def evaluate(self, model, half=False, distributed=False):
"""
COCO average precision (AP) Evaluation. Iterate inference on the test dataset
and the results are evaluated by COCO API.
Args:
model : model object
Returns:
ap50_95 (float) : calculated COCO AP for IoU=50:95
ap50 (float) : calculated COCO AP for IoU=50
"""
if isinstance(model, apex.parallel.DistributedDataParallel):
model = model.module
distributed=True
model=model.eval()
cuda = torch.cuda.is_available()
if half:
Tensor = torch.cuda.HalfTensor if cuda else torch.HalfTensor
else:
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
ids = []
data_dict = []
img_num = 0
indices = list(range(self.num_images))
if distributed:
dis_indices = indices[distributed_util.get_rank()::distributed_util.get_world_size()]
else:
dis_indices = indices
progress_bar = tqdm if distributed_util.is_main_process() else iter
num_classes = 80 if not self.voc else 20
inference_time=0
nms_time=0
n_samples=len(dis_indices)-10
for k, i in enumerate(progress_bar(dis_indices)):
img, _, info_img, id_ = self.dataset[i] # load a batch
info_img = [float(info) for info in info_img]
id_ = int(id_)
ids.append(id_)
with torch.no_grad():
img = Variable(img.type(Tensor).unsqueeze(0))
if k > 9:
start=time.time()
if self.vis:
outputs,fuse_weights,fused_f = model(img)
else:
outputs = model(img)
if k > 9:
infer_end=time.time()
inference_time += (infer_end-start)
outputs = postprocess(
outputs, num_classes, self.confthre, self.nmsthre)
if k > 9:
nms_end=time.time()
nms_time +=(nms_end-infer_end)
if outputs[0] is None:
continue
outputs = outputs[0].cpu().data
bboxes = outputs[:, 0:4]
bboxes[:, 0::2] *= info_img[0] / self.img_size[0]
bboxes[:, 1::2] *= info_img[1] / self.img_size[1]
bboxes[:, 2] = bboxes[:,2] - bboxes[:,0]
bboxes[:, 3] = bboxes[:,3] - bboxes[:,1]
cls = outputs[:, 6]
scores = outputs[:, 4]* outputs[:,5]
for ind in range(bboxes.shape[0]):
label = self.dataset.class_ids[int(cls[ind])]
A = {"image_id": id_, "category_id": label, "bbox": bboxes[ind].numpy().tolist(),
"score": scores[ind].numpy().item(), "segmentation": []} # COCO json format
data_dict.append(A)
if self.vis:
o_img,_,_,_ = self.dataset.pull_item(i)
make_vis('COCO', i, o_img, fuse_weights, fused_f)
class_names = self.dataset._classes
make_pred_vis('COCO', i, o_img, class_names, bboxes, cls, scores)
if DEBUG and distributed_util.is_main_process():
o_img,_ = self.dataset.pull_item(i)
class_names = self.dataset._classes
make_pred_vis('COCO', i, o_img, class_names, bboxes, cls, scores)
if distributed:
distributed_util.synchronize()
data_dict = _accumulate_predictions_from_multiple_gpus(data_dict)
inference_time = torch.FloatTensor(1).type(Tensor).fill_(inference_time)
nms_time = torch.FloatTensor(1).type(Tensor).fill_(nms_time)
n_samples = torch.LongTensor(1).type(Tensor).fill_(n_samples)
distributed_util.synchronize()
torch.distributed.reduce(inference_time, dst=0)
torch.distributed.reduce(nms_time, dst=0)
torch.distributed.reduce(n_samples, dst=0)
inference_time = inference_time.item()
nms_time = nms_time.item()
n_samples = n_samples.item()
if not distributed_util.is_main_process():
return 0, 0
print('Main process Evaluating...')
annType = ['segm', 'bbox', 'keypoints']
a_infer_time = 1000*inference_time / (n_samples)
a_nms_time= 1000*nms_time / (n_samples)
print('Average forward time: %.2f ms, Average NMS time: %.2f ms, Average inference time: %.2f ms' %(a_infer_time, \
a_nms_time, (a_infer_time+a_nms_time)))
# Evaluate the Dt (detection) json comparing with the ground truth
if len(data_dict) > 0:
cocoGt = self.dataset.coco
# workaround: temporarily write data to json file because pycocotools can't process dict in py36.
if self.testset:
json.dump(data_dict, open('yolov3_2017.json', 'w'))
cocoDt = cocoGt.loadRes('yolov3_2017.json')
else:
_, tmp = tempfile.mkstemp()
json.dump(data_dict, open(tmp, 'w'))
cocoDt = cocoGt.loadRes(tmp)
cocoEval = COCOeval(self.dataset.coco, cocoDt, annType[1])
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
return cocoEval.stats[0], cocoEval.stats[1]
else:
return 0, 0
def eval():
"""
YOLOv3 evaler. See README for details.
"""
args = parse_args()
print("Setting Arguments.. : ", args)
cuda = torch.cuda.is_available() and args.use_cuda
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl", init_method="env://")
# Parse config settings
with open(args.cfg, 'r') as f:
cfg = yaml.safe_load(f)
print("successfully loaded config file: ", cfg)
test_size = (args.test_size,args.test_size)
if args.dataset == 'COCO':
evaluator = COCOAPIEvaluator(
data_dir='data/COCO/',
img_size=test_size,
confthre=0.001,
nmsthre=0.65,
testset=args.testset,
vis=args.vis)
num_class=80
elif args.dataset == 'VOC':
'''
# COCO style evaluation, you have to convert xml annotation files into a json file.
evaluator = COCOAPIEvaluator(
data_dir='data/VOC/',
img_size=test_size,
confthre=cfg['TEST']['CONFTHRE'],
nmsthre=cfg['TEST']['NMSTHRE'],
testset=args.testset,
voc = True)
'''
evaluator = VOCEvaluator(
data_dir='data/VOC/',
img_size=test_size,
confthre=0.001,
nmsthre=0.65,
vis=args.vis)
num_class=20
# Initiate model
if args.asff:
from models.yolov3_asff import YOLOv3
print('Testing YOLOv3 with ASFF!')
model = YOLOv3(num_classes = num_class, rfb=args.rfb, vis=args.vis)
else:
from models.yolov3_baseline import YOLOv3
print('Testing YOLOv3 strong baseline!')
if args.vis:
print('Visualization is not supported for YOLOv3 baseline model')
args.vis = False
model = YOLOv3(num_classes = num_class, rfb=args.rfb)
save_to_disk = (not args.distributed) or distributed_util.get_rank() == 0
if args.checkpoint:
print("loading pytorch ckpt...", args.checkpoint)
cpu_device = torch.device("cpu")
ckpt = torch.load(args.checkpoint, map_location=cpu_device)
model.load_state_dict(ckpt,strict=False)
if cuda:
print("using cuda")
torch.backends.cudnn.benchmark = True
device = torch.device("cuda")
model = model.to(device)
if args.half:
model = model.half()
if args.ngpu > 1:
if args.distributed:
model = apex.parallel.DistributedDataParallel(model, delay_allreduce=True)
#model = apex.parallel.DistributedDataParallel(model)
else:
model = nn.DataParallel(model)
dtype = torch.float16 if args.half else torch.float32
if args.distributed:
distributed_util.synchronize()
ap50_95, ap50 = evaluator.evaluate(model, args.half, args.distributed)
if args.distributed:
distributed_util.synchronize()
sys.exit(0)
def main():
"""
YOLOv3 trainer. See README for details.
"""
args = parse_args()
print("Setting Arguments.. : ", args)
cuda = torch.cuda.is_available() and args.use_cuda
os.makedirs(args.log_dir, exist_ok=True)
os.makedirs(args.save_dir, exist_ok=True)
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
save_prefix = 'yolov3'
# Parse config settings
with open(args.cfg, 'r') as f:
cfg = yaml.safe_load(f)
print("successfully loaded config file: ", cfg)
lr = cfg['TRAIN']['LR']
epochs = cfg['TRAIN']['MAXEPOCH']
cos = cfg['TRAIN']['COS']
sybn = cfg['TRAIN']['SYBN']
mixup = cfg['TRAIN']['MIX']
no_mixup_epochs = cfg['TRAIN']['NO_MIXUP_EPOCHS']
label_smooth = cfg['TRAIN']['LABAL_SMOOTH']
momentum = cfg['TRAIN']['MOMENTUM']
burn_in = cfg['TRAIN']['BURN_IN']
batch_size = cfg['TRAIN']['BATCHSIZE']
decay = cfg['TRAIN']['DECAY']
ignore_thre = cfg['TRAIN']['IGNORETHRE']
random_resize = cfg['TRAIN']['RANDRESIZE']
input_size = (cfg['TRAIN']['IMGSIZE'], cfg['TRAIN']['IMGSIZE'])
test_size = (args.test_size, args.test_size)
step = (180, 240) # for no cos lr shedule training
# Learning rate setup
base_lr = lr
if args.dataset == 'COCO':
dataset = COCODataset(data_dir='data/COCO/',
img_size=input_size,
preproc=TrainTransform(rgb_means=(0.485, 0.456,
0.406),
std=(0.229, 0.224, 0.225),
max_labels=50),
debug=args.debug)
num_class = 80
elif args.dataset == 'VOC':
train_sets = [('2007', 'trainval'), ('2012', 'trainval')]
dataset = VOCDetection(root='data/VOC',
image_sets=train_sets,
input_dim=input_size,
preproc=TrainTransform(rgb_means=(0.485, 0.456,
0.406),
std=(0.229, 0.224,
0.225),
max_labels=30))
num_class = 20
else:
print('Only COCO and VOC datasets are supported!')
return
save_prefix += ('_' + args.dataset)
if label_smooth:
save_prefix += '_label_smooth'
# Initiate model
if args.asff:
save_prefix += '_asff'
from models.yolov3_asff import YOLOv3
print('Training YOLOv3 with ASFF!')
model = YOLOv3(num_classes=num_class,
ignore_thre=ignore_thre,
label_smooth=label_smooth,
rfb=args.rfb,
vis=args.vis)
else:
save_prefix += '_baseline'
from models.yolov3_baseline import YOLOv3
print('Training YOLOv3 strong baseline!')
if args.vis:
print('Visualization is not supported for YOLOv3 baseline model')
args.vis = False
model = YOLOv3(num_classes=num_class,
ignore_thre=ignore_thre,
label_smooth=label_smooth,
rfb=args.rfb)
save_to_disk = (not args.distributed) or distributed_util.get_rank() == 0
def init_yolo(m):
for key in m.state_dict():
if key.split('.')[-1] == 'weight':
if 'conv' in key:
init.kaiming_normal_(m.state_dict()[key],
a=0.1,
mode='fan_in')
if 'linear' in key:
init.kaiming_normal_(m.state_dict()[key],
a=0.0,
mode='fan_in')
if 'bn' in key:
m.state_dict()[key][...] = 1
elif key.split('.')[-1] == 'bias':
m.state_dict()[key][...] = 0
model.apply(init_yolo)
if sybn:
model = apex.parallel.convert_syncbn_model(model)
if args.checkpoint:
print("loading pytorch ckpt...", args.checkpoint)
cpu_device = torch.device("cpu")
ckpt = torch.load(args.checkpoint, map_location=cpu_device)
model.load_state_dict(ckpt, strict=False)
#model.load_state_dict(ckpt)
if cuda:
print("using cuda")
torch.backends.cudnn.benchmark = True
device = torch.device("cuda")
model = model.to(device)
if args.half:
model = model.half()
if args.ngpu > 1:
if args.distributed:
model = apex.parallel.DistributedDataParallel(model,
delay_allreduce=True)
#model = apex.parallel.DistributedDataParallel(model)
else:
model = nn.DataParallel(model)
if args.tfboard and save_to_disk:
print("using tfboard")
from torch.utils.tensorboard import SummaryWriter
tblogger = SummaryWriter(args.log_dir)
model.train()
if mixup:
from dataset.mixupdetection import MixupDetection
dataset = MixupDetection(
dataset,
preproc=TrainTransform(rgb_means=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
max_labels=50),
)
dataset.set_mixup(np.random.beta, 1.5, 1.5)
save_prefix += '_mixup'
if args.distributed:
sampler = torch.utils.data.DistributedSampler(dataset)
else:
sampler = torch.utils.data.RandomSampler(dataset)
batch_sampler = YoloBatchSampler(sampler=sampler,
batch_size=batch_size,
drop_last=False,
input_dimension=input_size)
dataloader = DataLoader(dataset,
batch_sampler=batch_sampler,
num_workers=args.n_cpu,
pin_memory=True)
dataiterator = iter(dataloader)
if args.dataset == 'COCO':
evaluator = COCOAPIEvaluator(data_dir='data/COCO/',
img_size=test_size,
confthre=cfg['TEST']['CONFTHRE'],
nmsthre=cfg['TEST']['NMSTHRE'],
testset=args.testset,
vis=args.vis)
elif args.dataset == 'VOC':
'''
# COCO style evaluation, you have to convert xml annotation files into a json file.
evaluator = COCOAPIEvaluator(
data_dir='data/VOC/',
img_size=test_size,
confthre=cfg['TEST']['CONFTHRE'],
nmsthre=cfg['TEST']['NMSTHRE'],
testset=args.testset,
voc = True)
'''
evaluator = VOCEvaluator(data_dir='data/VOC/',
img_size=test_size,
confthre=cfg['TEST']['CONFTHRE'],
nmsthre=cfg['TEST']['NMSTHRE'],
vis=args.vis)
dtype = torch.float16 if args.half else torch.float32
# optimizer setup
# set weight decay only on conv.weight
if args.no_wd:
params_dict = dict(model.named_parameters())
params = []
for key, value in params_dict.items():
if 'conv.weight' in key:
params += [{'params': value, 'weight_decay': decay}]
else:
params += [{'params': value, 'weight_decay': 0.0}]
save_prefix += '_no_wd'
else:
params = model.parameters()
optimizer = optim.SGD(params,
lr=base_lr,
momentum=momentum,
dampening=0,
weight_decay=decay)
if args.half:
optimizer = FP16_Optimizer(optimizer, verbose=False)
if cos:
save_prefix += '_cos'
tmp_lr = base_lr
def set_lr(tmp_lr):
for param_group in optimizer.param_groups:
param_group['lr'] = tmp_lr
# start training loop
start = time.time()
epoch = args.start_epoch
epoch_size = len(dataset) // (batch_size * args.ngpu)
while epoch < epochs + 1:
if args.distributed:
batch_sampler.sampler.set_epoch(epoch)
if epoch > epochs - no_mixup_epochs + 1:
args.eval_interval = 1
if mixup:
print('Disable mix up now!')
mixup = False
dataset.set_mixup(None)
if args.distributed:
sampler = torch.utils.data.DistributedSampler(dataset)
else:
sampler = torch.utils.data.RandomSampler(dataset)
batch_sampler = YoloBatchSampler(sampler=sampler,
batch_size=batch_size,
drop_last=False,
input_dimension=input_size)
dataloader = DataLoader(dataset,
batch_sampler=batch_sampler,
num_workers=args.n_cpu,
pin_memory=True)
#### DropBlock Shedule #####
Drop_layer = [16, 24, 33]
if args.asff:
Drop_layer = [16, 22, 29]
if (epoch == 5 or (epoch == args.start_epoch
and args.start_epoch > 5)) and (args.dropblock):
block_size = [1, 3, 5]
keep_p = [0.9, 0.9, 0.9]
for i in range(len(Drop_layer)):
model.module.module_list[Drop_layer[i]].reset(
block_size[i], keep_p[i])
if (epoch == 80 or (epoch == args.start_epoch
and args.start_epoch > 80)) and (args.dropblock):
block_size = [3, 5, 7]
keep_p = [0.9, 0.9, 0.9]
for i in range(len(Drop_layer)):
model.module.module_list[Drop_layer[i]].reset(
block_size[i], keep_p[i])
if (epoch == 150 or (epoch == args.start_epoch
and args.start_epoch > 150)) and (args.dropblock):
block_size = [7, 7, 7]
keep_p = [0.9, 0.9, 0.9]
for i in range(len(Drop_layer)):
model.module.module_list[Drop_layer[i]].reset(
block_size[i], keep_p[i])
for iter_i, (imgs, targets, img_info, idx) in enumerate(dataloader):
#evaluation
if ((epoch % args.eval_interval == 0) and epoch > args.start_epoch
and iter_i == 0) or args.test:
if not args.test and save_to_disk:
torch.save(
model.module.state_dict(),
os.path.join(args.save_dir,
save_prefix + '_' + repr(epoch) + '.pth'))
if args.distributed:
distributed_util.synchronize()
ap50_95, ap50 = evaluator.evaluate(model, args.half)
if args.distributed:
distributed_util.synchronize()
if args.test:
sys.exit(0)
model.train()
if args.tfboard and save_to_disk:
tblogger.add_scalar('val/COCOAP50', ap50, epoch)
tblogger.add_scalar('val/COCOAP50_95', ap50_95, epoch)
# learning rate scheduling (cos or step)
if epoch < burn_in:
tmp_lr = base_lr * pow((iter_i + epoch * epoch_size) * 1. /
(burn_in * epoch_size), 3)
set_lr(tmp_lr)
elif cos:
if epoch <= epochs - no_mixup_epochs and epoch > 20:
min_lr = 0.00001
tmp_lr = min_lr + 0.5*(base_lr-min_lr)*(1+math.cos(math.pi*(epoch-20)*1./\
(epochs-no_mixup_epochs-20)))
elif epoch > epochs - no_mixup_epochs:
tmp_lr = 0.00001
set_lr(tmp_lr)
elif epoch == burn_in:
tmp_lr = base_lr
set_lr(tmp_lr)
elif epoch in steps and iter_i == 0:
tmp_lr = tmp_lr * 0.1
set_lr(tmp_lr)
optimizer.zero_grad()
imgs = Variable(imgs.to(device).to(dtype))
targets = Variable(targets.to(device).to(dtype),
requires_grad=False)
loss_dict = model(imgs, targets, epoch)
loss_dict_reduced = reduce_loss_dict(loss_dict)
loss = sum(loss for loss in loss_dict['losses'])
if args.half:
optimizer.backward(loss)
else:
loss.backward()
#torch.nn.utils.clip_grad_norm_(model.parameters(), 10)
optimizer.step()
if iter_i % 10 == 0 and save_to_disk:
# logging
end = time.time()
print(
'[Epoch %d/%d][Iter %d/%d][lr %.6f]'
'[Loss: anchor %.2f, iou %.2f, l1 %.2f, conf %.2f, cls %.2f, imgsize %d, time: %.2f]'
%
(epoch, epochs, iter_i, epoch_size, tmp_lr,
sum(anchor_loss for anchor_loss in
loss_dict_reduced['anchor_losses']).item(),
sum(iou_loss for iou_loss in
loss_dict_reduced['iou_losses']).item(),
sum(l1_loss
for l1_loss in loss_dict_reduced['l1_losses']).item(),
sum(conf_loss for conf_loss in
loss_dict_reduced['conf_losses']).item(),
sum(cls_loss
for cls_loss in loss_dict_reduced['cls_losses']).item(
), input_size[0], end - start),
flush=True)
start = time.time()
if args.tfboard and save_to_disk:
tblogger.add_scalar(
'train/total_loss',
sum(loss
for loss in loss_dict_reduced['losses']).item(),
epoch * epoch_size + iter_i)
# random resizing
if random_resize and iter_i % 10 == 0 and iter_i > 0:
tensor = torch.LongTensor(1).to(device)
if args.distributed:
distributed_util.synchronize()
if save_to_disk:
if epoch > epochs - 10:
size = 416 if args.dataset == 'VOC' else 608
else:
size = random.randint(*(10, 19))
size = int(32 * size)
tensor.fill_(size)
if args.distributed:
distributed_util.synchronize()
dist.broadcast(tensor, 0)
input_size = dataloader.change_input_dim(
multiple=tensor.item(), random_range=None)
if args.distributed:
distributed_util.synchronize()
epoch += 1
if not args.test and save_to_disk:
torch.save(
model.module.state_dict(),
os.path.join(args.save_dir,
"yolov3_" + args.dataset + '_Final.pth'))
if args.distributed:
distributed_util.synchronize()
ap50_95, ap50 = evaluator.evaluate(model, args.half)
if args.tfboard and save_to_disk:
tblogger.close()
def evaluate(self, model, half=False, distributed=False):
"""
COCO average precision (AP) Evaluation. Iterate inference on the test dataset
and the results are evaluated by COCO API.
Args:
model : model object
Returns:
ap50_95 (float) : calculated COCO AP for IoU=50:95
ap50 (float) : calculated COCO AP for IoU=50
"""
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
model = model.module
model.eval()
cuda = torch.cuda.is_available()
if half:
Tensor = torch.cuda.HalfTensor if cuda else torch.HalfTensor
else:
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
ids = []
data_dict = []
dataiterator = iter(self.dataloader)
img_num = 0
indices = list(range(self.num_images))
dis_indices = indices[distributed_util.get_rank()::distributed_util.
get_world_size()]
progress_bar = tqdm if distributed_util.is_main_process() else iter
num_classes = 1
predictions = {}
if distributed_util.is_main_process():
inference_time = 0
nms_time = 0
n_samples = len(dis_indices)
for i in progress_bar(indices):
img, _, info_img, id_ = self.dataset[i] # load a batch
info_img = [float(info) for info in info_img]
ids.append(id_)
with torch.no_grad():
img = Variable(img.type(Tensor).unsqueeze(0))
if distributed_util.is_main_process() and i > 9:
start = time.time()
if self.vis:
outputs, fuse_weights, fused_f = model(img)
else:
outputs = model(img)
if distributed_util.is_main_process() and i > 9:
infer_end = time.time()
inference_time += (infer_end - start)
outputs = postprocess(outputs, 1, self.confthre, self.nmsthre)
if distributed_util.is_main_process() and i > 9:
nms_end = time.time()
nms_time += (nms_end - infer_end)
if outputs[0] is None:
predictions[i] = (None, None, None)
continue
outputs = outputs[0].cpu().data
bboxes = outputs[:, 0:4]
bboxes[:, 0::2] *= info_img[0] / self.img_size[0]
bboxes[:, 1::2] *= info_img[1] / self.img_size[1]
cls = outputs[:, 6]
scores = outputs[:, 4] * outputs[:, 5]
predictions[i] = (bboxes, cls, scores)
if self.vis:
o_img, _, _, _ = self.dataset.pull_item(i)
make_vis('SWIM', i, o_img, fuse_weights, fused_f)
class_names = self.dataset._classes
bbox = bboxes.clone()
bbox[:, 2] = bbox[:, 2] - bbox[:, 0]
bbox[:, 3] = bbox[:, 3] - bbox[:, 1]
make_pred_vis('SWIM', i, o_img, class_names, bbox, cls, scores)
if DEBUG and distributed_util.is_main_process():
o_img, _, _, _ = self.dataset.pull_item(i)
class_names = self.dataset._classes
bbox = bboxes.clone()
bbox[:, 2] = bbox[:, 2] - bbox[:, 0]
bbox[:, 3] = bbox[:, 3] - bbox[:, 1]
make_pred_vis('SWIM', i, o_img, class_names, bbox, cls, scores)
distributed_util.synchronize()
#predictions = _accumulate_predictions_from_multiple_gpus(predictions)
if not distributed_util.is_main_process():
return 0, 0
print('Main process Evaluating...')
a_infer_time = 1000 * inference_time / (n_samples - 10)
a_nms_time = 1000 * nms_time / (n_samples - 10)
print('Average forward time: %.2f ms, Average NMS time: %.2f ms, Average inference time: %.2f ms' %(a_infer_time, \
a_nms_time, (a_infer_time+a_nms_time)))
all_boxes = [[[] for _ in range(self.num_images)]
for _ in range(num_classes)]
for img_num in range(self.num_images):
bboxes, cls, scores = predictions[img_num]
if bboxes is None:
for j in range(num_classes):
all_boxes[j][img_num] = np.empty([0, 5], dtype=np.float32)
continue
for j in range(num_classes):
mask_c = (cls == j)
if sum(mask_c) == 0:
all_boxes[j][img_num] = np.empty([0, 5], dtype=np.float32)
continue
c_dets = torch.cat((bboxes, scores.unsqueeze(1)), dim=1)
all_boxes[j][img_num] = c_dets[mask_c].numpy()
sys.stdout.write('im_eval: {:d}/{:d} \r'.format(
img_num + 1, self.num_images))
sys.stdout.flush()
with tempfile.TemporaryDirectory() as tempdir:
mAP50, mAP70 = self.dataset.evaluate_detections(all_boxes, tempdir)
return mAP50, mAP70
