def eval_alexnet():
print("============== Starting Testing ==============")
device_num = get_device_num()
if device_num > 1:
# context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
context.set_context(mode=context.GRAPH_MODE,
device_target='Davinci',
save_graphs=False)
if config.device_target == "Ascend":
context.set_context(device_id=get_device_id())
init()
elif config.device_target == "GPU":
init()
if config.dataset_name == 'cifar10':
network = AlexNet(config.num_classes, phase='test')
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
opt = nn.Momentum(network.trainable_params(), config.learning_rate,
config.momentum)
ds_eval = create_dataset_cifar10(config.data_path, config.batch_size, status="test", \
target=config.device_target)
param_dict = load_checkpoint(load_path)
print("load checkpoint from [{}].".format(load_path))
load_param_into_net(network, param_dict)
network.set_train(False)
model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})
elif config.dataset_name == 'imagenet':
network = AlexNet(config.num_classes, phase='test')
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
ds_eval = create_dataset_imagenet(config.data_path,
config.batch_size,
training=False)
param_dict = load_checkpoint(load_path)
print("load checkpoint from [{}].".format(load_path))
load_param_into_net(network, param_dict)
network.set_train(False)
model = Model(network,
loss_fn=loss,
metrics={'top_1_accuracy', 'top_5_accuracy'})
else:
raise ValueError("Unsupported dataset.")
if ds_eval.get_dataset_size() == 0:
raise ValueError(
"Please check dataset size > 0 and batch_size <= dataset size")
result = model.eval(ds_eval, dataset_sink_mode=config.dataset_sink_mode)
print("result : {}".format(result))
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
print("============== Starting Testing ==============")
if args.dataset_name == 'cifar10':
cfg = alexnet_cifar10_cfg
network = AlexNet(cfg.num_classes)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
ds_eval = create_dataset_cifar10(args.data_path, cfg.batch_size, status="test", target=args.device_target)
param_dict = load_checkpoint(args.ckpt_path)
print("load checkpoint from [{}].".format(args.ckpt_path))
load_param_into_net(network, param_dict)
network.set_train(False)
model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})
elif args.dataset_name == 'imagenet':
cfg = alexnet_imagenet_cfg
network = AlexNet(cfg.num_classes)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
ds_eval = create_dataset_imagenet(args.data_path, cfg.batch_size, training=False)
param_dict = load_checkpoint(args.ckpt_path)
print("load checkpoint from [{}].".format(args.ckpt_path))
load_param_into_net(network, param_dict)
network.set_train(False)
model = Model(network, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})