67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
# -*- coding:utf-8 -*-
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# 该脚本用导出的模型进行前向推理，实现花分类
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import os
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import json
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import torch
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from PIL import Image
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from torchvision import transforms
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import matplotlib.pyplot as plt
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import sys
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sys.path.append(R"../0_SeResNet")
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from senet.se_resnet import se_resnet34
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def main():
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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data_transform = transforms.Compose(
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[
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225])])
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# 文件及参数配置
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img_path = R"../2_compile/qtset/flower/1.jpg"
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pt_path = '../2_compile/fmodel/SeResNet34_224x224.pt'
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class_indict = {
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"0": "daisy",
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"1": "dandelion",
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"2": "roses",
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"3": "sunflowers",
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"4": "tulips"
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}
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# load image
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assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
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img = Image.open(img_path)
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plt.imshow(img)
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img = data_transform(img)
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# expand batch dimension
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img = torch.unsqueeze(img, dim=0)
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# prepare class_indict
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print('class_indict =',class_indict)
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# load model
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assert os.path.exists(pt_path), "file: '{}' dose not exist.".format(pt_path)
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model = torch.load(pt_path,map_location=device)
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print('Model Load Done')
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# prediction
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model.eval()
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# predict class
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print('image-size',img.size())
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output = torch.squeeze(model(img.to(device))).cpu()
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predict = torch.softmax(output, dim=0)
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predict_cla = torch.argmax(predict).numpy()
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print('*'*10,'predict_class','*'*10)
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print_res = "class: {} prob: {:.3}".format(class_indict[str(predict_cla)],
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predict[predict_cla].detach().numpy())
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plt.title(print_res)
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for i in range(len(predict)):
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print("class: {:10} prob: {:.3}".format(class_indict[str(i)],
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predict[i].detach().numpy()))
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plt.show()
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if __name__ == '__main__':
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main() |