if not args_opt.dataset_path:
args_opt.dataset_path = download_dataset('cifar10')
# build the network
net = densenetBC_100(args_opt.num_classes)
net.update_parameters_name(prefix='huawei')
model = Model(net)
# define the loss function
net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
# define the optimizer
net_opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
model.compile(loss_fn=net_loss, optimizer=net_opt, metrics={"Accuracy": Accuracy()})
epoch_size = args_opt.epoch_size
batch_size = args_opt.batch_size
cifar10_path = args_opt.dataset_path
save_checkpoint_epochs = args_opt.save_checkpoint_epochs
dataset_sink_mode = not args_opt.device_target == "CPU"
if args_opt.do_eval: # as for evaluation, users could use model.eval
ds_eval = create_dataset(cifar10_path, batch_size=batch_size, is_training=False)
if args_opt.checkpoint_path:
model.load_checkpoint(args_opt.checkpoint_path)
acc = model.eval(ds_eval, dataset_sink_mode=dataset_sink_mode)
print("============== Accuracy:{} ==============".format(acc))
else: # as for train, users could use model.train
ds_train = create_dataset(cifar10_path, batch_size=batch_size)
ckpoint_cb = ModelCheckpoint(prefix="densenetBC_100_cifar10", config=CheckpointConfig(
save_checkpoint_steps=save_checkpoint_epochs * ds_train.get_dataset_size(),
keep_checkpoint_max=10))
model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor()],
dataset_sink_mode=dataset_sink_mode)
model.compile(loss_fn=net_loss,
optimizer=net_opt,
metrics={"Accuracy": Accuracy()})
epoch_size = args_opt.epoch_size
batch_size = args_opt.batch_size
cifar10_path = args_opt.dataset_path
save_checkpoint_epochs = args_opt.save_checkpoint_epochs
dataset_sink_mode = not args_opt.device_target == "CPU"
if args_opt.do_eval: # as for evaluation, users could use model.eval
ds_eval = create_dataset(cifar10_path,
batch_size=batch_size,
is_training=False)
if args_opt.load_pretrained == 'local':
if args_opt.checkpoint_path:
model.load_checkpoint(args_opt.checkpoint_path)
acc = model.eval(ds_eval, dataset_sink_mode=dataset_sink_mode)
print("============== Accuracy:{} ==============".format(acc))
else: # as for train, users could use model.train
ds_train = create_dataset(cifar10_path, batch_size=batch_size)
ckpoint_cb = ModelCheckpoint(
prefix="vgg16_cifar10",
config=CheckpointConfig(
save_checkpoint_steps=save_checkpoint_epochs *
ds_train.get_dataset_size(),
keep_checkpoint_max=10))
model.train(epoch_size,
ds_train,
callbacks=[ckpoint_cb, LossMonitor()],
dataset_sink_mode=dataset_sink_mode)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
lr, 0.9, 1.5e-4, loss_scale)
model = Model(TrainingWrapper(net, opt, loss_scale))
model.compile()
ckpoint_cb = ModelCheckpoint(
prefix="ssd300",
config=CheckpointConfig(
save_checkpoint_steps=args_opt.save_checkpoint_epochs *
dataset_size,
keep_checkpoint_max=10))
model.train(epoch_size,
ds_train,
callbacks=[
ckpoint_cb,
LossMonitor(),
TimeMonitor(data_size=dataset_size)
],
dataset_sink_mode=dataset_sink_mode)
else: # as for evaluation, users could use model.eval
ds_eval = create_dataset(voc_path, batch_size=1, is_training=False)
total = ds_eval.get_dataset_size()
# define the infer wrapper
eval_net = ssd300_infer(class_num=args_opt.num_classes)
model = Model(eval_net)
if args_opt.checkpoint_path:
model.load_checkpoint(args_opt.checkpoint_path)
# perform the model predict operation
print("\n========================================\n")
print("total images num: ", total)
print("Processing, please wait a moment...")