export_vision_onnx.py

import numpy as np
import os
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModel
import torch.nn.functional as F
from PIL import Image
import torchvision.transforms as T
from torchvision.transforms import InterpolationMode
from transformers.modeling_utils import PreTrainedModel

IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)

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

def load_image(image_file, input_size=448, max_num=12):
    image = Image.open(image_file).convert('RGB')
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values

# 加载本地模型
path = '.'
save_path = 'vision_encoder.onnx'
image_file = 'test.jpg'

def export_vision_InternVL(model_path: str, save_path: str):
    """
    Export the vision encoder and projector of Janus-Pro-1B model to ONNX format
    """
    # 设置默认数据类型为 float32
    torch.set_default_dtype(torch.float32)

    vl_gpt = AutoModel.from_pretrained(model_path,torch_dtype = torch.float32,trust_remote_code=True)

    # Move model to CPU and convert to float32
    vl_gpt = vl_gpt.cpu().eval().float()  # 确保模型是 float32
    
    # Create a wrapper class for vision encoder + projector
    class VisionWrapper(nn.Module):
        def __init__(self, model: PreTrainedModel):
            super().__init__()
            self.vision_model = model

        def forward(self, pixel_values: torch.FloatTensor) -> torch.FloatTensor:
            # Delegate to the built-in helper so we stay consistent with Transformers' implementation.
            return self.vision_model.get_image_features(pixel_values=pixel_values)
            
    # Create wrapper instance and convert to float32
    vision_wrapper = VisionWrapper(vl_gpt)
    vision_wrapper.eval().float()  # 确保包装器也是 float32
    
    # Create dummy input with float32
    batch_size = 1
    num_channels = 3
    height = 448  # InternVL2 default image size
    width = 448
    # dummy_input = load_image(image_file=image_file, max_num=12).to(torch.float32).cpu()
    dummy_input = torch.randn(batch_size, num_channels, height, width, dtype=torch.float32)
    # Export to ONNX with higher opset version
    torch.onnx.export(
        vision_wrapper,
        dummy_input,
        save_path,
        export_params=True,
        opset_version=17,  # 使用高版本 opset 以支持 scaled_dot_product_attention
        do_constant_folding=True,
        input_names=['pixel_values'],
        output_names=['projected_features'],
        dynamic_axes={
            'pixel_values': {0: 'batch_size'},
            'projected_features': {0: 'batch_size'}
        },
        # 添加额外的配置
        # operator_export_type=torch.onnx.OperatorExportTypes.ONNX,
        # training=torch.onnx.TrainingMode.EVAL,
        dynamo=True,
        verbose=False
    )
    
    print(f"Successfully exported vision components to {save_path}")
    
    # Verify the exported model
    import onnxruntime
    
    # Create inference session
    ort_session = onnxruntime.InferenceSession(save_path)
    
    # Run inference with dummy input
    ort_inputs = {
        'pixel_values': dummy_input.numpy()
    }
    ort_outputs = ort_session.run(None, ort_inputs)
    
    # Compare with PyTorch output
    torch_output = vision_wrapper(dummy_input)
    
    # Check numerical accuracy with更宽松的容忍度
    import numpy as np
    np.testing.assert_allclose(
        torch_output.detach().numpy(), 
        ort_outputs[0], 
        rtol=1e-1,  # 放宽相对误差容忍度
        atol=1e-2   # 放宽绝对误差容忍度
    )
    
    print("ONNX model verification successful!")
    
    # 打印一些统计信息
    torch_output_np = torch_output.detach().numpy()
    onnx_output_np = ort_outputs[0]
    
    abs_diff = np.abs(torch_output_np - onnx_output_np)
    rel_diff = np.abs((torch_output_np - onnx_output_np) / (torch_output_np + 1e-7))
    
    print(f"\nValidation Statistics:")
    print(f"Max absolute difference: {np.max(abs_diff):.6f}")
    print(f"Mean absolute difference: {np.mean(abs_diff):.6f}")
    print(f"Max relative difference: {np.max(rel_diff):.6f}")
    print(f"Mean relative difference: {np.mean(rel_diff):.6f}")

if __name__ == "__main__":
    try:
        import onnx
        try:
            onnx_version = onnx.__version__
        except AttributeError:
            try:
                onnx_version = onnx.version.version
            except AttributeError:
                onnx_version = "Unknown"
        print(f"ONNX version: {onnx_version}")
    except ImportError:
        print("ONNX not installed")
        
    import onnxruntime
    print(f"ONNX Runtime version: {onnxruntime.__version__}")
    
    export_vision_InternVL(path, save_path)