def __call__(self, output, target, *args, **kwargs):
"""Forward and calculate accuracy."""
if len(self.topk) == 1:
return Accuracy()
else:
return {
"accuracy": Accuracy(),
"accuracy_top1": nn.Top1CategoricalAccuracy(),
"accuracy_top5": nn.Top5CategoricalAccuracy()
}
}, {
'params': no_decayed_params
}, {
'order_params': net.trainable_params()
}]
optimizer = RMSProp(group_params,
lr,
decay=0.9,
weight_decay=cfg.weight_decay,
momentum=cfg.momentum,
epsilon=cfg.opt_eps,
loss_scale=cfg.loss_scale)
eval_metrics = {
'Loss': nn.Loss(),
'Top1-Acc': nn.Top1CategoricalAccuracy(),
'Top5-Acc': nn.Top5CategoricalAccuracy()
}
if args_opt.resume:
ckpt = load_checkpoint(args_opt.resume)
load_param_into_net(net, ckpt)
model = Model(net, loss_fn=loss, optimizer=optimizer, metrics={'acc'})
print("============== Starting Training ==============")
loss_cb = LossMonitor(per_print_times=batches_per_epoch)
time_cb = TimeMonitor(data_size=batches_per_epoch)
callbacks = [loss_cb, time_cb]
config_ck = CheckpointConfig(save_checkpoint_steps=batches_per_epoch,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix=f"inceptionv3-rank{cfg.rank}",
directory=cfg.ckpt_path,
dataset = create_dataset(args_opt.dataset_path,
do_train=False,
batch_size=config.batch_size,
device_num=1,
rank=0)
step_size = dataset.get_dataset_size()
# define net
net = xception(class_num=config.class_num)
# load checkpoint
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
# define loss, model
loss = CrossEntropySmooth(smooth_factor=config.label_smooth_factor,
num_classes=config.class_num)
# define model
eval_metrics = {
'Loss': nn.Loss(),
'Top_1_Acc': nn.Top1CategoricalAccuracy(),
'Top_5_Acc': nn.Top5CategoricalAccuracy()
}
model = Model(net, loss_fn=loss, metrics=eval_metrics)
# eval model
res = model.eval(dataset, dataset_sink_mode=False)
print("result:", res, "ckpt=", args_opt.checkpoint_path)
