File size: 1,519 Bytes
b816225 8658411 56153f5 8658411 b816225 8658411 b816225 3df38dc b816225 8658411 b816225 8658411 b816225 |
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 |
import gradio as gr
import torch
from torchvision import transforms
from PIL import Image
from timm import create_model
import json
with open('class_names.json', 'r') as json_file:
class_mapping = json.load(json_file)
# 加载模型
def load_model(model_path):
model = create_model('resnet18', pretrained=False, num_classes=len(class_mapping))
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.eval()
return model
model = load_model("res18_nabird555_acc596.pth")
# 定义图像预处理
def preprocess_image(image):
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
return transform(image).unsqueeze(0)
# 定义推理函数
def classify_image(image):
image = preprocess_image(image)
with torch.no_grad():
outputs = model(image)
_, predicted_class = torch.max(outputs, 1)
predicted_class_idx = predicted_class.item()
predicted_class_name = class_mapping[str(predicted_class_idx)]
return predicted_class_name
# 创建 Gradio 接口
title = "Bird Species Classifier"
description = "Upload an image of a bird, and the model will predict its species."
interface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil"),
outputs="text",
title=title,
description=description,
)
if __name__ == "__main__":
interface.launch()
|