Merge branch 'main' of https://huggingface.co/spaces/VatsalPatel18/HNSCC-MultiOmics-Risk-Feature-Extraction

app2.py

@@ -1,146 +0,0 @@
-import gradio as gr
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
-    return image
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
-
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
-                    step=1,
-                    value=2,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
-
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
-    )
-
-demo.queue().launch()

train.py

@@ -1,105 +0,0 @@
-import torch
-from torch.utils.data import DataLoader
-from torch_geometric.data import Batch
-from sklearn.model_selection import train_test_split
-import pickle
-
-from OmicsConfig import OmicsConfig
-from MultiOmicsGraphAttentionAutoencoderModel import MultiOmicsGraphAttentionAutoencoderModel
-from GATv2EncoderModel import GATv2EncoderModel
-from GATv2DecoderModel import GATv2DecoderModel
-from EdgeWeightPredictorModel import EdgeWeightPredictorModel
-
-def collate_graph_data(batch):
-    return Batch.from_data_list(batch)
-
-def create_data_loader(graph_data_dict, batch_size=1, shuffle=True):
-    graph_data = list(graph_data_dict.values())
-    return DataLoader(graph_data, batch_size=batch_size, shuffle=shuffle, collate_fn=collate_graph_data)
-
-# Load your data
-graph_data_dict = torch.load('data/graph_data_dictN.pth')
-
-# Split the data
-train_data, temp_data = train_test_split(list(graph_data_dict.items()), train_size=0.6, random_state=42)
-val_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42)
-
-# Convert lists back into dictionaries
-train_data = dict(train_data)
-val_data = dict(val_data)
-test_data = dict(test_data)
-
-# Define the configuration for the model
-autoencoder_config = OmicsConfig(
-    in_channels=17,
-    edge_attr_channels=1,
-    out_channels=1,
-    original_feature_size=17,
-    learning_rate=0.01,
-    num_layers=2,
-    edge_decoder_hidden_sizes=[128, 64],
-    edge_decoder_activations=['ReLU', 'ReLU']
-)
-
-# Initialize the model
-autoencoder_model = MultiOmicsGraphAttentionAutoencoderModel(autoencoder_config)
-
-# Create data loaders
-train_loader = create_data_loader(train_data, batch_size=4, shuffle=True)
-val_loader = create_data_loader(val_data, batch_size=4, shuffle=False)
-test_loader = create_data_loader(test_data, batch_size=4, shuffle=False)
-
-# Define the device
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-# Training process
-def train_autoencoder(autoencoder_model, train_loader, validation_loader, epochs, device):
-    autoencoder_model.to(device)
-    train_losses = []
-    val_losses = []
-
-    for epoch in range(epochs):
-        # Train
-        autoencoder_model.train()
-        train_loss, train_cosine_similarity = autoencoder_model.train_model(train_loader, device)
-        print(f"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, Train Cosine Similarity: {train_cosine_similarity:.4f}")
-        train_losses.append(train_loss)
-
-        # Validate
-        autoencoder_model.eval()
-        val_loss, val_cosine_similarity = autoencoder_model.validate(validation_loader, device)
-        print(f"Epoch {epoch+1}/{epochs}, Validation Loss: {val_loss:.4f}, Validation Cosine Similarity: {val_cosine_similarity:.4f}")
-        val_losses.append(val_loss)
-
-    # Save the trained encoder weights
-    trained_encoder_path = "lc_models/MultiOmicsAutoencoder/trained_encoder"
-    autoencoder_model.encoder.save_pretrained(trained_encoder_path)
-
-    # Save the trained decoder weights
-    trained_decoder_path = "lc_models/MultiOmicsAutoencoder/trained_decoder"
-    autoencoder_model.decoder.save_pretrained(trained_decoder_path)
-
-    # Save the trained edge weight predictor weights (if needed separately)
-    trained_edge_weight_predictor_path = "lc_models/MultiOmicsAutoencoder/trained_edge_weight_predictor"
-    autoencoder_model.decoder.edge_weight_predictor.save_pretrained(trained_edge_weight_predictor_path)
-
-    # Optionally save the entire autoencoder again if you want to have a complete package
-    trained_autoencoder_path = "lc_models/MultiOmicsAutoencoder/trained_autoencoder"
-    autoencoder_model.save_pretrained(trained_autoencoder_path)
-
-    return train_losses, val_losses
-
-# Train and save the model
-train_losses, val_losses = train_autoencoder(autoencoder_model, train_loader, val_loader, epochs=10, device=device)
-
-# Evaluate the model
-test_loss, test_accuracy = autoencoder_model.evaluate(test_loader, device)
-print(f"Test Loss: {test_loss:.4f}")
-print(f"Test Accuracy: {test_accuracy:.4%}")
-
-# Save the training and validation losses
-with open('./results/train_loss.pkl', 'wb') as f:
-    pickle.dump(train_losses, f)
-
-with open('./results/val_loss.pkl', 'wb') as f:
-    pickle.dump(val_losses, f)