Spaces:
Configuration error
Configuration error
File size: 3,871 Bytes
957df8a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
#!/usr/bin/env python3
"""
Test script to verify the diabetic retinopathy detection model can be loaded correctly.
Run this before starting the main app to check for any issues.
"""
import torch
import torch.nn as nn
from torchvision import models
import os
def test_model_loading():
"""Test if the model can be loaded successfully."""
print("🔍 Testing model loading...")
try:
# Check if model file exists
model_path = "resnet50_dr_classifier.pth"
if not os.path.exists(model_path):
print(f"❌ Error: Model file '{model_path}' not found!")
print(" Please ensure the model file is in the current directory.")
return False
print(f"✅ Model file found: {model_path}")
# Check file size
file_size = os.path.getsize(model_path) / (1024 * 1024) # MB
print(f"📁 Model file size: {file_size:.2f} MB")
# Try to load the model
device = torch.device("cpu")
print("🔄 Loading model...")
model = models.resnet50(weights=None)
model.fc = nn.Linear(model.fc.in_features, 2)
# Load state dict
state_dict = torch.load(model_path, map_location=device)
model.load_state_dict(state_dict)
model.to(device)
model.eval()
print("✅ Model loaded successfully!")
# Test with dummy input
print("🧪 Testing with dummy input...")
dummy_input = torch.randn(1, 3, 224, 224)
with torch.no_grad():
output = model(dummy_input)
probs = torch.nn.functional.softmax(output, dim=1)
pred = torch.argmax(probs, dim=1).item()
confidence = probs[0][pred].item()
print(f"✅ Model inference successful!")
print(f" Prediction: {'DR' if pred == 0 else 'NoDR'}")
print(f" Confidence: {confidence:.4f}")
return True
except Exception as e:
print(f"❌ Error loading model: {str(e)}")
return False
def test_dependencies():
"""Test if required packages are available."""
print("\n🔍 Testing dependencies...")
try:
import gradio
print("✅ Gradio imported successfully")
except ImportError:
print("❌ Gradio not found. Install with: pip install gradio")
return False
try:
import PIL
print("✅ PIL/Pillow imported successfully")
except ImportError:
print("❌ PIL/Pillow not found. Install with: pip install pillow")
return False
try:
import numpy
print("✅ NumPy imported successfully")
except ImportError:
print("❌ NumPy not found. Install with: pip install numpy")
return False
try:
from pytorch_grad_cam import GradCAM
print("✅ PyTorch Grad-CAM imported successfully")
except ImportError:
print("❌ PyTorch Grad-CAM not found. Install with: pip install pytorch-grad-cam")
return False
return True
def main():
"""Main test function."""
print("🚀 Diabetic Retinopathy Detection - Model Test")
print("=" * 50)
# Test dependencies
deps_ok = test_dependencies()
if not deps_ok:
print("\n❌ Dependency test failed. Please install missing packages.")
return
# Test model loading
model_ok = test_model_loading()
print("\n" + "=" * 50)
if model_ok:
print("🎉 All tests passed! The app should work correctly.")
print(" You can now run: python app.py")
else:
print("❌ Model test failed. Please check the error messages above.")
print(" Make sure the model file is correct and all dependencies are installed.")
if __name__ == "__main__":
main()
|