File size: 4,780 Bytes
4b35e49 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
#!/usr/bin/env python3
"""
Test script for the vision sentiment analysis model.
This script verifies that the ResNet-50 model can be loaded and run inference.
"""
import os
import sys
import torch
import torch.nn as nn
from torchvision import transforms, models
from PIL import Image
import numpy as np
def get_sentiment_mapping(num_classes):
"""Get the sentiment mapping based on number of classes"""
if num_classes == 3:
return {0: "Negative", 1: "Neutral", 2: "Positive"}
elif num_classes == 4:
# Common 4-class emotion mapping
return {0: "Angry", 1: "Sad", 2: "Happy", 3: "Neutral"}
elif num_classes == 7:
# FER2013 7-class emotion mapping
return {0: "Angry", 1: "Disgust", 2: "Fear", 3: "Happy", 4: "Sad", 5: "Surprise", 6: "Neutral"}
else:
# Generic mapping for unknown number of classes
return {i: f"Class_{i}" for i in range(num_classes)}
def test_vision_model():
"""Test the vision sentiment analysis model"""
print("🧠 Testing Vision Sentiment Analysis Model")
print("=" * 50)
# Check if model file exists
model_path = "models/resnet50_model.pth"
if not os.path.exists(model_path):
print(f"❌ Model file not found: {model_path}")
print("Please ensure the model file exists in the models/ directory")
return False
print(f"✅ Model file found: {model_path}")
try:
# Load the model weights first to check the architecture
print("📥 Loading model checkpoint...")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint = torch.load(model_path, map_location=device)
# Check the number of classes from the checkpoint
if 'fc.weight' in checkpoint:
num_classes = checkpoint['fc.weight'].shape[0]
print(f"📊 Model checkpoint has {num_classes} output classes")
else:
# Fallback: try to infer from the last layer
num_classes = 3 # Default assumption
print("⚠️ Could not determine number of classes from checkpoint, assuming 3")
# Initialize ResNet-50 model with the correct number of classes
print("🔧 Initializing ResNet-50 model...")
model = models.resnet50(weights=None) # Don't load ImageNet weights
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, num_classes) # Use actual number of classes
print(f"📥 Loading trained weights for {num_classes} classes...")
model.load_state_dict(checkpoint)
model.to(device)
model.eval()
print(f"✅ Model loaded successfully with {num_classes} classes!")
print(f"🖥️ Using device: {device}")
# Test with a dummy image
print("🧪 Testing inference with dummy image...")
# Create a dummy image (224x224 RGB)
dummy_image = Image.fromarray(
np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8)
)
# Apply transforms
transform = transforms.Compose(
[
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
)
image_tensor = transform(dummy_image).unsqueeze(0).to(device)
# Run inference
with torch.no_grad():
outputs = model(image_tensor)
print(f"🔍 Model output shape: {outputs.shape}")
probabilities = torch.nn.functional.softmax(outputs, dim=1)
confidence, predicted = torch.max(probabilities, 1)
# Get sentiment mapping based on number of classes
sentiment_map = get_sentiment_mapping(num_classes)
sentiment = sentiment_map[predicted.item()]
confidence_score = confidence.item()
print(f"🎯 Test prediction: {sentiment} (confidence: {confidence_score:.3f})")
print(f"📋 Available classes: {list(sentiment_map.values())}")
print("✅ Inference test passed!")
return True
except Exception as e:
print(f"❌ Error testing model: {str(e)}")
import traceback
traceback.print_exc()
return False
def main():
"""Main function"""
success = test_vision_model()
if success:
print("\n🎉 All tests passed! The vision model is ready to use.")
print("You can now run the Streamlit app with: streamlit run app.py")
else:
print("\n💥 Tests failed. Please check the error messages above.")
sys.exit(1)
if __name__ == "__main__":
main()
|