def test_bmn(args):
device = set_device(args.device)
fluid.enable_dygraph(device) if args.dynamic else None
#config setting
config = parse_config(args.config_file)
eval_cfg = merge_configs(config, 'test', vars(args))
feat_dim = config.MODEL.feat_dim
tscale = config.MODEL.tscale
dscale = config.MODEL.dscale
prop_boundary_ratio = config.MODEL.prop_boundary_ratio
num_sample = config.MODEL.num_sample
num_sample_perbin = config.MODEL.num_sample_perbin
#input and video index
inputs = [
Input([None, config.MODEL.feat_dim, config.MODEL.tscale],
'float32',
name='feat_input')
]
gt_iou_map = Input([None, config.MODEL.dscale, config.MODEL.tscale],
'float32',
name='gt_iou_map')
gt_start = Input([None, config.MODEL.tscale], 'float32', name='gt_start')
gt_end = Input([None, config.MODEL.tscale], 'float32', name='gt_end')
video_idx = Input([None, 1], 'int64', name='video_idx')
labels = [gt_iou_map, gt_start, gt_end, video_idx]
#data
eval_dataset = BmnDataset(eval_cfg, 'test')
#model
model = bmn(tscale,
dscale,
prop_boundary_ratio,
num_sample,
num_sample_perbin,
pretrained=args.weights is None)
model.prepare(loss_function=BmnLoss(tscale, dscale),
metrics=BmnMetric(config, mode='test'),
inputs=inputs,
labels=labels,
device=device)
#load checkpoint
if args.weights is not None:
assert os.path.exists(args.weights + '.pdparams'), \
"Given weight dir {} not exist.".format(args.weights)
logger.info('load test weights from {}'.format(args.weights))
model.load(args.weights)
model.evaluate(eval_data=eval_dataset,
batch_size=eval_cfg.TEST.batch_size,
num_workers=eval_cfg.TEST.num_workers,
log_freq=args.log_interval)
logger.info("[EVAL] eval finished")
def train_bmn(args):
device = set_device(args.device)
fluid.enable_dygraph(device) if args.dynamic else None
if not os.path.isdir(args.save_dir):
os.makedirs(args.save_dir)
#config setting
config = parse_config(args.config_file)
train_cfg = merge_configs(config, 'train', vars(args))
val_cfg = merge_configs(config, 'valid', vars(args))
feat_dim = config.MODEL.feat_dim
tscale = config.MODEL.tscale
dscale = config.MODEL.dscale
prop_boundary_ratio = config.MODEL.prop_boundary_ratio
num_sample = config.MODEL.num_sample
num_sample_perbin = config.MODEL.num_sample_perbin
# input and label list
inputs = [Input([None, feat_dim, tscale], 'float32', name='feat_input')]
gt_iou_map = Input([None, dscale, tscale], 'float32', name='gt_iou_map')
gt_start = Input([None, tscale], 'float32', name='gt_start')
gt_end = Input([None, tscale], 'float32', name='gt_end')
labels = [gt_iou_map, gt_start, gt_end]
# data
train_dataset = BmnDataset(train_cfg, 'train')
val_dataset = BmnDataset(val_cfg, 'valid')
# model
model = bmn(tscale,
dscale,
prop_boundary_ratio,
num_sample,
num_sample_perbin,
pretrained=False)
optim = optimizer(config, parameter_list=model.parameters())
model.prepare(optimizer=optim,
loss_function=BmnLoss(tscale, dscale),
inputs=inputs,
labels=labels,
device=device)
# if resume weights is given, load resume weights directly
if args.resume is not None:
model.load(args.resume)
model.fit(train_data=train_dataset,
eval_data=val_dataset,
batch_size=train_cfg.TRAIN.batch_size,
epochs=train_cfg.TRAIN.epoch,
eval_freq=args.valid_interval,
log_freq=args.log_interval,
save_dir=args.save_dir,
shuffle=train_cfg.TRAIN.use_shuffle,
num_workers=train_cfg.TRAIN.num_workers,
drop_last=True)
def infer_bmn(args):
device = set_device(args.device)
fluid.enable_dygraph(device) if args.dynamic else None
#config setting
config = parse_config(args.config_file)
infer_cfg = merge_configs(config, 'infer', vars(args))
feat_dim = config.MODEL.feat_dim
tscale = config.MODEL.tscale
dscale = config.MODEL.dscale
prop_boundary_ratio = config.MODEL.prop_boundary_ratio
num_sample = config.MODEL.num_sample
num_sample_perbin = config.MODEL.num_sample_perbin
#input and video index
inputs = [
Input([None, config.MODEL.feat_dim, config.MODEL.tscale],
'float32',
name='feat_input')
]
labels = [Input([None, 1], 'int64', name='video_idx')]
#data
infer_dataset = BmnDataset(infer_cfg, 'infer')
#model
model = bmn(tscale,
dscale,
prop_boundary_ratio,
num_sample,
num_sample_perbin,
pretrained=args.weights is None)
model.prepare(metrics=BmnMetric(config, mode='infer'),
inputs=inputs,
labels=labels,
device=device)
# load checkpoint
if args.weights is not None:
assert os.path.exists(
args.weights +
".pdparams"), "Given weight dir {} not exist.".format(args.weights)
logger.info('load test weights from {}'.format(args.weights))
model.load(args.weights)
# here use model.eval instead of model.test, as post process is required in our case
model.evaluate(eval_data=infer_dataset,
batch_size=infer_cfg.TEST.batch_size,
num_workers=infer_cfg.TEST.num_workers,
log_freq=args.log_interval)
logger.info("[INFER] infer finished")
def train_bmn(args):
device = paddle.set_device(args.device)
paddle.disable_static(device) if args.dynamic else None
if not os.path.isdir(args.save_dir):
os.makedirs(args.save_dir)
#config setting
config = parse_config(args.config_file)
train_cfg = merge_configs(config, 'train', vars(args))
val_cfg = merge_configs(config, 'valid', vars(args))
feat_dim = config.MODEL.feat_dim
tscale = config.MODEL.tscale
dscale = config.MODEL.dscale
prop_boundary_ratio = config.MODEL.prop_boundary_ratio
num_sample = config.MODEL.num_sample
num_sample_perbin = config.MODEL.num_sample_perbin
# data
train_dataset = BmnDataset(train_cfg, 'train')
val_dataset = BmnDataset(val_cfg, 'valid')
# model
model = bmn(tscale,
dscale,
feat_dim,
prop_boundary_ratio,
num_sample,
num_sample_perbin,
mode='train',
pretrained=False)
optim = optimizer(config, parameter_list=model.parameters())
model.prepare(optimizer=optim, loss=BmnLoss(tscale, dscale))
# if resume weights is given, load resume weights directly
if args.resume is not None:
model.load(args.resume)
model.fit(train_data=train_dataset,
eval_data=val_dataset,
batch_size=train_cfg.TRAIN.batch_size,
epochs=train_cfg.TRAIN.epoch,
eval_freq=args.valid_interval,
log_freq=args.log_interval,
save_dir=args.save_dir,
shuffle=train_cfg.TRAIN.use_shuffle,
num_workers=train_cfg.TRAIN.num_workers,
drop_last=True)
def infer_bmn(args):
device = paddle.set_device(args.device)
paddle.disable_static(device) if args.dynamic else None
#config setting
config = parse_config(args.config_file)
infer_cfg = merge_configs(config, 'infer', vars(args))
feat_dim = config.MODEL.feat_dim
tscale = config.MODEL.tscale
dscale = config.MODEL.dscale
prop_boundary_ratio = config.MODEL.prop_boundary_ratio
num_sample = config.MODEL.num_sample
num_sample_perbin = config.MODEL.num_sample_perbin
#data
infer_dataset = BmnDataset(infer_cfg, 'infer')
#model
model = bmn(tscale,
dscale,
feat_dim,
prop_boundary_ratio,
num_sample,
num_sample_perbin,
mode='infer',
pretrained=args.weights is None)
model.prepare(metrics=BmnMetric(config, mode='infer'))
# load checkpoint
if args.weights is not None:
assert os.path.exists(
args.weights +
".pdparams"), "Given weight dir {} not exist.".format(args.weights)
logger.info('load test weights from {}'.format(args.weights))
model.load(args.weights)
# here use model.eval instead of model.test, as post process is required in our case
model.evaluate(eval_data=infer_dataset,
batch_size=infer_cfg.TEST.batch_size,
num_workers=infer_cfg.TEST.num_workers,
log_freq=args.log_interval)
logger.info("[INFER] infer finished")
def test_bmn(args):
device = paddle.set_device(args.device)
paddle.disable_static(device) if args.dynamic else None
#config setting
config = parse_config(args.config_file)
eval_cfg = merge_configs(config, 'test', vars(args))
feat_dim = config.MODEL.feat_dim
tscale = config.MODEL.tscale
dscale = config.MODEL.dscale
prop_boundary_ratio = config.MODEL.prop_boundary_ratio
num_sample = config.MODEL.num_sample
num_sample_perbin = config.MODEL.num_sample_perbin
#data
eval_dataset = BmnDataset(eval_cfg, 'test')
#model
model = bmn(tscale,
dscale,
feat_dim,
prop_boundary_ratio,
num_sample,
num_sample_perbin,
mode='test',
pretrained=args.weights is None)
model.prepare(loss=BmnLoss(tscale, dscale),
metrics=BmnMetric(config, mode='test'))
#load checkpoint
if args.weights is not None:
assert os.path.exists(args.weights + '.pdparams'), \
"Given weight dir {} not exist.".format(args.weights)
logger.info('load test weights from {}'.format(args.weights))
model.load(args.weights)
model.evaluate(eval_data=eval_dataset,
batch_size=eval_cfg.TEST.batch_size,
num_workers=eval_cfg.TEST.num_workers,
log_freq=args.log_interval)
logger.info("[EVAL] eval finished")
