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# evaluate/evaluate_efficientnet_b4.py
import sys, os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
import torch
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import seaborn as sns
from torch.utils.data import DataLoader
from torchvision import transforms
from sklearn.metrics import (
accuracy_score, precision_score, recall_score, f1_score,
confusion_matrix, roc_auc_score
)
from models.efficientnet_b4 import EfficientNetB4Classifier
class ImageNpyDataset(torch.utils.data.Dataset):
def __init__(self, paths_file, labels_file, img_size=(380, 380)):
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
def evaluate(model_path="results_efficientnet_b4/efficientnetb4_best9799.pth"):
os.makedirs("results", exist_ok=True)
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)
model = EfficientNetB4Classifier(train_base=False)
model.load_state_dict(torch.load(model_path, map_location=device))
model.to(device)
model.eval()
y_true, y_pred, y_prob = [], [], []
with torch.no_grad():
for x, y in test_loader:
x = x.to(device)
out = model(x).squeeze()
probs = out.cpu().numpy().tolist()
preds = (out > 0.5).float().cpu().numpy().tolist()
y_true.extend(y.squeeze().numpy().tolist())
y_pred.extend(preds)
y_prob.extend(probs)
y_true = np.array(y_true)
y_pred = np.array(y_pred)
y_prob = np.array(y_prob)
acc = accuracy_score(y_true, y_pred)
prec = precision_score(y_true, y_pred)
rec = recall_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)
auc = roc_auc_score(y_true, y_prob)
print("\n📊 EfficientNetB4 Test Metrics:")
print(f"Accuracy : {acc:.4f}")
print(f"Precision: {prec:.4f}")
print(f"Recall : {rec:.4f}")
print(f"F1 Score : {f1:.4f}")
print(f"AUC : {auc:.4f}")
# Bar Chart
metrics = {"Accuracy": acc, "Precision": prec, "Recall": rec, "F1 Score": f1, "AUC": auc}
plt.figure()
sns.barplot(x=list(metrics.keys()), y=list(metrics.values()))
plt.ylim(0, 1)
plt.title("EfficientNetB4 Test Metrics")
plt.savefig("results/eval_metrics_efficientnetb4.png", dpi=300, bbox_inches="tight")
# Confusion Matrix
cm = confusion_matrix(y_true, y_pred)
plt.figure()
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues",
xticklabels=["Fresh", "Not Fresh"],
yticklabels=["Fresh", "Not Fresh"])
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.title("EfficientNetB4 Confusion Matrix")
plt.savefig("results/confusion_matrix_efficientnetb4.png", dpi=300, bbox_inches="tight")
if __name__ == "__main__":
evaluate()
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