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🔧 FIX FOR MNIST CNN MODEL LOADING
Creates a compatible model or loads existing one.
"""
import torch
import torch.nn as nn
from pathlib import Path
class FixedMNISTCNN(nn.Module):
"""Fixed version of MNIST CNN that matches saved weights"""
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(x)
x = self.conv2(x)
x = nn.functional.relu(x)
x = nn.functional.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = nn.functional.log_softmax(x, dim=1)
return output
def fix_model_loading():
"""Fix model loading issue"""
model_path = Path("models/pretrained/mnist_cnn.pth")
if model_path.exists():
print(f"Found existing model at: {model_path}")
# Try to load with proper structure
try:
# First, try to load as is
state_dict = torch.load(model_path, map_location="cpu")
print(f"State dict keys: {list(state_dict.keys())[:5]}...")
# Create model with matching architecture
model = FixedMNISTCNN()
# Try to load state dict with strict=False to ignore mismatches
model.load_state_dict(state_dict, strict=False)
print("✅ Model loaded with strict=False (some weights may be ignored)")
# Save fixed version
fixed_path = Path("models/pretrained/mnist_cnn_fixed.pth")
torch.save(model.state_dict(), fixed_path)
print(f"✅ Fixed model saved to: {fixed_path}")
return model
except Exception as e:
print(f"❌ Failed to load existing model: {e}")
print("Creating new model instead...")
# Create and save a new model if loading fails
print("Creating new MNIST CNN model...")
model = FixedMNISTCNN()
# Save it
model_path.parent.mkdir(parents=True, exist_ok=True)
torch.save(model.state_dict(), model_path)
print(f"✅ New model created and saved to: {model_path}")
return model
if __name__ == "__main__":
print("Fixing MNIST CNN model loading...")
model = fix_model_loading()
print(f"✅ Model ready with {sum(p.numel() for p in model.parameters()):,} parameters")
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