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import sys, os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
import torch
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from torch.utils.data import DataLoader
from torchvision import transforms
from PIL import Image
from sklearn.metrics import (
accuracy_score, precision_score, recall_score, f1_score,
confusion_matrix, roc_curve, auc
)
# --- Dataset class ---
class ImageNpyDataset(torch.utils.data.Dataset):
def __init__(self, paths_file, labels_file, img_size=(224, 224)):
self.image_paths = np.load(paths_file, allow_pickle=True).astype(str)
self.labels = np.load(labels_file, allow_pickle=True).astype(np.float32)
self.transform = transforms.Compose([
transforms.Resize(img_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
image = Image.open(self.image_paths[idx]).convert("RGB")
image = self.transform(image)
label = torch.tensor([self.labels[idx]], dtype=torch.float32)
return image, label
# --- Evaluation ---
def evaluate_model(model, dataloader, device):
model.eval()
y_true, y_pred, y_prob = [], [], []
with torch.no_grad():
for x, y in dataloader:
x, y = x.to(device), y.to(device)
out = model(x).squeeze()
pred = (out > 0.5).float()
y_true.extend(y.squeeze().cpu().numpy().tolist())
y_pred.extend(pred.cpu().numpy().tolist())
y_prob.extend(out.cpu().numpy().tolist())
return {
"accuracy": accuracy_score(y_true, y_pred),
"precision": precision_score(y_true, y_pred),
"recall": recall_score(y_true, y_pred),
"f1": f1_score(y_true, y_pred),
"y_true": y_true,
"y_pred": y_pred,
"y_prob": y_prob
}
if __name__ == "__main__":
import models.efficientnet_b0 as b0
import models.efficientnet_b4 as b4
import models.mobilenetv2 as mv2
import models.resnet50 as rsn
from models.hybrid_fusion import EnhancedHybridFusionClassifier
model_classes = {
"EfficientNetB0": (b0.EfficientNetB0Classifier, "results_efficientnet_b0/efficientnet_best9912.pth"),
"EfficientNetB4": (b4.EfficientNetB4Classifier, "results_efficientnet_b4/efficientnetb4_best9799.pth"),
"MobileNetV2": (mv2.MobileNetV2Classifier, "results_mobilenetv2/mobilenetv2_best9598.pth"),
"ResNet50": (rsn.ResNet50Classifier, "results_resnet50/resnet50_best9849.pth"),
"HybridFusion": (EnhancedHybridFusionClassifier, "results_hybrid_fusion/hybrid_fusion_best9799.pth"),
}
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
test_ds = ImageNpyDataset("test_paths.npy", "test_labels.npy")
test_loader = DataLoader(test_ds, batch_size=32)
results = {}
for name, (cls, path) in model_classes.items():
print(f"🔍 Evaluating {name}...")
model = cls()
model.load_state_dict(torch.load(path, map_location=device))
model.to(device)
results[name] = evaluate_model(model, test_loader, device)
os.makedirs("results", exist_ok=True)
# Confusion Matrices
fig, axes = plt.subplots(3, 2, figsize=(13, 15))
for ax, (name, m) in zip(axes.flat, results.items()):
cm = confusion_matrix(m["y_true"], m["y_pred"])
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues",
xticklabels=["Fresh", "Not Fresh"],
yticklabels=["Fresh", "Not Fresh"],
ax=ax)
ax.set_title(f"{name}")
ax.set_xlabel("Predicted")
ax.set_ylabel("Actual")
fig.tight_layout()
fig.savefig("results/confusion_matrices_comparison.png", dpi=300)
# ROC Curves
plt.figure(figsize=(8, 6))
for name, m in results.items():
fpr, tpr, _ = roc_curve(m["y_true"], m["y_prob"])
roc_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, label=f"{name} (AUC={roc_auc:.3f})")
plt.plot([0, 1], [0, 1], "k--")
plt.title("ROC Curve Comparison")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.legend(loc="lower right")
plt.grid(True)
plt.tight_layout()
plt.savefig("results/roc_curves_comparison.png", dpi=300)
# Bar Chart of Metrics
metrics = ["accuracy", "precision", "recall", "f1"]
x = np.arange(len(metrics))
bar_width = 0.15
plt.figure(figsize=(12, 6))
for i, (name, m) in enumerate(results.items()):
scores = [m[k] for k in metrics]
plt.bar(x + i * bar_width, scores, width=bar_width, label=name)
plt.xticks(x + bar_width * (len(results) / 2), [m.title() for m in metrics])
plt.ylim(0, 1)
plt.ylabel("Score")
plt.title("Model Metric Comparison")
plt.legend()
plt.grid(axis="y")
plt.tight_layout()
plt.savefig("results/metrics_bar_chart.png", dpi=300)
# Save to CSV
df = pd.DataFrame({
name: [results[name][k] for k in metrics]
for name in results
}, index=[m.title() for m in metrics]).T
df.to_csv("results/model_metrics_summary.csv")
print("📄 Saved metrics to results/model_metrics_summary.csv")
plt.show()
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