def parse_args():
"""
args for training.
"""
parser = argparse.ArgumentParser(description='finetune the supernet')
# general
parser.add_argument('--cfg',
type=str,
default='experiments/train/Ocean.yaml',
help='yaml configure file name')
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
parser.add_argument('--gpus', type=str, help='gpus')
parser.add_argument('--workers',
type=int,
help='num of dataloader workers')
parser.add_argument('--CHECKPOINT_DIR', type=str, default='')
parser.add_argument('--OUTPUT_DIR', type=str, default='')
parser.add_argument('--path_name', type=str, required=True)
parser.add_argument('--pretrain_dir', type=str, required=True)
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(description='Test localization network')
# general
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# testing
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
parser.add_argument('--dataDir', help='data path', type=str)
parser.add_argument('--modelDir', help='model path', type=str)
parser.add_argument('--logDir', help='log path', type=str)
parser.add_argument('--split',
default='val',
required=True,
choices=['train', 'val', 'test'],
type=str)
parser.add_argument('--verbose',
default=False,
action="store_true",
help='print progress bar')
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(description='Generate Data For RPSM')
parser.add_argument('--cfg',
help='configuration file name',
required=True,
type=str)
parser.add_argument('--heatmap',
help='heatmap file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
update_config(args.cfg)
parser.add_argument('--modelDir',
help='model directory',
type=str,
default='')
parser.add_argument('--logDir', help='log directory', type=str, default='')
parser.add_argument('--dataDir',
help='data directory',
type=str,
default='')
parser.add_argument('--no-distortion',
help='wheter use no distortion data',
action='store_true')
args = parser.parse_args()
update_dir(args.modelDir, args.logDir, args.dataDir)
return args
def parse_args():
parser = argparse.ArgumentParser(description='Keypoint detection')
# general
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
parser.add_argument('--model-file', help='model state file', type=str)
parser.add_argument('--flip-test',
help='use flip test',
action='store_true')
parser.add_argument('--post-process',
help='use post process',
action='store_true')
parser.add_argument('--shift-heatmap',
help='shift heatmap',
action='store_true')
parser.add_argument('--image-path',
help='image path',
default="/home/manhas/Pictures/crop_image/2.jpg")
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(description="Train keypoints network")
# general
parser.add_argument("--cfg",
help="experiment configure file name",
required=True,
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument("--frequent",
help="frequency of logging",
default=config.PRINT_FREQ,
type=int)
parser.add_argument("--gpus", help="gpus", type=str)
parser.add_argument("--workers",
help="num of dataloader workers",
type=int)
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
args = parser.parse_args()
print(
'final args !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!'
)
print(args)
return args
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate 3D Pose Estimation')
parser.add_argument('--cfg',
help='configuration file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
update_config(args.cfg)
parser.add_argument('--modelDir',
help='model directory',
type=str,
default='')
parser.add_argument('--logDir', help='log directory', type=str, default='')
parser.add_argument('--dataDir',
help='data directory',
type=str,
default='')
parser.add_argument('--withIMU',
help='use bone orientation in 3d',
type=lambda x:
(str(x).lower() in ['true', '1', 'yes']),
default=False)
args = parser.parse_args()
update_dir(args.modelDir, args.logDir, args.dataDir)
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
parser.add_argument(
'--model',
help='pretraind model',
type=str,
default=
'/export/home/lfy/zby/human-pose-estimation.pytorch/models/pytorch/pose_coco/pose_resnet_152_384x288.pth.tar'
)
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate 3D Pose Estimation')
parser.add_argument(
'--cfg', help='configuration file name', required=True, type=str)
args, rest = parser.parse_known_args()
update_config(args.cfg)
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--model-file', help='model state file', type=str)
parser.add_argument('--img-file',
help='input your test img',
type=str,
default='')
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
parser.add_argument(
'--cfg', help='experiment configure file name', required=True, type=str)
args, rest = parser.parse_known_args()
update_config(args.cfg)
parser.add_argument(
'--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument('--workers', help='num of dataloader workers', type=int)
parser.add_argument(
'--modelDir', help='model directory', type=str, default='')
parser.add_argument('--logDir', help='log directory', type=str, default='')
parser.add_argument(
'--dataDir', help='data directory', type=str, default='')
parser.add_argument(
'--data-format', help='data format', type=str, default='')
parser.add_argument(
'--on-server-cluster', help='if training on cluster', action='store_true')
parser.add_argument(
'--iteration', help='the kth times of training', type=int, choices=range(1,10), default=1)
parser.add_argument(
'--no-distortion', help='wheter use no distortion data', action='store_true')
args = parser.parse_args()
update_dir(args.modelDir, args.logDir, args.dataDir)
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument(
'--cfg',
help='experiment configure file name',
default=
'/home/gyh/human-pose-estimation/experiments/coco/resnet50/256x192_d256x3_adam_lr1e-3.yaml',
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
parser.add_argument(
'--cfg', help='experiment configure file name', required=True, type=str)
args, rest = parser.parse_known_args()
update_config(args.cfg)
parser.add_argument(
'--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument('--workers', help='num of dataloader workers', type=int)
parser.add_argument(
'--modelDir', help='model directory', type=str, default='')
parser.add_argument('--logDir', help='log directory', type=str, default='')
parser.add_argument(
'--dataDir', help='data directory', type=str, default='')
parser.add_argument(
'--data-format', help='data format', type=str, default='')
args = parser.parse_args()
update_dir(args.modelDir, args.logDir, args.dataDir)
return args
def get_pose_model():
args = GetArgs()
update_config(args.cfg)
reset_config(config, args)
# load pose-hrnet MODEL
pose_model = load_model(config)
pose_model.cuda().eval()
return pose_model
def parse_args():
parser = argparse.ArgumentParser(
description='Test Recursive Pictorial Structure Model')
parser.add_argument(
'--cfg', help='experiment configure file name', required=True, type=str)
args, rest = parser.parse_known_args()
update_config(args.cfg)
return args
def parse_args():
parser = argparse.ArgumentParser(description='Generate Data For RPSM')
parser.add_argument(
'--cfg', help='configuration file name', required=True, type=str)
parser.add_argument(
'--heatmap', help='heatmap file name', required=True, type=str)
args, rest = parser.parse_known_args()
update_config(args.cfg)
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument(
'--state',
help='the state of model which is used to test (best or final)',
default='best',
type=str)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
parser.add_argument('--model-file', help='model state file', type=str)
parser.add_argument('--flip-test',
help='use flip test',
action='store_true')
parser.add_argument('--post-process',
help='use post process',
action='store_true')
parser.add_argument('--shift-heatmap',
help='shift heatmap',
action='store_true')
# philly
parser.add_argument('--modelDir',
help='model directory',
type=str,
default='')
parser.add_argument('--logDir', help='log directory', type=str, default='')
parser.add_argument('--dataDir',
help='data directory',
type=str,
default='')
parser.add_argument('--data-format',
help='data format',
type=str,
default='')
args = parser.parse_args()
update_dir(args.modelDir, args.logDir, args.dataDir)
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
parser.add_argument('--model-file', help='model state file', type=str)
parser.add_argument('--use-detect-bbox',
help='use detect bbox',
action='store_true')
parser.add_argument('--flip-test',
help='use flip test',
action='store_true')
parser.add_argument('--post-process',
help='use post process',
action='store_true')
parser.add_argument('--shift-heatmap',
help='shift heatmap',
action='store_true')
parser.add_argument('--coco-bbox-file',
help='coco detection bbox file',
type=str)
parser.add_argument('--nltype', help='type of nonlocal block', type=str)
parser.add_argument('--spectrum_relu',
help='using spectrum_relu',
action="store_true")
parser.add_argument(
'--enc_no_ds',
help='no downsampling in the last two layers of encoder',
action="store_true")
parser.add_argument('--enc_nl',
help='add nonlocal block at encoder',
action="store_true")
parser.add_argument('--dec_nl',
help='add nonlocal block at decoder',
nargs='+',
type=int,
default=None)
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
parser.add_argument(
'--cfg', help='experiment configure file name', required=True, type=str)
args, rest = parser.parse_known_args()
update_config(args.cfg)
return args
def parse_args():
parser = argparse.ArgumentParser(
description='Generate Pairwise Constraint for RPSM')
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
update_config(args.cfg)
return args
def parse_args():
parser = argparse.ArgumentParser(description='Test Pseudo Labels')
# Required Param
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
parser.add_argument('--heatmap',
help='heatmap file name',
default='',
required=True,
type=str)
args, rest = parser.parse_known_args()
update_config(args.cfg)
parser.add_argument('--confidence-thre',
help='max pixel error to accept inliers',
type=float)
parser.add_argument('--ransac', help='use ransac', action='store_true')
parser.add_argument('--inliers',
help='min support num of inliers',
type=int)
parser.add_argument('--reproj-thre',
help='reprojection threshold to accept inliers',
type=int)
parser.add_argument('--use-reproj',
help='use reprojected 2d as label',
action='store_true')
parser.add_argument(
'--loop',
help='whether in loop training, only generate one pseudo_label',
action='store_true')
parser.add_argument('--no-distortion',
help='whether use no distortion data',
action='store_true')
parser.add_argument('--net-layers',
help='network layers',
type=int,
default=50,
choices=[50, 152])
parser.add_argument('--modelDir',
help='model directory',
type=str,
default='')
parser.add_argument('--logDir', help='log directory', type=str, default='')
parser.add_argument('--dataDir',
help='data directory',
type=str,
default='')
args = parser.parse_args()
update_dir(args.modelDir, args.logDir, args.dataDir)
return args
def parse_args():
parser = argparse.ArgumentParser(description='Test Pseudo Labels')
# Required Param
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
update_config(args.cfg)
args = parser.parse_args()
return args
def get_two_model():
args = GetArgs()
update_config(args.cfg)
reset_config(config, args)
# load pose-hrnet MODEL
pose_model = load_model(config)
pose_model.cuda().eval()
# load YoloV3 Model
bbox_model = yolo_model()
return bbox_model, pose_model
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument(
'--cfg',
help='experiment configure file name',
# required=True,
default='experiments/coco/resnet50/384x288_d256x3_adam_lr1e-3.yaml',
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
parser.add_argument(
'--model-file',
help='model state file',
default='models/pytorch/pose_resnet_50_384x288.pth.tar',
type=str)
parser.add_argument('--use-detect-bbox',
help='use detect bbox',
action='store_true')
parser.add_argument('--flip-test',
help='use flip test',
action='store_true')
parser.add_argument('--post-process',
help='use post process',
action='store_true')
parser.add_argument('--shift-heatmap',
help='shift heatmap',
action='store_true')
parser.add_argument('--coco-bbox-file',
help='coco detection bbox file',
type=str)
parser.add_argument('--det-file',
dest='det_file',
default='',
help='json file include image path and bbox',
type=str)
args = parser.parse_args()
return args
def main():
parser = argparse.ArgumentParser(description='model training')
parser.add_argument('--config_file', type=str, default=None, help='optional config file for training')
parser.add_argument('--save_dir', type=str, default='checkpoints', help='save model directory')
args = parser.parse_args()
if args.config_file is not None:
update_config(args.config_file)
opt.misc.save_dir = args.save_dir
#指定gpu
os.environ['CUDA_VISIBLE_DEVICES'] = opt.train.gpus
cudnn.benchmark = True
train(args)
def __init__(self, cuda_id=0, fast_yolo=False):
self.time_det = 0.0
self.time_run = 0.0
self.num_joints = 17
self.target_kps = [5, 6, 7, 8, 9, 10]
# Load yolo detection model
print('Loading YOLO model..')
if fast_yolo:
self.det_model = Darknet('./AlphaPose/yolo/cfg/yolov3-tiny.cfg')
self.det_model.load_weights(
'./AlphaPose/models/yolo/yolov3-tiny.weights')
else:
self.det_model = Darknet("./AlphaPose/yolo/cfg/yolov3.cfg")
self.det_model.load_weights(
'./AlphaPose/models/yolo/yolov3.weights')
self.det_model.cuda()
self.det_model.eval()
cfg_file = 'MSRAPose/experiments/coco/resnet50/256x192_d256x3_adam_lr1e-3.yaml'
model_file = 'MSRAPose/models/pytorch/pose_coco/pose_resnet_50_256x192.pth.tar'
# update config
update_config(cfg_file)
config.TEST.MODEL_FILE = model_file
# cudnn related setting
cudnn.benchmark = config.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = config.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = config.CUDNN.ENABLED
# load pre-trained model
self.model = eval('models_msra.' + config.MODEL.NAME +
'.get_pose_net')(config, is_train=False)
print('Loading MSRA pose model..')
print('=> loading model from {}'.format(config.TEST.MODEL_FILE))
self.model.load_state_dict(torch.load(config.TEST.MODEL_FILE))
gpus = [int(i) for i in config.GPUS.split(',')]
self.model = torch.nn.DataParallel(self.model, device_ids=gpus).cuda()
self.model.eval()
# image transform
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
def main():
parser = argparse.ArgumentParser(description='reid model testing')
parser.add_argument('--config_file', type=str, default=None,
help='Optional config file for params')
parser.add_argument('--load_model', type=str, required=True,
help='load trained model for testing')
args = parser.parse_args()
if args.config_file is not None:
update_config(args.config_file)
os.environ["CUDA_VISIBLE_DEVICES"] = opt.network.gpus
cudnn.benchmark = True
test(args)
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--gpus',
help='gpus',
type=str)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
parser.add_argument('opts',
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER)
# philly
parser.add_argument('--modelDir',
help='model directory',
type=str,
default='')
parser.add_argument('--logDir',
help='log directory',
type=str,
default='')
parser.add_argument('--dataDir',
help='data directory',
type=str,
default='')
parser.add_argument('--prevModelDir',
help='prev Model directory',
type=str,
default='')
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
update_config(args.cfg)
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
parser.add_argument('--cams', help='view/cam id to use', type=str)
# parser.add_argument(
# '--ablation', help='heatmap_only, consistency_only, both', type=str)
parser.add_argument('--modelDir',
help='model directory',
type=str,
default='')
parser.add_argument('--logDir', help='log directory', type=str, default='')
parser.add_argument('--dataDir',
help='data directory',
type=str,
default='')
parser.add_argument('--data-format',
help='data format',
type=str,
default='')
parser.add_argument('--runMode',
help='train or test',
type=str,
default='train')
parser.add_argument('--modelFile',
help='model for test',
type=str,
default='0')
parser.add_argument('--evaluate',
help='directly use provided model to evaluate results',
type=str2bool,
default='0')
args = parser.parse_args()
update_dir(args.modelDir, args.logDir, args.dataDir)
return args
def main():
args = parse_args()
# update config
update_config(args.cfg)
reset_config(config, args)
# output dir path
onnx_path = './models/onnx/'
logger, final_output_dir, tb_log_dir = create_logger(
config, args.cfg, 'convert')
if not os.path.isdir(onnx_path):
logger.info('Creating ' + onnx_path + 'folder...')
os.makedirs(onnx_path)
height = 0
width = 0
with open(args.cfg) as file:
documents = yaml.load(file, Loader=yaml.FullLoader)
height = documents['MODEL']['IMAGE_SIZE'][0]
width = documents['MODEL']['IMAGE_SIZE'][1]
# cudnn related setting
cudnn.benchmark = config.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = config.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = config.CUDNN.ENABLED
model = eval('models.' + config.MODEL.NAME + '.get_pose_net')(
config, is_train=False)
logger.info('=> loading model from {}'.format(config.TEST.MODEL_FILE))
filename = config.TEST.MODEL_FILE
cuda_available = torch.cuda.is_available()
model.load_state_dict(
torch.load(
config.TEST.MODEL_FILE,
map_location=None if cuda_available else torch.device('cpu')))
logger.info('=> Converting...')
data = torch.zeros((1, 3, height, width))
if cuda_available:
model.cuda()
data = data.cuda()
model.float()
head, filename = os.path.split(filename)
if 'pth.tar' in filename:
filename = filename[:-8]
torch.onnx.export(model, data, onnx_path + filename + '.onnx')
logger.info('=> Model saved as: ' + onnx_path + filename + '.onnx')
logger.info('=> Done.')
