75 lines
1.9 KiB
Python
75 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 efficientnet_b0 as create_model
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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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img_size = {
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"B0": 224,
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"B1": 240,
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"B2": 260,
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"B3": 300,
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"B4": 380,
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"B5": 456,
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"B6": 528,
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"B7": 600,
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}
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num_model = "B0"
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data_transform = transforms.Compose(
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[
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transforms.Resize(img_size[num_model]),
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transforms.CenterCrop(img_size[num_model]),
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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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]
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)
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img_path = "../dress.png"
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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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img = img.convert("RGB")
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plt.imshow(img)
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img = data_transform(img)
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img = torch.unsqueeze(img, dim=0)
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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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model = create_model(num_classes=5).to(device)
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model_weight_path = "./weights/model-1.pth"
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model.load_state_dict(torch.load(model_weight_path, map_location=device))
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model.eval()
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with torch.no_grad():
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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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