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app.py

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+import os
+import sys
+import math
+import numpy as np
+import torch
+import torchvision.transforms as T
+from torchvision.transforms.functional import InterpolationMode
+from PIL import Image
+import gradio as gr
+from transformers import AutoModel, AutoTokenizer
+
+
+# Enhanced debug printing
+import logging
+import traceback
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[logging.StreamHandler()]
+)
+logger = logging.getLogger("InternVL2.5-Debug")
+
+# Print environment info
+logger.info("Python version: %s", sys.version)
+logger.info("PyTorch version: %s", torch.__version__)
+logger.info("Transformers version: %s", __import__("transformers").__version__)
+try:
+    logger.info("Einops version: %s", __import__("einops").__version__)
+except ImportError:
+    logger.error("Einops is not installed!")
+# Constants
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+# Configuration
+MODEL_NAME = "OpenGVLab/InternVL2_5-8B"  # Smaller model for faster loading
+IMAGE_SIZE = 448
+
+# Set up environment variables
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
+
+# Utility functions for image processing
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+# Load and preprocess image for the model - following the official documentation pattern
+def load_image(image_pil, max_num=12):
+    # Process the image using dynamic_preprocess
+    processed_images = dynamic_preprocess(image_pil, image_size=IMAGE_SIZE, max_num=max_num)
+    
+    # Convert PIL images to tensor format expected by the model
+    transform = build_transform(IMAGE_SIZE)
+    pixel_values = [transform(img) for img in processed_images]
+    pixel_values = torch.stack(pixel_values)
+    
+    # Convert to appropriate data type
+    if torch.cuda.is_available():
+        pixel_values = pixel_values.cuda().to(torch.bfloat16)
+    else:
+        pixel_values = pixel_values.to(torch.float32)
+        
+    return pixel_values
+
+# Function to split model across GPUs
+def split_model(model_name):
+    device_map = {}
+    world_size = torch.cuda.device_count()
+    if world_size <= 1:
+        return "auto"
+    
+    num_layers = {
+        'InternVL2_5-1B': 24, 
+        'InternVL2_5-2B': 24, 
+        'InternVL2_5-4B': 36, 
+        'InternVL2_5-8B': 32,
+        'InternVL2_5-26B': 48, 
+        'InternVL2_5-38B': 64, 
+        'InternVL2_5-78B': 80
+    }[model_name]
+    
+    # Since the first GPU will be used for ViT, treat it as half a GPU.
+    num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5))
+    num_layers_per_gpu = [num_layers_per_gpu] * world_size
+    num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5)
+    layer_cnt = 0
+    for i, num_layer in enumerate(num_layers_per_gpu):
+        for j in range(num_layer):
+            device_map[f'language_model.model.layers.{layer_cnt}'] = i
+            layer_cnt += 1
+    device_map['vision_model'] = 0
+    device_map['mlp1'] = 0
+    device_map['language_model.model.tok_embeddings'] = 0
+    device_map['language_model.model.embed_tokens'] = 0
+    device_map['language_model.model.rotary_emb'] = 0
+    device_map['language_model.output'] = 0
+    device_map['language_model.model.norm'] = 0
+    device_map['language_model.lm_head'] = 0
+    device_map[f'language_model.model.layers.{num_layers - 1}'] = 0
+
+    return device_map
+
+# Get model dtype
+def get_model_dtype():
+    return torch.bfloat16 if torch.cuda.is_available() else torch.float32
+
+# Model loading function
+def load_model():
+    print(f"\n=== Loading {MODEL_NAME} ===")
+    print(f"CUDA available: {torch.cuda.is_available()}")
+    
+    model_dtype = get_model_dtype()
+    print(f"Using model dtype: {model_dtype}")
+    
+    if torch.cuda.is_available():
+        print(f"GPU count: {torch.cuda.device_count()}")
+        for i in range(torch.cuda.device_count()):
+            print(f"GPU {i}: {torch.cuda.get_device_name(i)}")
+            
+        # Memory info
+        print(f"Total GPU memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")
+        print(f"Allocated GPU memory: {torch.cuda.memory_allocated() / 1e9:.2f} GB")
+        print(f"Reserved GPU memory: {torch.cuda.memory_reserved() / 1e9:.2f} GB")
+    
+    # Determine device map
+    device_map = "auto"
+    if torch.cuda.is_available() and torch.cuda.device_count() > 1:
+        model_short_name = MODEL_NAME.split('/')[-1]
+        device_map = split_model(model_short_name)
+    
+    # Load model and tokenizer
+    try:
+        model = AutoModel.from_pretrained(
+            MODEL_NAME,
+            torch_dtype=model_dtype,
+            low_cpu_mem_usage=True,
+            trust_remote_code=True,
+            device_map=device_map
+        )
+        
+        tokenizer = AutoTokenizer.from_pretrained(
+            MODEL_NAME,
+            use_fast=False,
+            trust_remote_code=True
+        )
+        
+        print(f"✓ Model and tokenizer loaded successfully!")
+        return model, tokenizer
+    except Exception as e:
+        logger.error(f"❌ Error loading model: {e}")
+        logger.error("Detailed traceback:")
+        import traceback
+        traceback.print_exc()
+        
+        # Check if einops is available
+        try:
+            import einops
+            logger.info(f"einops is available, version: {einops.__version__}")
+        except ImportError:
+            logger.error("ImportError: einops is not installed! This is required for InternVL2.5.")
+            
+        # Check for CUDA availability
+        if torch.cuda.is_available():
+            logger.info(f"CUDA is available. Device count: {torch.cuda.device_count()}")
+            for i in range(torch.cuda.device_count()):
+                logger.info(f"Device {i}: {torch.cuda.get_device_name(i)}")
+                logger.info(f"Memory allocated: {torch.cuda.memory_allocated(i) / 1e9:.2f} GB")
+                logger.info(f"Memory reserved: {torch.cuda.memory_reserved(i) / 1e9:.2f} GB")
+        else:
+            logger.warning("CUDA is not available. Running on CPU.")        return None, None
+
+# Image analysis function using the chat method from documentation
+def analyze_image(model, tokenizer, image, prompt):
+    try:
+        # Check if image is valid
+        if image is None:
+            return "Please upload an image first."
+        
+        # Process the image following official pattern
+        pixel_values = load_image(image)
+        
+        # Debug info
+        print(f"Image processed: tensor shape {pixel_values.shape}, dtype {pixel_values.dtype}")
+        
+        # Define generation config
+        generation_config = {
+            "max_new_tokens": 512,
+            "do_sample": False
+        }
+        
+        # Use the model.chat method as shown in the official documentation
+        question = f"<image>\n{prompt}"
+        response, _ = model.chat(
+            tokenizer=tokenizer,
+            pixel_values=pixel_values,
+            question=question,
+            generation_config=generation_config,
+            history=None,
+            return_history=True
+        )
+        
+        return response
+    except Exception as e:
+        import traceback
+        error_msg = f"Error analyzing image: {str(e)}\n{traceback.format_exc()}"
+        return error_msg
+
+# Main function
+def main():
+    # Add debug info at the start of main
+    logger.info("Starting main() function...")
+    logger.info(f"MODEL_NAME: {MODEL_NAME}")
+    
+    # Load the model
+    model, tokenizer = load_model()
+    
+    if model is None:
+        # Create an error interface if model loading failed
+        demo = gr.Interface(
+            fn=lambda x: "Model loading failed. Please check the logs for details.",
+            inputs=gr.Textbox(),
+            outputs=gr.Textbox(),
+            title="InternVL2.5 Image Analyzer - Error",
+            description="The model failed to load. Please check the logs for more information."
+        )
+        return demo
+    
+    # Predefined prompts for analysis
+    prompts = [
+        "Describe this image in detail.",
+        "What can you tell me about this image?",
+        "Is there any text in this image? If so, can you read it?",
+        "What is the main subject of this image?", 
+        "What emotions or feelings does this image convey?",
+        "Describe the composition and visual elements of this image.",
+        "Summarize what you see in this image in one paragraph."
+    ]
+    
+    # Create the interface
+    demo = gr.Interface(
+        fn=lambda img, prompt: analyze_image(model, tokenizer, img, prompt),
+        inputs=[
+            gr.Image(type="pil", label="Upload Image"),
+            gr.Dropdown(choices=prompts, value=prompts[0], label="Select a prompt or write your own below", 
+                        allow_custom_value=True)
+        ],
+        outputs=gr.Textbox(label="Analysis Results", lines=15),
+        title="InternVL2.5 Image Analyzer",
+        description="Upload an image and ask the InternVL2.5 model to analyze it.",
+        examples=[
+            ["example_images/example1.jpg", "Describe this image in detail."],
+            ["example_images/example2.jpg", "What can you tell me about this image?"]
+        ],
+        theme=gr.themes.Soft(),
+        allow_flagging="never"
+    )
+    
+    return demo
+
+# Run the application
+if __name__ == "__main__":
+    try:
+        # Check for GPU
+        if not torch.cuda.is_available():
+            print("WARNING: CUDA is not available. The model requires a GPU to function properly.")
+            
+        # Create and launch the interface
+        demo = main()
+        demo.launch(server_name="0.0.0.0")
+    except Exception as e:
+        print(f"Error starting the application: {e}")
+        import traceback
+        traceback.print_exc()