def test(net,
val_data,
data_source_needs_reset,
dtype,
ctx,
input_image_size,
in_channels,
calc_weight_count=False,
calc_flops=False,
calc_flops_only=True,
extended_log=False):
if not calc_flops_only:
accuracy_metric = mx.metric.Accuracy()
tic = time.time()
err_val = validate1(accuracy_metric=accuracy_metric,
net=net,
val_data=val_data,
batch_fn=batch_fn,
data_source_needs_reset=data_source_needs_reset,
dtype=dtype,
ctx=ctx)
if extended_log:
logging.info('Test: err={err:.4f} ({err}))'.format(err=err_val))
else:
logging.info('Test: err={err:.4f}'.format(err=err_val))
logging.info('Time cost: {:.4f} sec'.format(time.time() - tic))
if calc_weight_count:
weight_count = calc_net_weight_count(net)
if not calc_flops:
logging.info("Model: {} trainable parameters".format(weight_count))
if calc_flops:
num_flops, num_macs, num_params = measure_model(
net, in_channels, input_image_size, ctx[0])
assert (not calc_weight_count) or (weight_count == num_params)
stat_msg = "Params: {params} ({params_m:.2f}M), FLOPs: {flops} ({flops_m:.2f}M)," \
" FLOPs/2: {flops2} ({flops2_m:.2f}M), MACs: {macs} ({macs_m:.2f}M)"
logging.info(
stat_msg.format(params=num_params,
params_m=num_params / 1e6,
flops=num_flops,
flops_m=num_flops / 1e6,
flops2=num_flops / 2,
flops2_m=num_flops / 2 / 1e6,
macs=num_macs,
macs_m=num_macs / 1e6))
def train_net(batch_size, num_epochs, start_epoch1, train_data, val_data,
data_source_needs_reset, dtype, net, trainer, lr_scheduler,
lp_saver, log_interval, mixup, mixup_epoch_tail, label_smoothing,
num_classes, grad_clip_value, batch_size_scale, ctx):
assert (not (mixup and label_smoothing))
if batch_size_scale != 1:
for p in net.collect_params().values():
p.grad_req = 'add'
if isinstance(ctx, mx.Context):
ctx = [ctx]
acc_metric_val = mx.metric.Accuracy()
acc_metric_train = mx.metric.Accuracy()
loss_func = gluon.loss.SoftmaxCrossEntropyLoss(
sparse_label=(not (mixup or label_smoothing)))
assert (type(start_epoch1) == int)
assert (start_epoch1 >= 1)
if start_epoch1 > 1:
logging.info('Start training from [Epoch {}]'.format(start_epoch1))
err_val = validate1(accuracy_metric=acc_metric_val,
net=net,
val_data=val_data,
batch_fn=batch_fn,
data_source_needs_reset=data_source_needs_reset,
dtype=dtype,
ctx=ctx)
logging.info('[Epoch {}] validation: err={:.4f}'.format(
start_epoch1 - 1, err_val))
gtic = time.time()
for epoch in range(start_epoch1 - 1, num_epochs):
err_train, train_loss = train_epoch(
epoch=epoch,
net=net,
acc_metric_train=acc_metric_train,
train_data=train_data,
data_source_needs_reset=data_source_needs_reset,
dtype=dtype,
ctx=ctx,
loss_func=loss_func,
trainer=trainer,
lr_scheduler=lr_scheduler,
batch_size=batch_size,
log_interval=log_interval,
mixup=mixup,
mixup_epoch_tail=mixup_epoch_tail,
label_smoothing=label_smoothing,
num_classes=num_classes,
num_epochs=num_epochs,
grad_clip_value=grad_clip_value,
batch_size_scale=batch_size_scale)
err_val = validate1(accuracy_metric=acc_metric_val,
net=net,
val_data=val_data,
batch_fn=batch_fn,
data_source_needs_reset=data_source_needs_reset,
dtype=dtype,
ctx=ctx)
logging.info('[Epoch {}] validation: err={:.4f}'.format(
epoch + 1, err_val))
if lp_saver is not None:
lp_saver_kwargs = {'net': net, 'trainer': trainer}
lp_saver.epoch_test_end_callback(
epoch1=(epoch + 1),
params=[err_val, err_train, train_loss, trainer.learning_rate],
**lp_saver_kwargs)
logging.info('Total time cost: {:.2f} sec'.format(time.time() - gtic))
if lp_saver is not None:
logging.info('Best err: {:.4f} at {} epoch'.format(
lp_saver.best_eval_metric_value, lp_saver.best_eval_metric_epoch))