test.py

from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
from data.dataset import hand_dataset

from vgg import vgg
import shutil

# Training settings
parser = argparse.ArgumentParser(description='PyTorch Slimming CIFAR training')
parser.add_argument('--dataset', type=str, default='cifar10',
                    help='training dataset (default: cifar10)')
parser.add_argument('--model', default='', type=str, metavar='PATH',
                    help='model for evaluation')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                    help='input batch size for testing (default: 1000)')
parser.add_argument('--no-cuda', action='store_true', default=False,
                    help='disables CUDA training')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()

kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
device = torch.device("cuda" if args.cuda else "cpu")
test_loader = torch.utils.data.DataLoader(
    hand_dataset('./data', 'test.txt', transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                   ])),
    batch_size=args.test_batch_size, shuffle=True, **kwargs)

checkpoint = torch.load(args.model)
if 'cfg' in checkpoint.keys():
    cfg = checkpoint['cfg']
else:
    cfg = None
model = vgg('handdata', cfg=cfg)
model.load_state_dict(checkpoint['state_dict'])
model.to(device)

def test():
    model.eval()
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        data, target = data.to(device), target.to(device)
        output = model(data)
        # test_loss += F.cross_entropy(output, target, size_average=False).data # sum up batch loss
        test_loss += F.cross_entropy(output, target, reduction='sum').item()
        pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
        correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.1f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))
    return correct / float(len(test_loader.dataset))

test()