test.py

import torch
import torch.nn as nn
from pathlib import Path
from data_prep import test_loader, device
from models.model import PlantCNN
from utils.config import load_config
from clearml import Task
import numpy as np
from utils.vis import visualize_preds, plot_cfm
from tqdm.auto import tqdm


def evaluate_on_test(model, loader, loss_fn, device, num_imgs):
    model.eval()
    all_labels = []
    all_preds = []
    running_loss = 0.0
    correct = 0
    total = 0
    imgs_to_display = []
    lbls_to_display = []
    prs_to_display = []
    
    with torch.no_grad():
        for batch_idx, batch in enumerate(tqdm(loader, desc="Val", leave=False)):
            images = batch["pixel_values"].to(device)
            labels = batch["labels"].to(device)
            
            output = model(images)
            loss = loss_fn(output, labels)
            
            running_loss += loss.item()*labels.size(0)
            
            _, preds = torch.max(output, dim=1)
            correct += (preds==labels).sum().item()
            total += labels.size(0)
            
            all_labels.extend(labels.cpu().numpy())
            all_preds.extend(preds.cpu().numpy())
            
            if len(imgs_to_display) < num_imgs:
                remaining = num_imgs - len(imgs_to_display)
                for img, lbl, pr in zip(images[:remaining], preds[:remaining], preds[:remaining]):
                    imgs_to_display.append(img.cpu())
                    lbls_to_display.append(lbl.item())
                    prs_to_display.append(pr.item())
                    
    test_loss = running_loss / total
    test_acc = correct / total
    return test_loss, test_acc, all_labels, all_preds, imgs_to_display, lbls_to_display, prs_to_display
    

def main():
    config = load_config()
    num_classes = config["num_classes"]
    channels = config["channels"]
    dropout = config["dropout"]
    lr = config["lr"]
    project_name = "GAP_plant_disease_classification"
    model_name = "PlantCNN"
    mean_nm = config["normalize_mean"]
    std_nm = config["normalize_std"]
    
    task = Task.init(project_name=project_name, task_name=f"{model_name}_test")
    task.connect(config)
    task.add_tags([model_name, "test"])
    logger = task.get_logger()
    
    dataset = test_loader.dataset
    class_names = dataset.features["label"].names
    
    model = PlantCNN(num_classes=num_classes, channels=channels, dropout=dropout).to(device)
    project_root = Path(__file__).resolve().parent
    model_path = project_root / "saved_models" / "plant_cnn.pt"
    state_dict = torch.load(model_path, map_location=device)
    model.load_state_dict(state_dict)
    
    loss_fn = nn.CrossEntropyLoss()
    
    test_loss, test_acc, all_labels, all_preds, display_images, display_labels, display_preds = evaluate_on_test(model, test_loader,
                                                                                                                 loss_fn, device,
                                                                                                                 num_imgs=24)
    
    print("\nTest results:")
    print(f"Test loss: {test_loss:.3f} | Test accuracy: {test_acc:.3f}")
    logger.report_scalar("loss", "test", test_loss, 0)
    logger.report_scalar("accuracy", "test", test_acc, 0)
    
    visualize_preds(display_images, display_labels, display_preds, logger, class_names, mean_nm, std_nm, num_images=24)
    plot_cfm(all_labels, all_preds, logger, class_names, num_classes)
    
if __name__ == "__main__":
    main()