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 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 train_epoch(epoch, net, teacher_net, discrim_net, train_metric,
loss_metrics, train_data, batch_fn, data_source_needs_reset,
dtype, ctx, loss_func, discrim_loss_func, trainer,
lr_scheduler, batch_size, log_interval, mixup,
mixup_epoch_tail, label_smoothing, num_classes, num_epochs,
grad_clip_value, batch_size_scale):
"""
Train model on particular epoch.
Parameters:
----------
epoch : int
Epoch number.
net : HybridBlock
Model.
teacher_net : HybridBlock or None
Teacher model.
discrim_net : HybridBlock or None
MEALv2 discriminator model.
train_metric : EvalMetric
Metric object instance.
loss_metric : list of EvalMetric
Metric object instances (loss values).
train_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).
dtype : str
Base data type for tensors.
ctx : Context
MXNet context.
loss_func : Loss
Loss function.
discrim_loss_func : Loss or None
MEALv2 adversarial loss function.
trainer : Trainer
Trainer.
lr_scheduler : LRScheduler
Learning rate scheduler.
batch_size : int
Training batch size.
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.
num_epochs : int
Number of training epochs.
grad_clip_value : float
Threshold for gradient clipping.
batch_size_scale : int
Manual batch-size increasing factor.
Returns:
-------
float
Loss value.
"""
labels_list_inds = None
batch_size_extend_count = 0
tic = time.time()
if data_source_needs_reset:
train_data.reset()
train_metric.reset()
for m in loss_metrics:
m.reset()
i = 0
btic = time.time()
for i, batch in enumerate(train_data):
data_list, labels_list = batch_fn(batch, ctx)
labels_one_hot = False
if teacher_net is not None:
labels_list = [
teacher_net(x.astype(dtype,
copy=False)).softmax(axis=-1).mean(axis=1)
for x in data_list
]
labels_list_inds = [y.argmax(axis=-1) for y in labels_list]
labels_one_hot = True
if label_smoothing and not (teacher_net is not None):
eta = 0.1
on_value = 1 - eta + eta / num_classes
off_value = eta / num_classes
if not labels_one_hot:
labels_list_inds = labels_list
labels_list = [
y.one_hot(depth=num_classes,
on_value=on_value,
off_value=off_value) for y in labels_list
]
labels_one_hot = True
if mixup:
if not labels_one_hot:
labels_list_inds = labels_list
labels_list = [
y.one_hot(depth=num_classes) for y in labels_list
]
labels_one_hot = True
if epoch < num_epochs - mixup_epoch_tail:
alpha = 1
lam = np.random.beta(alpha, alpha)
data_list = [lam * x + (1 - lam) * x[::-1] for x in data_list]
labels_list = [
lam * y + (1 - lam) * y[::-1] for y in labels_list
]
with ag.record():
outputs_list = [
net(x.astype(dtype, copy=False)) for x in data_list
]
loss_list = [
loss_func(yhat, y.astype(dtype, copy=False))
for yhat, y in zip(outputs_list, labels_list)
]
if discrim_net is not None:
d_pred_list = [
discrim_net(yhat.astype(dtype, copy=False).softmax())
for yhat in outputs_list
]
d_label_list = [
discrim_net(y.astype(dtype, copy=False))
for y in labels_list
]
d_loss_list = [
discrim_loss_func(yhat, y)
for yhat, y in zip(d_pred_list, d_label_list)
]
loss_list = [z + dz for z, dz in zip(loss_list, d_loss_list)]
for loss in loss_list:
loss.backward()
lr_scheduler.update(i, epoch)
if grad_clip_value is not None:
grads = [
v.grad(ctx[0]) for v in net.collect_params().values()
if v._grad is not None
]
gluon.utils.clip_global_norm(grads, max_norm=grad_clip_value)
if batch_size_scale == 1:
trainer.step(batch_size)
else:
if (i + 1) % batch_size_scale == 0:
batch_size_extend_count = 0
trainer.step(batch_size * batch_size_scale)
for p in net.collect_params().values():
p.zero_grad()
else:
batch_size_extend_count += 1
train_metric.update(
labels=(labels_list if not labels_one_hot else labels_list_inds),
preds=outputs_list)
loss_metrics[0].update(labels=None, preds=loss_list)
if (discrim_net is not None) and (len(loss_metrics) > 1):
loss_metrics[1].update(labels=None, preds=d_loss_list)
if log_interval and not (i + 1) % log_interval:
speed = batch_size * log_interval / (time.time() - btic)
btic = time.time()
train_accuracy_msg = report_accuracy(metric=train_metric)
loss_accuracy_msg = report_accuracy(metric=loss_metrics[0])
if (discrim_net is not None) and (len(loss_metrics) > 1):
dloss_accuracy_msg = report_accuracy(metric=loss_metrics[1])
logging.info(
"Epoch[{}] Batch [{}]\tSpeed: {:.2f} samples/sec\t{}\t{}\t{}\tlr={:.5f}"
.format(epoch + 1, i, speed, train_accuracy_msg,
loss_accuracy_msg, dloss_accuracy_msg,
trainer.learning_rate))
else:
logging.info(
"Epoch[{}] Batch [{}]\tSpeed: {:.2f} samples/sec\t{}\t{}\tlr={:.5f}"
.format(epoch + 1, i, speed, train_accuracy_msg,
loss_accuracy_msg, trainer.learning_rate))
if (batch_size_scale != 1) and (batch_size_extend_count > 0):
trainer.step(batch_size * batch_size_extend_count)
for p in net.collect_params().values():
p.zero_grad()
throughput = int(batch_size * (i + 1) / (time.time() - tic))
logging.info(
"[Epoch {}] speed: {:.2f} samples/sec\ttime cost: {:.2f} sec".format(
epoch + 1, throughput,
time.time() - tic))
train_accuracy_msg = report_accuracy(metric=train_metric)
loss_accuracy_msg = report_accuracy(metric=loss_metrics[0])
if (discrim_net is not None) and (len(loss_metrics) > 1):
dloss_accuracy_msg = report_accuracy(metric=loss_metrics[1])
logging.info("[Epoch {}] training: {}\t{}\t{}".format(
epoch + 1, train_accuracy_msg, loss_accuracy_msg,
dloss_accuracy_msg))
else:
logging.info("[Epoch {}] training: {}\t{}".format(
epoch + 1, train_accuracy_msg, loss_accuracy_msg))
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_epoch(epoch, net, train_metric, train_data, batch_fn,
data_source_needs_reset, dtype, ctx, loss_func, trainer,
lr_scheduler, batch_size, log_interval, mixup,
mixup_epoch_tail, label_smoothing, num_classes, num_epochs,
grad_clip_value, batch_size_scale):
labels_list_inds = None
batch_size_extend_count = 0
tic = time.time()
if data_source_needs_reset:
train_data.reset()
train_metric.reset()
train_loss = 0.0
btic = time.time()
for i, batch in enumerate(train_data):
data_list, labels_list = batch_fn(batch, ctx)
if label_smoothing:
eta = 0.1
on_value = 1 - eta + eta / num_classes
off_value = eta / num_classes
labels_list_inds = labels_list
labels_list = [
Y.one_hot(depth=num_classes,
on_value=on_value,
off_value=off_value) for Y in labels_list
]
if mixup:
if not label_smoothing:
labels_list_inds = labels_list
labels_list = [
Y.one_hot(depth=num_classes) for Y in labels_list
]
if epoch < num_epochs - mixup_epoch_tail:
alpha = 1
lam = np.random.beta(alpha, alpha)
data_list = [lam * X + (1 - lam) * X[::-1] for X in data_list]
labels_list = [
lam * Y + (1 - lam) * Y[::-1] for Y in labels_list
]
with ag.record():
outputs_list = [
net(X.astype(dtype, copy=False)) for X in data_list
]
loss_list = [
loss_func(yhat, y.astype(dtype, copy=False))
for yhat, y in zip(outputs_list, labels_list)
]
for loss in loss_list:
loss.backward()
lr_scheduler.update(i, epoch)
if grad_clip_value is not None:
grads = [
v.grad(ctx[0]) for v in net.collect_params().values()
if v._grad is not None
]
gluon.utils.clip_global_norm(grads, max_norm=grad_clip_value)
if batch_size_scale == 1:
trainer.step(batch_size)
else:
if (i + 1) % batch_size_scale == 0:
batch_size_extend_count = 0
trainer.step(batch_size * batch_size_scale)
for p in net.collect_params().values():
p.zero_grad()
else:
batch_size_extend_count += 1
train_loss += sum([loss.mean().asscalar()
for loss in loss_list]) / len(loss_list)
train_metric.update(
labels=(labels_list
if not (mixup or label_smoothing) else labels_list_inds),
preds=outputs_list)
if log_interval and not (i + 1) % log_interval:
speed = batch_size * log_interval / (time.time() - btic)
btic = time.time()
train_accuracy_msg = report_accuracy(metric=train_metric)
logging.info(
"Epoch[{}] Batch [{}]\tSpeed: {:.2f} samples/sec\t{}\tlr={:.5f}"
.format(epoch + 1, i, speed, train_accuracy_msg,
trainer.learning_rate))
if (batch_size_scale != 1) and (batch_size_extend_count > 0):
trainer.step(batch_size * batch_size_extend_count)
for p in net.collect_params().values():
p.zero_grad()
throughput = int(batch_size * (i + 1) / (time.time() - tic))
logging.info(
"[Epoch {}] speed: {:.2f} samples/sec\ttime cost: {:.2f} sec".format(
epoch + 1, throughput,
time.time() - tic))
train_loss /= (i + 1)
train_accuracy_msg = report_accuracy(metric=train_metric)
logging.info("[Epoch {}] training: {}\tloss={:.4f}".format(
epoch + 1, train_accuracy_msg, train_loss))
return train_loss
def train_epoch(epoch, net, train_metric, train_data, batch_fn,
data_source_needs_reset, dtype, ctx, loss_func, trainer,
lr_scheduler, batch_size, log_interval, mixup,
mixup_epoch_tail, label_smoothing, num_classes, num_epochs,
grad_clip_value, batch_size_scale):
"""
Train model on particular epoch.
Parameters:
----------
epoch : int
Epoch number.
net : HybridBlock
Model.
train_metric : EvalMetric
Metric object instance.
train_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).
dtype : str
Base data type for tensors.
ctx : Context
MXNet context.
loss_func : Loss
Loss function.
trainer : Trainer
Trainer.
lr_scheduler : LRScheduler
Learning rate scheduler.
batch_size : int
Training batch size.
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.
num_epochs : int
Number of training epochs.
grad_clip_value : float
Threshold for gradient clipping.
batch_size_scale : int
Manual batch-size increasing factor.
Returns
-------
float
Loss value.
"""
labels_list_inds = None
batch_size_extend_count = 0
tic = time.time()
if data_source_needs_reset:
train_data.reset()
train_metric.reset()
train_loss = 0.0
btic = time.time()
for i, batch in enumerate(train_data):
data_list, labels_list = batch_fn(batch, ctx)
# print("--> {}".format(labels_list[0][0].asscalar()))
# import cv2
# img = data_list[0][0].transpose((1, 2, 0)).asnumpy().copy()[:, :, [2, 1, 0]]
# img -= img.min()
# img *= 255 / img.max()
# cv2.imshow(winname="img", mat=img.astype(np.uint8))
# cv2.waitKey()
if label_smoothing:
eta = 0.1
on_value = 1 - eta + eta / num_classes
off_value = eta / num_classes
labels_list_inds = labels_list
labels_list = [
Y.one_hot(depth=num_classes,
on_value=on_value,
off_value=off_value) for Y in labels_list
]
if mixup:
if not label_smoothing:
labels_list_inds = labels_list
labels_list = [
Y.one_hot(depth=num_classes) for Y in labels_list
]
if epoch < num_epochs - mixup_epoch_tail:
alpha = 1
lam = np.random.beta(alpha, alpha)
data_list = [lam * X + (1 - lam) * X[::-1] for X in data_list]
labels_list = [
lam * Y + (1 - lam) * Y[::-1] for Y in labels_list
]
with ag.record():
outputs_list = [
net(X.astype(dtype, copy=False)) for X in data_list
]
loss_list = [
loss_func(yhat, y.astype(dtype, copy=False))
for yhat, y in zip(outputs_list, labels_list)
]
for loss in loss_list:
loss.backward()
lr_scheduler.update(i, epoch)
if grad_clip_value is not None:
grads = [
v.grad(ctx[0]) for v in net.collect_params().values()
if v._grad is not None
]
gluon.utils.clip_global_norm(grads, max_norm=grad_clip_value)
if batch_size_scale == 1:
trainer.step(batch_size)
else:
if (i + 1) % batch_size_scale == 0:
batch_size_extend_count = 0
trainer.step(batch_size * batch_size_scale)
for p in net.collect_params().values():
p.zero_grad()
else:
batch_size_extend_count += 1
train_loss += sum([loss.mean().asscalar()
for loss in loss_list]) / len(loss_list)
train_metric.update(
labels=(labels_list
if not (mixup or label_smoothing) else labels_list_inds),
preds=outputs_list)
if log_interval and not (i + 1) % log_interval:
speed = batch_size * log_interval / (time.time() - btic)
btic = time.time()
train_accuracy_msg = report_accuracy(metric=train_metric)
logging.info(
"Epoch[{}] Batch [{}]\tSpeed: {:.2f} samples/sec\t{}\tlr={:.5f}"
.format(epoch + 1, i, speed, train_accuracy_msg,
trainer.learning_rate))
if (batch_size_scale != 1) and (batch_size_extend_count > 0):
trainer.step(batch_size * batch_size_extend_count)
for p in net.collect_params().values():
p.zero_grad()
throughput = int(batch_size * (i + 1) / (time.time() - tic))
logging.info(
"[Epoch {}] speed: {:.2f} samples/sec\ttime cost: {:.2f} sec".format(
epoch + 1, throughput,
time.time() - tic))
train_loss /= (i + 1)
train_accuracy_msg = report_accuracy(metric=train_metric)
logging.info("[Epoch {}] training: {}\tloss={:.4f}".format(
epoch + 1, train_accuracy_msg, train_loss))
return train_loss
