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import os
import torch
from torchvision.transforms import transforms
import pathlib
from PIL import Image
from torchmetrics import ConfusionMatrix, Accuracy, F1Score
import matplotlib.pyplot as plt
from configs import *
from data_loader import load_data # Import the load_data function
image_path = "data/test/Task 1/"
# Constants
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Load the model
MODEL = MODEL.to(DEVICE)
MODEL.load_state_dict(torch.load(MODEL_SAVE_PATH, map_location=DEVICE))
MODEL.eval()
def predict_image(image_path, model, transform):
model.eval()
correct_predictions = 0
# Get a list of image files
images = list(pathlib.Path(image_path).rglob("*.png"))
total_predictions = len(images)
true_classes = []
predicted_labels = []
accuracy_metric = Accuracy(num_classes=NUM_CLASSES, task="multiclass")
f1_metric = F1Score(num_classes=NUM_CLASSES, task="multiclass")
with torch.no_grad():
for image_file in images:
print("---------------------------")
# Check the true label of the image by checking the sequence of the folder in Task 1
true_class = CLASSES.index(image_file.parts[-2])
print("Image path:", image_file)
print("True class:", true_class)
image = Image.open(image_file).convert("RGB")
image = transform(image).unsqueeze(0)
image = image.to(DEVICE)
output = model(image)
predicted_class = torch.argmax(output, dim=1).item()
# Print the predicted class
print("Predicted class:", predicted_class)
# Append true and predicted labels to their respective lists
true_classes.append(true_class)
predicted_labels.append(predicted_class)
# Check if the prediction is correct
if predicted_class == true_class:
correct_predictions += 1
# Calculate accuracy and f1 score
accuracy = correct_predictions / total_predictions
print("Accuracy:", accuracy)
f1 = f1_metric(torch.tensor(predicted_labels), torch.tensor(true_classes)).item()
print("Weighted F1 Score:", f1)
# Convert the lists to tensors
predicted_labels_tensor = torch.tensor(predicted_labels)
true_classes_tensor = torch.tensor(true_classes)
# Create a confusion matrix
conf_matrix = ConfusionMatrix(num_classes=NUM_CLASSES, task="multiclass")
conf_matrix(predicted_labels_tensor, true_classes_tensor)
# Plot the confusion matrix
conf_matrix.compute()
conf_matrix.plot()
plt.show()
# Call predict_image function
predict_image(image_path, MODEL, preprocess)
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