73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
# -*- coding:utf-8 -*-
|
|
# 该脚本用来加载权重，并执行模型前向推理，实现花分类
|
|
import os
|
|
import json
|
|
|
|
import torch
|
|
from PIL import Image
|
|
from torchvision import transforms
|
|
import matplotlib.pyplot as plt
|
|
import sys
|
|
sys.path.append(R"../0_SeResNet")
|
|
from senet.se_resnet import se_resnet34
|
|
|
|
|
|
def main():
|
|
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
|
|
|
data_transform = transforms.Compose(
|
|
[
|
|
transforms.Resize(256),
|
|
transforms.CenterCrop(224),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225])])
|
|
# 文件及参数配置
|
|
img_path = R"../2_compile/qtset/flower/1.jpg"
|
|
weights_path = "../weights/SeResNet34.pth"
|
|
class_indict = {
|
|
"0": "daisy",
|
|
"1": "dandelion",
|
|
"2": "roses",
|
|
"3": "sunflowers",
|
|
"4": "tulips"
|
|
}
|
|
# load image
|
|
assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
|
|
img = Image.open(img_path)
|
|
plt.imshow(img)
|
|
img = data_transform(img)
|
|
# expand batch dimension
|
|
img = torch.unsqueeze(img, dim=0)
|
|
|
|
# prepare class_indict
|
|
print('class_indict =',class_indict)
|
|
# create model
|
|
model = se_resnet34(num_classes=5).to(device)
|
|
|
|
# load model weights
|
|
assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(weights_path)
|
|
model.load_state_dict(torch.load(weights_path, map_location=device))
|
|
|
|
# prediction
|
|
model.eval()
|
|
|
|
# predict class
|
|
print('image-size',img.size())
|
|
result = model(img.to(device)).cpu()
|
|
print('result =',result)
|
|
output = torch.squeeze(model(img.to(device))).cpu()
|
|
predict = torch.softmax(output, dim=0)
|
|
predict_cla = torch.argmax(predict).numpy()
|
|
print('*'*10,'predict_class','*'*10)
|
|
print_res = "class: {} prob: {:.3}".format(class_indict[str(predict_cla)],
|
|
predict[predict_cla].detach().numpy())
|
|
plt.title(print_res)
|
|
for i in range(len(predict)):
|
|
print("class: {:10} prob: {:.3}".format(class_indict[str(i)],
|
|
predict[i].detach().numpy()))
|
|
plt.show()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|