<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.to
file(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?
先仔细阅读这部分内容，再来结合这上面的讲解，最终生成适合的转化rknn代码
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