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import os
import torch
from torchvision.transforms import transforms
from sklearn.metrics import f1_score
from models import *
import pathlib
from PIL import Image
from torchmetrics import ConfusionMatrix, Accuracy
import matplotlib.pyplot as plt
model_checkpoint_path = "model.pth"
image_path = "data/test/Task 1/"
# constants
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
NUM_CLASSES = 6
# load the model
images = list(pathlib.Path(image_path).rglob("*.png"))
classes = os.listdir(image_path)
print(images)
true_classs = []
predicted_labels = []
model = mobilenet_v2(pretrained=False, num_classes=NUM_CLASSES)
model.load_state_dict(torch.load(model_checkpoint_path, map_location=DEVICE))
model.eval()
model = model.to(DEVICE)
# Define transformation for preprocessing
preprocess = transforms.Compose(
[
transforms.Resize((64, 64)), # Resize images to 64x64
transforms.Grayscale(num_output_channels=3), # Convert to grayscale
transforms.ToTensor(), # Convert to tensor
transforms.Normalize((0.5,), (0.5,)), # Normalize (for grayscale)
]
)
# evaluate the model
all_predictions = []
true_labels = []
def predict_image(image_path, model, transform):
model.eval()
correct_predictions = 0
total_predictions = len(images)
with torch.no_grad():
for i in images:
print('---------------------------')
# Check the true label of the image by checking the sequence of the folder in Task 1
true_class = classes.index(i.parts[-2])
print("Image path:", i)
print("True class:", true_class)
image = Image.open(i)
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_classs.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 socre
accuracy = correct_predictions / total_predictions
print("Accuracy:", accuracy)
f1 = f1_score(true_classs, predicted_labels, average='weighted')
print("Weighted F1 Score:", f1)
# Call predict_image function
predict_image(image_path, model, preprocess)
# Convert the lists to tensors
predicted_labels_tensor = torch.tensor(predicted_labels)
true_classs_tensor = torch.tensor(true_classs)
conf_matrix = ConfusionMatrix(num_classes=NUM_CLASSES, task='multiclass')
conf_matrix.update(predicted_labels_tensor, true_classs_tensor)
# Plot confusion matrix
conf_matrix.plot()
plt.show() |