def get_dataloader(net, train_dataset, val_dataset, data_shape, batch_size,
num_workers, args):
"""Get dataloader."""
width, height = data_shape, data_shape
batchify_fn = Tuple(
*([Stack() for _ in range(6)] +
[Pad(axis=0, pad_val=-1)
for _ in range(1)])) # stack image, all targets generated
if args.no_random_shape:
train_loader = gluon.data.DataLoader(train_dataset.transform(
YOLO3DefaultTrainTransform(width, height, net, mixup=args.mixup)),
batch_size,
True,
batchify_fn=batchify_fn,
last_batch='rollover',
num_workers=num_workers)
else:
transform_fns = [
YOLO3DefaultTrainTransform(x * 32, x * 32, net, mixup=args.mixup)
for x in range(10, 20)
]
train_loader = RandomTransformDataLoader(transform_fns,
train_dataset,
batch_size=batch_size,
interval=10,
last_batch='rollover',
shuffle=True,
batchify_fn=batchify_fn,
num_workers=num_workers)
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
val_loader = gluon.data.DataLoader(val_dataset.transform(
YOLO3DefaultValTransform(width, height)),
batch_size,
False,
batchify_fn=val_batchify_fn,
last_batch='keep',
num_workers=num_workers)
return train_loader, val_loader
def _get_dataloader(net, test_dataset, data_shape, batch_size, num_workers, num_devices,
args):
"""Get dataloader."""
if args.meta_arch == 'yolo3':
width, height = data_shape, data_shape
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
test_loader = gluon.data.DataLoader(
test_dataset.transform(YOLO3DefaultValTransform(width, height)),
batch_size, False, batchify_fn=val_batchify_fn, last_batch='keep',
num_workers=num_workers)
return test_loader
elif args.meta_arch == 'faster_rcnn':
"""Get faster rcnn dataloader."""
test_bfn = Tuple(*[Append() for _ in range(3)])
short = net.short[-1] if isinstance(net.short, (tuple, list)) else net.short
# validation use 1 sample per device
test_loader = gluon.data.DataLoader(
test_dataset.transform(FasterRCNNDefaultValTransform(short, net.max_size)),
num_devices, False, batchify_fn=test_bfn, last_batch='keep',
num_workers=args.num_workers)
return test_loader
else:
raise NotImplementedError('%s not implemented.' % args.meta_arch)
def get_dataloader(net, train_dataset, val_dataset, data_shape, batch_size,
num_workers):
"""Get dataloader."""
width, height = data_shape, data_shape
batchify_fn = Tuple(
*([Stack() for _ in range(6)] +
[Pad(axis=0, pad_val=-1)
for _ in range(1)])) # stack image, all targets generated
train_loader = gluon.data.DataLoader(train_dataset.transform(
YOLO3DefaultTrainTransform(width, height, net)),
batch_size,
True,
batchify_fn=batchify_fn,
last_batch='rollover',
num_workers=num_workers)
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
val_loader = gluon.data.DataLoader(val_dataset.transform(
YOLO3DefaultValTransform(width, height)),
batch_size,
False,
batchify_fn=val_batchify_fn,
last_batch='keep',
num_workers=num_workers)
return train_loader, val_loader
def get_dataloader(self):
width, height = self.width, self.height
train_dataset = self.train_dataset
val_dataset = self.val_dataset
batch_size = self.batch_size
num_workers = self.num_workers
network = self.network
print('aqui 0')
if network == 'ssd':
# use fake data to generate fixed anchors for target generation
with autograd.train_mode():
_, _, anchors = self.net(mx.nd.zeros((1, 3, height, width)))
batchify_fn = Tuple(
Stack(), Stack(),
Stack()) # stack image, cls_targets, box_targets
train_loader = gluon.data.DataLoader(train_dataset.transform(
SSDDefaultTrainTransform(width, height, anchors)),
batch_size,
True,
batchify_fn=batchify_fn,
last_batch='rollover',
num_workers=num_workers)
# Val verdadeiro
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
val_loader = gluon.data.DataLoader(val_dataset.transform(
SSDDefaultValTransform(width, height)),
batch_size,
False,
batchify_fn=val_batchify_fn,
last_batch='keep',
num_workers=num_workers)
# use fake data to generate fixed anchors for target generation
with mx.Context(mx.gpu(0)):
anchors2 = anchors
val_loader_loss = gluon.data.DataLoader(val_dataset.transform(
SSDCustomValTransform(width, height, anchors2)),
batch_size,
True,
batchify_fn=batchify_fn,
last_batch='rollover',
num_workers=num_workers)
self.val_loader_loss = val_loader_loss
elif network == 'yolo':
print('aqui 1')
batchify_fn = Tuple(
*([Stack() for _ in range(6)] +
[Pad(axis=0, pad_val=-1)
for _ in range(1)])) # stack image, all targets generated
# if args.no_random_shape:
train_loader = gluon.data.DataLoader(train_dataset.transform(
YOLO3DefaultTrainTransform(width, height, self.net)),
batch_size,
True,
batchify_fn=batchify_fn,
last_batch='rollover',
num_workers=num_workers)
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
val_loader = gluon.data.DataLoader(val_dataset.transform(
YOLO3DefaultValTransform(width, height)),
batch_size,
False,
batchify_fn=val_batchify_fn,
last_batch='keep',
num_workers=num_workers)
print('aqui 2')
else:
raise ValueError("Network {} not implemented".format(network))
self.val_loader = val_loader
self.train_loader = train_loader
def get_dataloader(net, train_dataset, val_dataset, data_shape, batch_size, num_workers, args):
import gluoncv as gcv
gcv.utils.check_version("0.6.0")
from gluoncv import data as gdata
from gluoncv import utils as gutils
from gluoncv.data.batchify import Pad, Stack, Tuple
from gluoncv.data.dataloader import RandomTransformDataLoader
from gluoncv.data.transforms.presets.yolo import (
YOLO3DefaultTrainTransform,
YOLO3DefaultValTransform,
)
from gluoncv.model_zoo import get_model
from gluoncv.utils import LRScheduler, LRSequential
from gluoncv.utils.metrics.coco_detection import COCODetectionMetric
from gluoncv.utils.metrics.voc_detection import VOC07MApMetric
"""Get dataloader."""
width, height = data_shape, data_shape
batchify_fn = Tuple(
*([Stack() for _ in range(6)] + [Pad(axis=0, pad_val=-1) for _ in range(1)])
) # stack image, all targets generated
if args.no_random_shape:
print(len(train_dataset))
img, label = train_dataset[0]
print(img.shape, label.shape)
train_loader = gluon.data.DataLoader(
train_dataset.transform(
YOLO3DefaultTrainTransform(width, height, net, mixup=args.mixup)
),
batch_size,
True,
batchify_fn=batchify_fn,
last_batch="rollover",
num_workers=num_workers,
)
else:
transform_fns = [
YOLO3DefaultTrainTransform(x * 32, x * 32, net, mixup=args.mixup) for x in range(10, 20)
]
train_loader = RandomTransformDataLoader(
transform_fns,
train_dataset,
batch_size=batch_size,
interval=10,
last_batch="rollover",
shuffle=True,
batchify_fn=batchify_fn,
num_workers=num_workers,
)
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
val_loader = gluon.data.DataLoader(
val_dataset.transform(YOLO3DefaultValTransform(width, height)),
batch_size,
False,
batchify_fn=val_batchify_fn,
last_batch="keep",
num_workers=num_workers,
)
return train_loader, val_loader
def train(net, async_net, ctx, args):
"""Training pipeline"""
net.collect_params().reset_ctx(ctx)
if args.no_wd:
for k, v in net.collect_params(".*beta|.*gamma|.*bias").items():
v.wd_mult = 0.0
if args.label_smooth:
net._target_generator._label_smooth = True
if args.lr_decay_period > 0:
lr_decay_epoch = list(
range(args.lr_decay_period, args.epochs, args.lr_decay_period))
else:
lr_decay_epoch = [int(i) for i in args.lr_decay_epoch.split(',')]
lr_scheduler = LRSequential([
LRScheduler("linear",
base_lr=0,
target_lr=args.lr,
nepochs=args.warmup_epochs,
iters_per_epoch=args.batch_size),
LRScheduler(args.lr_mode,
base_lr=args.lr,
nepochs=args.epochs - args.warmup_epochs,
iters_per_epoch=args.batch_size,
step_epoch=lr_decay_epoch,
step_factor=args.lr_decay,
power=2),
])
if (args.optimizer == "sgd"):
trainer = gluon.Trainer(net.collect_params(),
args.optimizer, {
"wd": args.wd,
"momentum": args.momentum,
"lr_scheduler": lr_scheduler
},
kvstore="local")
elif (args.optimizer == "adam"):
trainer = gluon.Trainer(net.collect_params(),
args.optimizer, {"lr_scheduler": lr_scheduler},
kvstore="local")
else:
trainer = gluon.Trainer(net.collect_params(),
args.optimizer,
kvstore="local")
# targets
#sigmoid_ce = gluon.loss.SigmoidBinaryCrossEntropyLoss(from_sigmoid=False)
#l1_loss = gluon.loss.L1Loss()
# Intermediate Metrics:
train_metrics = (
mx.metric.Loss("ObjLoss"),
mx.metric.Loss("BoxCenterLoss"),
mx.metric.Loss("BoxScaleLoss"),
mx.metric.Loss("ClassLoss"),
mx.metric.Loss("TotalLoss"),
)
train_metric_ixs = range(len(train_metrics))
target_metric_ix = -1 # Train towards TotalLoss (the last one)
# Evaluation Metrics:
val_metric = VOC07MApMetric(iou_thresh=0.5)
# Data transformations:
train_dataset = gluon_pipe_mode.AugmentedManifestDetection(
args.train,
length=args.num_samples_train,
)
train_batchify_fn = batchify.Tuple(
*([batchify.Stack() for _ in range(6)] +
[batchify.Pad(axis=0, pad_val=-1) for _ in range(1)]))
if args.no_random_shape:
logger.debug("Creating train DataLoader without random transform")
train_transforms = YOLO3DefaultTrainTransform(args.data_shape,
args.data_shape,
net=async_net,
mixup=args.mixup)
train_dataloader = gluon.data.DataLoader(
train_dataset.transform(train_transforms),
batch_size=args.batch_size,
batchify_fn=train_batchify_fn,
last_batch="discard",
num_workers=args.num_workers,
shuffle=
False, # Note that shuffle *cannot* be used with AugmentedManifestDetection
)
else:
logger.debug("Creating train DataLoader with random transform")
train_transforms = [
YOLO3DefaultTrainTransform(x * 32,
x * 32,
net=async_net,
mixup=args.mixup)
for x in range(10, 20)
]
train_dataloader = RandomTransformDataLoader(
train_transforms,
train_dataset,
interval=10,
batch_size=args.batch_size,
batchify_fn=train_batchify_fn,
last_batch="discard",
num_workers=args.num_workers,
shuffle=
False, # Note that shuffle *cannot* be used with AugmentedManifestDetection
)
validation_dataset = None
validation_dataloader = None
if args.validation:
validation_dataset = gluon_pipe_mode.AugmentedManifestDetection(
args.validation,
length=args.num_samples_validation,
)
validation_dataloader = gluon.data.DataLoader(
validation_dataset.transform(
YOLO3DefaultValTransform(args.data_shape, args.data_shape), ),
args.batch_size,
shuffle=False,
batchify_fn=batchify.Tuple(batchify.Stack(),
batchify.Pad(pad_val=-1)),
last_batch="keep",
num_workers=args.num_workers,
)
# Prepare the inference-time configuration for our model's setup:
# (This will be saved alongside our network structure/params)
inference_config = config.InferenceConfig(image_size=args.data_shape)
logger.info(args)
logger.info(f"Start training from [Epoch {args.start_epoch}]")
prev_best_score = float("-inf")
best_epoch = args.start_epoch
logger.info("Sleeping for 3s in case training data file not yet ready")
time.sleep(3)
for epoch in range(args.start_epoch, args.start_epoch + args.epochs):
# if args.mixup:
# # TODO(zhreshold): more elegant way to control mixup during runtime
# try:
# train_data._dataset.set_mixup(np.random.beta, 1.5, 1.5)
# except AttributeError:
# train_data._dataset._data.set_mixup(np.random.beta, 1.5, 1.5)
# if epoch >= args.epochs - args.no_mixup_epochs:
# try:
# train_data._dataset.set_mixup(None)
# except AttributeError:
# train_data._dataset._data.set_mixup(None)
tic = time.time()
btic = time.time()
mx.nd.waitall()
net.hybridize()
logger.debug(
f"Input data dir contents: {os.listdir('/opt/ml/input/data/')}")
for i, batch in enumerate(train_dataloader):
logger.debug(f"Epoch {epoch}, minibatch {i}")
batch_size = batch[0].shape[0]
data = gluon.utils.split_and_load(batch[0],
ctx_list=ctx,
batch_axis=0,
even_split=False)
# objectness, center_targets, scale_targets, weights, class_targets
fixed_targets = [
gluon.utils.split_and_load(batch[it],
ctx_list=ctx,
batch_axis=0,
even_split=False)
for it in range(1, 6)
]
gt_boxes = gluon.utils.split_and_load(batch[6],
ctx_list=ctx,
batch_axis=0,
even_split=False)
loss_trackers = tuple([] for metric in train_metrics)
with autograd.record():
for ix, x in enumerate(data):
losses_raw = net(x, gt_boxes[ix],
*[ft[ix] for ft in fixed_targets])
# net outputs: [obj_loss, center_loss, scale_loss, cls_loss]
# Each a mx.ndarray 1xbatch_size. This is the same order as our
# train_metrics, so we just need to add a total vector:
total_loss = sum(losses_raw)
losses = losses_raw + [total_loss]
# If any sample's total loss is non-finite, sum will be:
if not isfinite(sum(total_loss)):
logger.error(
f"[Epoch {epoch}][Minibatch {i}] got non-finite losses: {losses_raw}"
)
# TODO: Terminate training if losses or gradient go infinite?
for ix in train_metric_ixs:
loss_trackers[ix].append(losses[ix])
autograd.backward(loss_trackers[target_metric_ix])
trainer.step(batch_size)
for ix in train_metric_ixs:
train_metrics[ix].update(0, loss_trackers[ix])
if args.log_interval and not (i + 1) % args.log_interval:
train_metrics_current = map(lambda metric: metric.get(),
train_metrics)
metrics_msg = "; ".join([
f"{name}={val:.3f}" for name, val in train_metrics_current
])
logger.info(
f"[Epoch {epoch}][Minibatch {i}] LR={trainer.learning_rate:.2E}; "
f"Speed={batch_size/(time.time()-btic):.3f} samples/sec; {metrics_msg};"
)
btic = time.time()
train_metrics_current = map(lambda metric: metric.get(), train_metrics)
metrics_msg = "; ".join(
[f"{name}={val:.3f}" for name, val in train_metrics_current])
logger.info(
f"[Epoch {epoch}] TrainingCost={time.time()-tic:.3f}; {metrics_msg};"
)
if not (epoch + 1) % args.val_interval:
logger.info(f"Validating [Epoch {epoch}]")
metric_names, metric_values = validate(
net, validation_dataloader, epoch, ctx,
VOC07MApMetric(iou_thresh=0.5), args)
if isinstance(metric_names, list):
val_msg = "; ".join(
[f"{k}={v}" for k, v in zip(metric_names, metric_values)])
current_score = float(metric_values[-1])
else:
val_msg = f"{metric_names}={metric_values}"
current_score = metric_values
logger.info(f"[Epoch {epoch}] Validation: {val_msg};")
else:
current_score = float("-inf")
save_progress(
net,
inference_config,
current_score,
prev_best_score,
args.model_dir,
epoch,
args.checkpoint_interval,
args.checkpoint_dir,
)
if current_score > prev_best_score:
prev_best_score = current_score
best_epoch = epoch
if (args.early_stopping and epoch >= args.early_stopping_min_epochs
and (epoch - best_epoch) >= args.early_stopping_patience):
logger.info(
f"[Epoch {epoch}] No improvement since epoch {best_epoch}: Stopping early"
)
break
def train(net, async_net, ctx, args):
"""Training pipeline"""
net.collect_params().reset_ctx(ctx)
if args.no_wd:
for k, v in net.collect_params(".*beta|.*gamma|.*bias").items():
v.wd_mult = 0.0
if args.label_smooth:
net._target_generator._label_smooth = True
if args.lr_decay_period > 0:
lr_decay_epoch = list(
range(args.lr_decay_period, args.epochs, args.lr_decay_period))
else:
lr_decay_epoch = [int(i) for i in args.lr_decay_epoch.split(',')]
lr_scheduler = LRSequential([
LRScheduler("linear",
base_lr=0,
target_lr=args.lr,
nepochs=args.warmup_epochs,
iters_per_epoch=args.batch_size),
LRScheduler(args.lr_mode,
base_lr=args.lr,
nepochs=args.epochs - args.warmup_epochs,
iters_per_epoch=args.batch_size,
step_epoch=lr_decay_epoch,
step_factor=args.lr_decay,
power=2),
])
if (args.optimizer == "sgd"):
trainer = gluon.Trainer(net.collect_params(),
args.optimizer, {
"wd": args.wd,
"momentum": args.momentum,
"lr_scheduler": lr_scheduler
},
kvstore="local")
elif (args.optimizer == "adam"):
trainer = gluon.Trainer(net.collect_params(),
args.optimizer, {"lr_scheduler": lr_scheduler},
kvstore="local")
else:
trainer = gluon.Trainer(net.collect_params(),
args.optimizer,
kvstore="local")
# targets
#sigmoid_ce = gluon.loss.SigmoidBinaryCrossEntropyLoss(from_sigmoid=False)
#l1_loss = gluon.loss.L1Loss()
# Intermediate Metrics:
train_metrics = (
mx.metric.Loss("ObjLoss"),
mx.metric.Loss("BoxCenterLoss"),
mx.metric.Loss("BoxScaleLoss"),
mx.metric.Loss("ClassLoss"),
mx.metric.Loss("TotalLoss"),
)
train_metric_ixs = range(len(train_metrics))
target_metric_ix = -1 # Train towards TotalLoss (the last one)
# Evaluation Metrics:
val_metric = VOC07MApMetric(iou_thresh=0.5)
# Data transformations:
train_batchify_fn = Tuple(*([Stack() for _ in range(6)] +
[Pad(axis=0, pad_val=-1) for _ in range(1)]))
train_transforms = (YOLO3DefaultTrainTransform(
args.data_shape, args.data_shape, net=async_net,
mixup=args.mixup) if args.no_random_shape else [
YOLO3DefaultTrainTransform(
x * 32, x * 32, net=async_net, mixup=args.mixup)
for x in range(10, 20)
])
validation_batchify_fn = None
validation_transforms = None
if args.validation:
validation_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
validation_transforms = YOLO3DefaultValTransform(
args.data_shape, args.data_shape)
logger.info(args)
logger.info(f"Start training from [Epoch {args.start_epoch}]")
prev_best_score = float("-inf")
best_epoch = args.start_epoch
logger.info("Sleeping for 3s in case training data file not yet ready")
time.sleep(3)
for epoch in range(args.start_epoch, args.epochs):
# if args.mixup:
# # TODO(zhreshold): more elegant way to control mixup during runtime
# try:
# train_data._dataset.set_mixup(np.random.beta, 1.5, 1.5)
# except AttributeError:
# train_data._dataset._data.set_mixup(np.random.beta, 1.5, 1.5)
# if epoch >= args.epochs - args.no_mixup_epochs:
# try:
# train_data._dataset.set_mixup(None)
# except AttributeError:
# train_data._dataset._data.set_mixup(None)
tic = time.time()
btic = time.time()
mx.nd.waitall()
net.hybridize()
logger.debug(
f'Input data dir contents: {os.listdir("/opt/ml/input/data/")}')
train_data_gen = pipe_detection_minibatch(
epoch, channel=args.train, batch_size=args.stream_batch_size)
for ix_streambatch, train_dataset in enumerate(train_data_gen):
# TODO: Mixup is kinda rubbish if it's only within a (potentially small) batch
if args.mixup:
train_dataset = MixupDetection(train_dataset)
# Create dataloader for the stream-batch:
if args.no_random_shape:
logger.debug(
"Creating train DataLoader without random transform")
train_dataloader = gluon.data.DataLoader(
train_dataset.transform(train_transforms),
batch_size=args.batch_size,
batchify_fn=train_batchify_fn,
last_batch="discard",
num_workers=args.num_workers,
shuffle=True,
)
else:
logger.debug("Creating train DataLoader with random transform")
train_dataloader = RandomTransformDataLoader(
train_transforms,
train_dataset,
interval=10,
batch_size=args.batch_size,
batchify_fn=train_batchify_fn,
last_batch="discard",
num_workers=args.num_workers,
shuffle=True,
)
if args.mixup:
logger.debug("Shuffling stream-batch")
# TODO(zhreshold): more elegant way to control mixup during runtime
try:
train_dataloader._dataset.set_mixup(
np.random.beta, 1.5, 1.5)
except AttributeError:
train_dataloader._dataset._data.set_mixup(
np.random.beta, 1.5, 1.5)
if epoch >= args.epochs - args.no_mixup_epochs:
try:
train_dataloader._dataset.set_mixup(None)
except AttributeError:
train_dataloader._dataset._data.set_mixup(None)
logger.debug(
f"Training on stream-batch {ix_streambatch} ({len(train_dataset)} records)"
)
# TODO: Improve stream-batching robustness to drop loop guard clauses
# While it would be nice to simply `for i, batch in enumerate(train_dataloader):`,
# corrupted image buffers are somehow sneaking through the stream-batch at the moment.
#
# For now, we catch and tolerate these errors - trying to resume stream-batch process
# where possible and otherwise discarding the remainder of the stream-batch :-(
done = False
i = -1
dataiter = iter(train_dataloader)
while not done:
i += 1
batch = None
while not batch:
try:
batch = next(dataiter)
except StopIteration:
done = True
break
except ValueError:
# Some problem with the minibatch prevented loading - try the next
logger.warn(
f"[Epoch {epoch}][Streambatch {ix_streambatch}] "
f"Failed to load minibatch {i}, trying next...")
i += 1
except:
logger.error(
f"[Epoch {epoch}][Streambatch {ix_streambatch}] "
f"Failed to iterate minibatch {i}: Discarding remainder"
)
break
if not batch:
logger.debug(
f"[Epoch {epoch}][Streambatch {ix_streambatch}] "
f"Done after {i} minibatches")
break
logger.debug(
f"Epoch {epoch}, stream batch {ix_streambatch}, minibatch {i}"
)
batch_size = batch[0].shape[0]
data = gluon.utils.split_and_load(batch[0],
ctx_list=ctx,
batch_axis=0,
even_split=False)
# objectness, center_targets, scale_targets, weights, class_targets
fixed_targets = [
gluon.utils.split_and_load(batch[it],
ctx_list=ctx,
batch_axis=0,
even_split=False)
for it in range(1, 6)
]
gt_boxes = gluon.utils.split_and_load(batch[6],
ctx_list=ctx,
batch_axis=0,
even_split=False)
loss_trackers = tuple([] for metric in train_metrics)
with autograd.record():
for ix, x in enumerate(data):
losses_raw = net(x, gt_boxes[ix],
*[ft[ix] for ft in fixed_targets])
# net outputs: [obj_loss, center_loss, scale_loss, cls_loss]
# Each a mx.ndarray 1xbatch_size. This is the same order as our
# train_metrics, so we just need to add a total vector:
total_loss = sum(losses_raw)
losses = losses_raw + [total_loss]
# If any sample's total loss is non-finite, sum will be:
if not isfinite(sum(total_loss)):
logger.error(
f"[Epoch {epoch}][Streambatch {ix_streambatch}][Minibatch {i}] "
f"got non-finite losses: {losses_raw}")
# TODO: Terminate training if losses or gradient go infinite?
for ix in train_metric_ixs:
loss_trackers[ix].append(losses[ix])
autograd.backward(loss_trackers[target_metric_ix])
trainer.step(batch_size)
for ix in train_metric_ixs:
train_metrics[ix].update(0, loss_trackers[ix])
if args.log_interval and not (i + 1) % args.log_interval:
train_metrics_current = map(lambda metric: metric.get(),
train_metrics)
metrics_msg = "; ".join([
f"{name}={val:.3f}"
for name, val in train_metrics_current
])
logger.info(
f"[Epoch {epoch}][Streambatch {ix_streambatch}][Minibatch {i}] "
f"LR={trainer.learning_rate:.2E}; "
f"Speed={batch_size/(time.time()-btic):.3f} samples/sec; {metrics_msg};"
)
btic = time.time()
train_metrics_current = map(lambda metric: metric.get(), train_metrics)
metrics_msg = "; ".join(
[f"{name}={val:.3f}" for name, val in train_metrics_current])
logger.info(
f"[Epoch {epoch}] TrainingCost={time.time()-tic:.3f}; {metrics_msg};"
)
if not (epoch + 1) % args.val_interval:
logger.info(f"Validating [Epoch {epoch}]")
metric_names, metric_values = validate(
net, args.validation, epoch, ctx,
VOC07MApMetric(iou_thresh=0.5), validation_transforms,
validation_batchify_fn, args)
if isinstance(metric_names, list):
val_msg = "; ".join(
[f"{k}={v}" for k, v in zip(metric_names, metric_values)])
current_score = float(metric_values[-1])
else:
val_msg = f"{metric_names}={metric_values}"
current_score = metric_values
logger.info(f"[Epoch {epoch}] Validation: {val_msg};")
else:
current_score = float("-inf")
save_progress(net, current_score, prev_best_score, args.model_dir,
epoch, args.checkpoint_interval, args.checkpoint_dir)
if current_score > prev_best_score:
prev_best_score = current_score
best_epoch = epoch
if (args.early_stopping and epoch >= args.early_stopping_min_epochs
and (epoch - best_epoch) >= args.early_stopping_patience):
logger.info(
f"[Epoch {epoch}] No improvement since epoch {best_epoch}: Stopping early"
)
break
args = parse_args()
# training contexts
ctx = [mx.gpu(int(i)) for i in args.gpus.split(',') if i.strip()]
ctx = ctx if ctx else [mx.cpu()]
# network name
net_name = '_'.join(('yolo3', args.network, args.dataset))
args.save_prefix += net_name
# get netework
net = get_model(net_name, pretrained_base=True)
# training data
width, height = 500, 500 # resize image to 416x416 after all data augmentation
train_transform = YOLO3DefaultTrainTransform(width, height, net)
val_transform = YOLO3DefaultValTransform(width, height, net)
width, height = data_shape, data_shape
batchify_fn = Tuple(*([Stack() for _ in range(6)] + [Pad(axis=0, pad_val=-1) for _ in range(1)])) # stack image, all targets generate
#get datasets
train_dataset, val_dataset, eval_metric = get_voc_dataset(args.dataset, args)
#get data loaders
train_loader = gluon.data.DataLoader(train_dataset.transform(train_transform),args.batch_size, True, batchify_fn=batchify_fn, last_batch='rollover', num_workers=args.num_workers)
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
val_loader = gluon.data.DataLoader(val_dataset.transform(val_transform),args.batch_size, False, batchify_fn=val_batchify_fn, last_batch='keep', num_workers=args.num_workers)
#define eval_metric
#eval_metric=VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)