def train_on_gpu():
config = config_gpu_quant if args_opt.quantization_aware else config_gpu
print("training args: {}".format(args_opt))
print("training configure: {}".format(config))
# define network
network = mobilenetV2(num_classes=config.num_classes)
# define loss
if config.label_smooth > 0:
loss = CrossEntropyWithLabelSmooth(smooth_factor=config.label_smooth,
num_classes=config.num_classes)
else:
loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
# define dataset
epoch_size = config.epoch_size
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=True,
config=config,
device_target=args_opt.device_target,
repeat_num=1,
batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
# resume
if args_opt.pre_trained:
param_dict = load_checkpoint(args_opt.pre_trained)
load_param_into_net(network, param_dict)
# convert fusion network to quantization aware network
if config.quantization_aware:
network = quant.convert_quant_network(network,
bn_fold=True,
per_channel=[True, False],
symmetric=[True, True])
# get learning rate
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
lr = Tensor(get_lr(global_step=config.start_epoch * step_size,
lr_init=0,
lr_end=0,
lr_max=config.lr,
warmup_epochs=config.warmup_epochs,
total_epochs=epoch_size + config.start_epoch,
steps_per_epoch=step_size))
# define optimization
opt = nn.Momentum(filter(lambda x: x.requires_grad, network.get_parameters()), lr, config.momentum,
config.weight_decay, config.loss_scale)
# define model
model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale)
print("============== Starting Training ==============")
callback = [Monitor(lr_init=lr.asnumpy())]
ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
if config.save_checkpoint:
config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
keep_checkpoint_max=config.keep_checkpoint_max)
ckpt_cb = ModelCheckpoint(prefix="mobilenetV2", directory=ckpt_save_dir, config=config_ck)
callback += [ckpt_cb]
model.train(epoch_size, dataset, callbacks=callback)
print("============== End Training ==============")
def train_on_ascend():
config = config_ascend_quant
print("training args: {}".format(args_opt))
print("training configure: {}".format(config))
print("parallel args: rank_id {}, device_id {}, rank_size {}".format(
rank_id, device_id, rank_size))
epoch_size = config.epoch_size
# distribute init
if run_distribute:
context.set_auto_parallel_context(
device_num=rank_size,
parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True)
init()
# define network
network = mobilenetV2(num_classes=config.num_classes)
# define loss
if config.label_smooth > 0:
loss = CrossEntropyWithLabelSmooth(smooth_factor=config.label_smooth,
num_classes=config.num_classes)
else:
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
# define dataset
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=True,
config=config,
device_target=args_opt.device_target,
repeat_num=1,
batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
# load pre trained ckpt
if args_opt.pre_trained:
param_dict = load_checkpoint(args_opt.pre_trained)
load_nonquant_param_into_quant_net(network, param_dict)
# convert fusion network to quantization aware network
network = quant.convert_quant_network(network,
bn_fold=True,
per_channel=[True, False],
symmetric=[True, False])
# get learning rate
lr = Tensor(
get_lr(global_step=config.start_epoch * step_size,
lr_init=0,
lr_end=0,
lr_max=config.lr,
warmup_epochs=config.warmup_epochs,
total_epochs=epoch_size + config.start_epoch,
steps_per_epoch=step_size))
# define optimization
opt = nn.Momentum(
filter(lambda x: x.requires_grad, network.get_parameters()), lr,
config.momentum, config.weight_decay)
# define model
model = Model(network, loss_fn=loss, optimizer=opt)
print("============== Starting Training ==============")
callback = None
if rank_id == 0:
callback = [Monitor(lr_init=lr.asnumpy())]
if config.save_checkpoint:
config_ck = CheckpointConfig(
save_checkpoint_steps=config.save_checkpoint_epochs *
step_size,
keep_checkpoint_max=config.keep_checkpoint_max)
ckpt_cb = ModelCheckpoint(prefix="mobilenetV2",
directory=config.save_checkpoint_path,
config=config_ck)
callback += [ckpt_cb]
model.train(epoch_size, dataset, callbacks=callback)
print("============== End Training ==============")
print("parallel args: rank_id {}, device_id {}, rank_size {}".format(rank_id, device_id, rank_size))
epoch_size = config.epoch_size
# distribute init
if run_distribute:
context.set_auto_parallel_context(device_num=rank_size,
parallel_mode=ParallelMode.DATA_PARALLEL,
parameter_broadcast=True,
mirror_mean=True)
init()
# define network
network = mobilenetV2(num_classes=config.num_classes)
# define loss
if config.label_smooth > 0:
loss = CrossEntropyWithLabelSmooth(smooth_factor=config.label_smooth, num_classes=config.num_classes)
else:
loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
# define dataset
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=True,
config=config,
device_target=args_opt.device_target,
repeat_num=1,
batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
# load pre trained ckpt
if args_opt.pre_trained:
param_dict = load_checkpoint(args_opt.pre_trained)
load_param_into_net(network, param_dict)