device_target=args_opt.device_target)
context.set_context(device_id=args_opt.device_id)
device_num = int(os.environ.get("DEVICE_NUM", 1))
if device_num > 1:
context.reset_auto_parallel_context()
context.set_auto_parallel_context(
device_num=device_num,
parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
init()
dataset = create_dataset(args_opt.data_path, cfg.epoch_size)
batch_num = dataset.get_dataset_size()
net = vgg16(num_classes=cfg.num_classes)
lr = lr_steps(0,
lr_max=cfg.lr_init,
total_epochs=cfg.epoch_size,
steps_per_epoch=batch_num)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
Tensor(lr),
cfg.momentum,
weight_decay=cfg.weight_decay)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True,
reduction='mean',
is_grad=False)
model = Model(net,
loss_fn=loss,
optimizer=opt,
metrics={'acc'},
parser.add_argument('--data_path',
type=str,
default='./cifar',
help='path where the dataset is saved')
parser.add_argument('--device_id',
type=int,
default=None,
help='device id of GPU or Ascend. (Default: None)')
args_opt = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE,
device_target=args_opt.device_target)
context.set_context(device_id=args_opt.device_id)
context.set_context(enable_mem_reuse=True, enable_hccl=False)
net = vgg16(batch_size=cfg.batch_size, num_classes=cfg.num_classes)
lr = lr_steps(0,
lr_max=cfg.lr_init,
total_epochs=cfg.epoch_size,
steps_per_epoch=50000 // cfg.batch_size)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
Tensor(lr),
cfg.momentum,
weight_decay=cfg.weight_decay)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True,
reduction='mean',
is_grad=False)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
dataset = dataset.create_dataset(args_opt.data_path, cfg.epoch_size)
batch_num = dataset.get_dataset_size()
def test_vgg16():
inputs = Tensor(np.random.rand(1, 3, 112, 112).astype(np.float32))
net = vgg16()
with pytest.raises(ValueError):
print(net.construct(inputs))
