def main():
args = parse_args()
os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0"
assert (args.batch_size == 1)
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
ctx, batch_size = prepare_mx_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs=ds_metainfo.net_extra_kwargs,
load_ignore_extra=False,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=False,
ctx=ctx)
test_data = get_val_data_source(ds_metainfo=ds_metainfo,
batch_size=args.batch_size,
num_workers=args.num_workers)
calc_detector_repeatability(test_data=test_data, net=net, ctx=ctx)
def main():
args = parse_args()
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ctx, batch_size = prepare_mx_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
tune_layers="",
ctx=ctx)
input_image_size = net.in_size if hasattr(
net, 'in_size') else (args.input_size, args.input_size)
if args.use_rec:
train_data, val_data, batch_fn = get_data_rec(
rec_train=args.rec_train,
rec_train_idx=args.rec_train_idx,
rec_val=args.rec_val,
rec_val_idx=args.rec_val_idx,
batch_size=batch_size,
num_workers=args.num_workers,
input_image_size=input_image_size,
resize_inv_factor=args.resize_inv_factor)
else:
train_data, val_data, batch_fn = get_data_loader(
data_dir=args.data_dir,
batch_size=batch_size,
num_workers=args.num_workers,
input_image_size=input_image_size,
resize_inv_factor=args.resize_inv_factor)
assert (args.use_pretrained or args.resume.strip())
test(
net=net,
val_data=val_data,
batch_fn=batch_fn,
use_rec=args.use_rec,
dtype=args.dtype,
ctx=ctx,
# calc_weight_count=(not log_file_exist),
calc_weight_count=True,
extended_log=True)
def main():
os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0"
args = parse_args()
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ctx, batch_size = prepare_mx_context(
num_gpus=args.num_gpus,
batch_size=1)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs={"aux": False, "fixed_size": False},
load_ignore_extra=True,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=False,
ctx=ctx)
input_image_size = net.in_size if hasattr(net, 'in_size') else (480, 480)
test_data = get_test_data_source(
dataset_name=args.dataset,
dataset_dir=args.data_dir,
batch_size=batch_size,
num_workers=args.num_workers)
assert (args.use_pretrained or args.resume.strip() or args.calc_flops_only)
test(
net=net,
test_data=test_data,
data_source_needs_reset=False,
dtype=args.dtype,
ctx=ctx,
input_image_size=input_image_size,
in_channels=args.in_channels,
classes=args.num_classes,
# calc_weight_count=(not log_file_exist),
calc_weight_count=True,
calc_flops=args.calc_flops,
calc_flops_only=args.calc_flops_only,
extended_log=True,
dataset_metainfo=get_metainfo(args.dataset))
def main():
args = parse_args()
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ctx, batch_size = prepare_mx_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=(not args.calc_flops),
ctx=ctx)
assert (hasattr(net, "in_size"))
input_image_size = net.in_size
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
val_data = get_val_data_source(dataset_metainfo=ds_metainfo,
dataset_dir=args.data_dir,
batch_size=batch_size,
num_workers=args.num_workers,
input_image_size=input_image_size,
resize_inv_factor=args.resize_inv_factor)
batch_fn = get_batch_fn(use_imgrec=ds_metainfo.use_imgrec)
assert (args.use_pretrained or args.resume.strip() or args.calc_flops_only)
test(
net=net,
val_data=val_data,
batch_fn=batch_fn,
data_source_needs_reset=ds_metainfo.use_imgrec,
val_metric=get_composite_metric(ds_metainfo.val_metric_names),
dtype=args.dtype,
ctx=ctx,
input_image_size=input_image_size,
in_channels=args.in_channels,
# calc_weight_count=(not log_file_exist),
calc_weight_count=True,
calc_flops=args.calc_flops,
calc_flops_only=args.calc_flops_only,
extended_log=True)
def main():
args = parse_args()
os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0"
assert (args.batch_size == 1)
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
ctx, batch_size = prepare_mx_context(
num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs=None,
load_ignore_extra=False,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=False,
ctx=ctx)
test_data = get_val_data_source(
ds_metainfo=ds_metainfo,
batch_size=args.batch_size,
num_workers=args.num_workers)
tic = time.time()
for batch in test_data:
data_src_list, data_dst_list, labels_list = batch_fn(batch, ctx)
outputs_src_list = [net(X) for X in data_src_list]
assert (outputs_src_list is not None)
pass
logging.info("Time cost: {:.4f} sec".format(
time.time() - tic))
def main():
args = parse_args()
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ctx, batch_size = prepare_mx_context(
num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=(not args.calc_flops),
ctx=ctx)
input_image_size = net.in_size if hasattr(net, 'in_size') else (32, 32)
val_data = get_val_data_source(
dataset_name=args.dataset,
dataset_dir=args.data_dir,
batch_size=batch_size,
num_workers=args.num_workers)
assert (args.use_pretrained or args.resume.strip() or args.calc_flops_only)
test(
net=net,
val_data=val_data,
data_source_needs_reset=False,
dtype=args.dtype,
ctx=ctx,
input_image_size=input_image_size,
in_channels=args.in_channels,
# calc_weight_count=(not log_file_exist),
calc_weight_count=True,
calc_flops=args.calc_flops,
calc_flops_only=args.calc_flops_only,
extended_log=True)
def main():
"""
Main body of script.
"""
args = parse_args()
args.seed = init_rand(seed=args.seed)
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ctx, batch_size = prepare_mx_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
use_teacher = (args.teacher_models
is not None) and (args.teacher_models.strip() != "")
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs=ds_metainfo.train_net_extra_kwargs,
tune_layers=args.tune_layers,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=(not args.not_hybridize),
initializer=get_initializer(initializer_name=args.initializer),
ctx=ctx)
assert (hasattr(net, "classes"))
num_classes = net.classes
teacher_net = None
discrim_net = None
discrim_loss_func = None
if use_teacher:
teacher_nets = []
for teacher_model in args.teacher_models.split(","):
teacher_net = prepare_model(
model_name=teacher_model.strip(),
use_pretrained=True,
pretrained_model_file_path="",
dtype=args.dtype,
net_extra_kwargs=ds_metainfo.train_net_extra_kwargs,
do_hybridize=(not args.not_hybridize),
ctx=ctx)
assert (teacher_net.classes == net.classes)
assert (teacher_net.in_size == net.in_size)
teacher_nets.append(teacher_net)
if len(teacher_nets) > 0:
teacher_net = Concurrent(stack=True,
prefix="",
branches=teacher_nets)
for k, v in teacher_net.collect_params().items():
v.grad_req = "null"
if not args.not_discriminator:
discrim_net = MealDiscriminator()
discrim_net.cast(args.dtype)
if not args.not_hybridize:
discrim_net.hybridize(static_alloc=True, static_shape=True)
discrim_net.initialize(mx.init.MSRAPrelu(), ctx=ctx)
for k, v in discrim_net.collect_params().items():
v.lr_mult = args.dlr_factor
discrim_loss_func = MealAdvLoss()
train_data = get_train_data_source(ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
val_data = get_val_data_source(ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
batch_fn = get_batch_fn(ds_metainfo=ds_metainfo)
num_training_samples = len(
train_data._dataset
) if not ds_metainfo.use_imgrec else ds_metainfo.num_training_samples
trainer, lr_scheduler = prepare_trainer(
net=net,
optimizer_name=args.optimizer_name,
wd=args.wd,
momentum=args.momentum,
lr_mode=args.lr_mode,
lr=args.lr,
lr_decay_period=args.lr_decay_period,
lr_decay_epoch=args.lr_decay_epoch,
lr_decay=args.lr_decay,
target_lr=args.target_lr,
poly_power=args.poly_power,
warmup_epochs=args.warmup_epochs,
warmup_lr=args.warmup_lr,
warmup_mode=args.warmup_mode,
batch_size=batch_size,
num_epochs=args.num_epochs,
num_training_samples=num_training_samples,
dtype=args.dtype,
gamma_wd_mult=args.gamma_wd_mult,
beta_wd_mult=args.beta_wd_mult,
bias_wd_mult=args.bias_wd_mult,
state_file_path=args.resume_state)
if args.save_dir and args.save_interval:
param_names = ds_metainfo.val_metric_capts + ds_metainfo.train_metric_capts + [
"Train.Loss", "LR"
]
lp_saver = TrainLogParamSaver(
checkpoint_file_name_prefix="{}_{}".format(ds_metainfo.short_label,
args.model),
last_checkpoint_file_name_suffix="last",
best_checkpoint_file_name_suffix=None,
last_checkpoint_dir_path=args.save_dir,
best_checkpoint_dir_path=None,
last_checkpoint_file_count=2,
best_checkpoint_file_count=2,
checkpoint_file_save_callback=save_params,
checkpoint_file_exts=(".params", ".states"),
save_interval=args.save_interval,
num_epochs=args.num_epochs,
param_names=param_names,
acc_ind=ds_metainfo.saver_acc_ind,
# bigger=[True],
# mask=None,
score_log_file_path=os.path.join(args.save_dir, "score.log"),
score_log_attempt_value=args.attempt,
best_map_log_file_path=os.path.join(args.save_dir, "best_map.log"))
else:
lp_saver = None
val_metric = get_composite_metric(ds_metainfo.val_metric_names,
ds_metainfo.val_metric_extra_kwargs)
train_metric = get_composite_metric(ds_metainfo.train_metric_names,
ds_metainfo.train_metric_extra_kwargs)
loss_metrics = [LossValue(name="loss"), LossValue(name="dloss")]
loss_kwargs = {
"sparse_label": (not (args.mixup or args.label_smoothing)
and not (use_teacher and (teacher_net is not None)))
}
if ds_metainfo.loss_extra_kwargs is not None:
loss_kwargs.update(ds_metainfo.loss_extra_kwargs)
loss_func = get_loss(ds_metainfo.loss_name, loss_kwargs)
train_net(batch_size=batch_size,
num_epochs=args.num_epochs,
start_epoch1=args.start_epoch,
train_data=train_data,
val_data=val_data,
batch_fn=batch_fn,
data_source_needs_reset=ds_metainfo.use_imgrec,
dtype=args.dtype,
net=net,
teacher_net=teacher_net,
discrim_net=discrim_net,
trainer=trainer,
lr_scheduler=lr_scheduler,
lp_saver=lp_saver,
log_interval=args.log_interval,
mixup=args.mixup,
mixup_epoch_tail=args.mixup_epoch_tail,
label_smoothing=args.label_smoothing,
num_classes=num_classes,
grad_clip_value=args.grad_clip,
batch_size_scale=args.batch_size_scale,
val_metric=val_metric,
train_metric=train_metric,
loss_metrics=loss_metrics,
loss_func=loss_func,
discrim_loss_func=discrim_loss_func,
ctx=ctx)
def test_model(args):
"""
Main test routine.
Parameters:
----------
args : ArgumentParser
Main script arguments.
Returns:
-------
float
Main accuracy value.
"""
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
assert (ds_metainfo.ml_type !=
"imgseg") or (args.data_subset != "test") or (args.batch_size == 1)
assert (ds_metainfo.ml_type != "imgseg") or args.disable_cudnn_autotune
ctx, batch_size = prepare_mx_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs=ds_metainfo.test_net_extra_kwargs,
load_ignore_extra=ds_metainfo.load_ignore_extra,
classes=(args.num_classes if ds_metainfo.ml_type != "hpe" else None),
in_channels=args.in_channels,
do_hybridize=(ds_metainfo.allow_hybridize and (not args.calc_flops)),
ctx=ctx)
assert (hasattr(net, "in_size"))
input_image_size = net.in_size
get_test_data_source_class = get_val_data_source if args.data_subset == "val" else get_test_data_source
test_data = get_test_data_source_class(ds_metainfo=ds_metainfo,
batch_size=args.batch_size,
num_workers=args.num_workers)
batch_fn = get_batch_fn(ds_metainfo=ds_metainfo)
if args.data_subset == "val":
test_metric = get_composite_metric(
metric_names=ds_metainfo.val_metric_names,
metric_extra_kwargs=ds_metainfo.val_metric_extra_kwargs)
else:
test_metric = get_composite_metric(
metric_names=ds_metainfo.test_metric_names,
metric_extra_kwargs=ds_metainfo.test_metric_extra_kwargs)
if not args.not_show_progress:
from tqdm import tqdm
test_data = tqdm(test_data)
assert (args.use_pretrained or args.resume.strip() or args.calc_flops_only)
acc_values = calc_model_accuracy(
net=net,
test_data=test_data,
batch_fn=batch_fn,
data_source_needs_reset=ds_metainfo.use_imgrec,
metric=test_metric,
dtype=args.dtype,
ctx=ctx,
input_image_size=input_image_size,
in_channels=args.in_channels,
# calc_weight_count=(not log_file_exist),
calc_weight_count=True,
calc_flops=args.calc_flops,
calc_flops_only=args.calc_flops_only,
extended_log=True)
return acc_values[
ds_metainfo.saver_acc_ind] if len(acc_values) > 0 else None
def main():
"""
Main body of script.
"""
args = parse_args()
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
ctx, batch_size = prepare_mx_context(
num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs=ds_metainfo.net_extra_kwargs,
load_ignore_extra=ds_metainfo.load_ignore_extra,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=(ds_metainfo.allow_hybridize and (not args.calc_flops)),
ctx=ctx)
assert (hasattr(net, "in_size"))
input_image_size = net.in_size
if args.data_subset == "val":
get_test_data_source_class = get_val_data_source
test_metric = get_composite_metric(
metric_names=ds_metainfo.val_metric_names,
metric_extra_kwargs=ds_metainfo.val_metric_extra_kwargs)
else:
get_test_data_source_class = get_test_data_source
test_metric = get_composite_metric(
metric_names=ds_metainfo.test_metric_names,
metric_extra_kwargs=ds_metainfo.test_metric_extra_kwargs)
test_data = get_test_data_source_class(
ds_metainfo=ds_metainfo,
batch_size=args.batch_size,
num_workers=args.num_workers)
batch_fn = get_batch_fn(use_imgrec=ds_metainfo.use_imgrec)
if not args.not_show_progress:
test_data = tqdm(test_data)
assert (args.use_pretrained or args.resume.strip() or args.calc_flops_only)
test(
net=net,
test_data=test_data,
batch_fn=batch_fn,
data_source_needs_reset=ds_metainfo.use_imgrec,
metric=test_metric,
dtype=args.dtype,
ctx=ctx,
input_image_size=input_image_size,
in_channels=args.in_channels,
calc_weight_count=True,
calc_flops=args.calc_flops,
calc_flops_only=args.calc_flops_only,
extended_log=True,
show_bad_samples=args.show_bad_samples)
def main():
args = parse_args()
args.seed = init_rand(seed=args.seed)
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ctx, batch_size = prepare_mx_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
tune_layers=args.tune_layers,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=(not args.not_hybridize),
ctx=ctx)
assert (hasattr(net, 'classes'))
num_classes = net.classes if hasattr(net, 'classes') else 10
train_data = get_train_data_source(dataset_name=args.dataset,
dataset_dir=args.data_dir,
batch_size=batch_size,
num_workers=args.num_workers)
val_data = get_val_data_source(dataset_name=args.dataset,
dataset_dir=args.data_dir,
batch_size=batch_size,
num_workers=args.num_workers)
trainer, lr_scheduler = prepare_trainer(
net=net,
optimizer_name=args.optimizer_name,
wd=args.wd,
momentum=args.momentum,
lr_mode=args.lr_mode,
lr=args.lr,
lr_decay_period=args.lr_decay_period,
lr_decay_epoch=args.lr_decay_epoch,
lr_decay=args.lr_decay,
target_lr=args.target_lr,
poly_power=args.poly_power,
warmup_epochs=args.warmup_epochs,
warmup_lr=args.warmup_lr,
warmup_mode=args.warmup_mode,
batch_size=batch_size,
num_epochs=args.num_epochs,
num_training_samples=get_num_training_samples(args.dataset),
dtype=args.dtype,
gamma_wd_mult=args.gamma_wd_mult,
beta_wd_mult=args.beta_wd_mult,
bias_wd_mult=args.bias_wd_mult,
state_file_path=args.resume_state)
if args.save_dir and args.save_interval:
lp_saver = TrainLogParamSaver(
checkpoint_file_name_prefix='{}_{}'.format(args.dataset.lower(),
args.model),
last_checkpoint_file_name_suffix="last",
best_checkpoint_file_name_suffix=None,
last_checkpoint_dir_path=args.save_dir,
best_checkpoint_dir_path=None,
last_checkpoint_file_count=2,
best_checkpoint_file_count=2,
checkpoint_file_save_callback=save_params,
checkpoint_file_exts=('.params', '.states'),
save_interval=args.save_interval,
num_epochs=args.num_epochs,
param_names=['Val.Err', 'Train.Err', 'Train.Loss', 'LR'],
acc_ind=0,
# bigger=[True],
# mask=None,
score_log_file_path=os.path.join(args.save_dir, 'score.log'),
score_log_attempt_value=args.attempt,
best_map_log_file_path=os.path.join(args.save_dir, 'best_map.log'))
else:
lp_saver = None
train_net(batch_size=batch_size,
num_epochs=args.num_epochs,
start_epoch1=args.start_epoch,
train_data=train_data,
val_data=val_data,
data_source_needs_reset=False,
dtype=args.dtype,
net=net,
trainer=trainer,
lr_scheduler=lr_scheduler,
lp_saver=lp_saver,
log_interval=args.log_interval,
mixup=args.mixup,
mixup_epoch_tail=args.mixup_epoch_tail,
label_smoothing=args.label_smoothing,
num_classes=num_classes,
grad_clip_value=args.grad_clip,
batch_size_scale=args.batch_size_scale,
ctx=ctx)
def main():
"""
Main body of script.
"""
args = parse_args()
args.seed = init_rand(seed=args.seed)
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ctx, batch_size = prepare_mx_context(num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
tune_layers=args.tune_layers,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=(not args.not_hybridize),
initializer=get_initializer(initializer_name=args.initializer),
ctx=ctx)
assert (hasattr(net, "classes"))
num_classes = net.classes
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
train_data = get_train_data_source(ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
val_data = get_val_data_source(ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
batch_fn = get_batch_fn(use_imgrec=ds_metainfo.use_imgrec)
num_training_samples = len(
train_data._dataset
) if not ds_metainfo.use_imgrec else ds_metainfo.num_training_samples
trainer, lr_scheduler = prepare_trainer(
net=net,
optimizer_name=args.optimizer_name,
wd=args.wd,
momentum=args.momentum,
lr_mode=args.lr_mode,
lr=args.lr,
lr_decay_period=args.lr_decay_period,
lr_decay_epoch=args.lr_decay_epoch,
lr_decay=args.lr_decay,
target_lr=args.target_lr,
poly_power=args.poly_power,
warmup_epochs=args.warmup_epochs,
warmup_lr=args.warmup_lr,
warmup_mode=args.warmup_mode,
batch_size=batch_size,
num_epochs=args.num_epochs,
num_training_samples=num_training_samples,
dtype=args.dtype,
gamma_wd_mult=args.gamma_wd_mult,
beta_wd_mult=args.beta_wd_mult,
bias_wd_mult=args.bias_wd_mult,
state_file_path=args.resume_state)
if args.save_dir and args.save_interval:
param_names = ds_metainfo.val_metric_capts + ds_metainfo.train_metric_capts + [
"Train.Loss", "LR"
]
lp_saver = TrainLogParamSaver(
checkpoint_file_name_prefix="{}_{}".format(ds_metainfo.short_label,
args.model),
last_checkpoint_file_name_suffix="last",
best_checkpoint_file_name_suffix=None,
last_checkpoint_dir_path=args.save_dir,
best_checkpoint_dir_path=None,
last_checkpoint_file_count=2,
best_checkpoint_file_count=2,
checkpoint_file_save_callback=save_params,
checkpoint_file_exts=(".params", ".states"),
save_interval=args.save_interval,
num_epochs=args.num_epochs,
param_names=param_names,
acc_ind=ds_metainfo.saver_acc_ind,
# bigger=[True],
# mask=None,
score_log_file_path=os.path.join(args.save_dir, "score.log"),
score_log_attempt_value=args.attempt,
best_map_log_file_path=os.path.join(args.save_dir, "best_map.log"))
else:
lp_saver = None
train_net(batch_size=batch_size,
num_epochs=args.num_epochs,
start_epoch1=args.start_epoch,
train_data=train_data,
val_data=val_data,
batch_fn=batch_fn,
data_source_needs_reset=ds_metainfo.use_imgrec,
dtype=args.dtype,
net=net,
trainer=trainer,
lr_scheduler=lr_scheduler,
lp_saver=lp_saver,
log_interval=args.log_interval,
mixup=args.mixup,
mixup_epoch_tail=args.mixup_epoch_tail,
label_smoothing=args.label_smoothing,
num_classes=num_classes,
grad_clip_value=args.grad_clip,
batch_size_scale=args.batch_size_scale,
val_metric=get_composite_metric(
ds_metainfo.val_metric_names,
ds_metainfo.val_metric_extra_kwargs),
train_metric=get_composite_metric(
ds_metainfo.train_metric_names,
ds_metainfo.train_metric_extra_kwargs),
ctx=ctx)
def main():
args = parse_args()
args.seed = init_rand(seed=args.seed)
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ctx, batch_size = prepare_mx_context(
num_gpus=args.num_gpus,
batch_size=args.batch_size)
# if args.convert_to_mxnet:
# batch_size = 1
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
tune_layers=args.tune_layers,
ctx=ctx)
num_classes = net.classes if hasattr(net, 'classes') else 1000
input_image_size = net.in_size if hasattr(net, 'in_size') else (224, 224)
if args.use_rec:
train_data, val_data, batch_fn = get_data_rec(
rec_train=args.rec_train,
rec_train_idx=args.rec_train_idx,
rec_val=args.rec_val,
rec_val_idx=args.rec_val_idx,
batch_size=batch_size,
num_workers=args.num_workers,
input_image_size=input_image_size)
else:
train_data, val_data, batch_fn = get_data_loader(
data_dir=args.data_dir,
batch_size=batch_size,
num_workers=args.num_workers,
input_image_size=input_image_size)
# if args.convert_to_mxnet:
# assert args.save_dir and os.path.exists(args.save_dir)
# assert (args.use_pretrained or args.resume.strip())
# x = mx.nd.array(np.zeros((1, 3, 224, 224), np.float32), ctx)
# net.forward(x)
# export_checkpoint_file_path_prefix = os.path.join(args.save_dir, 'imagenet_{}'.format(args.model))
# net.export(export_checkpoint_file_path_prefix)
# logging.info('Convert model to MXNet format: {}'.format(export_checkpoint_file_path_prefix))
num_training_samples = 1281167
trainer, lr_scheduler = prepare_trainer(
net=net,
optimizer_name=args.optimizer_name,
wd=args.wd,
momentum=args.momentum,
lr_mode=args.lr_mode,
lr=args.lr,
lr_decay_period=args.lr_decay_period,
lr_decay_epoch=args.lr_decay_epoch,
lr_decay=args.lr_decay,
target_lr=args.target_lr,
poly_power=args.poly_power,
warmup_epochs=args.warmup_epochs,
warmup_lr=args.warmup_lr,
warmup_mode=args.warmup_mode,
batch_size=batch_size,
num_epochs=args.num_epochs,
num_training_samples=num_training_samples,
dtype=args.dtype,
state_file_path=args.resume_state)
if args.save_dir and args.save_interval:
lp_saver = TrainLogParamSaver(
checkpoint_file_name_prefix='imagenet_{}'.format(args.model),
last_checkpoint_file_name_suffix="last",
best_checkpoint_file_name_suffix=None,
last_checkpoint_dir_path=args.save_dir,
best_checkpoint_dir_path=None,
last_checkpoint_file_count=2,
best_checkpoint_file_count=2,
checkpoint_file_save_callback=save_params,
checkpoint_file_exts=('.params', '.states'),
save_interval=args.save_interval,
num_epochs=args.num_epochs,
param_names=['Val.Top1', 'Train.Top1', 'Val.Top5', 'Train.Loss', 'LR'],
acc_ind=2,
# bigger=[True],
# mask=None,
score_log_file_path=os.path.join(args.save_dir, 'score.log'),
score_log_attempt_value=args.attempt,
best_map_log_file_path=os.path.join(args.save_dir, 'best_map.log'))
else:
lp_saver = None
train_net(
batch_size=batch_size,
num_epochs=args.num_epochs,
start_epoch1=args.start_epoch,
train_data=train_data,
val_data=val_data,
batch_fn=batch_fn,
use_rec=args.use_rec,
dtype=args.dtype,
net=net,
trainer=trainer,
lr_scheduler=lr_scheduler,
lp_saver=lp_saver,
log_interval=args.log_interval,
mixup=args.mixup,
mixup_epoch_tail=args.mixup_epoch_tail,
num_classes=num_classes,
ctx=ctx)
def main():
args = parse_args()
if args.disable_cudnn_autotune:
os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0"
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
assert (ds_metainfo.ml_type != "imgseg") or (args.batch_size == 1)
assert (ds_metainfo.ml_type != "imgseg") or args.disable_cudnn_autotune
ctx, batch_size = prepare_mx_context(
num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
dtype=args.dtype,
net_extra_kwargs=ds_metainfo.net_extra_kwargs,
load_ignore_extra=ds_metainfo.load_ignore_extra,
classes=args.num_classes,
in_channels=args.in_channels,
do_hybridize=(ds_metainfo.allow_hybridize and (not args.calc_flops)),
ctx=ctx)
assert (hasattr(net, "in_size"))
input_image_size = net.in_size
if args.data_subset == "val":
test_data = get_val_data_source(
ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
test_metric = get_composite_metric(
metric_names=ds_metainfo.val_metric_names,
metric_extra_kwargs=ds_metainfo.val_metric_extra_kwargs)
else:
test_data = get_test_data_source(
ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
test_metric = get_composite_metric(
metric_names=ds_metainfo.test_metric_names,
metric_extra_kwargs=ds_metainfo.test_metric_extra_kwargs)
batch_fn = get_batch_fn(use_imgrec=ds_metainfo.use_imgrec)
if args.show_progress:
from tqdm import tqdm
test_data = tqdm(test_data)
assert (args.use_pretrained or args.resume.strip() or args.calc_flops_only)
test(
net=net,
test_data=test_data,
batch_fn=batch_fn,
data_source_needs_reset=ds_metainfo.use_imgrec,
metric=test_metric,
dtype=args.dtype,
ctx=ctx,
input_image_size=input_image_size,
in_channels=args.in_channels,
# calc_weight_count=(not log_file_exist),
calc_weight_count=True,
calc_flops=args.calc_flops,
calc_flops_only=args.calc_flops_only,
extended_log=True)