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prediction.py
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prediction.py
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import torch
from nets.resnet50 import ResNet,Bottleneck
import os
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable
import torchvision
import cv2
import time
PATH = './logs/resnet50-mnist.pth'
Batch_Size = int(input('每次预测手写字体图片个数:'))
model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=10)
model.load_state_dict(torch.load(PATH))
model = model.cuda()
model.eval()
test_dataset = datasets.MNIST(root='data/', train=False,
transform=transforms.ToTensor(), download=False)
gen_test = DataLoader(dataset=test_dataset, batch_size=Batch_Size, shuffle=True)
while True:
images, lables = next(iter(gen_test))
img = torchvision.utils.make_grid(images, nrow=Batch_Size)
img_array = img.numpy().transpose(1, 2, 0)
start_time = time.time()
outputs = model(images.cuda())
_, id = torch.max(outputs.data, 1)
end_time = time.time()
print('预测用时:', end_time-start_time)
print('预测结果为', id.data.cpu().numpy())
cv2.imshow('img', img_array)
cv2.waitKey(0)