def build_optimizer(type, lr, kerasDefaults):
if type == 'sgd':
if kerasDefaults['nesterov_sgd']:
return optimizer.NAG(learning_rate=lr,
momentum=kerasDefaults['momentum_sgd'],
#rescale_grad=kerasDefaults['clipnorm'],
#clip_gradient=kerasDefaults['clipvalue'],
lr_scheduler=None)
else:
return optimizer.SGD(learning_rate=lr,
momentum=kerasDefaults['momentum_sgd'],
#rescale_grad=kerasDefaults['clipnorm'],
#clip_gradient=kerasDefaults['clipvalue'],
lr_scheduler=None)
elif type == 'rmsprop':
return optimizer.RMSProp(learning_rate=lr,
gamma1=kerasDefaults['rho'],
epsilon=kerasDefaults['epsilon'],
centered=False,
#rescale_grad=kerasDefaults['clipnorm'],
#clip_gradient=kerasDefaults['clipvalue'],
lr_scheduler=None)
elif type == 'adagrad':
return optimizer.AdaGrad(learning_rate=lr,
epsilon=kerasDefaults['epsilon'])#,
#rescale_grad=kerasDefaults['clipnorm'],
#clip_gradient=kerasDefaults['clipvalue'])
elif type == 'adadelta':
return optimizer.AdaDelta(epsilon=kerasDefaults['epsilon'],
rho=kerasDefaults['rho'])#,
#rescale_grad=kerasDefaults['clipnorm'],
#clip_gradient=kerasDefaults['clipvalue'])
elif type == 'adam':
return optimizer.Adam(learning_rate=lr, beta_1=kerasDefaults['beta_1'],
beta_2=kerasDefaults['beta_2'],
epsilon=kerasDefaults['epsilon'])#,
def main(args):
_seed = 727
random.seed(_seed)
np.random.seed(_seed)
mx.random.seed(_seed)
ctx = []
# cvd = os.environ['CUDA_VISIBLE_DEVICES'].strip()
# if len(cvd)>0:
# for i in range(len(cvd.split(','))):
# ctx.append(mx.gpu(i))
# if len(ctx)==0:
# ctx = [mx.cpu()]
# print('use cpu')
# else:
# print('gpu num:', len(ctx))
ctx = [mx.cpu()]
args.ctx_num = len(ctx)
args.batch_size = args.per_batch_size * args.ctx_num
config.per_batch_size = args.per_batch_size
print('Call with', args, config)
train_iter = FaceSegIter(
path_imgrec=os.path.join(config.dataset_path, 'train.rec'),
batch_size=args.batch_size,
per_batch_size=args.per_batch_size,
aug_level=1,
exf=args.exf,
args=args,
)
data_shape = train_iter.get_data_shape()
#label_shape = train_iter.get_label_shape()
sym = sym_heatmap.get_symbol(num_classes=config.num_classes)
if len(args.pretrained) == 0:
#data_shape_dict = {'data' : (args.per_batch_size,)+data_shape, 'softmax_label' : (args.per_batch_size,)+label_shape}
data_shape_dict = train_iter.get_shape_dict()
arg_params, aux_params = sym_heatmap.init_weights(sym, data_shape_dict)
else:
vec = args.pretrained.split(',')
print('loading', vec)
_, arg_params, aux_params = mx.model.load_checkpoint(
vec[0], int(vec[1]))
#sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params)
model = mx.mod.Module(
context=ctx,
symbol=sym,
label_names=train_iter.get_label_names(),
)
#lr = 1.0e-3
#lr = 2.5e-4
_rescale_grad = 1.0 / args.ctx_num
#_rescale_grad = 1.0/args.batch_size
#lr = args.lr
#opt = optimizer.Nadam(learning_rate=args.lr, wd=args.wd, rescale_grad=_rescale_grad, clip_gradient=5.0)
if args.optimizer == 'onadam':
opt = ONadam(learning_rate=args.lr,
wd=args.wd,
rescale_grad=_rescale_grad,
clip_gradient=5.0)
elif args.optimizer == 'nadam':
opt = optimizer.Nadam(learning_rate=args.lr,
rescale_grad=_rescale_grad)
elif args.optimizer == 'rmsprop':
opt = optimizer.RMSProp(learning_rate=args.lr,
rescale_grad=_rescale_grad)
elif args.optimizer == 'adam':
opt = optimizer.Adam(learning_rate=args.lr, rescale_grad=_rescale_grad)
else:
opt = optimizer.SGD(learning_rate=args.lr,
momentum=0.9,
wd=args.wd,
rescale_grad=_rescale_grad)
initializer = mx.init.Xavier(rnd_type='gaussian',
factor_type="in",
magnitude=2)
_cb = mx.callback.Speedometer(args.batch_size, args.frequent)
_metric = LossValueMetric()
#_metric = NMEMetric()
#_metric2 = AccMetric()
#eval_metrics = [_metric, _metric2]
eval_metrics = [_metric]
lr_steps = [int(x) for x in args.lr_step.split(',')]
print('lr-steps', lr_steps)
global_step = [0]
def val_test():
all_layers = sym.get_internals()
vsym = all_layers['heatmap_output']
vmodel = mx.mod.Module(symbol=vsym, context=ctx, label_names=None)
#model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
vmodel.bind(data_shapes=[('data', (args.batch_size, ) + data_shape)])
arg_params, aux_params = model.get_params()
vmodel.set_params(arg_params, aux_params)
for target in config.val_targets:
_file = os.path.join(config.dataset_path, '%s.rec' % target)
if not os.path.exists(_file):
continue
val_iter = FaceSegIter(
path_imgrec=_file,
batch_size=args.batch_size,
#batch_size = 4,
aug_level=0,
args=args,
)
_metric = NMEMetric()
val_metric = mx.metric.create(_metric)
val_metric.reset()
val_iter.reset()
for i, eval_batch in enumerate(val_iter):
#print(eval_batch.data[0].shape, eval_batch.label[0].shape)
batch_data = mx.io.DataBatch(eval_batch.data)
model.forward(batch_data, is_train=False)
model.update_metric(val_metric, eval_batch.label)
nme_value = val_metric.get_name_value()[0][1]
print('[%d][%s]NME: %f' % (global_step[0], target, nme_value))
def _batch_callback(param):
_cb(param)
global_step[0] += 1
mbatch = global_step[0]
for _lr in lr_steps:
if mbatch == _lr:
opt.lr *= 0.2
print('lr change to', opt.lr)
break
if mbatch % 1000 == 0:
print('lr-batch-epoch:', opt.lr, param.nbatch, param.epoch)
if mbatch > 0 and mbatch % args.verbose == 0:
val_test()
if args.ckpt == 1:
msave = mbatch // args.verbose
print('saving', msave)
arg, aux = model.get_params()
mx.model.save_checkpoint(args.prefix, msave, model.symbol, arg,
aux)
if mbatch == lr_steps[-1]:
if args.ckpt == 2:
#msave = mbatch//args.verbose
msave = 1
print('saving', msave)
arg, aux = model.get_params()
mx.model.save_checkpoint(args.prefix, msave, model.symbol, arg,
aux)
sys.exit(0)
train_iter = mx.io.PrefetchingIter(train_iter)
model.fit(
train_iter,
begin_epoch=0,
num_epoch=9999,
#eval_data = val_iter,
eval_data=None,
eval_metric=eval_metrics,
kvstore='device',
optimizer=opt,
initializer=initializer,
arg_params=arg_params,
aux_params=aux_params,
allow_missing=True,
batch_end_callback=_batch_callback,
epoch_end_callback=None,
)
def main(args):
_seed = 727
random.seed(_seed)
np.random.seed(_seed)
mx.random.seed(_seed)
ctx = []
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
cvd = os.environ['CUDA_VISIBLE_DEVICES'].strip()
if len(cvd) > 0:
for i in xrange(len(cvd.split(','))):
ctx.append(mx.gpu(i))
if len(ctx) == 0:
ctx = [mx.cpu()]
print('use cpu')
else:
print('gpu num:', len(ctx))
#ctx = [mx.gpu(0)]
args.ctx_num = len(ctx)
args.batch_size = args.per_batch_size * args.ctx_num
config.per_batch_size = args.per_batch_size
print('Call with', args, config)
train_iter = FaceSegIter(
path_imgrec=os.path.join(config.dataset_path, 'train.rec'),
batch_size=args.batch_size,
per_batch_size=args.per_batch_size,
aug_level=1,
exf=args.exf,
args=args,
)
data_shape, data_size = train_iter.get_data_shape()
#label_shape = train_iter.get_label_shape()
sym = eval(config.network).get_symbol(num_classes=config.num_classes)
if len(args.pretrained) == 0:
#data_shape_dict = {'data' : (args.per_batch_size,)+data_shape, 'softmax_label' : (args.per_batch_size,)+label_shape}
data_shape_dict = train_iter.get_shape_dict()
arg_params, aux_params = init_weights(sym, data_shape_dict)
else:
vec = args.pretrained.split(',')
print('loading', vec)
_, arg_params, aux_params = mx.model.load_checkpoint(
vec[0], int(vec[1]))
#sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params)
model = mx.mod.Module(
context=ctx,
symbol=sym,
label_names=train_iter.get_label_names(),
)
#lr = 1.0e-3
#lr = 2.5e-4
_rescale_grad = 1.0 / args.ctx_num
#_rescale_grad = 1.0/args.batch_size
#lr = args.lr
#opt = optimizer.Nadam(learning_rate=args.lr, wd=args.wd, rescale_grad=_rescale_grad, clip_gradient=5.0)
if args.optimizer == 'onadam':
opt = ONadam(learning_rate=args.lr,
wd=args.wd,
rescale_grad=_rescale_grad,
clip_gradient=5.0)
elif args.optimizer == 'nadam':
opt = optimizer.Nadam(learning_rate=args.lr,
rescale_grad=_rescale_grad)
elif args.optimizer == 'rmsprop':
opt = optimizer.RMSProp(learning_rate=args.lr,
rescale_grad=_rescale_grad)
elif args.optimizer == 'adam':
opt = optimizer.Adam(learning_rate=args.lr, rescale_grad=_rescale_grad)
else:
opt = optimizer.SGD(learning_rate=args.lr,
momentum=0.9,
wd=args.wd,
rescale_grad=_rescale_grad)
initializer = mx.init.Xavier(rnd_type='gaussian',
factor_type="in",
magnitude=2)
_cb = mx.callback.Speedometer(args.batch_size, args.frequent)
_metric = LossValueMetric()
#_metric = NMEMetric()
#_metric2 = AccMetric()
#eval_metrics = [_metric, _metric2]
eval_metrics = [_metric]
lr_epoch_steps = [int(x) for x in args.lr_epoch_step.split(',')]
print('lr-epoch-steps', lr_epoch_steps)
global_step = [0]
highest_acc = [1.0, 1.0]
def _batch_callback(param):
_cb(param)
global_step[0] += 1
mbatch = global_step[0]
mepoch = mbatch * args.batch_size // data_size
pre = mbatch * args.batch_size % data_size
is_highest = False
for _lr in lr_epoch_steps[0:-1]:
if mepoch == _lr and pre < args.batch_size:
opt.lr *= 0.2
print('lr change to', opt.lr)
break
if mbatch % 1000 == 0:
print('lr:', opt.lr, 'batch:', param.nbatch, 'epoch:', param.epoch)
if mbatch > 0 and mbatch % args.verbose == 0:
acc_list = val_test(sym, model, ctx, data_shape, global_step)
score = np.mean(acc_list)
if acc_list[0] < highest_acc[0]: # ibug
is_highest = True
highest_acc[0] = acc_list[0]
if score < highest_acc[1]: # mean
is_highest = True
highest_acc[1] = score
if args.ckpt == 1 and is_highest == True:
msave = mbatch // args.verbose
print('saving', msave)
arg, aux = model.get_params()
mx.model.save_checkpoint(args.prefix, msave, model.symbol, arg,
aux)
if mepoch == lr_epoch_steps[-1]:
if args.ckpt == 1:
acc_list = val_test(sym, model, ctx, data_shape, global_step)
msave = mbatch // args.verbose
print('saving', msave)
arg, aux = model.get_params()
mx.model.save_checkpoint(args.prefix, msave, model.symbol, arg,
aux)
sys.exit(0)
train_iter = mx.io.PrefetchingIter(train_iter)
model.fit(
train_iter,
begin_epoch=0,
num_epoch=9999,
#eval_data = val_iter,
eval_data=None,
eval_metric=eval_metrics,
kvstore='device',
optimizer=opt,
initializer=initializer,
arg_params=arg_params,
aux_params=aux_params,
allow_missing=True,
batch_end_callback=_batch_callback,
epoch_end_callback=None,
)
