def main():
fcnxs = symbol_fcnxs.get_fcn32s_symbol(numclass=2, workspace_default=1536)
fcnxs_model_prefix = "model_pascal/FCN32s_VGG16"
num_epoch = 1
learning_rate = 1e-3
if args.model == "fcn16s":
fcnxs = symbol_fcnxs.get_fcn16s_symbol(numclass=2, workspace_default=1536)
fcnxs_model_prefix = "model_pascal/FCN16s_VGG16"
learning_rate = 1e-5
elif args.model == "fcn8s":
fcnxs = symbol_fcnxs.get_fcn8s_symbol(numclass=2, workspace_default=1536)
fcnxs_model_prefix = "model_pascal/FCN8s_VGG16"
num_epoch = 30
learning_rate = 1e-7
arg_names = fcnxs.list_arguments()
_, fcnxs_args, fcnxs_auxs = mx.model.load_checkpoint(args.prefix, args.epoch)
if not args.retrain:
if args.init_type == "vgg16":
fcnxs_args, fcnxs_auxs = init_fcnxs.init_from_vgg16(ctx, fcnxs, fcnxs_args, fcnxs_auxs)
elif args.init_type == "fcnxs":
fcnxs_args, fcnxs_auxs = init_fcnxs.init_from_fcnxs(ctx, fcnxs, fcnxs_args, fcnxs_auxs)
train_dataiter = FileIter(
root_dir = ".",
flist_name = "train.lst",
# cut_off_size = 400,
rgb_mean = (123.68, 116.779, 103.939),
)
# val_dataiter = FileIter(
# root_dir = "/home/zw/dataset/VOC2012Segmentation/VOC2012",
# flist_name = "val.lst",
# rgb_mean = (123.68, 116.779, 103.939),
# )
model = Solver(
ctx = ctx,
symbol = fcnxs,
begin_epoch = 0,
num_epoch = num_epoch,
arg_params = fcnxs_args,
aux_params = fcnxs_auxs,
learning_rate = learning_rate,
momentum = 0.9,
wd = 0.0001)
model.fit(
train_data = train_dataiter,
# eval_data = val_dataiter,
batch_end_callback = mx.callback.Speedometer(1, 10),
epoch_end_callback = mx.callback.do_checkpoint(fcnxs_model_prefix))
def main():
fcnxs = symbol_fcnxs.get_fcn32s_symbol(numclass=21, workspace_default=1536)
fcnxs_model_prefix = "model_pascal/FCN32s_VGG16"
if args.model == "fcn16s":
fcnxs = symbol_fcnxs.get_fcn16s_symbol(numclass=21, workspace_default=1536)
fcnxs_model_prefix = "model_pascal/FCN16s_VGG16"
elif args.model == "fcn8s":
fcnxs = symbol_fcnxs.get_fcn8s_symbol(numclass=21, workspace_default=1536)
fcnxs_model_prefix = "model_pascal/FCN8s_VGG16"
arg_names = fcnxs.list_arguments()
_, fcnxs_args, fcnxs_auxs = mx.model.load_checkpoint(args.prefix, args.epoch)
# print arg_names
if not args.retrain:
if args.init_type == "vgg16":
fcnxs_args, fcnxs_auxs = init_fcnxs.init_from_vgg16(ctx, fcnxs, fcnxs_args, fcnxs_auxs)
elif args.init_type == "fcnxs":
fcnxs_args, fcnxs_auxs = init_fcnxs.init_from_fcnxs(ctx, fcnxs, fcnxs_args, fcnxs_auxs)
train_dataiter = FileIter(
root_dir = "./VOC2012",
flist_name = "train.lst",
# cut_off_size = 400,
rgb_mean = (123.68, 116.779, 103.939),
)
val_dataiter = FileIter(
root_dir = "./VOC2012",
flist_name = "val.lst",
rgb_mean = (123.68, 116.779, 103.939),
)
model = Solver(
ctx = ctx,
symbol = fcnxs,
begin_epoch = 0,
num_epoch = 50,
arg_params = fcnxs_args,
aux_params = fcnxs_auxs,
learning_rate = 1e-10,
momentum = 0.99,
wd = 0.0005)
model.fit(
train_data = train_dataiter,
eval_data = val_dataiter,
batch_end_callback = mx.callback.Speedometer(1, 10),
epoch_end_callback = mx.callback.do_checkpoint(fcnxs_model_prefix))
def main():
ctx = mx.cpu() if not args.gpu else mx.gpu(args.gpu)
fcnxs = symbol_fcnxs.get_fcn32s_symbol(numclass=21, workspace_default=1536)
fcnxs_model_prefix = "model_pascal/FCN32s_VGG16"
if args.model == "fcn16s":
fcnxs = symbol_fcnxs.get_fcn16s_symbol(numclass=21, workspace_default=1536)
fcnxs_model_prefix = "model_pascal/FCN16s_VGG16"
elif args.model == "fcn8s":
fcnxs = symbol_fcnxs.get_fcn8s_symbol(numclass=21, workspace_default=1536)
fcnxs_model_prefix = "model_pascal/FCN8s_VGG16"
arg_names = fcnxs.list_arguments()
_, fcnxs_args, fcnxs_auxs = mx.model.load_checkpoint(args.prefix, args.epoch)
if not args.retrain:
if args.init_type == "vgg16":
fcnxs_args, fcnxs_auxs = init_fcnxs.init_from_vgg16(ctx, fcnxs, fcnxs_args, fcnxs_auxs)
elif args.init_type == "fcnxs":
fcnxs_args, fcnxs_auxs = init_fcnxs.init_from_fcnxs(ctx, fcnxs, fcnxs_args, fcnxs_auxs)
train_dataiter = FileIter(
root_dir = "./VOC2012",
flist_name = "train.lst",
# cut_off_size = 400,
rgb_mean = (123.68, 116.779, 103.939),
)
val_dataiter = FileIter(
root_dir = "./VOC2012",
flist_name = "val.lst",
rgb_mean = (123.68, 116.779, 103.939),
)
model = Solver(
ctx = ctx,
symbol = fcnxs,
begin_epoch = 0,
num_epoch = 50,
arg_params = fcnxs_args,
aux_params = fcnxs_auxs,
learning_rate = 1e-10,
momentum = 0.99,
wd = 0.0005)
model.fit(
train_data = train_dataiter,
eval_data = val_dataiter,
batch_end_callback = mx.callback.Speedometer(1, 10),
epoch_end_callback = mx.callback.do_checkpoint(fcnxs_model_prefix))
def main():
devs = mx.cpu() if args.gpus is None or args.gpus is '' else [
mx.gpu(int(i)) for i in args.gpus.split(',')
]
carvn_root = ''
num_classes = 2
cutoff = None if args.cutoff == 0 else args.cutoff
epochs = [74, 2, 1, 1]
if not os.path.exists(args.model_dir):
os.mkdir(args.model_dir)
model_prefixes = [
'VGG_FC_ILSVRC_16_layers', args.model_dir + "/FCN32s_VGG16",
args.model_dir + "/FCN16s_VGG16", args.model_dir + "/FCN8s_VGG16"
]
if args.model == "fcn16s":
fcnxs = symbol_fcnxs.get_fcn16s_symbol(numclass=num_classes,
workspace_default=1536)
fcnxs_model_prefix = model_prefixes[2]
load_prefix = model_prefixes[1]
lr = 1e-5
run_epochs = epochs[2]
load_epoch = epochs[1]
elif args.model == "fcn8s":
fcnxs = symbol_fcnxs.get_fcn8s_symbol(numclass=num_classes,
workspace_default=1536)
fcnxs_model_prefix = model_prefixes[3]
load_prefix = model_prefixes[2]
lr = 1e-6
run_epochs = epochs[3]
load_epoch = epochs[2]
else:
fcnxs = symbol_fcnxs.get_fcn32s_symbol(numclass=num_classes,
workspace_default=1536)
fcnxs_model_prefix = model_prefixes[1]
load_prefix = model_prefixes[0]
lr = 1e-4
run_epochs = epochs[1]
load_epoch = epochs[0]
arg_names = fcnxs.list_arguments()
print('loading', load_prefix, load_epoch)
print('lr', lr)
print('model_prefix', fcnxs_model_prefix)
print('running epochs', run_epochs)
print('cutoff size', cutoff)
args.batch_size = len(devs)
_, fcnxs_args, fcnxs_auxs = mx.model.load_checkpoint(
load_prefix, load_epoch)
ctx = mx.cpu()
if not args.retrain:
if args.model == "fcn16s" or args.model == "fcn8s":
fcnxs_args, fcnxs_auxs = init_fcnxs.init_from_fcnxs(
ctx, fcnxs, fcnxs_args, fcnxs_auxs)
else:
fcnxs_args, fcnxs_auxs = init_fcnxs.init_from_vgg16(
ctx, fcnxs, fcnxs_args, fcnxs_auxs)
train_dataiter = BatchFileIter(
path_imglist="../data/train.lst",
cut_off_size=cutoff,
rgb_mean=(123.68, 116.779, 103.939),
batch_size=args.batch_size,
)
val_dataiter = BatchFileIter(
path_imglist="../data/val.lst",
cut_off_size=cutoff,
rgb_mean=(123.68, 116.779, 103.939),
batch_size=args.batch_size,
)
# learning rate
kv = mx.kvstore.create('local')
# create model
model = mx.mod.Module(
context=devs,
symbol=fcnxs,
#label_names = ['softmax_label', 'softmax2_label']
)
optimizer_params = {'learning_rate': lr, 'momentum': 0.99, 'wd': 0.0005}
_dice = DiceMetric()
_acc = AccMetric()
eval_metrics = [mx.metric.create(_dice), mx.metric.create(_acc)]
initializer = mx.init.Xavier(rnd_type='gaussian',
factor_type="in",
magnitude=2)
model.fit(
train_dataiter,
begin_epoch=0,
num_epoch=run_epochs,
#eval_data = val_dataiter,
eval_metric=eval_metrics,
kvstore=kv,
optimizer='sgd',
optimizer_params=optimizer_params,
initializer=initializer,
arg_params=fcnxs_args,
aux_params=fcnxs_auxs,
batch_end_callback=mx.callback.Speedometer(args.batch_size, 10),
epoch_end_callback=mx.callback.do_checkpoint(fcnxs_model_prefix),
allow_missing=True)
def main():
gpu_list = []
_gpus = args.gpu.split(',')
for _gpu in _gpus:
_gpu = _gpu.strip()
if len(_gpu)==0:
continue
gpu_list.append(int(_gpu))
assert len(gpu_list)>0
ctx = mx.gpu(gpu_list[0])
carvn_root = ''
num_classes = 2
cutoff = None if args.cutoff==0 else args.cutoff
epochs = [74,31,27,19]
model_prefixes = ['VGG_FC_ILSVRC_16_layers', args.model_dir+"/FCN32s_VGG16", args.model_dir+"/FCN16s_VGG16", args.model_dir+"/FCN8s_VGG16"]
if args.model == "fcn16s":
fcnxs = symbol_fcnxs.get_fcn16s_symbol(numclass=num_classes, workspace_default=1536)
fcnxs_model_prefix = model_prefixes[2]
load_prefix = model_prefixes[1]
lr = 1e-5
run_epochs = epochs[2]
load_epoch = epochs[1]
elif args.model == "fcn8s":
fcnxs = symbol_fcnxs.get_fcn8s_symbol(numclass=num_classes, workspace_default=1536)
fcnxs_model_prefix = model_prefixes[3]
load_prefix = model_prefixes[2]
lr = 1e-6
run_epochs = epochs[3]
load_epoch = epochs[2]
else:
fcnxs = symbol_fcnxs.get_fcn32s_symbol(numclass=num_classes, workspace_default=1536)
fcnxs_model_prefix = model_prefixes[1]
load_prefix = model_prefixes[0]
lr = 1e-4
run_epochs = epochs[1]
load_epoch = epochs[0]
arg_names = fcnxs.list_arguments()
print('loading', load_prefix, load_epoch)
print('lr', lr)
print('model_prefix', fcnxs_model_prefix)
print('running epochs', run_epochs)
_, fcnxs_args, fcnxs_auxs = mx.model.load_checkpoint(load_prefix, load_epoch)
if not args.retrain:
if args.model == "fcn16s" or args.model == "fcn8s":
fcnxs_args, fcnxs_auxs = init_fcnxs.init_from_fcnxs(ctx, fcnxs, fcnxs_args, fcnxs_auxs)
else:
fcnxs_args, fcnxs_auxs = init_fcnxs.init_from_vgg16(ctx, fcnxs, fcnxs_args, fcnxs_auxs)
train_dataiter = FileIter(
root_dir = carvn_root,
flist_name = "../data/train.lst",
cut_off_size = cutoff,
rgb_mean = (123.68, 116.779, 103.939),
)
val_dataiter = FileIter(
root_dir = carvn_root,
flist_name = "../data/val.lst",
cut_off_size = cutoff,
rgb_mean = (123.68, 116.779, 103.939),
)
model = Solver(
ctx = ctx,
symbol = fcnxs,
begin_epoch = 0,
num_epoch = run_epochs,
arg_params = fcnxs_args,
aux_params = fcnxs_auxs,
learning_rate = lr,
momentum = 0.99,
wd = 0.0005)
_metric = DiceMetric()
model.fit(
train_data = train_dataiter,
eval_data = val_dataiter,
eval_metric = _metric,
batch_end_callback = mx.callback.Speedometer(1, 10),
epoch_end_callback = mx.callback.do_checkpoint(fcnxs_model_prefix))