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aicandy_alexnet_test_ovpridiv.py
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aicandy_alexnet_test_ovpridiv.py
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"""
@author: AIcandy
@website: aicandy.vn
"""
import torch
from PIL import Image
from torchvision import transforms
from aicandy_model_src_rimrtnqo.aicandy_alexnet_model_dndvgvhk import AlexNet
# python aicandy_alexnet_test_ovpridiv.py --image_path ../image_test.jpg --model_path aicandy_model_out_uoebddte/aicandy_model_pth_luveqrpt.pth --label_path label.txt
def load_labels(label_path):
with open(label_path, 'r') as f:
labels = {int(line.split(": ")[0]): line.split(": ")[1].strip() for line in f}
print('labels: ',labels)
return labels
def predict(image_path, model_path, label_path):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
labels = load_labels(label_path)
num_classes = len(labels)
# Khởi tạo mô hình và tải trọng số
model = AlexNet(num_classes=num_classes).to(device)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
# Chuyển đổi ảnh
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
image = Image.open(image_path)
image = transform(image).unsqueeze(0).to(device)
with torch.no_grad():
outputs = model(image)
_, predicted = torch.max(outputs, 1)
predicted_class = predicted.item()
return labels.get(predicted_class, "Unknown")
if __name__ == "__main__":
import sys
import argparse
parser = argparse.ArgumentParser(description='AIcandy.vn')
parser.add_argument('--image_path', type=str, required=True, help='Path to the image')
parser.add_argument('--model_path', type=str, required=True, help='Path to the model')
parser.add_argument('--label_path', type=str, required=True, help='Path to the label file')
args = parser.parse_args()
predicted_class = predict(args.image_path, args.model_path, args.label_path)
print(f'Predicted class: {predicted_class}')