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test.py
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import argparse
import os,sys
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import transforms
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from thop import profile
import glob
from tqdm import tqdm
import numpy as np
from skimage import io
from PIL import Image
from utils.common import get_network, load_data, get_compress_rate
from data.data_loader import RescaleT
from data.data_loader import ToTensorLab
from data.data_loader import SalObjDataset
cudnn.benchmark = True
cudnn.enabled=True
def test():
net.eval()
test_loss = 0
correct = 0
for data, target in val_loader:
if torch.cuda.is_available():
data, target = data.cuda(), target.cuda()
with torch.no_grad():
data, target = Variable(data), Variable(target)
output = net(data)
test_loss += loss_function(output, target).data.item()
pred = output.data.max(1)[1]
correct += pred.eq(target.data).cpu().sum()
test_loss /= len(val_loader)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'.format(test_loss, correct, len(val_loader.dataset), 100. * correct / len(val_loader.dataset)))
return correct.item() / len(val_loader.dataset)
def test_u2netp():
net.eval()
for i_test, data_test in tqdm(enumerate(test_salobj_dataloader)):
print("inferencing:",img_name_list[i_test].split(os.sep)[-1])
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
d1,d2,d3,d4,d5,d6,d7= net(inputs_test)
# normalization
pred = d1[:,0,:,:]
pred = normPRED(pred)
# save results to test_results folder
if not os.path.exists(prediction_dir):
os.makedirs(prediction_dir, exist_ok=True)
save_output(img_name_list[i_test],pred,prediction_dir)
del d1,d2,d3,d4,d5,d6,d7
def save_output(image_name,pred,d_dir):
predict = pred
predict = predict.squeeze()
predict_np = predict.cpu().data.numpy()
im = Image.fromarray(predict_np*255).convert('RGB')
img_name = image_name.split(os.sep)[-1]
image = io.imread(image_name)
imo = im.resize((image.shape[1],image.shape[0]),resample=Image.BILINEAR)
aaa = img_name.split(".")
bbb = aaa[0:-1]
imidx = bbb[0]
for i in range(1,len(bbb)):
imidx = imidx + "." + bbb[i]
imo.save(d_dir+imidx+'.png')
def normPRED(d):
ma = torch.max(d)
mi = torch.min(d)
dn = (d-mi)/(ma-mi)
return dn
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Networks Pruning')
parser.add_argument('--dataset',type=str,default='cifar10',choices=('cifar10','imagenet','DUTS'),help='dataset')
parser.add_argument('--data_dir',type=str,default='./data',help='path to dataset')
parser.add_argument('--batch_size',type=int,default=32,help='batch size')
parser.add_argument('--test_model_dir',type=str,default='',help='test model path')
parser.add_argument('--compress_rate',type=str,default='[0.]*99',help='compress rate of each conv')
parser.add_argument(
'--net',
type=str,
default='vgg_16_bn',
choices=('resnet_50','vgg_16_bn','resnet_56',
'resnet_110','densenet_40','googlenet','u2netp'),
help='net type')
args = parser.parse_args()
print('==> Loading data of {}..'.format(args.dataset))
_, val_loader = load_data(args)
print('==> Building model..')
compress_rate = get_compress_rate(args)
net = get_network(args, compress_rate)
print('{}:'.format(args.net))
flops, params = profile(net, inputs=(torch.randn(1, 3, 32, 32,
device='cuda' if torch.cuda.is_available() else None),))
print('Params: %.2f' % (params))
print('Flops: %.2f' % (flops))
print('Compress_Rate: {}'.format(compress_rate))
if args.dataset == 'cifar10':
if os.path.isfile(args.test_model_dir):
print('loading checkpoint {} ..........'.format(args.test_model_dir))
checkpoint = torch.load(args.test_model_dir, map_location='cpu')
net.load_state_dict(checkpoint)
else:
print('please specify a checkpoint file')
sys.exit()
loss_function = nn.CrossEntropyLoss()
if torch.cuda.is_available():
loss_function.cuda()
test()
if args.dataset == 'DUTS':
prediction_dir = os.path.join(args.data_dir, args.net + '_DUTS-TE_results' + os.sep)
image_dir = os.path.join(args.data_dir, 'DUTS-TE/DUTS-TE-Image')
img_name_list = glob.glob(image_dir + os.sep + '*')
test_salobj_dataset = SalObjDataset(img_name_list = img_name_list,
lbl_name_list = [],
transform=transforms.Compose([RescaleT(320),
ToTensorLab(flag=0)])
)
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
if os.path.isfile(args.test_model_dir):
print('loading checkpoint {} ..........'.format(args.test_model_dir))
checkpoint = torch.load(args.test_model_dir, map_location='cpu')
net.load_state_dict(checkpoint)
else:
print('please specify a checkpoint file')
sys.exit()
test_u2netp()
if args.dataset == 'imagenet':
if os.path.isfile(args.test_model_dir):
print('loading checkpoint {} ..........'.format(args.test_model_dir))
checkpoint = torch.load(args.test_model_dir, map_location='cpu')
net.load_state_dict(checkpoint)
else:
print('please specify a checkpoint file')
sys.exit()
loss_function = nn.CrossEntropyLoss()
if torch.cuda.is_available():
loss_function.cuda()
test()