def run(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
anchor_alloc_size=[256, 256],
anchor_sizes=[32, 64, 128, 256, 512],
anchor_size_ratios=[1, pow(2, 1 / 3), pow(2, 2 / 3)],
anchor_aspect_ratios=[0.5, 1, 2],
anchor_box_clip=True,
graphviz=True,
epoch=100,
input_size=[512, 512],
batch_log=100,
batch_size=16,
batch_interval=10,
subdivision=4,
train_dataset_path="Dataset/train",
valid_dataset_path="Dataset/valid",
multiscale=True,
factor_scale=[8, 5],
foreground_iou_thresh=0.5,
background_iou_thresh=0.4,
data_augmentation=True,
num_workers=4,
optimizer="ADAM",
weight_decay=0.000001,
save_period=5,
load_period=10,
learning_rate=0.001,
decay_lr=0.999,
decay_step=10,
GPU_COUNT=0,
base=0,
AMP=True,
valid_size=8,
eval_period=5,
tensorboard=True,
valid_graph_path="valid_Graph",
valid_html_auto_open=True,
using_mlflow=True,
decode_number=5000,
multiperclass=True,
nms_thresh=0.5,
nms_topk=500,
iou_thresh=0.5,
except_class_thresh=0.05,
plot_class_thresh=0.5):
if GPU_COUNT == 0:
ctx = mx.cpu(0)
AMP = False
elif GPU_COUNT == 1:
ctx = mx.gpu(0)
else:
ctx = [mx.gpu(i) for i in range(GPU_COUNT)]
# 운영체제 확인
if platform.system() == "Linux":
logging.info(f"{platform.system()} OS")
elif platform.system() == "Windows":
logging.info(f"{platform.system()} OS")
else:
logging.info(f"{platform.system()} OS")
if isinstance(ctx, (list, tuple)):
for i, c in enumerate(ctx):
free_memory, total_memory = mx.context.gpu_memory_info(i)
free_memory = round(free_memory / (1024 * 1024 * 1024), 2)
total_memory = round(total_memory / (1024 * 1024 * 1024), 2)
logging.info(
f'Running on {c} / free memory : {free_memory}GB / total memory {total_memory}GB'
)
else:
if GPU_COUNT == 1:
free_memory, total_memory = mx.context.gpu_memory_info(0)
free_memory = round(free_memory / (1024 * 1024 * 1024), 2)
total_memory = round(total_memory / (1024 * 1024 * 1024), 2)
logging.info(
f'Running on {ctx} / free memory : {free_memory}GB / total memory {total_memory}GB'
)
else:
logging.info(f'Running on {ctx}')
if GPU_COUNT > 0 and batch_size < GPU_COUNT:
logging.info("batch size must be greater than gpu number")
exit(0)
if AMP:
amp.init()
if multiscale:
logging.info("Using MultiScale")
if data_augmentation:
logging.info("Using Data Augmentation")
logging.info("training Efficient Detector")
input_shape = (1, 3) + tuple(input_size)
net = Efficient(version=base,
anchor_sizes=anchor_sizes,
anchor_size_ratios=anchor_size_ratios,
anchor_aspect_ratios=anchor_aspect_ratios,
anchor_box_clip=anchor_box_clip,
alloc_size=anchor_alloc_size,
ctx=mx.cpu())
train_dataloader, train_dataset = traindataloader(
multiscale=multiscale,
factor_scale=factor_scale,
augmentation=data_augmentation,
path=train_dataset_path,
input_size=input_size,
batch_size=batch_size,
batch_interval=batch_interval,
num_workers=num_workers,
shuffle=True,
mean=mean,
std=std,
net=net,
foreground_iou_thresh=foreground_iou_thresh,
background_iou_thresh=background_iou_thresh,
make_target=True)
train_update_number_per_epoch = len(train_dataloader)
if train_update_number_per_epoch < 1:
logging.warning("train batch size가 데이터 수보다 큼")
exit(0)
valid_list = glob.glob(os.path.join(valid_dataset_path, "*"))
if valid_list:
valid_dataloader, valid_dataset = validdataloader(
path=valid_dataset_path,
input_size=input_size,
batch_size=valid_size,
num_workers=num_workers,
shuffle=True,
mean=mean,
std=std,
net=net,
foreground_iou_thresh=foreground_iou_thresh,
background_iou_thresh=background_iou_thresh,
make_target=True)
valid_update_number_per_epoch = len(valid_dataloader)
if valid_update_number_per_epoch < 1:
logging.warning("valid batch size가 데이터 수보다 큼")
exit(0)
num_classes = train_dataset.num_class # 클래스 수
name_classes = train_dataset.classes
optimizer = optimizer.upper()
model = str(input_size[0]) + "_" + str(
input_size[1]) + "_" + optimizer + "_EFF_" + str(base)
weight_path = os.path.join("weights", f"{model}")
sym_path = os.path.join(weight_path, f'{model}-symbol.json')
param_path = os.path.join(weight_path, f'{model}-{load_period:04d}.params')
optimizer_path = os.path.join(weight_path,
f'{model}-{load_period:04d}.opt')
if os.path.exists(param_path) and os.path.exists(sym_path):
start_epoch = load_period
logging.info(f"loading {os.path.basename(param_path)}\n")
net = gluon.SymbolBlock.imports(sym_path, ['data'],
param_path,
ctx=ctx)
else:
start_epoch = 0
net = Efficient(
version=base,
input_size=input_size,
anchor_sizes=anchor_sizes,
anchor_size_ratios=anchor_size_ratios,
anchor_aspect_ratios=anchor_aspect_ratios,
num_classes=num_classes, # foreground만
anchor_box_clip=anchor_box_clip,
alloc_size=anchor_alloc_size,
ctx=ctx)
if isinstance(ctx, (list, tuple)):
net.summary(mx.nd.ones(shape=input_shape, ctx=ctx[0]))
else:
net.summary(mx.nd.ones(shape=input_shape, ctx=ctx))
'''
active (bool, default True) – Whether to turn hybrid on or off.
static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.
static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.
'''
if multiscale:
net.hybridize(active=True, static_alloc=True, static_shape=False)
else:
net.hybridize(active=True, static_alloc=True, static_shape=True)
if start_epoch + 1 >= epoch + 1:
logging.info("this model has already been optimized")
exit(0)
if tensorboard:
summary = SummaryWriter(logdir=os.path.join("mxboard", model),
max_queue=10,
flush_secs=10,
verbose=False)
if isinstance(ctx, (list, tuple)):
net.forward(mx.nd.ones(shape=input_shape, ctx=ctx[0]))
else:
net.forward(mx.nd.ones(shape=input_shape, ctx=ctx))
summary.add_graph(net)
if graphviz:
gluoncv.utils.viz.plot_network(net,
shape=input_shape,
save_prefix=model)
# optimizer
unit = 1 if (len(train_dataset) //
batch_size) < 1 else len(train_dataset) // batch_size
step = unit * decay_step
lr_sch = mx.lr_scheduler.FactorScheduler(step=step,
factor=decay_lr,
stop_factor_lr=1e-12,
base_lr=learning_rate)
for p in net.collect_params().values():
if p.grad_req != "null":
p.grad_req = 'add'
'''
update_on_kvstore : bool, default None
Whether to perform parameter updates on kvstore. If None, then trainer will choose the more
suitable option depending on the type of kvstore. If the `update_on_kvstore` argument is
provided, environment variable `MXNET_UPDATE_ON_KVSTORE` will be ignored.
'''
if optimizer.upper() == "ADAM":
trainer = gluon.Trainer(net.collect_params(),
optimizer,
optimizer_params={
"learning_rate": learning_rate,
"lr_scheduler": lr_sch,
"wd": weight_decay,
"beta1": 0.9,
"beta2": 0.999,
'multi_precision': False
},
update_on_kvstore=False
if AMP else None) # for Dynamic loss scaling
elif optimizer.upper() == "RMSPROP":
trainer = gluon.Trainer(net.collect_params(),
optimizer,
optimizer_params={
"learning_rate": learning_rate,
"lr_scheduler": lr_sch,
"wd": weight_decay,
"gamma1": 0.9,
"gamma2": 0.999,
'multi_precision': False
},
update_on_kvstore=False
if AMP else None) # for Dynamic loss scaling
elif optimizer.upper() == "SGD":
trainer = gluon.Trainer(net.collect_params(),
optimizer,
optimizer_params={
"learning_rate": learning_rate,
"lr_scheduler": lr_sch,
"wd": weight_decay,
"momentum": 0.9,
'multi_precision': False
},
update_on_kvstore=False
if AMP else None) # for Dynamic loss scaling
else:
logging.error("optimizer not selected")
exit(0)
if AMP:
amp.init_trainer(trainer)
# optimizer weight 불러오기
if os.path.exists(optimizer_path):
try:
trainer.load_states(optimizer_path)
except Exception as E:
logging.info(E)
else:
logging.info(f"loading {os.path.basename(optimizer_path)}\n")
'''
localization loss -> Smooth L1 loss
confidence loss -> Focal
'''
confidence_loss = FocalLoss(alpha=0.25,
gamma=2,
sparse_label=True,
from_sigmoid=False,
batch_axis=None,
num_class=num_classes,
reduction="sum",
exclude=False)
localization_loss = HuberLoss(rho=1,
batch_axis=None,
reduction="sum",
exclude=False)
prediction = Prediction(batch_size=batch_size,
from_sigmoid=False,
num_classes=num_classes,
decode_number=decode_number,
nms_thresh=nms_thresh,
nms_topk=nms_topk,
except_class_thresh=except_class_thresh,
multiperclass=multiperclass)
precision_recall = Voc_2007_AP(iou_thresh=iou_thresh,
class_names=name_classes)
ctx_list = ctx if isinstance(ctx, (list, tuple)) else [ctx]
start_time = time.time()
for i in tqdm(range(start_epoch + 1, epoch + 1, 1),
initial=start_epoch + 1,
total=epoch):
conf_loss_sum = 0
loc_loss_sum = 0
time_stamp = time.time()
for batch_count, (image, _, cls_all, box_all,
_) in enumerate(train_dataloader, start=1):
td_batch_size = image.shape[0]
image = mx.nd.split(data=image, num_outputs=subdivision, axis=0)
cls_all = mx.nd.split(data=cls_all,
num_outputs=subdivision,
axis=0)
box_all = mx.nd.split(data=box_all,
num_outputs=subdivision,
axis=0)
if subdivision == 1:
image = [image]
cls_all = [cls_all]
box_all = [box_all]
'''
autograd 설명
https://mxnet.apache.org/api/python/docs/tutorials/getting-started/crash-course/3-autograd.html
'''
with autograd.record(train_mode=True):
cls_all_losses = []
box_all_losses = []
for image_split, cls_split, box_split in zip(
image, cls_all, box_all):
image_split = gluon.utils.split_and_load(image_split,
ctx_list,
even_split=False)
cls_split = gluon.utils.split_and_load(cls_split,
ctx_list,
even_split=False)
box_split = gluon.utils.split_and_load(box_split,
ctx_list,
even_split=False)
# prediction, target space for Data Parallelism
cls_losses = []
box_losses = []
total_loss = []
# gpu N 개를 대비한 코드 (Data Parallelism)
for img, cls_target, box_target in zip(
image_split, cls_split, box_split):
cls_pred, box_pred, anchor = net(img)
except_ignore_samples = cls_target > -1
positive_samples = cls_target > 0
positive_numbers = positive_samples.sum()
conf_loss = confidence_loss(
cls_pred, cls_target,
except_ignore_samples.expand_dims(axis=-1))
conf_loss = mx.nd.divide(conf_loss,
positive_numbers + 1)
cls_losses.append(conf_loss.asscalar())
loc_loss = localization_loss(
box_pred, box_target,
positive_samples.expand_dims(axis=-1))
box_losses.append(loc_loss.asscalar())
total_loss.append(conf_loss + loc_loss)
if AMP:
with amp.scale_loss(total_loss,
trainer) as scaled_loss:
autograd.backward(scaled_loss)
else:
autograd.backward(total_loss)
cls_all_losses.append(sum(cls_losses))
box_all_losses.append(sum(box_losses))
trainer.step(batch_size=td_batch_size, ignore_stale_grad=False)
# 비우기
for p in net.collect_params().values():
p.zero_grad()
conf_loss_sum += sum(cls_all_losses) / td_batch_size
loc_loss_sum += sum(box_all_losses) / td_batch_size
if batch_count % batch_log == 0:
logging.info(
f'[Epoch {i}][Batch {batch_count}/{train_update_number_per_epoch}],'
f'[Speed {td_batch_size / (time.time() - time_stamp):.3f} samples/sec],'
f'[Lr = {trainer.learning_rate}]'
f'[confidence loss = {sum(cls_all_losses) / td_batch_size:.3f}]'
f'[localization loss = {sum(box_all_losses) / td_batch_size:.3f}]'
)
time_stamp = time.time()
train_conf_loss_mean = np.divide(conf_loss_sum,
train_update_number_per_epoch)
train_loc_loss_mean = np.divide(loc_loss_sum,
train_update_number_per_epoch)
train_total_loss_mean = train_conf_loss_mean + train_loc_loss_mean
logging.info(
f"train confidence loss : {train_conf_loss_mean} / train localization loss : {train_loc_loss_mean} / train total loss : {train_total_loss_mean}"
)
if i % save_period == 0:
weight_epoch_path = os.path.join(weight_path, str(i))
if not os.path.exists(weight_epoch_path):
os.makedirs(weight_epoch_path)
# optimizer weight 저장하기
try:
trainer.save_states(
os.path.join(weight_path, f'{model}-{i:04d}.opt'))
except Exception as E:
logging.error(f"optimizer weight export 예외 발생 : {E}")
else:
logging.info("optimizer weight export 성공")
'''
Hybrid models can be serialized as JSON files using the export function
Export HybridBlock to json format that can be loaded by SymbolBlock.imports, mxnet.mod.Module or the C++ interface.
When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.
'''
if GPU_COUNT >= 1:
context = mx.gpu(0)
else:
context = mx.cpu(0)
'''
mxnet1.6.0 버전 에서 AMP 사용시 위에 미리 선언한 prediction을 사용하면 문제가 될 수 있다.
-yolo v3, gaussian yolo v3 에서는 문제가 발생한다.
mxnet 1.5.x 버전에서는 아래와 같이 새로 선언하지 않아도 정상 동작한다.
block들은 함수 인자로 보낼 경우 자기 자신이 보내진다.(복사되는 것이 아님)
export_block_for_cplusplus 에서 prediction 이 hybridize 되면서
미리 선언한 prediction도 hybridize화 되면서 symbol 형태가 된다.
이런 현상을 보면 아래와같이 다시 선언해 주는게 맞는 것 같다.
'''
auxnet = Prediction(from_sigmoid=False,
num_classes=num_classes,
decode_number=decode_number,
nms_thresh=nms_thresh,
nms_topk=nms_topk,
except_class_thresh=except_class_thresh,
multiperclass=multiperclass)
postnet = PostNet(net=net, auxnet=auxnet)
try:
net.export(os.path.join(weight_path, f"{model}"),
epoch=i,
remove_amp_cast=True)
net.save_parameters(os.path.join(weight_path,
f"{i}.params")) # onnx 추출용
# network inference, decoder, nms까지 처리됨 - mxnet c++에서 편리함
export_block_for_cplusplus(
path=os.path.join(weight_epoch_path, f"{model}_prepost"),
block=postnet,
data_shape=tuple(input_size) + tuple((3, )),
epoch=i,
preprocess=
True, # c++ 에서 inference시 opencv에서 읽은 이미지 그대로 넣으면 됨
layout='HWC',
ctx=context,
remove_amp_cast=True)
except Exception as E:
logging.error(f"json, param model export 예외 발생 : {E}")
else:
logging.info("json, param model export 성공")
net.collect_params().reset_ctx(ctx)
if i % eval_period == 0 and valid_list:
conf_loss_sum = 0
loc_loss_sum = 0
# loss 구하기
for image, label, cls_all, box_all, _ in valid_dataloader:
vd_batch_size = image.shape[0]
image = gluon.utils.split_and_load(image,
ctx_list,
even_split=False)
label = gluon.utils.split_and_load(label,
ctx_list,
even_split=False)
cls_all = gluon.utils.split_and_load(cls_all,
ctx_list,
even_split=False)
box_all = gluon.utils.split_and_load(box_all,
ctx_list,
even_split=False)
# prediction, target space for Data Parallelism
cls_losses = []
box_losses = []
# gpu N 개를 대비한 코드 (Data Parallelism)
for img, lb, cls_target, box_target in zip(
image, label, cls_all, box_all):
gt_box = lb[:, :, :4]
gt_id = lb[:, :, 4:5]
cls_pred, box_pred, anchor = net(img)
id, score, bbox = prediction(cls_pred, box_pred, anchor)
precision_recall.update(pred_bboxes=bbox,
pred_labels=id,
pred_scores=score,
gt_boxes=gt_box,
gt_labels=gt_id)
except_ignore_samples = cls_target > -1
positive_samples = cls_target > 0
positive_numbers = positive_samples.sum()
conf_loss = confidence_loss(
cls_pred, cls_target,
except_ignore_samples.expand_dims(axis=-1))
conf_loss = mx.nd.divide(conf_loss, positive_numbers + 1)
cls_losses.append(conf_loss.asscalar())
loc_loss = localization_loss(
box_pred, box_target,
positive_samples.expand_dims(axis=-1))
box_losses.append(loc_loss.asscalar())
conf_loss_sum += sum(cls_losses) / vd_batch_size
loc_loss_sum += sum(box_losses) / vd_batch_size
valid_conf_loss_mean = np.divide(conf_loss_sum,
valid_update_number_per_epoch)
valid_loc_loss_mean = np.divide(loc_loss_sum,
valid_update_number_per_epoch)
valid_total_loss_mean = valid_conf_loss_mean + valid_loc_loss_mean
logging.info(
f"valid confidence loss : {valid_conf_loss_mean} / valid localization loss : {valid_loc_loss_mean} / valid total loss : {valid_total_loss_mean}"
)
AP_appender = []
round_position = 2
class_name, precision, recall, true_positive, false_positive, threshold = precision_recall.get_PR_list(
)
for j, c, p, r in zip(range(len(recall)), class_name, precision,
recall):
name, AP = precision_recall.get_AP(c, p, r)
logging.info(
f"class {j}'s {name} AP : {round(AP * 100, round_position)}%"
)
AP_appender.append(AP)
AP_appender = np.nan_to_num(AP_appender)
mAP_result = np.mean(AP_appender)
logging.info(f"mAP : {round(mAP_result * 100, round_position)}%")
precision_recall.get_PR_curve(name=class_name,
precision=precision,
recall=recall,
threshold=threshold,
AP=AP_appender,
mAP=mAP_result,
folder_name=valid_graph_path,
epoch=i,
auto_open=valid_html_auto_open)
precision_recall.reset()
if tensorboard:
# gpu N 개를 대비한 코드 (Data Parallelism)
dataloader_iter = iter(valid_dataloader)
image, label, _, _, _ = next(dataloader_iter)
image = gluon.utils.split_and_load(image,
ctx_list,
even_split=False)
label = gluon.utils.split_and_load(label,
ctx_list,
even_split=False)
ground_truth_colors = {}
for k in range(num_classes):
ground_truth_colors[k] = (0, 1, 0)
batch_image = []
for img, lb in zip(image, label):
gt_boxes = lb[:, :, :4]
gt_ids = lb[:, :, 4:5]
cls_pred, box_pred, anchor = net(img)
ids, scores, bboxes = prediction(cls_pred, box_pred,
anchor)
for ig, gt_id, gt_box, id, score, bbox in zip(
img, gt_ids, gt_boxes, ids, scores, bboxes):
ig = ig.transpose((1, 2, 0)) * mx.nd.array(
std, ctx=ig.context) + mx.nd.array(mean,
ctx=ig.context)
ig = (ig * 255).clip(0, 255)
ig = ig.astype(np.uint8)
# ground truth box 그리기
ground_truth = plot_bbox(
ig,
gt_box,
scores=None,
labels=gt_id,
thresh=None,
reverse_rgb=False,
class_names=valid_dataset.classes,
absolute_coordinates=True,
colors=ground_truth_colors)
# prediction box 그리기
prediction_box = plot_bbox(
ground_truth,
bbox,
scores=score,
labels=id,
thresh=plot_class_thresh,
reverse_rgb=False,
class_names=valid_dataset.classes,
absolute_coordinates=True)
# Tensorboard에 그리기 (height, width, channel) -> (channel, height, width) 를한다.
prediction_box = np.transpose(prediction_box,
axes=(2, 0, 1))
batch_image.append(
prediction_box) # (batch, channel, height, width)
summary.add_image(tag="valid_result",
image=np.array(batch_image),
global_step=i)
summary.add_scalar(tag="conf_loss",
value={
"train_conf_loss": train_conf_loss_mean,
"valid_conf_loss": valid_conf_loss_mean
},
global_step=i)
summary.add_scalar(tag="loc_loss",
value={
"train_loc_loss": train_loc_loss_mean,
"valid_loc_loss": valid_loc_loss_mean
},
global_step=i)
summary.add_scalar(tag="total_loss",
value={
"train_total_loss":
train_total_loss_mean,
"valid_total_loss":
valid_total_loss_mean
},
global_step=i)
for p in net.collect_params().values():
summary.add_histogram(tag=p.name,
values=p.data(ctx=ctx_list[0]),
global_step=i,
bins='default')
end_time = time.time()
learning_time = end_time - start_time
logging.info(f"learning time : 약, {learning_time / 3600:0.2f}H")
logging.info("optimization completed")
if using_mlflow:
ml.log_metric("learning time", round(learning_time / 3600, 2))
def run(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
load_name="512_512_ADAM_PEFF_0",
load_period=10,
GPU_COUNT=0,
test_weight_path="weights",
test_dataset_path="Dataset/test",
test_save_path="result",
test_graph_path="test_Graph",
foreground_iou_thresh=0.5,
background_iou_thresh=0.4,
num_workers=4,
show_flag=True,
save_flag=True,
decode_number=5000,
multiperclass=True,
nms_thresh=0.5,
nms_topk=500,
except_class_thresh=0.05,
plot_class_thresh=0.5):
if GPU_COUNT <= 0:
ctx = mx.cpu(0)
elif GPU_COUNT > 0:
ctx = mx.gpu(0)
# 운영체제 확인
if platform.system() == "Linux":
logging.info(f"{platform.system()} OS")
elif platform.system() == "Windows":
logging.info(f"{platform.system()} OS")
else:
logging.info(f"{platform.system()} OS")
if GPU_COUNT > 0:
free_memory, total_memory = mx.context.gpu_memory_info(0)
free_memory = round(free_memory / (1024 * 1024 * 1024), 2)
total_memory = round(total_memory / (1024 * 1024 * 1024), 2)
logging.info(
f'Running on {ctx} / free memory : {free_memory}GB / total memory {total_memory}GB'
)
else:
logging.info(f'Running on {ctx}')
logging.info(f"test {load_name}")
netheight = int(load_name.split("_")[0])
netwidth = int(load_name.split("_")[1])
if not isinstance(netheight, int) and not isinstance(netwidth, int):
logging.info("height is not int")
logging.info("width is not int")
raise ValueError
else:
logging.info(f"network input size : {(netheight, netwidth)}")
try:
test_dataloader, test_dataset = testdataloader(path=test_dataset_path,
input_size=(netheight,
netwidth),
num_workers=num_workers,
mean=mean,
std=std)
except Exception:
logging.info("The dataset does not exist")
exit(0)
weight_path = os.path.join(test_weight_path, load_name)
sym = os.path.join(weight_path, f'{load_name}-symbol.json')
params = os.path.join(weight_path, f'{load_name}-{load_period:04d}.params')
test_update_number_per_epoch = len(test_dataloader)
if test_update_number_per_epoch < 1:
logging.warning(" test batch size가 데이터 수보다 큼 ")
exit(0)
num_classes = test_dataset.num_class # 클래스 수
name_classes = test_dataset.classes
logging.info("symbol model test")
try:
net = gluon.SymbolBlock.imports(sym, ['data'], params, ctx=ctx)
except Exception:
# DEBUG, INFO, WARNING, ERROR, CRITICAL 의 5가지 등급
logging.info("loading symbol weights 실패")
exit(0)
else:
logging.info("loading symbol weights 성공")
net.hybridize(active=True, static_alloc=True, static_shape=True)
confidence_loss = FocalLoss(
alpha=0.25, # 논문에서 가장 좋다고 한 숫자
gamma=1.5, # 논문에서 가장 좋다고 한 숫자
sparse_label=True,
from_sigmoid=False,
batch_axis=None,
num_class=num_classes,
reduction="sum",
exclude=False)
localization_loss = HuberLoss(rho=1,
batch_axis=None,
reduction="sum",
exclude=False)
targetgenerator = TargetGenerator(
foreground_iou_thresh=foreground_iou_thresh,
background_iou_thresh=background_iou_thresh)
prediction = Prediction(from_sigmoid=False,
num_classes=num_classes,
decode_number=decode_number,
nms_thresh=nms_thresh,
nms_topk=nms_topk,
except_class_thresh=except_class_thresh,
multiperclass=multiperclass)
precision_recall = Voc_2007_AP(iou_thresh=0.5, class_names=name_classes)
ground_truth_colors = {}
for i in range(num_classes):
ground_truth_colors[i] = (0, 0, 1)
conf_loss_sum = 0
loc_loss_sum = 0
for image, label, name, origin_image, origin_box in tqdm(test_dataloader):
_, height, width, _ = origin_image.shape
logging.info(f"real input size : {(height, width)}")
origin_image = origin_image.asnumpy()[0]
origin_box = origin_box.asnumpy()[0]
image = image.as_in_context(ctx)
label = label.as_in_context(ctx)
gt_boxes = label[:, :, :4]
gt_ids = label[:, :, 4:5]
cls_preds, box_preds, anchors = net(image)
ids, scores, bboxes = prediction(cls_preds, box_preds, anchors)
precision_recall.update(pred_bboxes=bboxes,
pred_labels=ids,
pred_scores=scores,
gt_boxes=gt_boxes,
gt_labels=gt_ids)
bbox = box_resize(bboxes[0], (netwidth, netheight), (width, height))
ground_truth = plot_bbox(origin_image,
origin_box[:, :4],
scores=None,
labels=origin_box[:, 4:5],
thresh=None,
reverse_rgb=True,
class_names=test_dataset.classes,
absolute_coordinates=True,
colors=ground_truth_colors)
plot_bbox(ground_truth,
bbox,
scores=scores[0],
labels=ids[0],
thresh=plot_class_thresh,
reverse_rgb=False,
class_names=test_dataset.classes,
absolute_coordinates=True,
image_show=show_flag,
image_save=save_flag,
image_save_path=test_save_path,
image_name=name[0])
cls_targets, box_targets = targetgenerator(anchors, gt_boxes, gt_ids)
except_ignore_samples = cls_targets > -1
positive_samples = cls_targets > 0
positive_numbers = positive_samples.sum()
conf_loss = confidence_loss(cls_preds, cls_targets,
except_ignore_samples.expand_dims(axis=-1))
conf_loss = mx.nd.divide(conf_loss, positive_numbers + 1)
conf_loss_sum += conf_loss.asscalar()
loc_loss = localization_loss(box_preds, box_targets,
positive_samples.expand_dims(axis=-1))
loc_loss_sum += loc_loss.asscalar()
# epoch 당 평균 loss
test_conf_loss_mean = np.divide(conf_loss_sum,
test_update_number_per_epoch)
test_loc_loss_mean = np.divide(loc_loss_sum, test_update_number_per_epoch)
test_total_loss_mean = test_conf_loss_mean + test_loc_loss_mean
logging.info(
f"test confidence loss : {test_conf_loss_mean} / test localization loss : {test_loc_loss_mean} / test total loss : {test_total_loss_mean}"
)
AP_appender = []
round_position = 2
class_name, precision, recall, true_positive, false_positive, threshold = precision_recall.get_PR_list(
)
for j, c, p, r in zip(range(len(recall)), class_name, precision, recall):
name, AP = precision_recall.get_AP(c, p, r)
logging.info(
f"class {j}'s {name} AP : {round(AP * 100, round_position)}%")
AP_appender.append(AP)
mAP_result = np.mean(AP_appender)
logging.info(f"mAP : {round(mAP_result * 100, round_position)}%")
precision_recall.get_PR_curve(name=class_name,
precision=precision,
recall=recall,
threshold=threshold,
AP=AP_appender,
mAP=mAP_result,
folder_name=test_graph_path)
def run(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
anchor_alloc_size=[256, 256],
box_sizes=[21, 51.2, 133.12, 215.04, 296.96, 378.88, 460.8, 542.72],
box_ratios=[[1, 2, 0.5]] + [[1, 2, 0.5, 3, 1.0 / 3]] * 4 +
[[1, 2, 0.5]] * 2,
anchor_box_clip=True,
graphviz=True,
epoch=100,
input_size=[400, 600],
batch_log=100,
batch_size=16,
batch_interval=10,
subdivision=4,
train_dataset_path="Dataset/train",
valid_dataset_path="Dataset/valid",
multiscale=True,
factor_scale=[8, 5],
foreground_iou_thresh=0.5,
data_augmentation=True,
num_workers=4,
optimizer="ADAM",
save_period=10,
load_period=10,
learning_rate=0.001,
decay_lr=0.999,
decay_step=10,
GPU_COUNT=0,
base="VGG16_512",
pretrained_base=True,
pretrained_path="modelparam",
classHardNegativeMining=True,
boxHardNegativeMining=True,
AMP=True,
valid_size=8,
eval_period=5,
tensorboard=True,
valid_graph_path="valid_Graph",
using_mlflow=True,
decode_number=-1,
multiperclass=True,
nms_thresh=0.45,
nms_topk=500,
iou_thresh=0.5,
except_class_thresh=0.01,
plot_class_thresh=0.5):
if GPU_COUNT == 0:
ctx = mx.cpu(0)
AMP = False
elif GPU_COUNT == 1:
ctx = mx.gpu(0)
else:
ctx = [mx.gpu(i) for i in range(GPU_COUNT)]
# 운영체제 확인
if platform.system() == "Linux":
logging.info(f"{platform.system()} OS")
elif platform.system() == "Windows":
logging.info(f"{platform.system()} OS")
else:
logging.info(f"{platform.system()} OS")
if isinstance(ctx, (list, tuple)):
for i, c in enumerate(ctx):
free_memory, total_memory = mx.context.gpu_memory_info(i)
free_memory = round(free_memory / (1024 * 1024 * 1024), 2)
total_memory = round(total_memory / (1024 * 1024 * 1024), 2)
logging.info(
f'Running on {c} / free memory : {free_memory}GB / total memory {total_memory}GB'
)
else:
if GPU_COUNT == 1:
free_memory, total_memory = mx.context.gpu_memory_info(0)
free_memory = round(free_memory / (1024 * 1024 * 1024), 2)
total_memory = round(total_memory / (1024 * 1024 * 1024), 2)
logging.info(
f'Running on {ctx} / free memory : {free_memory}GB / total memory {total_memory}GB'
)
else:
logging.info(f'Running on {ctx}')
if GPU_COUNT > 0 and batch_size < GPU_COUNT:
logging.info("batch size must be greater than gpu number")
exit(0)
if AMP:
amp.init()
if multiscale:
logging.info("Using MultiScale")
if data_augmentation:
logging.info("Using Data Augmentation")
logging.info("training SSD Detector")
input_shape = (1, 3) + tuple(input_size)
try:
if base.upper() == "VGG16_300": # 입력 사이즈 300 x 300 추천
net = SSD_VGG16(version=300,
input_size=input_size,
box_sizes=box_sizes,
box_ratios=box_ratios,
anchor_box_clip=anchor_box_clip,
alloc_size=anchor_alloc_size,
ctx=mx.cpu())
elif base.upper() == "VGG16_512": # 입력 사이즈 512 x 512 추천
net = SSD_VGG16(version=512,
input_size=input_size,
box_sizes=box_sizes,
box_ratios=box_ratios,
anchor_box_clip=anchor_box_clip,
ctx=mx.cpu())
train_dataloader, train_dataset = traindataloader(
multiscale=multiscale,
factor_scale=factor_scale,
augmentation=data_augmentation,
path=train_dataset_path,
input_size=input_size,
batch_size=batch_size,
batch_interval=batch_interval,
num_workers=num_workers,
shuffle=True,
mean=mean,
std=std,
net=net,
foreground_iou_thresh=foreground_iou_thresh,
make_target=True)
valid_dataloader, valid_dataset = validdataloader(
path=valid_dataset_path,
input_size=input_size,
batch_size=valid_size,
num_workers=num_workers,
shuffle=True,
mean=mean,
std=std,
net=net,
foreground_iou_thresh=foreground_iou_thresh,
make_target=True)
except Exception:
logging.info("dataset 없음")
exit(0)
train_update_number_per_epoch = len(train_dataloader)
if train_update_number_per_epoch < 1:
logging.warning("train batch size가 데이터 수보다 큼")
exit(0)
valid_list = glob.glob(os.path.join(valid_dataset_path, "*"))
if valid_list:
valid_update_number_per_epoch = len(valid_dataloader)
if valid_update_number_per_epoch < 1:
logging.warning("valid batch size가 데이터 수보다 큼")
exit(0)
num_classes = train_dataset.num_class # 클래스 수
name_classes = train_dataset.classes
# 이름 다시 붙이기
optimizer = optimizer.upper()
base = base.upper()
if pretrained_base:
model = str(input_size[0]) + "_" + str(
input_size[1]) + "_" + optimizer + "_P" + base
else:
model = str(input_size[0]) + "_" + str(
input_size[1]) + "_" + optimizer + "_" + base
weight_path = f"weights/{model}"
sym_path = os.path.join(weight_path, f'{model}-symbol.json')
param_path = os.path.join(weight_path, f'{model}-{load_period:04d}.params')
if os.path.exists(param_path) and os.path.exists(sym_path):
start_epoch = load_period
logging.info(f"loading {os.path.basename(param_path)} weights\n")
net = gluon.SymbolBlock.imports(sym_path, ['data'],
param_path,
ctx=ctx)
else:
start_epoch = 0
if base.upper() == "VGG16_300": # 입력 사이즈 300 x 300 추천
net = SSD_VGG16(
version=300,
input_size=input_size,
# box_sizes=[21, 45, 101.25, 157.5, 213.75, 270, 326.25],
# box_ratios=[[1, 2, 0.5]] + # conv4_3
# [[1, 2, 0.5, 3, 1.0 / 3]] * 3 + # conv7, conv8_2, conv9_2, conv10_2
# [[1, 2, 0.5]] * 2, # conv11_2, conv12_2
box_sizes=box_sizes,
box_ratios=box_ratios,
num_classes=num_classes,
pretrained=pretrained_base,
pretrained_path=pretrained_path,
anchor_box_clip=anchor_box_clip,
alloc_size=anchor_alloc_size,
ctx=ctx)
elif base.upper() == "VGG16_512": # 입력 사이즈 512 x 512 추천
net = SSD_VGG16(
version=512,
input_size=input_size,
# box_sizes=[21, 51.2, 133.12, 215.04, 296.96, 378.88, 460.8, 542.72],
# box_ratios=[[1, 2, 0.5]] + # conv4_3
# [[1, 2, 0.5, 3, 1.0 / 3]] * 4 + # conv7, conv8_2, conv9_2, conv10_2
# [[1, 2, 0.5]] * 2, # conv11_2, conv12_2
box_sizes=box_sizes,
box_ratios=box_ratios,
num_classes=num_classes,
pretrained=pretrained_base,
pretrained_path=pretrained_path,
anchor_box_clip=anchor_box_clip,
ctx=ctx)
else:
logging.warning("backbone 없음")
exit(0)
if isinstance(ctx, (list, tuple)):
net.summary(mx.nd.ones(shape=input_shape, ctx=ctx[0]))
else:
net.summary(mx.nd.ones(shape=input_shape, ctx=ctx))
'''
active (bool, default True) – Whether to turn hybrid on or off.
static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.
static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.
'''
if multiscale:
net.hybridize(active=True, static_alloc=True, static_shape=False)
else:
net.hybridize(active=True, static_alloc=True, static_shape=True)
if start_epoch + 1 >= epoch + 1:
logging.info("this model has already been optimized")
exit(0)
if tensorboard:
summary = SummaryWriter(logdir=os.path.join("mxboard", model),
max_queue=10,
flush_secs=10,
verbose=False)
if isinstance(ctx, (list, tuple)):
net.forward(mx.nd.ones(shape=input_shape, ctx=ctx[0]))
else:
net.forward(mx.nd.ones(shape=input_shape, ctx=ctx))
summary.add_graph(net)
if graphviz:
gluoncv.utils.viz.plot_network(net,
shape=input_shape,
save_prefix=model)
# optimizer
unit = 1 if (len(train_dataset) //
batch_size) < 1 else len(train_dataset) // batch_size
step = unit * decay_step
lr_sch = mx.lr_scheduler.FactorScheduler(step=step,
factor=decay_lr,
stop_factor_lr=1e-12,
base_lr=learning_rate)
for p in net.collect_params().values():
if p.grad_req != "null":
p.grad_req = 'add'
if AMP:
'''
update_on_kvstore : bool, default None
Whether to perform parameter updates on kvstore. If None, then trainer will choose the more
suitable option depending on the type of kvstore. If the `update_on_kvstore` argument is
provided, environment variable `MXNET_UPDATE_ON_KVSTORE` will be ignored.
'''
if optimizer.upper() == "ADAM":
trainer = gluon.Trainer(
net.collect_params(),
optimizer,
optimizer_params={
"learning_rate": learning_rate,
"lr_scheduler": lr_sch,
"beta1": 0.9,
"beta2": 0.999,
'multi_precision': False
},
update_on_kvstore=False) # for Dynamic loss scaling
elif optimizer.upper() == "RMSPROP":
trainer = gluon.Trainer(
net.collect_params(),
optimizer,
optimizer_params={
"learning_rate": learning_rate,
"lr_scheduler": lr_sch,
"gamma1": 0.9,
"gamma2": 0.999,
'multi_precision': False
},
update_on_kvstore=False) # for Dynamic loss scaling
elif optimizer.upper() == "SGD":
trainer = gluon.Trainer(
net.collect_params(),
optimizer,
optimizer_params={
"learning_rate": learning_rate,
"lr_scheduler": lr_sch,
"wd": 0.0005,
"momentum": 0.9,
'multi_precision': False
},
update_on_kvstore=False) # for Dynamic loss scaling
else:
logging.error("optimizer not selected")
exit(0)
amp.init_trainer(trainer)
else:
if optimizer.upper() == "ADAM":
trainer = gluon.Trainer(net.collect_params(),
optimizer,
optimizer_params={
"learning_rate": learning_rate,
"lr_scheduler": lr_sch,
"beta1": 0.9,
"beta2": 0.999,
'multi_precision': False
})
elif optimizer.upper() == "RMSPROP":
trainer = gluon.Trainer(net.collect_params(),
optimizer,
optimizer_params={
"learning_rate": learning_rate,
"lr_scheduler": lr_sch,
"gamma1": 0.9,
"gamma2": 0.999,
'multi_precision': False
})
elif optimizer.upper() == "SGD":
trainer = gluon.Trainer(net.collect_params(),
optimizer,
optimizer_params={
"learning_rate": learning_rate,
"lr_scheduler": lr_sch,
"wd": 0.0005,
"momentum": 0.9,
'multi_precision': False
})
else:
logging.error("optimizer not selected")
exit(0)
'''
localization loss -> Smooth L1 loss
confidence loss -> Softmax
'''
if not classHardNegativeMining:
confidence_loss = SoftmaxCrossEntropyLoss(axis=-1,
sparse_label=True,
from_log_softmax=False,
batch_axis=None,
reduction="sum",
exclude=False)
if not boxHardNegativeMining:
localization_loss = HuberLoss(rho=1,
batch_axis=None,
reduction="sum",
exclude=False)
prediction = Prediction(from_softmax=False,
num_classes=num_classes,
decode_number=decode_number,
nms_thresh=nms_thresh,
nms_topk=nms_topk,
except_class_thresh=except_class_thresh,
multiperclass=multiperclass)
precision_recall = Voc_2007_AP(iou_thresh=iou_thresh,
class_names=name_classes)
start_time = time.time()
for i in tqdm(range(start_epoch + 1, epoch + 1, 1),
initial=start_epoch + 1,
total=epoch):
conf_loss_sum = 0
loc_loss_sum = 0
time_stamp = time.time()
for batch_count, (image, _, cls_all, box_all,
_) in enumerate(train_dataloader, start=1):
td_batch_size = image.shape[0]
image = mx.nd.split(data=image, num_outputs=subdivision, axis=0)
cls_all = mx.nd.split(data=cls_all,
num_outputs=subdivision,
axis=0)
box_all = mx.nd.split(data=box_all,
num_outputs=subdivision,
axis=0)
if subdivision == 1:
image = [image]
cls_t_all = [cls_t_all]
box_t_all = [box_t_all]
with autograd.record(train_mode=True):
cls_all_losses = []
box_all_losses = []
for image_split, cls_split, box_split in zip(
image, cls_all, box_all):
if GPU_COUNT <= 1:
image_split = gluon.utils.split_and_load(
image_split, [ctx], even_split=False)
cls_split = gluon.utils.split_and_load(
cls_split, [ctx], even_split=False)
box_split = gluon.utils.split_and_load(
box_split, [ctx], even_split=False)
else:
image_split = gluon.utils.split_and_load(
image_split, ctx, even_split=False)
cls_split = gluon.utils.split_and_load(
cls_split, ctx, even_split=False)
box_split = gluon.utils.split_and_load(
box_split, ctx, even_split=False)
# prediction, target space for Data Parallelism
cls_losses = []
box_losses = []
total_loss = []
# gpu N 개를 대비한 코드 (Data Parallelism)
for img, cls_target, box_target in zip(
image_split, cls_split, box_split):
# 1. SSD network Inference
cls_pred, box_pred, anchor = net(img)
'''
4. Hard negative mining (class에만 loss 계산)
Hard negative mining After the matching step, most of the default boxes are negatives,
especially when the number of possible default boxes is large. This introduces a
significant imbalance between the positive and negative training examples. Instead of
using all the negative examples, we sort them using the highest confidence loss for each
default box and pick the top ones so that the ratio between the negatives and positives is
at most 3:1. We found that this leads to faster optimization and a more stable training
'''
weight_term_alpha = 1
negative_mining_ratio = 3
positive_samples = cls_target > 0 # True or False
positive_numbers = positive_samples.sum()
if classHardNegativeMining:
pred = mx.nd.log_softmax(cls_pred, axis=-1)
negative_samples = 1 - positive_samples
conf_loss = -mx.nd.pick(
pred, cls_target,
axis=-1) # (batch, all feature number)
'''
we sort them using the highest confidence loss for each
default box and pick the top ones so that the ratio between the negatives and positives is
at most 3:1.
'''
negative_samples_conf_loss = (conf_loss *
negative_samples)
# 아래 3줄의 코드 출처 : from gluoncv.loss import SSDMultiBoxLoss
negative_samples_index = mx.nd.argsort(
negative_samples_conf_loss,
axis=-1,
is_ascend=False)
selection = mx.nd.argsort(negative_samples_index,
axis=-1,
is_ascend=True)
hard_negative_samples = selection <= mx.nd.multiply(
positive_numbers,
negative_mining_ratio).expand_dims(-1)
pos_hardnega = positive_samples + hard_negative_samples
conf_loss = mx.nd.where(
pos_hardnega > 0, conf_loss,
mx.nd.zeros_like(conf_loss))
conf_loss = mx.nd.sum(conf_loss)
if positive_numbers:
conf_loss = mx.nd.divide(
conf_loss, positive_numbers)
else:
conf_loss = mx.nd.multiply(conf_loss, 0)
cls_losses.append(conf_loss.asscalar())
else:
conf_loss = confidence_loss(
cls_pred, cls_target,
positive_samples.expand_dims(axis=-1))
if positive_numbers:
conf_loss = mx.nd.divide(
conf_loss, positive_numbers)
else:
conf_loss = mx.nd.multiply(conf_loss, 0)
cls_losses.append(conf_loss.asscalar())
if boxHardNegativeMining:
# loc loss에도 hard HardNegativeMining 적용해보자.
pred = mx.nd.log_softmax(cls_pred, axis=-1)
negative_samples = 1 - positive_samples
conf_loss_for_box = -mx.nd.pick(
pred, cls_target,
axis=-1) # (batch, all feature number)
negative_samples_conf_loss = (conf_loss_for_box *
negative_samples)
negative_samples_index = mx.nd.argsort(
negative_samples_conf_loss,
axis=-1,
is_ascend=False)
selection = mx.nd.argsort(negative_samples_index,
axis=-1,
is_ascend=True)
hard_negative_samples = selection <= mx.nd.multiply(
positive_numbers,
negative_mining_ratio).expand_dims(-1)
pos_hardnega = positive_samples + hard_negative_samples
pos_hardnega = mx.nd.repeat(
pos_hardnega.reshape(shape=(0, 0, 1)),
repeats=4,
axis=-1)
loc_loss = mx.nd.abs(box_pred - box_target)
loc_loss = mx.nd.where(loc_loss > 1,
loc_loss - 0.5, (0.5 / 1) *
mx.nd.square(loc_loss))
loc_loss = mx.nd.where(pos_hardnega > 0, loc_loss,
mx.nd.zeros_like(loc_loss))
loc_loss = mx.nd.sum(loc_loss)
if positive_numbers:
loc_loss = mx.nd.divide(
loc_loss, positive_numbers)
else:
loc_loss = mx.nd.multiply(loc_loss, 0)
box_losses.append(loc_loss.asscalar())
else:
loc_loss = localization_loss(
box_pred, box_target,
positive_samples.expand_dims(axis=-1))
if positive_numbers:
loc_loss = mx.nd.divide(
loc_loss, positive_numbers)
else:
loc_loss = mx.nd.multiply(loc_loss, 0)
box_losses.append(loc_loss.asscalar())
total_loss.append(conf_loss +
weight_term_alpha * loc_loss)
if AMP:
with amp.scale_loss(total_loss,
trainer) as scaled_loss:
autograd.backward(scaled_loss)
else:
autograd.backward(total_loss)
cls_all_losses.append(sum(cls_losses))
box_all_losses.append(sum(box_losses))
trainer.step(batch_size=td_batch_size, ignore_stale_grad=False)
# 비우기
for p in net.collect_params().values():
p.zero_grad()
conf_loss_sum += sum(cls_all_losses) / td_batch_size
loc_loss_sum += sum(box_all_losses) / td_batch_size
if batch_count % batch_log == 0:
logging.info(
f'[Epoch {i}][Batch {batch_count}/{train_update_number_per_epoch}],'
f'[Speed {td_batch_size / (time.time() - time_stamp):.3f} samples/sec],'
f'[Lr = {trainer.learning_rate}]'
f'[confidence loss = {sum(cls_all_losses) / td_batch_size:.3f}]'
f'[localization loss = {sum(box_all_losses) / td_batch_size:.3f}]'
)
time_stamp = time.time()
train_conf_loss_mean = np.divide(conf_loss_sum,
train_update_number_per_epoch)
train_loc_loss_mean = np.divide(loc_loss_sum,
train_update_number_per_epoch)
train_total_loss_mean = train_conf_loss_mean + train_loc_loss_mean
logging.info(
f"train confidence loss : {train_conf_loss_mean} / train localization loss : {train_loc_loss_mean} / train total loss : {train_total_loss_mean}"
)
if i % eval_period == 0 and valid_list:
if classHardNegativeMining:
confidence_loss = SoftmaxCrossEntropyLoss(
axis=-1,
sparse_label=True,
from_log_softmax=False,
batch_axis=None,
reduction="sum",
exclude=False)
if boxHardNegativeMining:
localization_loss = HuberLoss(rho=1,
batch_axis=None,
reduction="sum",
exclude=False)
conf_loss_sum = 0
loc_loss_sum = 0
for image, label, cls_all, box_all, _ in valid_dataloader:
vd_batch_size = image.shape[0]
if GPU_COUNT <= 1:
image = gluon.utils.split_and_load(image, [ctx],
even_split=False)
label = gluon.utils.split_and_load(label, [ctx],
even_split=False)
cls_all = gluon.utils.split_and_load(cls_all, [ctx],
even_split=False)
box_all = gluon.utils.split_and_load(box_all, [ctx],
even_split=False)
else:
image = gluon.utils.split_and_load(image,
ctx,
even_split=False)
label = gluon.utils.split_and_load(label,
ctx,
even_split=False)
cls_all = gluon.utils.split_and_load(cls_all, [ctx],
even_split=False)
box_all = gluon.utils.split_and_load(box_all, [ctx],
even_split=False)
# prediction, target space for Data Parallelism
cls_losses = []
box_losses = []
# gpu N 개를 대비한 코드 (Data Parallelism)
for img, lb, cls_target, box_target in zip(
image, label, cls_all, box_all):
gt_box = lb[:, :, :4]
gt_id = lb[:, :, 4:5]
cls_pred, box_pred, anchor = net(img)
id, score, bbox = prediction(cls_pred, box_pred, anchor)
precision_recall.update(pred_bboxes=bbox,
pred_labels=id,
pred_scores=score,
gt_boxes=gt_box,
gt_labels=gt_id)
positive_samples = cls_target > 0
positive_numbers = positive_samples.sum()
conf_loss = confidence_loss(
cls_pred, cls_target,
positive_samples.expand_dims(axis=-1))
if positive_numbers:
conf_loss = mx.nd.divide(conf_loss, positive_numbers)
else:
conf_loss = mx.nd.multiply(conf_loss, 0)
cls_losses.append(conf_loss.asscalar())
loc_loss = localization_loss(
box_pred, box_target,
positive_samples.expand_dims(axis=-1))
if positive_numbers:
loc_loss = mx.nd.divide(loc_loss, positive_numbers)
else:
loc_loss = mx.nd.multiply(loc_loss, 0)
box_losses.append(loc_loss.asscalar())
conf_loss_sum += sum(cls_losses) / vd_batch_size
loc_loss_sum += sum(box_losses) / vd_batch_size
valid_conf_loss_mean = np.divide(conf_loss_sum,
valid_update_number_per_epoch)
valid_loc_loss_mean = np.divide(loc_loss_sum,
valid_update_number_per_epoch)
valid_total_loss_mean = valid_conf_loss_mean + valid_loc_loss_mean
logging.info(
f"valid confidence loss : {valid_conf_loss_mean} / valid localization loss : {valid_loc_loss_mean} / valid total loss : {valid_total_loss_mean}"
)
AP_appender = []
round_position = 2
class_name, precision, recall, true_positive, false_positive, threshold = precision_recall.get_PR_list(
)
for j, c, p, r in zip(range(len(recall)), class_name, precision,
recall):
name, AP = precision_recall.get_AP(c, p, r)
logging.info(
f"class {j}'s {name} AP : {round(AP * 100, round_position)}%"
)
AP_appender.append(AP)
mAP_result = np.mean(AP_appender)
logging.info(f"mAP : {round(mAP_result * 100, round_position)}%")
precision_recall.get_PR_curve(name=class_name,
precision=precision,
recall=recall,
threshold=threshold,
AP=AP_appender,
mAP=mAP_result,
folder_name=valid_graph_path,
epoch=i)
precision_recall.reset()
if tensorboard:
# gpu N 개를 대비한 코드 (Data Parallelism)
dataloader_iter = iter(valid_dataloader)
image, label, _, _, _ = next(dataloader_iter)
if GPU_COUNT <= 1:
image = gluon.utils.split_and_load(image, [ctx],
even_split=False)
label = gluon.utils.split_and_load(label, [ctx],
even_split=False)
else:
image = gluon.utils.split_and_load(image,
ctx,
even_split=False)
label = gluon.utils.split_and_load(label,
ctx,
even_split=False)
ground_truth_colors = {}
for k in range(num_classes):
ground_truth_colors[k] = (0, 0, 1)
batch_image = []
for img, lb in zip(image, label):
gt_boxes = lb[:, :, :4]
gt_ids = lb[:, :, 4:5]
cls_pred, box_pred, anchor = net(img)
ids, scores, bboxes = prediction(cls_pred, box_pred,
anchor)
for ig, gt_id, gt_box, id, score, bbox in zip(
img, gt_ids, gt_boxes, ids, scores, bboxes):
ig = ig.transpose((1, 2, 0)) * mx.nd.array(
std, ctx=ig.context) + mx.nd.array(mean,
ctx=ig.context)
ig = (ig * 255).clip(0, 255)
# ground truth box 그리기
ground_truth = plot_bbox(
ig,
gt_box,
scores=None,
labels=gt_id,
thresh=None,
reverse_rgb=True,
class_names=valid_dataset.classes,
absolute_coordinates=True,
colors=ground_truth_colors)
# prediction box 그리기
prediction_box = plot_bbox(
ground_truth,
bbox,
scores=score,
labels=id,
thresh=plot_class_thresh,
reverse_rgb=False,
class_names=valid_dataset.classes,
absolute_coordinates=True)
# Tensorboard에 그리기 위해 BGR -> RGB / (height, width, channel) -> (channel, height, width) 를한다.
prediction_box = cv2.cvtColor(prediction_box,
cv2.COLOR_BGR2RGB)
prediction_box = np.transpose(prediction_box,
axes=(2, 0, 1))
batch_image.append(
prediction_box) # (batch, channel, height, width)
summary.add_image(tag="valid_result",
image=np.array(batch_image),
global_step=i)
summary.add_scalar(tag="conf_loss",
value={
"train_conf_loss": train_conf_loss_mean,
"valid_conf_loss": valid_conf_loss_mean
},
global_step=i)
summary.add_scalar(tag="loc_loss",
value={
"train_loc_loss": train_loc_loss_mean,
"valid_loc_loss": valid_loc_loss_mean
},
global_step=i)
summary.add_scalar(tag="total_loss",
value={
"train_total_loss":
train_total_loss_mean,
"valid_total_loss":
valid_total_loss_mean
},
global_step=i)
params = net.collect_params().values()
if GPU_COUNT > 1:
for c in ctx:
for p in params:
summary.add_histogram(tag=p.name,
values=p.data(ctx=c),
global_step=i,
bins='default')
else:
for p in params:
summary.add_histogram(tag=p.name,
values=p.data(),
global_step=i,
bins='default')
if i % save_period == 0:
weight_epoch_path = os.path.join(weight_path, str(i))
if not os.path.exists(weight_epoch_path):
os.makedirs(weight_epoch_path)
'''
Hybrid models can be serialized as JSON files using the export function
Export HybridBlock to json format that can be loaded by SymbolBlock.imports, mxnet.mod.Module or the C++ interface.
When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.
'''
if GPU_COUNT >= 1:
context = mx.gpu(0)
else:
context = mx.cpu(0)
postnet = PostNet(net=net, auxnet=prediction)
try:
net.export(os.path.join(weight_path, f"{model}"),
epoch=i,
remove_amp_cast=True)
net.save_parameters(os.path.join(weight_path,
f"{i}.params")) # onnx 추출용
# network inference, decoder, nms까지 처리됨 - mxnet c++에서 편리함 / onnx로는 추출 못함.
export_block_for_cplusplus(
path=os.path.join(weight_epoch_path, f"{model}_prepost"),
block=postnet,
data_shape=tuple(input_size) + tuple((3, )),
epoch=i,
preprocess=
True, # c++ 에서 inference시 opencv에서 읽은 이미지 그대로 넣으면 됨
layout='HWC',
ctx=context,
remove_amp_cast=True)
except Exception as E:
logging.error(f"json, param model export 예외 발생 : {E}")
else:
logging.info("json, param model export 성공")
net.collect_params().reset_ctx(ctx)
end_time = time.time()
learning_time = end_time - start_time
logging.info(f"learning time : 약, {learning_time / 3600:0.2f}H")
logging.info("optimization completed")
if using_mlflow:
ml.log_metric("learning time", round(learning_time / 3600, 2))