def test(net,
val_data,
batch_fn,
use_rec,
dtype,
ctx,
calc_weight_count=False,
extended_log=False):
acc_top1 = mx.metric.Accuracy()
acc_top5 = mx.metric.TopKAccuracy(5)
tic = time.time()
err_top1_val, err_top5_val = validate(acc_top1=acc_top1,
acc_top5=acc_top5,
net=net,
val_data=val_data,
batch_fn=batch_fn,
use_rec=use_rec,
dtype=dtype,
ctx=ctx)
if calc_weight_count:
weight_count = calc_net_weight_count(net)
logging.info('Model: {} trainable parameters'.format(weight_count))
if extended_log:
logging.info(
'Test: err-top1={top1:.4f} ({top1})\terr-top5={top5:.4f} ({top5})'.
format(top1=err_top1_val, top5=err_top5_val))
else:
logging.info('Test: err-top1={top1:.4f}\terr-top5={top5:.4f}'.format(
top1=err_top1_val, top5=err_top5_val))
logging.info('Time cost: {:.4f} sec'.format(time.time() - tic))
def test(net,
test_data,
batch_fn,
data_source_needs_reset,
metric,
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:
tic = time.time()
validate(metric=metric,
net=net,
val_data=test_data,
batch_fn=batch_fn,
data_source_needs_reset=data_source_needs_reset,
dtype=dtype,
ctx=ctx)
accuracy_msg = report_accuracy(metric=metric,
extended_log=extended_log)
logging.info("Test: {}".format(accuracy_msg))
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 test(net,
val_data,
batch_fn,
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:
acc_top1 = mx.metric.Accuracy()
acc_top5 = mx.metric.TopKAccuracy(5)
tic = time.time()
err_top1_val, err_top5_val = validate(
acc_top1=acc_top1,
acc_top5=acc_top5,
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-top1={top1:.4f} ({top1})\terr-top5={top5:.4f} ({top5})'
.format(top1=err_top1_val, top5=err_top5_val))
else:
logging.info(
'Test: err-top1={top1:.4f}\terr-top5={top5:.4f}'.format(
top1=err_top1_val, top5=err_top5_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,
batch_fn, data_source_needs_reset, dtype, net, teacher_net,
discrim_net, trainer, lr_scheduler, lp_saver, log_interval,
mixup, mixup_epoch_tail, label_smoothing, num_classes,
grad_clip_value, batch_size_scale, val_metric, train_metric,
loss_metrics, loss_func, discrim_loss_func, ctx):
"""
Main procedure for training model.
Parameters:
----------
batch_size : int
Training batch size.
num_epochs : int
Number of training epochs.
start_epoch1 : int
Number of starting epoch (1-based).
train_data : DataLoader or ImageRecordIter
Data loader or ImRec-iterator (training subset).
val_data : DataLoader or ImageRecordIter
Data loader or ImRec-iterator (validation subset).
batch_fn : func
Function for splitting data after extraction from data loader.
data_source_needs_reset : bool
Whether to reset data (if test_data is ImageRecordIter).
dtype : str
Base data type for tensors.
net : HybridBlock
Model.
teacher_net : HybridBlock or None
Teacher model.
discrim_net : HybridBlock or None
MEALv2 discriminator model.
trainer : Trainer
Trainer.
lr_scheduler : LRScheduler
Learning rate scheduler.
lp_saver : TrainLogParamSaver
Model/trainer state saver.
log_interval : int
Batch count period for logging.
mixup : bool
Whether to use mixup.
mixup_epoch_tail : int
Number of epochs without mixup at the end of training.
label_smoothing : bool
Whether to use label-smoothing.
num_classes : int
Number of model classes.
grad_clip_value : float
Threshold for gradient clipping.
batch_size_scale : int
Manual batch-size increasing factor.
val_metric : EvalMetric
Metric object instance (validation subset).
train_metric : EvalMetric
Metric object instance (training subset).
loss_metrics : list of EvalMetric
Metric object instances (loss values).
loss_func : Loss
Loss object instance.
discrim_loss_func : Loss or None
MEALv2 adversarial loss function.
ctx : Context
MXNet context.
"""
if batch_size_scale != 1:
for p in net.collect_params().values():
p.grad_req = "add"
if isinstance(ctx, mx.Context):
ctx = [ctx]
# 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))
validate(metric=val_metric,
net=net,
val_data=val_data,
batch_fn=batch_fn,
data_source_needs_reset=data_source_needs_reset,
dtype=dtype,
ctx=ctx)
val_accuracy_msg = report_accuracy(metric=val_metric)
logging.info("[Epoch {}] validation: {}".format(
start_epoch1 - 1, val_accuracy_msg))
gtic = time.time()
for epoch in range(start_epoch1 - 1, num_epochs):
train_epoch(epoch=epoch,
net=net,
teacher_net=teacher_net,
discrim_net=discrim_net,
train_metric=train_metric,
loss_metrics=loss_metrics,
train_data=train_data,
batch_fn=batch_fn,
data_source_needs_reset=data_source_needs_reset,
dtype=dtype,
ctx=ctx,
loss_func=loss_func,
discrim_loss_func=discrim_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)
validate(metric=val_metric,
net=net,
val_data=val_data,
batch_fn=batch_fn,
data_source_needs_reset=data_source_needs_reset,
dtype=dtype,
ctx=ctx)
val_accuracy_msg = report_accuracy(metric=val_metric)
logging.info("[Epoch {}] validation: {}".format(
epoch + 1, val_accuracy_msg))
if lp_saver is not None:
lp_saver_kwargs = {"net": net, "trainer": trainer}
val_acc_values = val_metric.get()[1]
train_acc_values = train_metric.get()[1]
val_acc_values = val_acc_values if type(
val_acc_values) == list else [val_acc_values]
train_acc_values = train_acc_values if type(
train_acc_values) == list else [train_acc_values]
lp_saver.epoch_test_end_callback(
epoch1=(epoch + 1),
params=(val_acc_values + train_acc_values +
[loss_metrics[0].get()[1], trainer.learning_rate]),
**lp_saver_kwargs)
logging.info("Total time cost: {:.2f} sec".format(time.time() - gtic))
if lp_saver is not None:
opt_metric_name = get_metric_name(val_metric, lp_saver.acc_ind)
logging.info("Best {}: {:.4f} at {} epoch".format(
opt_metric_name, lp_saver.best_eval_metric_value,
lp_saver.best_eval_metric_epoch))
def calc_model_accuracy(net,
test_data,
batch_fn,
data_source_needs_reset,
metric,
dtype,
ctx,
input_image_size,
in_channels,
calc_weight_count=False,
calc_flops=False,
calc_flops_only=True,
extended_log=False):
"""
Main test routine.
Parameters:
----------
net : HybridBlock
Model.
test_data : DataLoader or ImageRecordIter
Data loader or ImRec-iterator.
batch_fn : func
Function for splitting data after extraction from data loader.
data_source_needs_reset : bool
Whether to reset data (if test_data is ImageRecordIter).
metric : EvalMetric
Metric object instance.
dtype : str
Base data type for tensors.
ctx : Context
MXNet context.
input_image_size : tuple of 2 ints
Spatial size of the expected input image.
in_channels : int
Number of input channels.
calc_weight_count : bool, default False
Whether to calculate count of weights.
calc_flops : bool, default False
Whether to calculate FLOPs.
calc_flops_only : bool, default True
Whether to only calculate FLOPs without testing.
extended_log : bool, default False
Whether to log more precise accuracy values.
Returns:
-------
list of floats
Accuracy values.
"""
if not calc_flops_only:
tic = time.time()
validate(metric=metric,
net=net,
val_data=test_data,
batch_fn=batch_fn,
data_source_needs_reset=data_source_needs_reset,
dtype=dtype,
ctx=ctx)
accuracy_msg = report_accuracy(metric=metric,
extended_log=extended_log)
logging.info("Test: {}".format(accuracy_msg))
logging.info("Time cost: {:.4f} sec".format(time.time() - tic))
acc_values = metric.get()[1]
acc_values = acc_values if type(acc_values) == list else [acc_values]
else:
acc_values = []
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))
return acc_values
def train_net(batch_size, num_epochs, start_epoch1, train_data, val_data,
batch_fn, 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,
val_metric, train_metric, ctx):
if batch_size_scale != 1:
for p in net.collect_params().values():
p.grad_req = "add"
if isinstance(ctx, mx.Context):
ctx = [ctx]
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))
validate(metric=val_metric,
net=net,
val_data=val_data,
batch_fn=batch_fn,
data_source_needs_reset=data_source_needs_reset,
dtype=dtype,
ctx=ctx)
val_accuracy_msg = report_accuracy(metric=val_metric)
logging.info("[Epoch {}] validation: {}".format(
start_epoch1 - 1, val_accuracy_msg))
gtic = time.time()
for epoch in range(start_epoch1 - 1, num_epochs):
train_loss = train_epoch(
epoch=epoch,
net=net,
train_metric=train_metric,
train_data=train_data,
batch_fn=batch_fn,
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)
validate(metric=val_metric,
net=net,
val_data=val_data,
batch_fn=batch_fn,
data_source_needs_reset=data_source_needs_reset,
dtype=dtype,
ctx=ctx)
val_accuracy_msg = report_accuracy(metric=val_metric)
logging.info("[Epoch {}] validation: {}".format(
epoch + 1, val_accuracy_msg))
if lp_saver is not None:
lp_saver_kwargs = {"net": net, "trainer": trainer}
val_acc_values = val_metric.get()[1]
train_acc_values = train_metric.get()[1]
val_acc_values = val_acc_values if type(
val_acc_values) == list else [val_acc_values]
train_acc_values = train_acc_values if type(
train_acc_values) == list else [train_acc_values]
lp_saver.epoch_test_end_callback(
epoch1=(epoch + 1),
params=(val_acc_values + train_acc_values +
[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:
opt_metric_name = get_metric_name(val_metric, lp_saver.acc_ind)
logging.info("Best {}: {:.4f} at {} epoch".format(
opt_metric_name, lp_saver.best_eval_metric_value,
lp_saver.best_eval_metric_epoch))
def train_net(batch_size,
num_epochs,
start_epoch1,
train_data,
val_data,
batch_fn,
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_top1_val = mx.metric.Accuracy()
acc_top5_val = mx.metric.TopKAccuracy(5)
acc_top1_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_top1_val, err_top5_val = validate(
acc_top1=acc_top1_val,
acc_top5=acc_top5_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-top1={:.4f}\terr-top5={:.4f}'.format(
start_epoch1 - 1, err_top1_val, err_top5_val))
gtic = time.time()
for epoch in range(start_epoch1 - 1, num_epochs):
err_top1_train, train_loss = train_epoch(
epoch=epoch,
net=net,
acc_top1_train=acc_top1_train,
train_data=train_data,
batch_fn=batch_fn,
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_top1_val, err_top5_val = validate(
acc_top1=acc_top1_val,
acc_top5=acc_top5_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-top1={:.4f}\terr-top5={:.4f}'.format(
epoch + 1, err_top1_val, err_top5_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_top1_val, err_top1_train, err_top5_val, 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-top5: {:.4f} at {} epoch'.format(
lp_saver.best_eval_metric_value, lp_saver.best_eval_metric_epoch))