def __init__(self, gpu_id=0, model_path=None):
self.cfg_file = '${PGPT_ROOT}/cfgs/pose_res152.yaml'
self.flag = 0
update_config(self.cfg_file)
print('----------------------------------------')
print('PoseEstimation: Initilizing pose estimation network...')
self.gpu_id = gpu_id
self.pixel_std = 200
self.image_size = cfg.MODEL.IMAGE_SIZE
self.image_width = self.image_size[0]
self.image_height = self.image_size[1]
self.aspect_ratio = self.image_width * 1.0 / self.image_height
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.flip_pairs = [[5, 6], [7, 8], [9, 10], [11, 12], [13, 14],
[15, 16]]
cfg.TEST.FLIP_TEST = True
cfg.TEST.POST_PROCESS = True
cfg.TEST.SHIFT_HEATMAP = True
cfg.TEST.MODEL_FILE = model_path
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
with torch.cuda.device(self.gpu_id):
self.model = get_pose_net(cfg, is_train=False, flag=self.flag)
self._load_model()
self.model.eval()
def __init__(self, gpu_id=0, model_path='/export/home/zby/SiamFC/data/models/model_99.pth.tar'):
self.cfg_file='/export/home/zby/SiamFC/cfgs/pose_res152.yaml'
self.flag = 0
update_config(self.cfg_file)
print('----------------------------------------')
print('PoseEstimation: Initilizing pose estimation network...')
self.gpu_id = gpu_id
self.pixel_std = 200
self.image_size = cfg.MODEL.IMAGE_SIZE
self.image_width = self.image_size[0]
self.image_height = self.image_size[1]
self.aspect_ratio = self.image_width * 1.0 /self.image_height
self.transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(mean = [0.485,0.456,0.406],
std = [0.229,0.224,0.225])
])
self.flip_pairs = [[5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]
cfg.TEST.FLIP_TEST = True
cfg.TEST.POST_PROCESS = True
cfg.TEST.SHIFT_HEATMAP = True
cfg.TEST.MODEL_FILE = model_path
#cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
with torch.cuda.device(self.gpu_id):
self.model = get_pose_net(cfg, is_train=False, flag = self.flag)
#logger, final_output_dir, tb_log_dir = create_logger(cfg, cfg_file, 'valid')
#logger.info(pprint.pformat(cfg))
self._load_model()
self.model.eval()
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument(
'--cfg',
help='experiment configure file name',
required=False,
default='/export/home/zby/SiamFC/data/pose_res152.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, default='0')
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
parser.add_argument('--model-file',
help='model state file',
default='data/models/model_99.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',
default=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)
args = parser.parse_args()
return args
def __init__(
self,
data_path,
annotation_path,
num_video,
gpu_id=0,
model_path='/export/home/zby/SiamFC/data/models/final_new.pth.tar'
):
self.cfg_file = '/export/home/zby/SiamFC/cfgs/pose_res152.yaml'
update_config(self.cfg_file)
self.data_path = data_path
self.annotation_path = annotation_path
self.num_video = num_video
print('----------------------------------------')
print('Pose & Embedding: Initilizing pose & embedding network...')
#self.test_thresh_list = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18 ,0.19, 0.2]
self.test_thresh_list = [
0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2,
0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 1
]
self.gpu_id = gpu_id
self.pixel_std = 200
self.image_size = cfg.MODEL.IMAGE_SIZE
self.image_width = self.image_size[0]
self.image_height = self.image_size[1]
self.aspect_ratio = self.image_width * 1.0 / self.image_height
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
cfg.TEST.FLIP_TEST = True
cfg.TEST.POST_PROCESS = True
cfg.TEST.SHIFT_HEATMAP = True
cfg.TEST.MODEL_FILE = model_path
#cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
with torch.cuda.device(self.gpu_id):
self.model = get_pose_net(cfg, is_train=False)
self._load_model()
self.model.eval()
print('Pose & Embedding: Test thresh list is set as {}'.format(
self.test_thresh_list))
def __init__(self, gpu_id, train=True): self.cfg_file='/export/home/zby/SiamFC/cfgs/pose_res152.yaml' update_config(self.cfg_file) super(SiameseAlexNet, self).__init__() self.PoseNet = get_pose_net(cfg, is_train=False, flag = 4) self.exemplar_size = (8,8) self.multi_instance_size = [(24,24),(22,22)] self.features = nn.Sequential( nn.Conv2d(3, 96, 11, 2), nn.BatchNorm2d(96), nn.ReLU(inplace=True), nn.MaxPool2d(3, 2), nn.Conv2d(96, 256, 5, 1, groups=2), nn.BatchNorm2d(256), nn.ReLU(inplace=True), nn.MaxPool2d(3, 2), nn.Conv2d(256, 384, 3, 1), nn.BatchNorm2d(384), nn.ReLU(inplace=True), nn.Conv2d(384, 384, 3, 1, groups=2), nn.BatchNorm2d(384), nn.ReLU(inplace=True)) self.conv_pose = nn.Conv2d(2048, 384, 3, 1, padding=1) self.conv_final = nn.Conv2d(384, 256, 3, 1, groups=2) self.corr_bias = nn.Parameter(torch.zeros(1)) if train: gt, weight = self._create_gt_mask((config.train_response_sz, config.train_response_sz),mode='train') with torch.cuda.device(gpu_id): self.train_gt = torch.from_numpy(gt).cuda() self.train_weight = torch.from_numpy(weight).cuda() gt, weight = self._create_gt_mask((config.response_sz, config.response_sz), mode='valid') with torch.cuda.device(gpu_id): self.valid_gt = torch.from_numpy(gt).cuda() self.valid_weight = torch.from_numpy(weight).cuda() self.exemplar = None