FantasyJXF
commented
5 years ago #
# demo.py
#
import argparse
import os
import numpy as np
from modeling.deeplab import *
from dataloaders import custom_transforms as tr
from PIL import Image
from torchvision import transforms
from dataloaders.utils import *
from torchvision.utils import make_grid, save_image
def main():
parser = argparse.ArgumentParser(description="PyTorch DeeplabV3Plus Training")
parser.add_argument('--in-path', type=str, required=True, help='image to test')
parser.add_argument('--out-path', type=str, required=True, help='mask image to save')
parser.add_argument('--backbone', type=str, default='resnet',
choices=['resnet', 'xception', 'drn', 'mobilenet'],
help='backbone name (default: resnet)')
parser.add_argument('--ckpt', type=str, default='deeplab-resnet.pth',
help='saved model')
parser.add_argument('--out-stride', type=int, default=16,
help='network output stride (default: 8)')
parser.add_argument('--no-cuda', action='store_true', default=True,
help='disables CUDA training')
parser.add_argument('--gpu-ids', type=str, default='0',
help='use which gpu to train, must be a \
comma-separated list of integers only (default=0)')
parser.add_argument('--dataset', type=str, default='pascal',
choices=['pascal', 'coco', 'cityscapes'],
help='dataset name (default: pascal)')
parser.add_argument('--crop-size', type=int, default=513,
help='crop image size')
parser.add_argument('--sync-bn', type=bool, default=None,
help='whether to use sync bn (default: auto)')
parser.add_argument('--freeze-bn', type=bool, default=False,
help='whether to freeze bn parameters (default: False)')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
try:
args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')]
except ValueError:
raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only')
if args.sync_bn is None:
if args.cuda and len(args.gpu_ids) > 1:
args.sync_bn = True
else:
args.sync_bn = False
model = DeepLab(num_classes=21,
backbone=args.backbone,
output_stride=args.out_stride,
sync_bn=args.sync_bn,
freeze_bn=args.freeze_bn)
ckpt = torch.load(args.ckpt, map_location='cpu')
model.load_state_dict(ckpt['state_dict'])
composed_transforms = transforms.Compose([
tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
tr.ToTensor()])
image = Image.open(args.in_path).convert('RGB')
target = Image.open(args.in_path).convert('L')
sample = {'image': image, 'label': target}
tensor_in = composed_transforms(sample)['image'].unsqueeze(0)
model.eval()
if args.cuda:
image = image.cuda()
with torch.no_grad():
output = model(tensor_in)
grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy()),
3, normalize=False, range=(0, 255))
print("type(grid) is: ", type(grid_image))
print("grid_image.shape is: ", grid_image.shape)
save_image(grid_image, args.out_path)
if __name__ == "__main__":
main()
download the pretrained model and test with the following line:
python demo.py --in-path your_file --out-path your_dst_fileJust enjoy it.
beizhengren
TomSirLiu
JasonChenhx
raghavgurbaxani
wangwangteam
Ellsionjeep
Thunder003
ouyang11111
hello,i also want to test single one image in my own path,have you solve it?