[Bug]: openvino.tools.ovc.error.Error: Unknown model type: <class 'onnx.onnx_ml_pb2.ModelProto'>

[huangqiu15444]

OpenVINO Version

openvino 2024.3.0 + onnx 1.14.0

Operating System

Ubuntu 20.04 (LTS)

Device used for inference

CPU

Framework

ONNX

Model used

resnet18

Issue description

Model has been converted to ONNX format and saved as mobilenet_v2.onnx WARNING:nncf:ONNX models with 10 < opset version < 13 do not support per-channel quantization. Per-tensor quantization will be applied. Statistics collection ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 1/1 • 0:00:00 • 0:00:00 Applying Fast Bias correction ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 21/21 • 0:00:01 • 0:00:00 Traceback (most recent call last): File "nncf_tool.py", line 145, in ov_quantized_model = ov.convert_model(quantized_model) File "/home/huangq/anaconda3/envs/py38/lib/python3.8/site-packages/openvino/tools/ovc/convert.py", line 100, in convert_model ov_model, _ = _convert(cli_parser, params, True) File "/home/huangq/anaconda3/envs/py38/lib/python3.8/site-packages/openvino/tools/ovc/convert_impl.py", line 548, in _convert raise e File "/home/huangq/anaconda3/envs/py38/lib/python3.8/site-packages/openvino/tools/ovc/convert_impl.py", line 448, in _convert model_framework = check_model_object(args) File "/home/huangq/anaconda3/envs/py38/lib/python3.8/site-packages/openvino/tools/ovc/convert_impl.py", line 241, in check_model_object raise Error('Unknown model type: {}'.format(type(model))) openvino.tools.ovc.error.Error: Unknown model type: <class 'onnx.onnx_ml_pb2.ModelProto'>

Step-by-step reproduction

if name == 'main': #onnx_quantization() import torch import torch.nn as nn import torchvision.models as models import onnx

# 1. 创建 MobileNetV2 模型
model = models.resnet18(pretrained=True)  # 使用预训练的 MobileNetV2 模型

# 2. 设置模型为评估模式
model.eval()

# 3. 创建一个虚拟输入张量
# 假设输入图像的大小为 224x224，批量大小为 1
dummy_input = torch.randn(1, 3, 224, 224)

# 4. 定义 ONNX 导出路径
onnx_file_path = "mobilenet_v2.onnx"

# 5. 导出模型到 ONNX 格式
torch.onnx.export(
    model,                # 要导出的模型
    dummy_input,          # 输入张量
    onnx_file_path,       # 导出的 ONNX 文件路径
    verbose=True,         # 是否打印导出过程的信息
    input_names=['input'],# 输入张量的名称
    output_names=['output'], # 输出张量的名称
    opset_version=12      # ONNX 操作集版本
)

print(f"Model has been converted to ONNX format and saved as {onnx_file_path}")


from torchvision import datasets, transforms

# Instantiate your uncompressed model
onnx_model = onnx.load_model(onnx_file_path)

# Provide validation part of the dataset to collect statistics needed for the compression algorithm
val_dataset = datasets.ImageFolder("/home/huangq/nfs/common_train_tools/qunat/", transform=transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()]))
dataset_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1)

# Step 1: Initialize transformation function
input_name = onnx_model.graph.input[0].name
def transform_fn(data_item):
    images, _ = data_item
    return {input_name: images.numpy()}

# Step 2: Initialize NNCF Dataset
calibration_dataset = nncf.Dataset(dataset_loader, transform_fn)
# Step 3: Run the quantization pipeline
quantized_model = nncf.quantize(onnx_model, calibration_dataset)

ov_quantized_model = ov.convert_model(quantized_model)

Relevant log output

No response

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