def _exp_top_boxes_per_hoi(out_base_dir, data_const):
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=['model_num'],
optional_args=[
'verb_given_appearance',
'verb_given_human_appearance',
'verb_given_object_appearance',
'verb_given_boxes_and_object_label',
'verb_given_human_pose',
'rcnn_det_prob'])
exp_name = 'factors'
if args.rcnn_det_prob:
exp_name += '_rcnn_det_prob'
if args.verb_given_appearance:
exp_name += '_appearance'
if args.verb_given_human_appearance:
exp_name += '_human_appearance'
if args.verb_given_object_appearance:
exp_name += '_object_appearance'
if args.verb_given_boxes_and_object_label:
exp_name += '_boxes_and_object_label'
if args.verb_given_human_pose:
exp_name += '_human_pose'
exp_const = ExpConstants(
exp_name=exp_name,
out_base_dir=out_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir,'models')
exp_const.num_to_vis = 10
data_const.pred_hoi_dets_h5py = os.path.join(
exp_const.exp_dir,
f'pred_hoi_dets_test_{args.model_num}.hdf5')
hoi_cand_dir = os.path.join(
os.getcwd(),
'data_symlinks/hico_exp/hoi_candidates')
data_const.human_pose_feats_hdf5 = os.path.join(
hoi_cand_dir,
'human_pose_feats_test.hdf5')
data_const.num_pose_keypoints = 18
model_const = Constants()
model_const.model_num = args.model_num
model_const.hoi_classifier = HoiClassifierConstants()
model_const.hoi_classifier.verb_given_appearance = args.verb_given_appearance
model_const.hoi_classifier.verb_given_boxes_and_object_label = args.verb_given_boxes_and_object_label
model_const.hoi_classifier.verb_given_human_pose = args.verb_given_human_pose
model_const.hoi_classifier.rcnn_det_prob = args.rcnn_det_prob
model_const.hoi_classifier.model_pth = os.path.join(
exp_const.model_dir,
f'hoi_classifier_{model_const.model_num}')
vis_top_boxes_per_hoi.main(exp_const, data_const, model_const)
def _exp_eval(out_base_dir, data_const):
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=['model_num'],
optional_args=[
'verb_given_appearance',
'verb_given_human_appearance',
'verb_given_object_appearance',
'verb_given_boxes_and_object_label',
'verb_given_human_pose',
'rcnn_det_prob'])
exp_name = 'factors'
if args.rcnn_det_prob:
exp_name += '_rcnn_det_prob'
if args.verb_given_appearance:
exp_name += '_appearance'
if args.verb_given_human_appearance:
exp_name += '_human_appearance'
if args.verb_given_object_appearance:
exp_name += '_object_appearance'
if args.verb_given_boxes_and_object_label:
exp_name += '_boxes_and_object_label'
if args.verb_given_human_pose:
exp_name += '_human_pose'
exp_const = ExpConstants(
exp_name=exp_name,
out_base_dir=out_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
data_const.balanced_sampling = False
model_const = Constants()
model_const.model_num = args.model_num
model_const.hoi_classifier = HoiClassifierConstants()
model_const.hoi_classifier.verb_given_appearance = args.verb_given_appearance
model_const.hoi_classifier.verb_given_human_appearance = args.verb_given_human_appearance
model_const.hoi_classifier.verb_given_object_appearance = args.verb_given_object_appearance
model_const.hoi_classifier.verb_given_boxes_and_object_label = args.verb_given_boxes_and_object_label
model_const.hoi_classifier.verb_given_human_pose = args.verb_given_human_pose
model_const.hoi_classifier.rcnn_det_prob = args.rcnn_det_prob
model_const.hoi_classifier.model_pth = os.path.join(
exp_const.model_dir,
f'hoi_classifier_{model_const.model_num}')
if isinstance(data_const, FeatureConstantsVcoco):
data_sign = 'vcoco'
else:
data_sign = 'hico'
evaluate.main(exp_const, data_const, model_const, data_sign)
def _exp_train(out_base_dir,
data_const_train,
data_const_val,
data_sign='hico'):
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=['imgs_per_batch', 'fp_to_tp_ratio'],
optional_args=[
'verb_given_appearance', 'verb_given_human_appearance',
'verb_given_object_appearance',
'verb_given_boxes_and_object_label', 'verb_given_human_pose',
'rcnn_det_prob'
])
exp_name = 'factors'
if args.rcnn_det_prob:
exp_name += '_rcnn_det_prob'
if args.verb_given_appearance:
exp_name += '_appearance'
if args.verb_given_human_appearance:
exp_name += '_human_appearance'
if args.verb_given_object_appearance:
exp_name += '_object_appearance'
if args.verb_given_boxes_and_object_label:
exp_name += '_boxes_and_object_label'
if args.verb_given_human_pose:
pose = '_human_pose'
exp_name += pose
exp_const = ExpConstants(exp_name=exp_name, out_base_dir=out_base_dir)
exp_const.log_dir = os.path.join(exp_const.exp_dir, 'log')
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.num_epochs = 10
exp_const.imgs_per_batch = args.imgs_per_batch
exp_const.lr = 1e-3
model_const = Constants()
model_const.hoi_classifier = HoiClassifierConstants(data_sign)
model_const.hoi_classifier.verb_given_appearance = args.verb_given_appearance
model_const.hoi_classifier.verb_given_human_appearance = args.verb_given_human_appearance
model_const.hoi_classifier.verb_given_object_appearance = args.verb_given_object_appearance
model_const.hoi_classifier.verb_given_boxes_and_object_label = args.verb_given_boxes_and_object_label
model_const.hoi_classifier.verb_given_human_pose = args.verb_given_human_pose
model_const.hoi_classifier.rcnn_det_prob = args.rcnn_det_prob
train.main(exp_const, data_const_train, data_const_val, model_const,
data_sign)
def exp_train():
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=['fappend', 'imgs_per_batch', 'fp_to_tp_ratio'],
optional_args=[
'verb_given_appearance', 'verb_given_human_appearance',
'verb_given_object_appearance',
'verb_given_boxes_and_object_label', 'rcnn_det_prob'
])
exp_name = 'factors'
exp_name += '_' + args.fappend
out_base_dir = os.path.join(os.getcwd(),
'data_symlinks/hico_exp/hoi_classifier')
exp_const = ExpConstants(exp_name=exp_name, out_base_dir=out_base_dir)
exp_const.log_dir = os.path.join(exp_const.exp_dir, 'log')
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.num_epochs = 10
exp_const.imgs_per_batch = args.imgs_per_batch
exp_const.lr = 1e-3
data_const_train = FeatureConstants(subset='train')
data_const_val = FeatureConstants(subset='val')
model_const = Constants()
model_const.hoi_classifier = HoiClassifierConstants()
model_const.hoi_classifier.verb_given_appearance = args.verb_given_appearance
model_const.hoi_classifier.verb_given_human_appearance = args.verb_given_human_appearance
model_const.hoi_classifier.verb_given_object_appearance = args.verb_given_object_appearance
model_const.hoi_classifier.verb_given_boxes_and_object_label = args.verb_given_boxes_and_object_label
model_const.hoi_classifier.verb_given_human_pose = args.verb_given_human_pose
model_const.hoi_classifier.rcnn_det_prob = args.rcnn_det_prob
train.main(exp_const, data_const_train, data_const_val, model_const)
def exp_eval():
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=['fappend', 'model_num'],
optional_args=[
'verb_given_appearance', 'verb_given_human_appearance',
'verb_given_object_appearance',
'verb_given_boxes_and_object_label', 'verb_given_human_pose',
'rcnn_det_prob'
])
exp_name = 'factors'
exp_name += '_' + args.fappend
out_base_dir = os.path.join(os.getcwd(),
'data_symlinks/hico_exp/hoi_classifier')
exp_const = ExpConstants(exp_name=exp_name, out_base_dir=out_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
data_const = FeatureConstants(subset='test')
data_const.balanced_sampling = False
model_const = Constants()
model_const.model_num = args.model_num
model_const.hoi_classifier = HoiClassifierConstants()
model_const.hoi_classifier.verb_given_appearance = args.verb_given_appearance
model_const.hoi_classifier.verb_given_human_appearance = args.verb_given_human_appearance
model_const.hoi_classifier.verb_given_object_appearance = args.verb_given_object_appearance
model_const.hoi_classifier.verb_given_boxes_and_object_label = args.verb_given_boxes_and_object_label
model_const.hoi_classifier.verb_given_human_pose = args.verb_given_human_pose
model_const.hoi_classifier.rcnn_det_prob = args.rcnn_det_prob
model_const.hoi_classifier.model_pth = os.path.join(
exp_const.model_dir, f'hoi_classifier_{model_const.model_num}')
evaluate.main(exp_const, data_const, model_const)
