73 lines
1.9 KiB
Python
73 lines
1.9 KiB
Python
import json
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import os
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import matplotlib.pyplot as plt
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import torch
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from model import GoogLeNet
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from PIL import Image
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from torchvision import transforms
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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((224, 224)),
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transforms.ToTensor(),
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
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]
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)
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# load image
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img_path = "./rose.jpg"
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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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# [N, C, H, W]
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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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# read class_indict
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json_path = "./class_indices.json"
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assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)
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json_file = open(json_path, "r")
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class_indict = json.load(json_file)
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# create model
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model = GoogLeNet(num_classes=5, aux_logits=False).to(device)
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# load model weights
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weights_path = "./googleNet.pth"
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assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(
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weights_path
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)
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missing_keys, unexpected_keys = model.load_state_dict(
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torch.load(weights_path, map_location=device), strict=False
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)
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model.eval()
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with torch.no_grad():
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# predict class
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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_res = "class: {} prob: {:.3}".format(
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class_indict[str(predict_cla)], predict[predict_cla].numpy()
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)
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plt.title(print_res)
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for i in range(len(predict)):
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print(
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"class: {:10} prob: {:.3}".format(
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class_indict[str(i)], predict[i].numpy()
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)
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)
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plt.show()
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if __name__ == "__main__":
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main()
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