<think>
Let me analyze this code and provide a helpful response. This appears to be a Python script for training a convolutional neural network (CNN) on the
MNIST handwritten digit dataset using PyTorch, and then exporting the trained
model to ONNX format.
The code consists of several parts:
Importing necessary libraries and setting hyperparameters
Preparing the
MNIST dataset with transformations
Visualizing some examples from the
dataset
Defining a CNN
model architecture using PyTorch
Setting up loss function and optimizer
Defining training and testing functions
Training the
model for 10 epochs and saving it
Exporting the trained
model to ONNX format
At the end, the user mentions they want to convert their ONNX
model to RKNN format but are having version discrepancies. They're using rknn-toolkit2 version 2.3.0 in their code, but on their Ubuntu system they have rknn-toolkit2 version 1.6.0 with Python 3.10.
I'll focus on providing a solution for converting their ONNX
model to RKNN format, considering the version discrepancy they're experiencing.
Let me first understand the
model architecture better:
Input:
MNIST images (1 channel, 28x28 pixels)
First convolutional layer: 1→10 channels, 5x5 kernel, followed by ReLU and 2x2 max pooling
Second convolutional layer: 10→20 channels, 5x5 kernel, followed by ReLU and 2x2 max pooling
Fully connected layers: 320→50→10 (output classes for
digits 0-9)
The exported ONNX
model uses:
Input shape: [1, 1, 28, 28]
Output shape: [1, 10] (representing probabilities for
digits 0-9)
ONNX opset version: 19
Now, the issue is the version discrepancy between the rknn-toolkit2 they're using in their code (2.3.0) and what's installed on their Ubuntu system (1.6.0). I need to provide code that will work with their installed version (1.6.0) to convert the ONNX
model to RKNN format.
</think>
I see you've shared a PyTorch script for training a CNN
model on the
MNIST dataset, followed by ONNX conversion. You mentioned that you need to convert this ONNX
model to RKNN format, but there's a version mismatch between the rknn-toolkit2 in your code (2.3.0) and what's installed on your Ubuntu system (1.6.0).
Let me create a compatible RKNN conversion script for your system:
<artifact id="rknn_conversion_script" type="application/vnd.ant.code" language="python" title="
MNIST ONNX to RKNN Conversion Script"> from rknn.api import RKNN
Configuration
ONNX_
MODEL = './
model_
Mnist_fixed.onnx'
RKNN_
MODEL = './
model_
Mnist.rknn'
Initialize RKNN object
rknn = RKNN(verbose=True)
Pre-process config for input
print('--> Config
model')
rknn.config(mean_values=[127.5], std_values=[127.5], target_platform='rk3588')
print('done')
Load ONNX
model
print('--> Loading
model')
ret = rknn.load_onnx(
model=ONNX_
MODEL)
if ret != 0:
print('Load ONNX
model failed!')
exit(ret)
print('done')
Build
model
print('--> Building
model')
ret = rknn.build(do_quantization=True,
dataset='./
dataset.txt')
if ret != 0:
print('Build
model failed!')
exit(ret)
print('done')
Export RKNN
model
print('--> Export RKNN
model')
ret = rknn.export_rknn(RKNN_
MODEL)
if ret != 0:
print('Export RKNN
model failed!')
exit(ret)
print('done')
Optional: Inference testing
print('--> Testing
model on sample data')
Set inputs
img = cv2.imread('sample_
digit.png', cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (28, 28))
img = np.expand_dims(img, axis=0) # Add batch dimension
img = np.expand_dims(img, axis=0) # Add channel dimension
Init runtime environment
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
Inference
print('--> Running
model')
outputs = rknn.inference(inputs=[img])
predicted = np.argmax(outputs[0][0])
print(f'Predicted
digit: {predicted}')
Release RKNN context
rknn.release()
</artifact>
Additional Setup for RKNN Conversion
For the above script to work properly, you'll need:
Create a calibration
dataset file: RKNN quantization requires a
dataset file listing paths to sample images:
<artifact id="
dataset_generator" type="application/vnd.ant.code" language="python" title="Calibration
Dataset Generator"> import os import numpy as np from torchvision import
datasets, transforms import torch
Create
dataset directory if it doesn't exist
os.makedirs('./calibration_images', exist_ok=True)
Load a subset of
MNIST images for calibration
transform = transforms.Compose([transforms.ToTensor()])
dataset =
datasets.
MNIST(root='./data/
mnist', train=True, transform=transform, download=True)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=1, shuffle=True)
Save calibration images (using 100 images)
with open('./
dataset.txt', 'w') as f:
for i, (img, ) in enumerate(data_loader):
if i >= 100: # Use 100 images for calibration
break
# Convert tensor to numpy and save as raw format
img_np = img.numpy().reshape(28, 28) * 255
img_np = img_np.astype(np.uint8)
img_path = f'./calibration_images/img{i:03d}.bin'
img_np.tofile(img_path)
f.write(f'{img_path}\n')
print(f"Created
dataset.txt with {min(100, i+1)} image paths for calibration")
</artifact>
Version Compatibility Notes
Since you're using rknn-toolkit2 version 1.6.0 on Ubuntu with Python 3.10, here are some important compatibility considerations:
API differences: The script I provided uses the API structure compatible with older versions. The newer 2.3.0 version might have slightly different parameter names or methods.
Target platform: I specified 'rk3588' as the target platform. Adjust this if you're targeting a different RK chip.
Quantization parameters: The script uses basic quantization settings. You may need to adjust these (particularly mean_values and std_values) based on your
model's preprocessing.
Testing the
model: The optional testing section requires you to have a sample image. You can generate one from your test
dataset or skip this section if not needed.
Troubleshooting Common Issues
If you encounter errors:
ONNX compatibility: Older RKNN versions may not support ONNX opset 19. Try re-exporting your ONNX
model with a lower opset version (e.g., 11 or 13).
Layer support: Some operations might not be supported. Check error messages for unsupported operations and consider simplifying your
model if needed.
Quantization issues: If quantization fails, try do_quantization=False first to verify the conversion works without quantization.
Would you like me to explain any specific part of the conversion process in more detail?
按照这个分析来
博主在优快云上分享了其运行特定代码或实验的结果，详细记录了过程与发现，为读者提供了实践参考。
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