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import sys, os |
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) |
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import torch |
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import numpy as np |
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from PIL import Image |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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from torch.utils.data import DataLoader |
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from torchvision import transforms |
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from sklearn.metrics import ( |
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accuracy_score, precision_score, recall_score, f1_score, |
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confusion_matrix, roc_auc_score |
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) |
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from models.efficientnet_b4 import EfficientNetB4Classifier |
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class ImageNpyDataset(torch.utils.data.Dataset): |
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def __init__(self, paths_file, labels_file, img_size=(380, 380)): |
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self.image_paths = np.load(paths_file, allow_pickle=True).astype(str) |
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self.labels = np.load(labels_file, allow_pickle=True).astype(np.float32) |
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self.transform = transforms.Compose([ |
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transforms.Resize(img_size), |
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transforms.ToTensor(), |
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transforms.Normalize([0.485, 0.456, 0.406], |
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[0.229, 0.224, 0.225]) |
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]) |
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def __len__(self): |
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return len(self.image_paths) |
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def __getitem__(self, idx): |
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image = Image.open(self.image_paths[idx]).convert("RGB") |
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image = self.transform(image) |
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label = torch.tensor([self.labels[idx]], dtype=torch.float32) |
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return image, label |
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def evaluate(model_path="results_efficientnet_b4/efficientnetb4_best9799.pth"): |
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os.makedirs("results", exist_ok=True) |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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test_ds = ImageNpyDataset("test_paths.npy", "test_labels.npy") |
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test_loader = DataLoader(test_ds, batch_size=32) |
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model = EfficientNetB4Classifier(train_base=False) |
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model.load_state_dict(torch.load(model_path, map_location=device)) |
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model.to(device) |
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model.eval() |
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y_true, y_pred, y_prob = [], [], [] |
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with torch.no_grad(): |
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for x, y in test_loader: |
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x = x.to(device) |
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out = model(x).squeeze() |
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probs = out.cpu().numpy().tolist() |
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preds = (out > 0.5).float().cpu().numpy().tolist() |
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y_true.extend(y.squeeze().numpy().tolist()) |
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y_pred.extend(preds) |
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y_prob.extend(probs) |
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y_true = np.array(y_true) |
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y_pred = np.array(y_pred) |
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y_prob = np.array(y_prob) |
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acc = accuracy_score(y_true, y_pred) |
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prec = precision_score(y_true, y_pred) |
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rec = recall_score(y_true, y_pred) |
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f1 = f1_score(y_true, y_pred) |
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auc = roc_auc_score(y_true, y_prob) |
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print("\n📊 EfficientNetB4 Test Metrics:") |
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print(f"Accuracy : {acc:.4f}") |
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print(f"Precision: {prec:.4f}") |
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print(f"Recall : {rec:.4f}") |
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print(f"F1 Score : {f1:.4f}") |
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print(f"AUC : {auc:.4f}") |
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metrics = {"Accuracy": acc, "Precision": prec, "Recall": rec, "F1 Score": f1, "AUC": auc} |
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plt.figure() |
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sns.barplot(x=list(metrics.keys()), y=list(metrics.values())) |
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plt.ylim(0, 1) |
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plt.title("EfficientNetB4 Test Metrics") |
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plt.savefig("results/eval_metrics_efficientnetb4.png", dpi=300, bbox_inches="tight") |
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cm = confusion_matrix(y_true, y_pred) |
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plt.figure() |
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sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", |
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xticklabels=["Fresh", "Not Fresh"], |
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yticklabels=["Fresh", "Not Fresh"]) |
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plt.xlabel("Predicted") |
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plt.ylabel("Actual") |
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plt.title("EfficientNetB4 Confusion Matrix") |
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plt.savefig("results/confusion_matrix_efficientnetb4.png", dpi=300, bbox_inches="tight") |
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if __name__ == "__main__": |
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evaluate() |
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