Spaces:
Runtime error
Runtime error
File size: 3,612 Bytes
1882b96 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
import os
import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
from models import * # Make sure you import your model correctly from the 'models' module
from torchmetrics import ConfusionMatrix
import matplotlib.pyplot as plt
import pathlib
# Define the path to your model checkpoint
model_checkpoint_path = "model.pth"
# Define the path to the image you want to classify
image_path = "data/test/Task 1/" # Use forward slashes for file paths
# Define images variable to recursively list all the data file in the image_path
images = list(pathlib.Path(image_path).rglob("*.png"))
classes = os.listdir(image_path)
print(images)
true_classs = []
predicted_labels = []
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
NUM_CLASSES = 5 # Update with the correct number of classes
# Load your model (change this according to your model definition)
model = resnet18(pretrained=False, num_classes=NUM_CLASSES)
model.load_state_dict(torch.load(model_checkpoint_path, map_location=DEVICE)) # Load the model on the same device
model.eval()
model = model.to(DEVICE)
# Define transformation for preprocessing the input image
preprocess = transforms.Compose(
[
transforms.Resize((64, 64)), # Resize the image to match training input size
transforms.Grayscale(num_output_channels=3), # Convert the image to grayscale
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), # Normalize the image
]
)
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)
# softmax algorithm
probabilities = torch.softmax(output, dim=1)[0] * 100
predicted_class = torch.argmax(output, dim=1).item()
# 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
# Report the prediction
print("Predicted class:", predicted_class)
print("Probability:", probabilities[predicted_class].item())
print("Predicted label:", classes[predicted_class])
print("Correct predictions:", correct_predictions)
print("Correct?", "Yes" if predicted_class == true_class else "No")
print("---------------------------")
# Calculate accuracy
accuracy = correct_predictions / total_predictions
print("Accuracy:", accuracy)
# Call the 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)
# Create confusion matrix
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()
|