def get_parser():
parser = argparse.ArgumentParser(
description='PyTorch Semantic Segmentation')
parser.add_argument('--config',
type=str,
default='config/ade20k/ade20k_pspnet50.yaml',
help='config file')
parser.add_argument(
'opts',
help='see config/ade20k/ade20k_pspnet50.yaml for all options',
default=None,
nargs=argparse.REMAINDER)
args = parser.parse_args()
assert args.config is not None
cfg = config.load_cfg_from_cfg_file(args.config)
if args.opts is not None:
cfg = config.merge_cfg_from_list(cfg, args.opts)
check(cfg)
# backup the config file
if os.path.exists(cfg.save_path):
shutil.rmtree(cfg.save_path)
os.makedirs(cfg.save_path)
shutil.copyfile(args.config, os.path.join(cfg.save_path, "config.yaml"))
return cfg
def get_parser():
parser = argparse.ArgumentParser(description='Few-Shot Semantic Segmentation')
parser.add_argument('--config', type=str, default='config/MSD/fold0_resnet50.yaml', help='config file')
args = parser.parse_args()
cfg = config.load_cfg_from_cfg_file(args.config)
return cfg
def get_parser():
parser = argparse.ArgumentParser(description='PyTorch Semantic Segmentation')
parser.add_argument('--config', type=str, default='config/ade20k/ade20k_pspnet50.yaml', help='config file')
parser.add_argument('opts', help='see config/ade20k/ade20k_pspnet50.yaml for all options', default=None, nargs=argparse.REMAINDER)
args = parser.parse_args()
assert args.config is not None
cfg = config.load_cfg_from_cfg_file(args.config)
if args.opts is not None:
cfg = config.merge_cfg_from_list(cfg, args.opts)
return cfg
def get_parser():
#parser = argparse.ArgumentParser(description='PyTorch Semantic Segmentation')
#parser.add_argument('--config', type=str, default='/home/agalex/PycharmProjects/torch_1_3_0/segmentation/config/voc2012/voc2012_deeplabv3_resnet101.yaml', help='config file')
#parser.add_argument('opts', help='see /home/agalex/PycharmProjects/torch_1_3_0/segmentation/config/voc2012/voc2012_pspnet101.yaml for all options', default=None, nargs=argparse.REMAINDER)
#args = parser.parse_args()
#assert args.config is not None
cfg = config.load_cfg_from_cfg_file('/content/drive/My Drive/Segmentation/config/voc2012/voc2012_deeplabv3_resnet101.yaml')
#if args.opts is not None:
# cfg = config.merge_cfg_from_list(cfg, args.opts)
return cfg
def get_parser():
parser = argparse.ArgumentParser(description='PyTorch Semantic Segmentation')
parser.add_argument('--config', type=str, default='config/cityscapes/cityscapes_pspnet18.yaml', help='config file')
parser.add_argument('--image', type=str, default='figure/demo/cityscapes.jpg', help='input image')
parser.add_argument('opts', help='see config/cityscapes/cityscapes_pspnet18.yaml for all options', default=None, nargs=argparse.REMAINDER)
args = parser.parse_args()
assert args.config is not None
cfg = config.load_cfg_from_cfg_file(args.config)
cfg.image = args.image
if args.opts is not None:
cfg = config.merge_cfg_from_list(cfg, args.opts)
return cfg
def __init__(self, config_file=CONFIG_FILE):
# Load Parameters
self.args_ = config.load_cfg_from_cfg_file(config_file)
self.logger_ = get_logger()
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(
str(x) for x in self.args_.test_gpu)
value_scale = 255
mean = [0.485, 0.456, 0.406]
self.mean_ = [item * value_scale for item in mean]
std = [0.229, 0.224, 0.225]
self.std_ = [item * value_scale for item in std]
# self.colors_ = np.loadtxt(self.args_.colors_path).astype('uint8')
# Load Model
if self.args_.arch == 'psp':
from model.pspnet import PSPNet
self.model_ = PSPNet(layers=self.args_.layers,
classes=self.args_.classes,
zoom_factor=self.args_.zoom_factor,
pretrained=False)
elif self.args_.arch == 'psa':
from model.psanet import PSANet
self.model_ = PSANet(
layers=self.args_.layers,
classes=self.args_.classes,
zoom_factor=self.args_.zoom_factor,
compact=self.args_.compact,
shrink_factor=self.args_.shrink_factor,
mask_h=self.args_.mask_h,
mask_w=self.args_.mask_w,
normalization_factor=self.args_.normalization_factor,
psa_softmax=self.args_.psa_softmax,
pretrained=False)
self.model_ = torch.nn.DataParallel(self.model_).cuda()
cudnn.benchmark = True
if os.path.isfile(self.args_.model_path):
self.logger_ = get_logger().info(
"=> loading checkpoint '{}'".format(self.args_.model_path))
checkpoint = torch.load(self.args_.model_path)
self.model_.load_state_dict(checkpoint['state_dict'], strict=False)
self.logger_ = get_logger().info(
"=> loaded checkpoint '{}'".format(self.args_.model_path))
else:
raise RuntimeError("=> no checkpoint found at '{}'".format(
self.args_.model_path))
def get_parser():
parser = argparse.ArgumentParser(
description='PyTorch Point Cloud Classification / Semantic Segmentation'
)
# parser.add_argument('--config', type=str, default='config/s3dis/s3dis_pointweb.yaml', help='config file')
parser.add_argument('--config',
type=str,
default='config/modelnet40/modelnet40_pointweb.yaml',
help='config file')
parser.add_argument(
'opts',
help='see config/s3dis/s3dis_pointweb.yaml for all options',
default=None,
nargs=argparse.REMAINDER)
args = parser.parse_args()
assert args.config is not None
cfg = config.load_cfg_from_cfg_file(args.config)
if args.opts is not None:
cfg = config.merge_cfg_from_list(cfg, args.opts)
return cfg
def get_parser():
parser = argparse.ArgumentParser(
description='PyTorch Semantic Segmentation') # 创建 ArgumentParser() 对象
# 添加参数：--表示可选参数，否则为定位参数
parser.add_argument(
'--config',
type=str,
default='/data/ss/URISC/CODE/FullVersion/config/URISC/urisc_unet.yaml',
help='config file')
parser.add_argument(
'opts',
help='see config/URISC/urisc_unet.yaml for all options',
default=None,
nargs=argparse.REMAINDER)
# 解析添加的参数
args = parser.parse_args()
assert args.config is not None
# 将yaml文件加载为CfgNode
cfg = config.load_cfg_from_cfg_file(args.config)
if args.opts is not None:
cfg = config.merge_cfg_from_list(cfg, args.opts)
return cfg
def main(
config_name,
weights_url='https://github.com/deepparrot/semseg/releases/download/0.1/pspnet50-ade20k.pth',
weights_name='pspnet50-ade20k.pth'):
args = config.load_cfg_from_cfg_file(config_name)
check(args)
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(
str(x) for x in args.test_gpu)
value_scale = 255
mean = [0.485, 0.456, 0.406]
mean = [item * value_scale for item in mean]
std = [0.229, 0.224, 0.225]
std = [item * value_scale for item in std]
gray_folder = os.path.join(args.save_folder, 'gray')
color_folder = os.path.join(args.save_folder, 'color')
args.data_root = './.data/vision/ade20k'
args.val_list = './.data/vision/ade20k/validation.txt'
args.test_list = './.data/vision/ade20k/validation.txt'
print(args.data_root)
test_transform = transform.Compose([transform.ToTensor()])
test_data = dataset.SemData(split=args.split,
data_root=args.data_root,
data_list=args.test_list,
transform=test_transform)
index_start = args.index_start
if args.index_step == 0:
index_end = len(test_data.data_list)
else:
index_end = min(index_start + args.index_step,
len(test_data.data_list))
test_data.data_list = test_data.data_list[index_start:index_end]
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=1,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
colors = np.loadtxt(args.colors_path).astype('uint8')
names = []
if not args.has_prediction:
if args.arch == 'psp':
from model.pspnet import PSPNet
model = PSPNet(layers=args.layers,
classes=args.classes,
zoom_factor=args.zoom_factor,
pretrained=False)
elif args.arch == 'psa':
from model.psanet import PSANet
model = PSANet(layers=args.layers,
classes=args.classes,
zoom_factor=args.zoom_factor,
compact=args.compact,
shrink_factor=args.shrink_factor,
mask_h=args.mask_h,
mask_w=args.mask_w,
normalization_factor=args.normalization_factor,
psa_softmax=args.psa_softmax,
pretrained=False)
model = torch.nn.DataParallel(model).cuda()
cudnn.benchmark = True
local_checkpoint, _ = urllib.request.urlretrieve(
weights_url, weights_name)
if os.path.isfile(local_checkpoint):
checkpoint = torch.load(local_checkpoint)
model.load_state_dict(checkpoint['state_dict'], strict=False)
else:
raise RuntimeError(
"=> no checkpoint found at '{}'".format(local_checkpoint))
test(test_loader, test_data.data_list, model, args.classes, mean, std,
args.base_size, args.test_h, args.test_w, args.scales,
gray_folder, color_folder, colors)
if args.split != 'test':
cal_acc(test_data.data_list, gray_folder, args.classes, names)
# Model 1
config_path = 'config/imagenet/imagenet_san10_pairwise.yaml'
file_id = '1lv5TYfJFYvNWt_Ik0E-nAuI5h4PqSwuk'
destination = './tmp/'
filename = 'imagenet_san10_pairwise.pth'
download_file_from_google_drive(file_id, destination, filename=filename)
checkpoint = torch.load(os.path.join(destination, filename))
sd = {}
for key in checkpoint['state_dict']:
sd[key.replace('module.', '')] = checkpoint['state_dict'][key]
# Define the transforms need to convert ImageNet data to expected model input
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
input_transform = transforms.Compose(
[transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean, std)])
args = config.load_cfg_from_cfg_file(config_path)
model = san(args.sa_type, args.layers, args.kernels, args.classes)
model.load_state_dict(sd)
# Run the benchmark
ImageNet.benchmark(
model=model,
paper_model_name='SAN10-pairwise',
paper_arxiv_id='2004.13621',
input_transform=input_transform,
batch_size=256,
num_gpu=1,
paper_results={'Top 1 Accuracy': 0.749, 'Top 5 Accuracy': 0.921},
model_description="Official weights from the authors of the paper.",
)
torch.cuda.empty_cache()
def get_parser():
cfg = config.load_cfg_from_cfg_file(CONFIG_FILE)
return cfg
