nr_aug = 4
pixel_means = np.array([[[102.9801, 115.9465, 122.7717]]]) # BGR
pixel_norm = True
data_shape = (384, 288) #height, width
output_shape = (96, 72) #height, width
gaussain_kernel = (13, 13)
#
gk15 = (23, 23)
gk11 = (17, 17)
gk9 = (13, 13)
gk7 = (9, 9)
gt_path = osp.join(root_dir, 'data', 'COCO', 'MSCOCO', 'annotations',
'person_keypoints_minival2014.json')
det_path = osp.join(root_dir, 'data', 'COCO', 'dets',
'person_detection_minival411_human553.json')
cfg = Config()
sys.path.insert(0, osp.join(cfg.root_dir, 'lib'))
sys.path.insert(0, cfg.cur_dir)
from tfflat.utils import add_pypath, make_link, make_dir
add_pypath(osp.join(cfg.root_dir, 'data'))
add_pypath(osp.join(cfg.root_dir, 'data', 'COCO'))
make_link(cfg.output_dir, './log')
make_dir(cfg.output_dir)
make_dir(cfg.model_dump_dir)
def set_args(self, gpu_ids, continue_train=False):
self.gpu_ids = gpu_ids
self.num_gpus = len(self.gpu_ids.split(','))
self.continue_train = continue_train
os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu_ids
print('>>> Using /gpu:{}'.format(self.gpu_ids))
cfg = Config()
sys.path.insert(0, osp.join(cfg.root_dir, 'lib'))
from tfflat.utils import add_pypath, make_dir
add_pypath(osp.join(cfg.data_dir))
add_pypath(osp.join(cfg.data_dir, cfg.dataset))
make_dir(cfg.model_dump_dir)
make_dir(cfg.vis_dir)
make_dir(cfg.log_dir)
make_dir(cfg.result_dir)
from dataset import dbcfg
cfg.num_kps = dbcfg.num_kps
cfg.kps_names = dbcfg.kps_names
cfg.kps_lines = dbcfg.kps_lines
cfg.kps_symmetry = dbcfg.kps_symmetry
cfg.img_path = dbcfg.img_path
cfg.human_det_path = dbcfg.human_det_path
cfg.vis_keypoints = dbcfg.vis_keypoints
def set_config(cfg_path, train=True, valid_dataset=""):
global cfg
with open(cfg_path) as f:
cfg = EasyDict(yaml.load(f))
cfg.root_dir = root_dir
cfg.output_dir = osp.join(cfg.root_dir, 'output', cfg.EXPERIMENT_NAME)
with open(cfg.DATASET) as f:
cfg.DATASET = EasyDict(yaml.load(f))
cfg.loader_dir = osp.join(cfg.root_dir, 'loader')
cfg.init_model = osp.join(cfg.root_dir, 'imagenet_weights',
'resnet_v1_' + cfg.MODEL.backbone[6:] + '.ckpt')
cfg.tensorboard_dir = osp.join(cfg.output_dir, 't_log')
cfg.model_dump_dir = osp.join(cfg.output_dir, 'model_dump',
cfg.DATASET.name)
cfg.vis_dir = osp.join(cfg.output_dir, 'vis', cfg.DATASET.name)
cfg.log_dir = osp.join(cfg.output_dir, 'log', cfg.DATASET.name)
cfg.result_dir = osp.join(cfg.output_dir, 'result', cfg.DATASET.name)
if (valid_dataset != ""):
with open(valid_dataset) as f:
cfg.DATASET = EasyDict(yaml.load(f))
cfg.vis_dir = osp.join(cfg.vis_dir, cfg.DATASET.name)
cfg.result_dir = osp.join(cfg.result_dir, cfg.DATASET.name)
cfg.DATASET.root = os.path.abspath(osp.join(os.getcwd(), cfg.DATASET.root))
if cfg.TRAIN.structure_aware_loss:
if (cfg.DATASET.loader == "CrowdPose"):
cfg.joint_graph = np.array([[16, 14, 12], [15, 13, 11], [10, 8, 6],
[9, 7, 5], [0, 6, 5]]) # todo put in
elif (cfg.DATASET.loader == "COCO"):
cfg.joint_graph = np.array([[0, 2, 4], [1, 3, 5], [6, 8, 10],
[7, 9, 10], [0, 1, 13]])
else:
print("Joint Graph is undefined for {} loader".format(
cfg.DATASET.loader))
cfg.additional_outputs = len(cfg.joint_graph)
else:
cfg.additional_outputs = 0
cfg.MODEL.output_shape = (cfg.MODEL.input_shape[0] // 4,
cfg.MODEL.input_shape[1] // 4)
if cfg.MODEL.output_shape[0] == 64:
cfg.sigma = 2
elif cfg.MODEL.output_shape[0] == 96:
cfg.sigma = 3
cfg.pixel_means = np.array([[[123.68, 116.78, 103.94]]])
cfg.TRAIN.coco_person_cutout = cfg.TRAIN.coco_person_cutout or cfg.TRAIN.fullbodycut
cfg.MODEL.occluded_detection = cfg.MODEL.occluded_detection or cfg.MODEL.occluded_cross_branch
cfg.MODEL.occluded_hard_loss = cfg.MODEL.occluded_detection and cfg.MODEL.occluded_hard_loss
cfg.multi_thread_enable = True
cfg.num_thread = 12
cfg.gpu_ids = '0'
cfg.num_gpus = 1
cfg.continue_train = False
cfg.display = 1
cfg.tensorboard_update = 100
from tfflat.utils import add_pypath, make_dir
add_pypath(osp.join(cfg.loader_dir))
add_pypath(osp.join(cfg.loader_dir, cfg.DATASET.kpt_format))
make_dir(cfg.model_dump_dir)
make_dir(cfg.vis_dir)
make_dir(cfg.log_dir)
make_dir(cfg.result_dir)
from dataset import dbcfg
dbcfg.setup_paths(cfg)
cfg.num_kps = dbcfg.num_kps
cfg.kps_names = dbcfg.kps_names
cfg.kps_lines = dbcfg.kps_lines
cfg.kps_symmetry = dbcfg.kps_symmetry
cfg.human_det_path = dbcfg.human_det_path
cfg.vis_keypoints = dbcfg.vis_keypoints
cfg.get_lr = get_lr
cfg.normalize_input = normalize_input
cfg.denormalize_input = denormalize_input
if (train):
cfg.img_path = dbcfg.train_images
elif (cfg.TEST.testset == "test"):
cfg.img_path = dbcfg.test_images
elif (cfg.TEST.testset == "valid"):
cfg.img_path = dbcfg.val_images
else:
print("{} is not a defined test set.".format(cfg.TEST.testset))
