def create_network(name, *args, **kwargs):
if name == "mobilenetv3_large":
net = mobilenet_v3_large(*args, **kwargs)
elif name == "mobilenetv3_small":
net = mobilenet_v3_small(*args, **kwargs)
else:
raise NotImplementedError(f"{name} is not implemented in the repo")
return net
1, cb_params.epoch_num, cur_step_in_epoch, cb_params.batch_num, step_loss,
np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
if __name__ == '__main__':
if args_opt.platform == "GPU":
# train on gpu
print("train args: ", args_opt, "\ncfg: ", config_gpu)
init('nccl')
context.set_auto_parallel_context(parallel_mode="data_parallel",
mirror_mean=True,
device_num=get_group_size())
# define net
net = mobilenet_v3_large(num_classes=config_gpu.num_classes)
# define loss
if config_gpu.label_smooth > 0:
loss = CrossEntropyWithLabelSmooth(
smooth_factor=config_gpu.label_smooth, num_classes=config_gpu.num_classes)
else:
loss = SoftmaxCrossEntropyWithLogits(
is_grad=False, sparse=True, reduction='mean')
# define dataset
epoch_size = config_gpu.epoch_size
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=True,
config=config_gpu,
platform=args_opt.platform,
repeat_num=epoch_size,
batch_size=config_gpu.batch_size)
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=False,
config=config,
device_target=args_opt.device_target,
batch_size=config.batch_size)
elif args_opt.device_target == "CPU":
config = config_cpu
context.set_context(mode=context.GRAPH_MODE,
device_target="CPU",
save_graphs=False)
dataset = create_dataset_cifar(dataset_path=args_opt.dataset_path,
do_train=False,
batch_size=config.batch_size)
else:
raise ValueError("Unsupported device_target.")
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
net = mobilenet_v3_large(num_classes=config.num_classes,
activation="Softmax")
step_size = dataset.get_dataset_size()
if args_opt.checkpoint_path:
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
model = Model(net, loss_fn=loss, metrics={'acc'})
res = model.eval(dataset)
print("result:", res, "ckpt=", args_opt.checkpoint_path)
if __name__ == '__main__':
config_ = None
if args_opt.device_target == "GPU":
config_ = config_gpu
elif args_opt.device_target == "CPU":
config_ = config_cpu
else:
raise ValueError("Unsupported device_target.")
# train on device
print("train args: ", args_opt)
print("cfg: ", config_)
# define net
net = mobilenet_v3_large(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 = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
# define dataset
epoch_size = config_.epoch_size
if args_opt.device_target == "GPU":
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,
config_platform = None
if args_opt.platform == "Ascend":
config_platform = config_ascend
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
device_id=device_id, save_graphs=False)
elif args_opt.platform == "GPU":
config_platform = config_gpu
context.set_context(mode=context.GRAPH_MODE,
device_target="GPU", save_graphs=False)
else:
raise ValueError("Unsupport platform.")
loss = nn.SoftmaxCrossEntropyWithLogits(
is_grad=False, sparse=True, reduction='mean')
net = mobilenet_v3_large(num_classes=config_platform.num_classes)
if args_opt.platform == "Ascend":
net.to_float(mstype.float16)
for _, cell in net.cells_and_names():
if isinstance(cell, nn.Dense):
cell.to_float(mstype.float32)
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=False,
config=config_platform,
platform=args_opt.platform,
batch_size=config_platform.batch_size)
step_size = dataset.get_dataset_size()
if args_opt.checkpoint_path:
