def test(epoch, name):
###################
# params #
###################
cuda = True
cudnn.benchmark = True
batch_size = 64
image_size = 28
###################
# load data #
###################
img_transform = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
model_root = 'model'
if name == 'mnist':
mode = 'source'
image_root = os.path.join('dataset', 'mnist')
dataset = datasets.MNIST(root=image_root,
train=False,
transform=img_transform)
dataloader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=False,
num_workers=8)
elif name == 'mnist_m':
mode = 'target'
image_root = os.path.join('dataset', 'mnist_m', 'mnist_m_test')
test_list = os.path.join('dataset', 'mnist_m',
'mnist_m_test_labels.txt')
dataset = GetLoader(data_root=image_root,
data_list=test_list,
transform=img_transform)
dataloader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=False,
num_workers=8)
else:
print 'error dataset name'
####################
# load model #
####################
my_net = DSN()
checkpoint = torch.load(
os.path.join(model_root,
'dsn_mnist_mnistm_epoch_' + str(epoch) + '.pth'))
my_net.load_state_dict(checkpoint)
my_net.eval()
if cuda:
my_net = my_net.cuda()
####################
# transform image #
####################
def tr_image(img):
img_new = (img + 1) / 2
return img_new
len_dataloader = len(dataloader)
data_iter = iter(dataloader)
i = 0
n_total = 0
n_correct = 0
while i < len_dataloader:
data_input = data_iter.next()
img, label = data_input
batch_size = len(label)
input_img = torch.FloatTensor(batch_size, 3, image_size, image_size)
class_label = torch.LongTensor(batch_size)
if cuda:
img = img.cuda()
label = label.cuda()
input_img = input_img.cuda()
class_label = class_label.cuda()
input_img.resize_as_(input_img).copy_(img)
class_label.resize_as_(label).copy_(label)
inputv_img = Variable(input_img)
classv_label = Variable(class_label)
result = my_net(input_data=inputv_img,
mode='source',
rec_scheme='share')
pred = result[3].data.max(1, keepdim=True)[1]
result = my_net(input_data=inputv_img, mode=mode, rec_scheme='all')
rec_img_all = tr_image(result[-1].data)
result = my_net(input_data=inputv_img, mode=mode, rec_scheme='share')
rec_img_share = tr_image(result[-1].data)
result = my_net(input_data=inputv_img, mode=mode, rec_scheme='private')
rec_img_private = tr_image(result[-1].data)
if i == len_dataloader - 2:
vutils.save_image(rec_img_all, name + '_rec_image_all.png', nrow=8)
vutils.save_image(rec_img_share,
name + '_rec_image_share.png',
nrow=8)
vutils.save_image(rec_img_private,
name + '_rec_image_private.png',
nrow=8)
n_correct += pred.eq(classv_label.data.view_as(pred)).cpu().sum()
n_total += batch_size
i += 1
accu = n_correct * 1.0 / n_total
print 'epoch: %d, accuracy of the %s dataset: %f' % (epoch, name, accu)
def test(epoch, name):
cuda = False
cudnn.benchmark = True
batch_size = 16
image_size = 28
p = str(8)
model_root = 'dataset1' + p + 'dataset2'
################
# load data #
################
# 图形变换
source_img_transform = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(), # 归一化,进行图像的灰度处理
transforms.Lambda(lambda x: x.repeat(3, 1, 1)), # 单通道变为三通道
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
img_transform_source = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=(0.1307, ), std=(0.3081, )) #?
])
img_tranform_target = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
if name == 'dataset1':
mode = 'source'
image_root = r'D:\study\graduation_project\grdaution_project\instru_identify\dataset\dataset1\dataset1_test'
# image_root.replace("\\",'/')
test_list = r'D:\study\graduation_project\grdaution_project\instru_identify\dataset\dataset1\dataset1_test_labels.txt'
dataset = GetLoader(
data_root=image_root,
data_list=test_list,
transform=img_transform_source,
)
dataloader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=False,
num_workers=0)
# print('success')
elif name == 'dataset2':
mode = 'target'
image_root = os.path.join('D:\\', 'study', 'graduation_project',
'grdaution_project', 'instru_identify',
'dataset', 'dataset2', 'dataset2_test')
test_list = os.path.join('D:\\', 'study', 'graduation_project',
'grdaution_project', 'instru_identify',
'dataset', 'dataset2',
'dataset2_test_labels.txt')
dataset = GetLoader(
data_root=image_root,
data_list=test_list,
transform=img_tranform_target,
)
dataloader = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=batch_size,
shuffle=False,
num_workers=0 #?
)
else:
print('error dataset name')
###############
# load model #
###############
# print('image_root:', image_root)
# print('test_list:',test_list)
my_net = DSN()
checkpoint = torch.load(
os.path.join(model_root, 'dsn_epoch_' + str(epoch) + '.pth'))
my_net.load_state_dict(checkpoint)
my_net.eval() #?
if cuda:
my_net = my_net #.cuda()
###################
# transform image #
###################
# 这个函数对图片做了什么操作？
def tr_image(img):
img_new = (img + 1) / 2
return img_new
# print(dataloader)
len_dataloader = len(dataloader)
# print('len_dataloader:',len_dataloader)
data_iter = iter(dataloader) # 获取迭代器
# print('data_iter:',data_iter)
i = 0
n_total = 0
n_correct = 0
total_accu = 0
while i < len_dataloader - 1:
#print(i)
data_input = data_iter.next()
#print('data_input:', data_input)
img, label = data_input
# print('label:', label)
batch_size = len(label) # batch_size为一个batch中图片的数量
input_img = torch.FloatTensor(batch_size, 3, image_size, image_size)
class_label = torch.LongTensor(batch_size)
if cuda:
img = img #.cuda()
label = label #.cuda()
input_img = input_img #.cuda()
class_label = class_label #.cuda()
input_img.resize_as_(input_img).copy_(img)
class_label.resize_as_(class_label).copy_(label)
inputv_img = Variable(input_img) #?
classv_label = Variable(class_label)
# 输入网络
result = my_net(input_data=inputv_img,
mode='source',
rec_scheme='share')
pred = result[3].data.max(1, keepdim=True)[1]
# print('pred:',pred)
result = my_net(input_data=inputv_img, mode=mode, rec_scheme='all')
rec_img_all = tr_image(result[-1].data)
result = my_net(input_data=inputv_img, mode=mode, rec_scheme='share')
rec_img_share = tr_image(result[-1].data)
result = my_net(input_data=inputv_img, mode=mode, rec_scheme='private')
rec_img_private = tr_image(result[-1].data)
if i == len_dataloader - 2:
image_save_path = os.path.join(model_root, 'images')
if not os.path.exists(image_save_path):
os.mkdir(image_save_path)
vutils.save_image(rec_img_all,
image_save_path + '/' + name +
'_rec_image_all.png',
nrow=8)
vutils.save_image(rec_img_share,
image_save_path + '/' + name +
'rec_image_share.png',
nrow=8)
vutils.save_image(rec_img_private,
image_save_path + '/' + name +
'rec_image_private.png',
nrow=8)
n_correct += pred.eq(classv_label.data.view_as(pred)).cpu().sum()
n_total += batch_size
i += 1
accu = n_correct * 1.0 / n_total
# print('n_correct:', n_correct)
# print('n_total:', n_total)
# print('epoch: %d,accuracy of the %s dataset: %f' % (epoch, name, accu))
return accu