在rk3588上部署InternVL2-1B

准备

首先要在hf上下载InternVL2-1B的模型传送门（镜像）

git clone https://hf-mirror.com/OpenGVLab/InternVL2-1B

还要安装rknn转换的必要软件，这里不多赘述

转换

将vision_model和mlp转为onnx的代码如下：

import numpy as npimport osimport torchimport torch.nn as nnfrom transformers import AutoTokenizer, AutoModelimport torch.nn.functional as Ffrom PIL import Imageimport torchvision.transforms as Tfrom torchvision.transforms import InterpolationModefrom transformers.modeling_utils import PreTrainedModelIMAGENET_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 transformdef 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_ratiodef 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_imagesdef 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 = './InternVL2-1B'save_path = 'onnx/InternVL2-1B_vision.onnx'image_file = 'InternVL2-1B/examples/image1.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.Sequential): def __init__(self, model): super().__init__() self.vision_town = model.vision_model self.mlp = model.mlp1 self.vision_mlp = model.extract_feature def forward(self, pixel_values): projected_features = self.vision_mlp(pixel_values) return projected_features # 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=16, # 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, 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__": os.makedirs('onnx', exist_ok=True) 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)

将onnx转为rknn的代码如下：

from rknn.api import RKNNimport numpy as npimport osmodel_path ="./onnx/InternVL2-1B_vision.onnx"target_platform ="rk3588"rknn = RKNN(verbose=False)rknn.config( target_platform=target_platform, mean_values=[[0.5 * 255, 0.5 * 255, 0.5 * 255]], std_values=[[0.5 * 255, 0.5 * 255, 0.5 * 255]], )rknn.load_onnx(model_path, inputs=['pixel_values'], input_size_list=[[1,3,448,448]])rknn.build(do_quantization=False, dataset=None)os.makedirs("rknn", exist_ok=True)rknn.export_rknn("./rknn/" + model_path.split("/")[-1].split(".")[0] +"_{}.rknn".format(target_platform))

语言模型参考rkllm的Qwen模型转换

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