求助：运行NiftyNet Promise12 Demo推理模块时报错如何解决？

Hey there, let's tackle this NiftyNet Promise12 inference error you're hitting with Python 3.4 and TensorFlow 1.4.1. I've debugged similar issues before, so here are the most likely fixes to try out:

1. Lock in Compatible NiftyNet Version #

Older TensorFlow versions have strict compatibility with NiftyNet. Since you're on TF 1.4.1, you need to use the last NiftyNet release that supports TF 1.x and Python 3.4:

• Run this command to install the correct version: pip install niftynet==0.2.0
• Newer NiftyNet versions (>=0.3.0) dropped support for TF 1.x entirely, so avoid those at all costs.

2. Fix Mismatched Dependencies #

Older NiftyNet releases require specific versions of core libraries. Resolve conflicts with these steps:

• First, uninstall any conflicting packages: pip uninstall -y numpy scipy pillow
• Install the exact compatible versions:
  • pip install numpy==1.13.3
  • pip install scipy==0.19.1
  • pip install pillow==5.4.1
• Don't forget the medical image handling dependency: pip install SimpleITK==1.1.0

3. Correct the Inference Config File #

Misconfigured settings are a top culprit for inference errors. Open your inference_config.ini and double-check:

• In the [data] section, replace placeholder paths with your actual test image directory: path_to_test_images = /your/local/test/data/path
• In [network], confirm pretrained_model_path points to a valid, final pre-trained weight file (not a mid-training checkpoint)
• Set inference_iter = 0 to load the full trained weights (this is a common oversight)

4. Validate Pre-trained Weights #

If the error relates to loading model weights:

• Ensure you downloaded the TF 1.4.1-compatible Promise12 weights from the NiftyNet model zoo
• Check the file isn't corrupted: verify its size matches the expected ~150MB
• Use absolute paths for the weights in your config—Jupyter's working directory can break relative paths

5. Jupyter-Specific Environment Fixes #

Jupyter has unique quirks that don't affect regular scripts:

• Restart your kernel after installing packages to load the new versions
• Add this cell at the top to confirm your environment is correct:

  import sys
  import tensorflow as tf
  import niftynet
  print(f"Python version: {sys.version}")
  print(f"TensorFlow version: {tf.__version__}")
  print(f"NiftyNet version: {niftynet.__version__}")
  

  Make sure outputs show Python 3.4.x, TF 1.4.1, and NiftyNet 0.2.0
• Before initializing inference, run tf.reset_default_graph() to clear leftover graph variables from previous runs

6. Debug the Exact Error Traceback #

If none of the above work, zero in on the error message:

• For ImportError: No module named 'xyz', install the missing package with a Python 3.4-compatible version
• For tensor shape-related InvalidArgumentError, resize your test images to match the model's expected 64x64x64 3D size
• For NotFoundError, triple-check all file paths in your config exist and are spelled correctly

内容的提问来源于stack exchange，提问作者YChen