
 EXPOSE 8888
 
 # Set the entrypoint to a shell command

train_omics_plip_model.py

@@ -35,7 +35,7 @@ def train_model(data_dir, model_save_path, pretrained_model_path, lr, num_epochs
             for img, score in zip(batch_images, batch_scores):
                 vision_features, score_features = custom_model(img.unsqueeze(0), score.unsqueeze(0))
                 cos_sim = criterion(score_features, vision_features)
-                loss = -cos_sim.mean()
+                loss = 1-cos_sim.mean()
 
                 batch_loss += loss.item()
                 loss.backward()
@@ -57,7 +57,7 @@ def train_model(data_dir, model_save_path, pretrained_model_path, lr, num_epochs
                 for img, score in zip(batch_images, batch_scores):
                     vision_features, score_features = custom_model(img.unsqueeze(0), score.unsqueeze(0))
                     cos_sim = criterion(score_features, vision_features)
-                    loss = -cos_sim.mean()
+                    loss = 1-cos_sim.mean()
 
                     batch_loss += loss.item()