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predict.py
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predict.py
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import torch
import numpy as np
from PIL import Image, ImageOps
import matplotlib.pyplot as plt
from model import DuckNet
from utils.config import Configs
def load_model(config:Configs, model_path:str):
model = DuckNet(config.input_channels, config.num_classes, config.num_filters)
model.load_state_dict(torch.load(model_path))
return model
def predict(model:DuckNet, image_path:str, device:torch.device):
image = ImageOps.exif_transpose(Image.open(image_path)).convert('RGB')
image = np.array(image.resize((512, 512)))
image = torch.from_numpy(image).unsqueeze(0).permute(0, 3, 1, 2).float() / 255.0
image = image.to(device)
model.eval()
with torch.no_grad():
output = model(image)
output_image = output.squeeze().cpu().numpy()
input_image = image.squeeze(0).permute(1, 2, 0).cpu().numpy()
return input_image, output_image
def main(config:Configs, model_path:str, image_path:str, output_path:str):
device = torch.device(f'cuda:{config.gpu_id}' if torch.cuda.is_available() else 'cpu')
print(f'Using device: {device}')
model = load_model(config, model_path)
model.to(device)
print(f'Model loaded from {model_path}')
input_image, output_image = predict(model, image_path, device)
plt.subplot(1, 2, 1)
plt.imshow(input_image)
plt.title('Input Image')
plt.axis('off')
plt.subplot(1, 2, 2)
plt.imshow(output_image, cmap='gray')
plt.title('Prediction')
plt.axis('off')
plt.tight_layout()
plt.savefig(output_path)
plt.close()
print(f'Prediction saved at {output_path}')
if __name__ == '__main__':
config = Configs(num_filters=34)
model_path = 'checkpoints/best_model.pt'
image_path = 'sample_1.jpg'
output_path = 'output_1.jpg'
main(config, model_path, image_path, output_path)