Fixing PyTorch Model Loading Error: _pickle.UnpicklingError: invalid load key, 'x1f'

Resolving PyTorch Model Checkpoint Error: Invalid Load Key

Python backend solution using proper file handling and model loading

import os
import torch
import numpy as np
import timm
import zipfile
import io
# Device setup
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device being used:', device)
# Correct method to load a corrupted or zipped model checkpoint
mname = os.path.join('./CDF2_0.pth')
try:
    # Attempt to open as a zip if initial loading fails
    if zipfile.is_zipfile(mname):
        with zipfile.ZipFile(mname) as archive:
            for file in archive.namelist():
                with archive.open(file) as f:
                    buffer = io.BytesIO(f.read())
                    checkpoints = torch.load(buffer, map_location=device)
    else:
        checkpoints = torch.load(mname, map_location=device)
    print("Checkpoint loaded successfully.")
except Exception as e:
    print("Error loading the checkpoint file:", e)
# Model creation and state_dict loading
model = timm.create_model('legacy_xception', pretrained=True, num_classes=2).to(device)
if 'state_dict' in checkpoints:
    model.load_state_dict(checkpoints['state_dict'])
else:
    model.load_state_dict(checkpoints)
model.eval()
print("Model loaded and ready for inference.")

Alternate Solution: Re-saving the Checkpoint File

Python-based solution to fix corrupted checkpoint file

import os
import torch
# Device setup
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device being used:', device)
# Original and corrected file paths
original_file = './CDF2_0.pth'
corrected_file = './fixed_CDF2_0.pth'
try:
    # Load and re-save the checkpoint
    checkpoints = torch.load(original_file, map_location=device)
    torch.save(checkpoints, corrected_file)
    print("Checkpoint file re-saved successfully.")
except Exception as e:
    print("Failed to fix checkpoint file:", e)
# Verify loading from the corrected file
checkpoints_fixed = torch.load(corrected_file, map_location=device)
print("Verified: Corrected checkpoint loaded.")

Unit Test for Both Solutions

Unit tests to validate checkpoint loading and model state_dict integrity

import torch
import unittest
import os
import timm
class TestCheckpointLoading(unittest.TestCase):
    def setUp(self):
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model_path = './fixed_CDF2_0.pth'
        self.model = timm.create_model('legacy_xception', pretrained=True, num_classes=2).to(self.device)
    def test_checkpoint_loading(self):
        try:
            checkpoints = torch.load(self.model_path, map_location=self.device)
            if 'state_dict' in checkpoints:
                self.model.load_state_dict(checkpoints['state_dict'])
            else:
                self.model.load_state_dict(checkpoints)
            self.model.eval()
            self.assertTrue(True)
            print("Checkpoint loaded successfully in unit test.")
        except Exception as e:
            self.fail(f"Checkpoint loading failed with error: {e}")
if __name__ == '__main__':
    unittest.main()

Frequently Asked Questions on PyTorch Checkpoint Loading Issues

1. Why am I getting _pickle.UnpicklingError when loading my PyTorch model?
2. This error usually occurs due to an incompatible or corrupted checkpoint file. It can also happen when using different PyTorch versions between saving and loading.
3. How do I fix a corrupted PyTorch checkpoint file?
4. You can use zipfile.ZipFile() to check if the file is a ZIP archive or re-save the checkpoint with torch.save() after repairing it.
5. What is the role of the state_dict in PyTorch?
6. The state_dict contains the model's weights and parameters in a dictionary format. Always save and load the state_dict for better portability.
7. How can I load a PyTorch checkpoint on a CPU?
8. Use the map_location='cpu' argument in torch.load() to remap tensors from GPU to CPU.
9. Can PyTorch checkpoints fail due to version conflicts?
10. Yes, older checkpoints may not load in newer versions of PyTorch. It’s recommended to use consistent PyTorch versions when saving and loading.
11. How can I check if a PyTorch checkpoint file is corrupted?
12. Try loading the file using torch.load(). If that fails, inspect the file with tools like zipfile.is_zipfile().
13. What is the correct way to save and load PyTorch models?
14. Always save using torch.save(model.state_dict()) and load using model.load_state_dict().
15. Why does my model fail to load on a different device?
16. This happens when tensors are saved for GPU but loaded on a CPU. Use map_location to resolve this.
17. How can I validate checkpoints across environments?
18. Write unit tests using unittest to check model loading on different setups (CPU, GPU, OS).
19. Can I inspect checkpoint files manually?
20. Yes, you can change the extension to .zip and open it with zipfile or archive managers to inspect the contents.

Overcoming PyTorch Model Loading Errors

Loading PyTorch checkpoints can sometimes throw errors due to corrupted files or version mismatches. By verifying the file format and using proper tools like zipfile or remapping tensors, you can recover your trained models efficiently and save hours of re-training.

Developers should follow best practices like saving the state_dict only and validating models across environments. Remember, the time spent resolving these issues ensures your models remain functional, portable, and compatible with any deployment system. 🚀